Samin Ravanshadi

افزايش روزافزون كاربرد سيست مهاي مخابراتي و راداري په نباند و حساسيت اين تجهيزات به تداخ لهاي الكترومغناطيسي (EMI) ، نياز مبرمي به توسع هي جاذبهاي موج پيشرفته با قابليت جذب بالا و پهناي باند عملياتي گسترده ايجاد كرده است. جاذبهاي فراماد هاي (Metamaterial Absorbers - MMA) به دليل امكان كنترل بيسابق هي پارامترهاي مؤثر الكتريكي و مغناطيسي از طريق طراحي هندسه، پاسخي ايد هآل براي اين نياز فناورانه هستند. با اين حال، اغلب اين ساختارها ذاتاً باند باريكي دارند . هدف اصلي اين پاياننامه، طراحي، بهين هسازي و تحليل يك جاذب فراماد هاي صفح هاي فوقنازك و په نبان دو قابل تنظيم براي كاربرد در محدود هي فركانسي X/Ku ( ??.?? تا ??.? گيگاهرتز( است. در اين راستا، ابتدا يك ساختار پايه مرور شد و سپس از طريق يكرويه گا مب هگام سيستماتيك، با اعمال تغييرات هدفمند در هندس هي سلول واحد، نظير افزودن حلق ههاي ه ممركز، ايجاد و تنظيم شكا فهاي تشديدي و معرفي مسيرهاي مارپيچ، ساختار اصلي توسعه يافت . مكانيسم عملكرد مبتني بر ايجاد همزمان رزونان سهاي الكتريكي و مغناطيسي و كوپلينگ مؤثر بين آ نها است كه منجر به تطبيق امپدانس بهينه با فضاي آزاد و در نتيجه كاهش چشمگير بازتاب ميشود . براي تحليل عملكرد، از شبي هسازي الكترومغناطيسي تما مموج در نرمافزار CST Studio Suite استفاده شد و پارامترهاي پراكندگي، توزيع ميدان و جريان سطحي به دقت بررسي گرديد. در مرحلهي بعد، اثر آراي هسازي و كوپلينگ متقابل بين سلولها با بررسي دو آرايه با فواصل سلولي متفاوت مورد مطالعه قرار گرفت . دستاوردهاي كليدي اين پژوه شعبارتند از : ? ( دستيابي به يكسلول واحد بهينه با ضخامت تنها ?.??? ميل يمتر معادل تقريبي ? ? در فركانس مركزي كه جذب بالاي ??? را در پهناي باند ?.?? گيگاهرتزي ارائه م يدهد و در نقاط اوج به بازدهي حدود ??? ميرسد. ? ( نشان دادن اين كه كوپلينگ متقابل در آراي ههاي فشرد ه ميتواند به عنوان يك پارامتر طراحي مثبت عمل كرده و منحني پاسخ را هموارتر نمايد . ساختار پيشنهادي به دليل سازگاري با فناوري ساخت PCB و عملكرد پايدار در زواياي تابش مختلف، گزينهاي عملي و اميدبخش برا ي كاربرد در سيستمهاي راداري، مخابراتي نوين و فناور يهاي كاهش سطح مقطع رادار ي (RCS) محسوب ميشود  

 This research investigates and optimizes routing protocols in Internet of Things (IoT) based telecommunication networks. Considering the increasing number of connected devices, resource constraints, and the need for fast and stable data transmission, optimization of routing protocols plays a key role in improving network efficiency, reducing energy consumption, and increasing reliability. In this research, an improved DCUR algorithm with the Whale Optimization algorithm is proposed to select optimal routes and balance load distribution in the IoT network. This method determines the optimal data transmission routes using criteria such as received signal strength (RSS), distance between nodes, energy status, and temperature of nodes. Simulation results show that implementing the optimal protocol reduces data transmission latency, increases network lifetime, reduces

   Schizophrenia is one of the most complex psychiatric disorders characterized by widespread abnormalities in functional brain connectivity. This study aims to develop a convolutional neural network-based >In this study, four functional connectivity metrics (PLV, PCC, MSC, and MI) were extracted from EEG signals across five frequency bands. Using a novel brain anatomy-based approach, the 19×19 connectivity matrices were reduced to 5×15, achieving over 90% reduction in feature dimensionality while preserving neurologically meaningful information. For >Results demonstrated that in the subject-independent approach, the multi-branch architecture utilizing all four metrics simultaneously achieved 99.92% accuracy, 100% sensitivity, and 99.82% specificity. In the subject-dependent approach, 78.44% accuracy was obtained with 76.70% sensitivity and 80.53% specificity. Mutual Information (MI) showed the best single-metric performance with 99.43% accuracy in subject-independent evaluation, while Phase Locking Value (PLV) demonstrated the best performance with 79.98% accuracy in subject-dependent evaluation. The innovations of this research include the anatomy-based dimensionality reduction method, comprehensive comparison of four connectivity metrics, multi-branch architecture for simultaneous processing of spatial-frequency information, and dual evaluation paradigm. The proposed system demonstrates high potential for use as a diagnostic aid in early schizophrenia detection, other psychiatric disorders, and brain-computer interface applications. Schizophrenia is one of the most complex psychiatric disorders characterized by widespread abnormalities in functional brain connectivity. This study aims to develop a convolutional neural network-based >In this study, four functional connectivity metrics (PLV, PCC, MSC, and MI) were extracted from EEG signals across five frequency bands. Using a novel brain anatomy-based approach, the 19×19 connectivity matrices were reduced to 5×15, achieving over 90% reduction in feature dimensionality while preserving neurologically meaningful information. For >Results demonstrated that in the subject-independent approach, the multi-branch architecture utilizing all four metrics simultaneously achieved 99.92% accuracy, 100% sensitivity, and 99.82% specificity. In the subject-dependent approach, 78.44% accuracy was obtained with 76.70% sensitivity and 80.53% specificity. Mutual Information (MI) showed the best single-metric performance with 99.43% accuracy in subject-independent evaluation, while Phase Locking Value (PLV) demonstrated the best performance with 79.98% accuracy in subject-dependent evaluation. The innovations of this research include the anatomy-based dimensionality reduction method, comprehensive comparison of four connectivity metrics, multi-branch architecture for simultaneous processing of spatial-frequency information, and dual evaluation paradigm. The proposed system demonstrates high potential for use as a diagnostic aid in early schizophrenia detection, other psychiatric disorders, and brain-computer interface applications.

   Abstract In this thesis, an innovative compact dual-band branch-line coupler is designed, simulated, and analyzed to operate efficiently at 2.24 GHz and 5 GHz. The primary objective of the thesis is to develop a miniaturized and low-loss microwave coupler capable of effectively suppressing higher-order harmonics, tailored for modern wireless communication systems such as 5G and advanced Wi-Fi networks. To achieve this, modern design techniques have been employed, including the use of a modified interdigital capacitor, a rectangular-shaped resonator for dual-band performance, and a custom-designed harmonic suppression cell. The proposed structure was simulated using ADS 2023, and results show outstanding performance. In the first band (2.24 GHz), the coupler achieves a coupling level of S21?=?0.13 dB and a return loss of S11?=?24.02 dB. In the second band (5 GHz), these values are -0.21 dB and below -20 dB, respectively. The fractional bandwidth (FBW) is measured to be 46% in the first band and 20% in the second. One of the most significant achievements of this work is the suppression of harmonics up to the seventh order, alongside a remarkable reduction in physical size to just 0.0154?g2 . The circuit was fabricated on a Rogers 5880 substrate and tested at the Computational Intelligence Laboratory of Razi University, Kermanshah. The experimental measurements closely match the simulation results, confirming the stability and reliability of the design. Given its unique features, the proposed coupler is a strong candidate for integration into compact, multi-band, and next-generation RF systems. Keywords: Branch-line coupler, dual-band structure, modified interdigital capacitor, fractional bandwidth, microstrip lines, 5G systems

Given the increasing need for compact, high-performance passive devices with harmonic suppression capability in modern telecommunication systems, this thesis addresses the design of a compact Wilkinson power divider using modified resonator structures. The main objective is to reduce size, improve bandwidth, and effectively eliminate harmonics. The proposed design is implemented on a Rogers RT/duroid 5880 substrate with a thickness of 25 mil and operates at a center frequency of 2 GHz. Results demonstrate a significant size reduction of 86% compared to the conventional design, a wide fractional bandwidth of 65%, and effective suppression of 6 harmonics (2nd to 7th order). Electrical performance at 2 GHz includes return loss better than 26 dB, insertion loss approximately 3.031 dB, and isolation better than 20 dB. The total physical dimensions are only 0.078?g?×0.11?g?. This power divider, offering compact dimensions, wide bandwidth, and harmonic suppression capability, is considered an ideal option for modern high-density RF/microwave systems.

   The Wilkinson power divider is a key component in the design of RF and microwave circuits, used to split the input power into multiple outputs while maintaining impedance matching and high efficiency. The importance of designing a Wilkinson power divider lies in its ability to not only effectively divide power but also address the need for isolation between output ports and reduce return losses. These features make the Wilkinson power divider widely used in many communication, radar, and other RF applications that require precise and reliable performance. Proper design of this divider can lead to improved system efficiency and reduced signal interference across different frequencies.

   Cardiovascular diseases, particularly arrhythmias, have been among the leading causes of mortality in recent years. Consequently, the medical community has been actively seeking efficient and rapid methods for diagnosing these conditions. To enhance diagnostic speed and minimize potential human errors, the use of automated methods for detecting arrhythmias has gained significant attention. This study aims to achieve accurate and timely detection of various arrhythmias with minimal computational complexity and a reduced number of features. in this thesis, three types of arrhythmias—atrial, sinus, and ventricular—are analyzed, with each category comprising 100 ECG signal samples sourced from the SHEDB database. Two models, the Multilayer Perceptron (MLP) neural network and the Radial Basis Function (RBF) neural network, were employed for arrhythmia classification. Results indicate that the MLP model, achieving a test accuracy of 97.8%, significantly outperformed the RBF model, which achieved a test accuracy of 76.7%. These models were selected to reduce computational overhead compared to more complex models like Convolutional Neural Networks (C  ). furthermore, various temporal, statistical, and frequency domain features were examined during the feature extraction process. The best performance was achieved using eight selected features: Root Mean Square (RMS), Waveform Length (WL), Absolute Sum of Squares( ASS), Mean (MEAN), Skewness (SKW), Kurtosis (KUR), Dominant Frequency (DF), and Amplitude of Dominant Frequency (AFDF).

  Diabetes mellitus is a chronic metabolic disease characterized by the body's inability to effectively use blood sugar or produce sufficient insulin to regulate it. If not properly diagnosed and treated, this disease can lead to serious complications such as heart disease, kidney damage, nerve disorders, and blindness. Given the increasing global prevalence of diabetes, early identification and prediction of this disease is of paramount importance. This research focuses on predicting the onset of diabetes using machine learning algorithms. For this purpose, the Pima Indian Diabetes dataset is employed, which includes features such as age, weight, blood pressure, fasting blood glucose levels, Body Mass Index (BMI), number of pregnancies, family history of diabetes, and other biological parameters. These data, extracted from a population of Native American women, are used to train and test various machine-learning models. In this study, different algorithms including Logistic Regression, XGBoost, AdaBoost, LightGBM, Decision Tree, CatBoost, and Gradient Boosting, were employed to predict the onset of diabetes. The results of this research, which compares different algorithms, particularly boosting algorithms, indicate that some of these algorithms demonstrate higher accuracy in predicting diabetes and can be used as effective tools for early detection and optimal management of the disease. The models achieved the following accuracy: Logistic Regression (0.92), XGBoost (0.96), AdaBoost (0.94), LightGBM (0.96), Gradient Boosting (0.91), and Decision Tree (0.91), with the best performance achieved by CatBoost with an accuracy of 0.98. Finally, suggestions for future research are offered.

 Brain tumors are caused by abnormal cell growth in the brain. Magnetic resonance imaging (MRI) is the most widely used method for diagnosing brain tumors. Through these MRIs, doctors analyze and identify abnormal tissue growth and can confirm whether the brain is affected by a tumor or not. Today, with the advent of artificial intelligence techniques, the diagnosis of brain tumors is performed using machine learning techniques and algorithms and artificial neural networks. The advantages of using these algorithms include rapid prediction of brain tumors, fewer errors, and greater accuracy, which helps in decision-making and choosing the most appropriate treatment for patients. In this study, an artificial neural network will be used to detect the presence of a brain tumor and its performance will be analyzed. The main goal of this research is to design an artificial neural network-based system for automatic detection of brain tumors from MRI images and classify MRI images into two categories: "brain tumor" and "normal" and ultimately achieve high diagnostic accuracy in MRI images. Keywords: Brain tumor detection, Artificial neural networks (ANN), MRI images, Convolutional neural network (CNN).

 Breast cancer is one of the most common types of cancer among women, which is caused by the abnormal growth of breast cells. Determining the boundaries and analyzing the shape of tumors in ultrasound images are considered to be key aspects of medical diagnosis of breast cancer. Accurate identification of tumor edges and accurate diagnosis of cancer have always created challenges for doctors due to image noise or tumor characteristics. Therefore, in this thesis, an intelligent and automatic method for identifying the border of breast masses and classifying breast cancer using advanced techniques of deep learning and machine learning based on ultrasound images is presented. The data used includes 647 ultrasound images of women between 25 and 75 years of age with benign and malignant masses collected at Bahia Hospital. In the pre-processing stage, the images were removed from unnecessary noise and resized to the standard size of 256,256 pixels. Then, using Deep Lab method 3, the images were accurately segmented and the boundaries of the masses were determined. Next, textural and statistical features were extracted from the masses and using different machine learning models, including support vector machine (SVM), nearest neighbor (KNN) and decision tree, the masses were classified into benign and malignant categories. The results show that the decision tree model with an average accuracy of 89.92%, sensitivity of 73.96%, detectability of 97.72% and a score of 19.3% has performed best in breast cancer classification. This research is considered as an important step towards improving the quality of health and treatment services in breast cancer diagnosis and can help to improve the process of diagnosis and treatment of this disease.

Classifying non-stationary signals, such as electroencephalogram (EEG) signals, is a major challenge in signal processing. Non-stationary signals are signals whose statistical properties, such as mean and variance, change over time, making them more difficult to analyze compared to stationary signals, where these properties remain constant. EEG signals, commonly used to study brain activity, are non-stationary, meaning their patterns change over time. Detecting abnormal brain activity, like epilepsy, which shows different patterns from normal activity, requires advanced techniques to classify these signals accurately. In this study, we explore how well a combination of Continuous Wavelet Transform (CWT) and pre-trained Convolutional Neural Networks (C  ) can classify non-stationary signals. As a case study, we use multi-channel neonatal EEG data, where each segment of the signal is labeled as either healthy or showing a seizure. A novel approach was developed in this research, where continuous wavelet transform representations from all EEG channels were combined into a single RGB image by stacking the continuous wavelet transform representations into the red, green, and blue channels. This creates a multivariable image that captures important time-frequency information from all EEG channels. These images were then classified using two advanced convolutional neural network models, ResNet and MobileNet, which were pre-trained on large datasets. Both models achieved high classification accuracy, with results above 80%, as well as high area under receiver operating characteristic curve scores, also above 80%. The findings show that combining continuous wavelet transform with convolutional neural networks is effective for recognizing patterns and classifying non-stationary signals. This work highlights the potential of this method, especially for medical applications like detecting seizures in newborns.

The design and construction of water wells are unfamiliar to many engineers. Due to this unfamiliarity, their design is often done inappropriately or completely ignored. In the mechanical design of ground source heat pump systems, we should be familiar with the terminology of water wells and key issues related to their construction. In wells, the still water level and the pumping level are two different states, and in case of low water or no water, sensors and automatic cut-offs can remove the float from the circuit. In this research, we have designed a circuit that differs from existing circuits in that the circuit we have designed can be adjusted at any water level.   

In this thesis, the investigation and management of interference for device-to-device (D2D) communication in fifth generation (5G) networks has been discussed. Due to the ever-increasing number of devices connected to the network and the need for more transmission with high speed and low latency, interference management in D2D communication has become a fundamental challenge. As one of the key technologies in 5G networks, D2D communications allow devices to communicate directly with a connection without the need for a base station. This increases efficiency and at the same time reduces, but can increase interference in the network. To solve this issue, two optimization methods using meta-heuristic algorithms are compared: Genetic Algorithm (GA) and Wall Optimization Algorithm (WOA). Genetic algorithm is mainly used in optimization problems due to its high search power and ability to adapt to complex problems. This algorithm searches for optimal solutions using selection, intersection and mutation operations. On the other hand, Wall's optimization algorithm is a new method i  ired by predator behavior that searches for optimal solutions by bubble and circular search. To evaluate the performance of these two algorithms, MATLAB software is used for simulation. Evaluation criteria included efficiency, execution time, and performance volume increase (CDF). The results of the simulations show that the Wall Algorithm (WOA) has performed significantly better than the Genetic Algorithm (GA) in reducing the interference and improving the efficiencies. Also, WOA algorithm has shown shorter execution time and better performance compared to GA. These results show that the optimization algorithm can be used as a treatment solution to manage interference in D2D communication in 5G networks. These findings can help improve the performance of future wireless networks and translate into increased quality of service (QoS) for end users.  

 Detecting brain tumors from the levels of MRI images is one of the biggest challenges in the world of artificial intelligence and engineering sciences Medicine goes to the number. Brain tumors, which can lead to the death of people with their growth, need to be staged Identify, identify. There are two main categories of tumors, which include benign and malignant tumors. made An intelligent medical diagnosis system in the field of brain tumor diagnosis from the levels of MRI images is one of the important parts In medical engineering science, it is numbered, which can help doctors in the early detection and identification of tumors and then diagnosis. Care and maintenance of people until full recovery is helpful. Glioma is the most common malignant brain tumor with grades It is different, which greatly determines the survival rate of patients.Tumor segmentation and grading using contrast-enhanced imaging Magnetic resonance imaging (MRI) is essential for diagnosis and treatment planning. To achieve this clinical need, a feature Part of Convolutional Neural Networks (CNN) based on UNet network for tumor segmentation and transmission Then, based on the pre-trained convolution of the Vgg16 base and a perfection classifier for tumor grading, it is developed. Segmentation and gradation models from the same pipeline T1 - Coping, weak liquid damping inversion recovery (FLAIR) and post-contrast T1 MRI images of 110 low-grade glioma patients (LGG) for teaching and evaluating the use of do Dice similarity coefficient (DSC) and tumor detection accuracy obtained by the segmentation model are 0.84 and 0.92, respectively.Model Grading LGG with accuracy, sensitivity and specificity of 89.0, 87.0 and 92.0 respectively at the level of MRI images and 95.0, 97.0 and 98.0 classifies patients as benign and malignant. This work has the potential to use deep learning MRI images to provide a non-invasive tool for simultaneous and automated tumor segmentation, diagnosis and tumor grading shows for clinical applications.

Sleep is a state of reduced mental and physical activity and is vital for part of daily activities. When sleep is insufficient, several problems such as learning disorders and increased risk of stress diseases such as mood disorders and cardiovascular diseases appear. The older population, adults over 60, have more problems with sleep and sleep disorders. They are more prone to sleep disorders, such as insomnia. These problems together can lead to an increased risk of other diseases. A common condition among the elderly is dementia. Lack of sleep and bad sleeping habits are risk factors for dementia, however, sleep is not the only risk factor for dementia. Education, age and gender of people are other factors that play a role in the risk level of a given person. Since data is available on the older population, we can make inferences about different aspects of their lives. Machine learning is a program that uses experience to learn and make predictions based on its experiences. These programs can find the patterns in the data or explain the relationship between them. We use ML to study the relationship between sleep and dementia. We also use other features such as methodological approaches and other aspects of sleep such as REM sleep, so using machine learning with different features is useful in other aspects of sleep. The current thesis evaluates different methods to investigate sleep disorders on dementia using five machine learning algorithms (gradient boosting, logistic regression, Gaussian smoothing, random forest and support vector machine). Data on the older population (60+) in Sweden from the Swedish National Study on Aging and Care - Blacking = 4175 (number of samples) Algorithm from 10-fold stratified cross-validation to obtain results, including Brier score to check accuracy And feature importance is used to investigate factors affecting dementia. Algorithms use 16 features that are based on personal factors and sleep disorders.  

   This thesis presents the investigation, design, simulation, and fabrication of a compact Gysel power divider with a hybrid structure and wide pa  and. The primary objective of this research is to enhance the performance and reduce the size of the Gysel power divider by employing novel resonators and lumped elements. Rectangular and hairpin resonators were designed to create transmission zeros and improve impedance matching. These resonators are capable of generating six transmission zeros in their stopband, significantly improving filtering performance and unwanted frequency suppression. The circuit implementation utilized a substrate with the specifications of Rogers RT/Duroid 5880. The initial simulations of this circuit were conducted using the Advanced Design System (ADS) software. Simulation results indicated that the designed circuit exhibits very low return loss (S11) and adequate power transmission (S21) at the frequencies of 8.74 GHz and 9.23 GHz. Specifically, at these frequencies, S21 was measured to be -3.08 dB and -3.1 dB, respectively, while the return loss (S11) was -22.3 dB and -20.8 dB, respectively. Furthermore, the isolation (S32) was found to be less than -25 dB, demonstrating good signal separation between ports. These simulation and measurement results show good agreement and confirm the optimal performance of the circuit. Following the simulation phase, the designed Gysel power divider was practically fabricated and tested. The fabrication process included preparing the printed circuit board (PCB) mask, exposing and etching the board, and soldering the lumped elements. The practical measurement results closely matched the simulation results, validating the circuit's optimal performance in real-world conditions. One significant achievement of this research is the reduction of the circuit's size by 86% compared to the initial structure, highlighting a considerable advancement in circuit design and optimization. This dual-band power divider, with its optimized design and compact size, is suitable for various telecommunications, radar, and electronic systems that require precise and efficient power division. Recommendations for future work include further optimization of the pa  and width, the use of new materials with better dielectric properties, and examining the environmental and thermal effects on the circuit's performance. These advancements can further develop and enhance the technology of power dividers, improving the efficiency of telecommunications and radar systems. Given the results obtained, it is anticipated that these optimization and design methods will continue to be of interest and lead to further improvements in telecommunications and electronic equipment. This research offers an innovative and efficient solution, marking a significant step forward in developing advanced technologies in the field of telecommunications and electronics.

  سيستم هاي ميكروالكترومكانيكي يا به اختصار ممز[1]) (MEMS، كارايي قابل توجهي در فركانس هاي مايكروويوو راديويي (RF) دارند . اين تكنولوژي كاربردهاي MEMS (سيستم هاي ميكرو الكترومكانيكي) را به خصوص در سيستم هاي مخابراتي بيسيم و ماهواره نشان مي دهد. در واقع سوييچ هاي ممز   RF، ميكروماشين هاي سطحي هستندكه از حركت مكانيكي براي ايجاد يك"اتصال كوتاه" يا " مدار باز " در خط انتقال RF استفاده مي كنند .اين سوييچ ها با استفاده از روش هاي ميكروالكترومكانيكي و با اتصال الكتريكي–فلزي و با استفاده از فواصل هوايي ساخته مي شو ند . در طراحي و ساخت سوييچ هاي ممز، دو نوع اتصال مختلف وجود دارد. اين اتصال شامل پيكربندي خازني (يا شنت) و نوع فلز به فلز (يا سري) مي باشد .در سوييچ هاي خازني به دليل وجود لايه دي الكتريك بين خط انتقال و پل ، چه در حالت بالا(مدار باز) و چه در حالت پايين(مدار بسته) بين پل و خط انتقال هيچ اتصالي وجود نخواهد داشت . به دليل وجود اين فاصله سوييچ در حالت بالا و در حالت پايين مانند يك خازن عمل مي كند كه با افزايش نسبت خازني ، سوييچ عملكرد فركانس بالاي بهتري خواهد داشت. در روش پيشنهادي يك سوييچ خازني با يك لايه پلي سيليكان و يك دي الكتريك از جنس هوا و مواد ديگر كه بر بستري از سيليكان و يا خطوط CPW قرار ميگيرند ، بررسي مي شود و سعي ميشود ولتاژ تحريك سوييچ را كاهش داد چرا كه يكي از مهم ترين پارامتر ها در طراحي و ساخت سوييچ هاي ممز، ولتاژ تحريك مي باشد كه در واقع پايين ترين ولتاژي است كه با اعمال آن به مدار، سوييچ در حالت روشن قرار ميگيرد و اساسا كوچك بودن اين مقدار ولتاژ براي ما مطلوب است اما از آنجاكه اين پارامتر با پارامتر زمان سوييچينگ نسبت عكس دارد، نميتوان آن را به ميزان زياد كاهش داد. لذا با روش هاي ديگري مانند كاهش يا افزايش فواصل هوايي و تغيير جنس ماده ي دي الكتريك ، به بهبود پارامترهاي مهم سوييچ ، مانند زمان سوييچينگ و عملكرد فركانس بالا مي پردازيم

One of the biggest problems of mobile phone operators is mobile network coverage in densely populated urban areas; In mobile networks, communication signals are transmitted between mobile phones and transceiver base stations. These stations include telecommunication equipment such as antenna, radio, processing equipment and other electronic components, which enable the sending and receiving of wireless signals. Factors such as the location of urbanization do not allow the correct transmission of signals from the operator to the subscriber. Artificial obstacles such as buildings block the passage of mobile waves so that the waves reach the subscriber in a weakened form; In dense urban areas, the number of mobile phone users is very high, and synchronizing a large number of users with the limited space of network servers is facing problems. There are a large number of telecommunication equipment and wireless networks in these areas, which cause interference in the radio space. This interference reduces the efficiency and coverage of the network. The subject investigated in this thesis is the improvement of mobile network coverage using the transfer and control of changeable parameters in the transceiver base station in the urban environment. Blind spots in the mobile network are areas where the direct signal from the transceiver base station does not reach them and the quality of communication decreases there. Reducing blind spots is very important. In this thesis, a new software model is presented to improve the coverage of city area  

  Vibrationenergy harvesting is an ideal source of renewable energy, in this thesis a newmicro-electromagnetic harvesting mechanism for low-power applications isintroduced that can be used at a vibration frequency of less than 11 Hz, whichis used for motion harvesting. Fits humans, moving vehicles, and structuressuch as buildings, bridges, and streets. The energy harvesting mechanismresulting from the moving electromagnetic field (magnet) in the vicinity of afixed coil made of material (copper) induces a current in the coil, examples ofdifferent energy harvesting geometries to achieve the best and lowest frequencyand harvesting performance The energy generator is specified in terms ofdimensions, output power, stable sinusoidal voltage. As far as the builtprototypes showed the ability to harvest energy at low frequencies in the rangeof 2 to 10 Hz, with a voltage between 330 and 800 mV, and an output power of upto 2800 µW.

  Architecture is a process of planning, designing and building. Considering that the roots of these constructions originate from human thoughts and human thoughts are influenced by culture and customs, it can be said that architecture is a symbol of its culture. is the area. The architecture of any society can represent and show the identity of the ruler of that society. With the emergence of modernists and relying on technology, in many cases, they fought against traditional and native architectures, and as a result, it caused a lack of identity in architecture. Now in Iran, the identity crisis is one of the controversial issues, especially in the housing sector and facades. It is a city. According to the conducted studies and the existence of identity crisis problems at the level of residential facades, returning to authentic Iranian architecture can be one of the appropriate solutions to solve this problem. Since dealing with the architectural structure and urban development of contemporary facades in order to reread the identity, it requires a critical thinking on the concepts and components that influence the formation of architectural elements and the characteristics of urban facades, by knowing the influential and structural concepts, it is possible to revive It provided visual identity in the form of architectural landscape of contemporary facades. Considering the emergence of this identityle  ess in Iran, now the main goal in this thesis is to investigate the residential house during the Safavid period and find the components of the identity of the part in the Safavid architecture, especially in the facade part, and use the mentioned components in the form of The design of a residential complex in Kermanshah city and Set street. The research method of this thesis is descriptive-analytical by means of library and field study.

In this research, a lowpass-bandpass diplexer with ultra-wide stopband and low insertion loss using hexagon-shaped resonators. The proposed diplexer consists of a bandpass (BPF) and a lowpass filter (LPF), representing the core concept of the proposed design method that aims to concurrently design BPF and LPF. In this proposed design method, the influence of the LPF filter on the BPF's design has been identified through coupling matrix analysis for the first time. Initially, an LPF is designed based on three coupled hexagon-shaped elliptical resonators. Subsequently, a novel model for BPF design, utilizing coupled high-impedance lines, has been introduced. Following this, the BPF model is developed using coupling matrix analysis while considering the impact of LPF resonators. The LPF have a 1.32 GHz cut-off frequency and ultra-wide stopband up to 17.42 GHz. The BPF consisted of four resonators and the hexagon-shaped structure is used instead of low impedance lines. The utilization of hexagon-shaped resonators serves the purpose of enhancing the precision of the coupling effect, aligning with the proposed coupling matrix analysis. Additionally, hexagon-shaped resonators exhibit a greater capacitive effect, leading to a reduction in insertion loss within the pa  and when compared to rectangular-shaped resonators. The BPF has narrow pa  and with center frequency of is 2.25 GHz and 0.31 GHz bandwidth. The measured insertion losses of LPF and BPF are less than 0.75 dB and 0.81 dB, respectively in 60% of pa  ands

آنتن‌هاي آرايه‌اي 1 از چندين آنتن مستقل تشكيل شده‌اند كه به صورت هم‌محور يا غير هم‌محور در كنار يكديگر قرار مي‌گيرند. اين آنتن‌ها كاربردهاي فراواني در حوزه‌هاي مختلف مانند ارتباطات بي‌سيم، رادار، سنجش از دور و غيره دارند. يكي از چالش‌هاي مهم در طراحي آنتن‌هاي آرايه‌اي، اثر تزويج متقابل 2 بين المان‌هاي آنتن است. اين اثر باعث مي‌شود كه انرژي سيگنال ارسالي از يك المان، به المان‌هاي ديگر نيز منتقل شود. اين امر مي‌تواند باعث كاهش بازده آنتن، تغيير الگوي تابش و ساير مشكلات شود. در اين پژوهش، از دو روش ساختار DGS و عنصرها پارازيتي براي كاهش اثر تزويج متقابل استفاده شده است. ساختار DGS يك تكنيك مؤثر براي بهبود خصوصيات آنتن‌هاي ريزنوار است. اين ساختار با ايجاد تغييراتي در ساختار زمين آنتن، تأثيرات قابل توجهي در عملكرد آنتن دارد. عنصرها پارازيتي نيز هر المان غير از خود آنتن‌هاي فعال كه در ميدان نزديك آنتن‌هاي آرايه‌اي قرار مي‌گيرد، به عنوان عنصر پارازيتي تعريف مي‌شود. نتايج اين پژوهش نشان مي‌دهد كه استفاده از ساختار DGS و عنصرها پارازيتي 3 ، مي‌تواند اثر تزويج متقابل را به ميزان قابل توجهي كاهش دهد. اين امر باعث بهبود بازده، الگوي تابش و ساير مشخصات آنتن مي‌شود. نتايج اين پژوهش مي‌تواند به طراحان و محققان در زمينه آنتن‌ها كمك كند تا طراحي‌هاي بهتر و با عملكرد بهتري را ارائه دهند. اين نتايج همچنين مي‌تواند در دستيابي به  

در سالهاي اخير پژوهش و مطالعه بر روي سيگنالهاي الكترومايوگرافي به دليل سادگي ثبت اين سيگنالها و نمود خوبي كه از فعاليت بدني افراد دارد توسط محققان حوزه مهندسي پزشكي مورد توجه ويژه اي قرار گرفته شده است. يكي از راه هاي بررسي عملكرد، جداسازي موقعيت وجهت حركت عضلات وتعيين نيروي آنها، ثبت و پردازش سيگنال EMG (الكترومايوگرافي) مي باشد. در پژوهش هاي پيشين به دليل وابستگي موقعيت عضلات به عوامل متعدد، محدوديت¬هايي در ارايه¬ي يك روش به منظورپيش بيني موقعيت عضلات ايجاد شده است. به علت اهميت اين رابطه در مسايل مختلف اسكلت عضلاني و در زمينه¬ي آناليزحركت، ارايه¬ي راهكارهايي براي تخمين تئوري نيروي عضلاني امري ضروري است كه در اين پژوهش به آن پرداخته شده است. هر چند در مطالعات مختلف،روابط گوناگوني براي اين منظور مطرح شده است، اما پيچيدگي ارتباط ميان موقعيت و جهت حركت ايجاد شده در عضله و عوامل مؤثر در آن، باعث شده است تا تلاش براي ارايه¬ي يك روش با بازده محاسباتي بالا با مشكلات زياد روبه روشود. در اين پايان نامه روش تخمين موفعيت و جهت عضلات از روي مدل هاي پارامتري سيگنال الكترومايوگرافي به كمك كلاسيفاير مبتني بر شبكه عصبي عميق و با كمك سه دسته از آنتروپي¬هاي شناخته شده و پركاربرد از قبيل آنتروپي شانون ريني و تساليس مورد بررسي قرار گرفته است. نتايج به دست آمده از شبيه سازي¬ها و دستيابي به دقت 93 درصد حاكي از اين بوده كه استفاده از اين فرايند براي تخمين جهت و موقعيت عضله دست عملكرد مطلوبي را در بر داشته است. 

  Abstract: In this project, firstly, a 2-port cut rectangular microstrip patch antenna (port A: frequency 1.756 and port B: 2.47 GHz) with a fractal structure engraved in the patch, for use in the RFEH system in the LTE band in order to improve the performance in harvesting the energy of electromagnetic waves. The original patch antenna (1) without fractal geometry is designed as the proposed antenna (1). The antenna is made on FR4 substrate with relative transmittance of 4.4, loss tangent of 0.02 and dimensions of 48 x 48 mm2. Also with new changes. In the structure and dimensions of the primary antenna (1) without fractals, with the aim of further reducing the size of the antenna, a new two-port right-angled triangular microstrip patch antenna with a resonant frequency of 2.4 GHz in each port is obtained as the primary antenna (2), which is obtained by applying changes In the right-angled triangular patch antenna, the gain efficiency of the antenna is improved compared to the previous state of the proposed antenna (2). In the first chapter: definitions, introduction, purpose, history and background of the research are introduced. In the second chapter: the structure, challenges and components of the RFEH system (antenna, impedance matching network and rectifier circuit) are presented, in the third chapter: first, the theory and technique of the proposed antenna design (1) and (2) are discussed. Then, the theory and technique of designing a horn transmitter antenna (RF transmitter) and a broadband rectifier including a voltage doubler rectifier circuit to reduce losses, the role of the Schottky diode HSMS2852 used in this part is very effective, also for a good match between The antenna and the rectifier circuit use an innovative matching network that is a combination of microstrip transmission lines and compact modules. The rectifier is made on the FR4 substrate with relative transmittance of 4.4, loss tangent of 0.02 and dimensions of 48 x 48 mm2. The use of fractal structure in the proposed antenna (1) makes effective use of space, reduces the size of the antenna, increases the effective length of the antenna and improves the reflection coefficient. And the change in the structure of the primary antenna patch (2) is from a rectangle to a right-angled triangle in order to reduce the size of the antenna. Also, the purpose of having 2 ports of antennas is to compress the volume of the antenna and increase the energy harvesting of electromagnetic waves. In the fourth chapter: at the beginning, a simple microstrip patch antenna without fractal structure is simulated as the primary antenna (1). And as a result, it led to the shift of resonance frequencies to the desired frequency in the LTE band and the improvement of the S11 coefficient and efficiency compared to the primary antenna (1). In the next step, the two-port microstrip patch antenna in the form of a simple right-angled triangle (initial antenna 2) is simulated with new dimensions for use at 2.4 GHz frequency (in both ports) and finally by applying special changes to the initial triangular patch antenna ( 2) We see the optimal gain efficiency and realization of the proposed antenna (2). By simulating the trumpet antenna (as a transmitter of RF signal to the receiving antennas) and the matching network and the desired rectifier circuit, and in the final stage, by connecting each of the antennas in the role to the matching network and the rectifier circuit, Rectna is formed. And then the simulated output voltage obtained from the proposed antenna (1) is compared with the original antenna (1) and the proposed antenna (2) with the right triangle antenna (primary antenna 2) and finally the result obtained is due to the better performance of the proposed antenna (1) and (2) in harvesting energy and subsequently producing more voltage compared to the primary antennas.

Analysis of Four Electromagnetic Launcher and Calculated the Mutual Gradient Inductance using 2D-FEM

   Smart antenna is one of the latest technologies that has a higher capacity in wireless networks by effectively reducing multi-path and co-channel interference. Smart antennas use a set of radiating elements arranged in an array. In a smart antenna system, the arrays are not smart by themselves, it is the digital signal processing that makes them smart. The method of combining signals and then focusing radiation in a specific direction is often referred to as digital. Beamforming will be widely used in the term. In this thesis, it consists of a presentation of 8 elements. Each element or antenna is a T-shaped dipole designed on an FR4 substrate with a relative electrical conductivity of 4.4 and a thickness of 1.60 mm. Each element is fed using 50-ohm microstrip lines. For each row, four elements of one ground are considered, in other words, four arrays facing each other have separate ground. The operating frequency is equal to 3.5 GHz and the arrangement of the array elements is designed in two rows of four facing each other. The obtained frequency bandwidth is 3.3-3.7 GHz and the radiation efficiency for each antenna is about 97% and the overall efficiency is about 85%.

  In

   Due to the growth of the use of electronic devices such as electronic amplifiers, wireless devices, etc., power dividers have gained special importance, and the most important of these dividers are Wilkinson and Gysel, which are widely used. take The main task of dividers in the electronic circuit is power division, which is evaluated in symmetrical and asymmetrical divider models today.    The problem discussed in this thesis is to pay attention to the reduction of the physical size that affects the size of the electric circuit, that the reduction of the designed dividers reduces the dimensions of the circuit. It increases the quality of the output wave and also increases the conduction bandwidth, which plays a significant role in the selection of dividers in devices, which is investigated using microstrip technology today. In this thesis, ADS software has been used to simulate a Gysel divider, which has been obtained by reducing the dimensions, removing additional harmonics, and providing proper isolation, and good results have been obtained.

با توجه به شيوع گسترده بيماري هاي واگيردار در جهان مدل‌هاي رياضي مي‌توانند به پيش‌بيني و كنترل اين پاندمي كمك كنند. پاندمي بيماري نشان داده كه كشورها عليرغم توسعه يافتگي و دسترسي به منابع و تجهيزات در مقابل يك بيماري جديد ناشناخته بايستي زير ساخت‌هاي فيزيكي اعم از بيمارستان‌ها، مراكز بهداشتي درماني و نيروي انساني را در برابر بحران آماده نمايند. اپيدمي ها ممكن است ظرفيت بيمارستاني ارائه خدمات بهداشتي درماني را در هم بشكنند و منابع مادي و انساني، از جمله فضاي بيمارستان، تجهيزات و داروها براي برآوردن تقاضا كافي نباشند، مخصوصاً در مورد اپيدمي كه چندين هفته يا چندين ماه طول بكشد و به ويژه اگر بحران‌هاي همزمان اتفاق افتد، بيمارستان جهت كمك به اقدامات انجام شده در كنترل اپيدمي، بايد بسياري از كاركردها و منابعش را مهار كرده و به طور هماهنگ شده مورد استفاده قرار دهدكه برآوردن اين الزامات مي‌تواند چالش برانگيز باشد. يك اپيدمي نياز به مركز بهداشتي درماني دارد تا اولويت‌هايش را تغيير داده و با روش‌هاي كاري منطبق باشد تا پاسخي سيستميك و هماهنگ به يك موقعيت با   سرعت در حال تغيير بدهد.   بر اساس پيش‌بيني و صحت نتايج مربوطه، آماده‌سازي مراكز درماني در مقابل بحران از جنبه‌هاي مختلف مديريتي، ارتباطات، منابع انساني، تامين تجهيزات، دارو و ساير خدمات تشخيصي، درماني و پشتيباني مورد نياز بيماران، همراهان و كاركنان بهداشت و درمان مهم‌ترين هدف اين تحقيق مي‌باشد كه با مدل‌سازي همه‌گيري بيماري در شرايط مختلف محقق مي‌شود. شبيه سازي با در نظر گرفتن مواردي مانند: تاثيرات اندازه جمعيت، دوره نهفتگي بيماري، دوره بيماري، نرخ آلودگي، مكان‌هاي آلوده بر گسترش بيماري، واكسيناسيون عمومي و ... اجرا و تحليل خواهدشد.

Abstract: In this thesis, a four-element Adcock directional antenna is designed and the angle of the input (received) signal is calculated. Considering that one of the sources of error is the interaction effect, we have gone to design an EBG to reduce the interaction effect. First, we have designed an EBG between two dipole antennas for the 750 MHz frequency band. Then we have designed a four-element Adcock array and checked its error. Then, for this Adcock array, we have designed an EBG and checked its error and compared it with the case without EBG. We observed that the error in this case has decreased by about 3 degrees (more than half).   

  On March 11, 2020, the World Health Organization (WHO) declared thecoronavirus (covid-19) as a pandemic due to its widespread seriousnessthroughout the world. The new corona virus caused by the SARS-COV2 virusoriginated in the city of Wuhan, China and has spread worldwide. Covid-19 is arespiratory disease that has infected so many people around the world that theWorld Health Organization (WHO) declared this disease as a pandemic due to itshigh mortality among people with poor medical history. has done.The corona virus (covid-19) pandemic has had a negative impact on thehealth of people worldwide. To reduce the impact of this widespread epidemic,it is essential to identify covid-19 cases as quickly as possible. Earlydetection of the virus is very important in the complete recovery of thepatient, but if it is detected at a later stage, it can be fatal. Since thesymptoms of the corona virus are similar to the flu, it is difficult to detectit, so we are looking for a method that will detect the corona virus in theshortest time and with the highest accuracy.Currently, CT scans (computed tomography) and X-rays are the most commonand effective methods used by hospitals to evaluate lung images for covid-19.Since there is a problem with RT-PCR kits being stained and also these kitshave a sensitivity of less than 60%-70%, researchers are trying to find thebest and fastest methods for identifying Covid-19.Chest X-ray images combined with emerging artificial intelligence (AI)methods, especially deep learning (DL) algorithms, have become a suitableoption for initial screening of Covid-19.  Keywords: Covid-19, X-ray, deep learning, artificial intelligence, CT,RT-PCR.

In order to diagnose epilepsy, a series of features must be extracted from the EEG signal of the patients, in order to extract the features, the entropy criterion has been used. In this research, permutation entropy, which is the most suitable for the analysis of biomedical signals, is investigated. The purpose of this thesis is to implement one-variable and multi-variable entropy and use it for the analysis of biomedical signals. The experimental results show the highest >  

  Abstract:Today, due to the reduction in the size of electronic and telecommunicationcircuits and boards and the increasing use of circuits with high speed andaccuracy, the frequency spectrum known as microwave is widely used. Amongthese, one of the most widely used elements In high frequency and radiocircuits, they are power dividers and combiners (couplers). Among theapplications of power dividers, it can be mentioned that they are used insatellite receivers, antennas, power amplifiers, communication and radiosystems, etc., during which these inactive circuits, they divide or combine thepower of signals in radio frequencies.There are various types of power divider circuits, among the mostreliable ones, we can mention the Wilkinson, Gysel, T-shaped power dividers,etc., which are used in different equipment depending on the efficiency. In themeantime, Gysel power divider is more popular than other dividers due to itsmany applications and having advantages such as suitable thermal conductivitycompared to Wilkinson, high bandwidth, optimal matching and high isolation betweenoutput ports. Gysel power amplifiers can be referred to isolation of outputports, input and output impedance matching and their flat structure. Nowadays,many Gysel power dividers have been designed. In this thesis, a new structureof Gysel power divider has been proposed using microstrip lines, and also inthis thesis, in order to remove unwanted harmonics, by placing modifiedresonators with a new structure of quarter-wavelength lines is presented. Thedimensions of the resonators were determined using the PSO optimizationalgorithm, and the results show that the designed power divider has a returnloss of less than -20 dB, an appropriate insertion loss of 3.09 dB, a compactsize of 0.14 ?g × 0.42 ?g , and a high bandwidth of FBW=125%.  

The design of a reconfigurable antenna that is able to change its radiation pattern characteristics at a certain frequency in an organized and reversible manner using control switches such as (semiconductor switches, microelectromechanical switches and varactor switches), and application in the fifth generation of wireless communication networks have The present research method is descriptive-analytical and practical in terms of applying the results. Collecting the required data and information is also done by reading articles, books, using the knowledge of professors and observing the microstrip antennas that have been designed and built in the past, and to analyze and analyze the obtained data and simulate the desired models, high frequency circuit simulation software is used. CST has been used. In the research, a microstrip antenna (printed circuit patch antenna) has been designed to achieve reconfiguration of the radiation pattern. Two capacitors work simultaneously to achieve the radiation pattern reconfiguration operation. This microstrip antenna is i  ired by the design of a circular loop. The basic structure of the loop is changed and the capacitors are integrated into the patch. By changing the capacity of the capacitors in the range of (0.01-10) picofarad, the radiation pattern of the antenna can be changed to six different modes. An electromagnetic model of the proposed antenna is simulated in CST software for numerical analysis and observation of different radiation patterns. The proposed antenna structure has been implemented and built using a material (FR4) substrate with relative transmittance (4.4) and loss tangent (0.02) and thickness (1.6) mm. Antennas have been used since 1886 to send and receive information. They have special characteristics, this divides antennas into two categories, the first category is antennas with fixed characteristics and the second category is tunable antennas, tunable antennas are divided into several categories, the main ones are tunable antennas (frequency, polarization, bandwidth and radiation pattern), these types of antennas are very useful in the technology of the fifth generation of wireless communication systems. One of the achievements of this research is the design and simulation of a microstrip antenna that can reset the radiation pattern by using capacitors as control switches in order to change the surface current and as a result change the radiation pattern of the antenna.   

A microelectromechanical switch is an electronic device that disconnects or connects electric current by changing the position or shape of its microscopic structure. These switches consist of micro and even nanometer structures and are used as an alternative to conventional electromagnetic switches in electronic devices. With the change the dimensions, the characteristics of the switch change. For example, by reducing, the response time is improved, as well as an increase in sensitivity to electric current occurs. Also, with the change, the application frequency was higher. Microelectromechanical switches are used in many electronic applications, including memories, sensors, electronic chips, and communication devices. It is also used in the automotive, medical and industry. In this thesis, the basic structure of switches based on micro-electromechanical systems for use in radio frequency has been investigated and analyzed, and the design challenges of different analyzes have been investigated in the analysis. Then, by evaluating the previous switches and previous examples, their results and the weak and strong points of each have been analyzed. Finally, a new microelectromechanical switch for high frequency applications has been designed and simulated using polysilicon material. The presented structure is operated with 5V voltage and its switching time is less than 38 microseconds. COMSOL version 6.1 software is used to simulate the proposed structure. One of the most prominent features of the provided switches is its size, which is only 60?m × 220?m, and this value is much smaller compared to other articles and designed switches. Also, one of the other terms in the design of high frequency switches is the value of capacitor at the time of the switch, or in other words, high capacitor and low capacitor, which is referred to as the capacitor ratio. This parameter for the presented switch is 65, which is very ideal and provides various applications for the switch. At the end, the presented switch structure has been modified and with the help of twisted anchors and different structures, different switches have been presented under the title of the first and second modified structure. Each of these structures moves with the same voltage of 5 volts, and for each of these structures, the structures of switching time, capacitance ratio, displacement, and the effect of contact force have been simulated and analyzed at the level of the switch. Also, the type of material in the function of the switch has been investigated and by changing the material to gallium arsenide, it is found that the contact forces on the surface of the switch are much less and the switching time increases.   

آنتن هاي شيپوري كاربرد زيادي در طيف فركانسي امواج ميليمتري دارند. ويژگي هاي اين آنتن ها از قبيل بهره زياد، تلفات بازگشتي كم، پهناي باند مناسب و ساخت نسبتا راحت   باعث شده در كاربردهاي مختلفي نظير تغذيه آنتن­هاي انعكاسِي، رادار، سيستم­هاي رديابي ماهواره، جنگ الكترونيك، سامانه هاي جهت يابي و تشخيص هدف مورد استفاده قرار گيرد. به دليل كاربردهاي وسيع اين نوع آنتن­ها، بهينه سازي پارامترهاي آن­ها   همواره توجه تخصصين، طراح آنتن را به خود جلب كرده است. آنتن هاي شيپوري مبتني بر موجبرهاي فلزي كه اولين بار در محدوده ريز موجي ساخته شده اندداراي قدرت توان انتقال بالا و ضريب كيفيت مناسب بوده اند اما متاسفانه حجم و اندازه بزرگي داشته­اند. علاوه بر اين ساختار سخت و انعطاف ناپذير آن­­ها، پياده سازي اين گونه آنتن­ها را دشوار نموده است[1]. به دليل عملكرد مناسب فناوري SIW در باند فركانسي موج ميليمتري تلاش­هاي زيادي   در جهت پياده سازي اين آنتن بر اساس فناوري SIW صورت گرفته است. همچنين اين فناوري در ادوات فركانس بالا مانند فيلترها، تشديد كننده ها و غيره كاربرد دارد. در واقع امروزه   نياز به آنتن­هاي جديد به صورت صفحه­اي، فشرده، كم حجم و كم هزينه، بيش از بيش احساس مي­شود. لذا در سال هاي اخير با استفاده از فناوري SIW بخش بزرگي از اين نيازمندي ها مرتفع شده است. اين نوع موجبر كه شامل دو صفحه فلزي در دو طرف يك زير لايه دي الكتريك و حفره هاي توخالي فلزي در دوطرف موجبر است، مي­تواند با طراحي مناسب جايگزين موجبر مستطيلي شود.

Today, SOI-MESFET transistors have many applications in the electronic world and due to their advantages such as high switching speed and working at high voltage and frequencies and reducing power consumption compared to BULK silicon body technology, but with this advantage There are also some limitations, such as the effect of self-heating and the effect of body buoyancy. In this research, we introduce a new silicon-on-insulator structure, which compared to the conventional structure, has advantages such as higher breakdown voltage, higher drain current, and improvement in RF parameters. In this structure, we have used an oxide region in the channel region. The oxide region is located between the gate and the drain, which causes an increase in the breakdown voltage, and the reason for this is that the breakdown tolerance of the oxide is higher than that of the semiconductor. A metal region is buried inside the oxide region. The metal region improves RF parameters and prevents electric field congestion. In this structure, the breakdown voltage is around 22 volts, compared to the basic structure, which is about 19 volts, we can see an increase of 3 volts, and also the drain current has increased compared to the basic structure, and the RF parameters have all improved, and as a result This structure is ordered to work in high power applications. This research is about a completely new structure that has excellent efficiency for working at very high powers. In this structure, by using an oxide region in the channel, it improves the breakdown voltage from 19 volts in the basic structure to 22 volts. We are new in the structure, and on the other hand, by using nickel and SI3N4 areas, we have improved the ac parameters and increased the maximum transmission power from 0.9 W/mm in the basic structure to 0.998 W/mm in the new structure, so we can safely say that It is an excellent structure for working at high powers  

  The purpose of thisthesis is to investigate different deep learning techniques that can be used toimplement a system that learns how to detect breast cancer cases inmammography. Today, breast cancer has become one of the deadliest diseases.Mammography is the gold standard for detecting early signs of breast cancer,which can help treat the disease in its early stages. However, mammographymisdiagnosis is common and may harm patients through unnecessary treatments andprocedures (or lack of treatment). Therefore, systems that can learn to detectbreast cancer on their own can help reduce the number of misinterpretations andmissed cases.Convolutional NeuralNetworks (C  ) are used as part of the deep learning process, initially ontheir own. To analyze the effects on performance and efficiency, various deeplearning techniques such as different architectures (VGG16), dropout, dataaugmentation, changing network layers, fine-tuning, etc. are used.Finally, the accuracy of88.69% in the CBIS-DDSM dataset with the pre-trained model with VGG16architecture in two-class classification for the detection of mass andcalcification, as well as the accuracy of 61.31% in the four-classclassification for the simultaneous detection of mass and Calcification andtheir benign and malignant nature are obtained in mammography images. Othertested techniques such as data augmentation, dropout, and fine-tuning alsoincrease accuracy. Finally, these results have been compared with otherarticles using the CBIS-DDSM dataset.

   Currently, fMRIis the most widely used brain function technique. fMRI provides an oppurtunity to observe the neural activity in the brain. In fMRI tests, a blood oxygen level dependent (BOLD) signal is measured. Dynamic causal modeling is a Bayesian framework for inferring latent neural states from measured brain activity. DCM is increasingly used in the analysis of a wide range of neuro imaging and electrophysiological data. One of the methods of functinal magnetic resonance imaging is functional magnetic resonance imaging at rest. The idea that cerebral blood flow can reflect neural activity is the basis of all imaging techniques today. rs-fMRI imaging is used to describe areas of the brain that are correlated with time signals. Spectral Dynamic causal modelling (sp DCM) is introduced to estimate the intrinsic effective connectivity of rs-fMRI data. spDCM estimate the effective connectivity that results in a functional connectivity. spDCM reliably estimates the intrinsic efffective connectivity in the absence of an external stimulus. Spectral DCM models a range of endogenous activities that reproduce the observed functional connectivity fMRI at rest. The aim of this thesis is to investigate both DCM methods for inferring latent neural states from measured brain activity and spDCM methods for estimating the intrinsic effective connectivity of rs-fMRI data. Currently, fMRIis the most widely used brain function technique. fMRI provides an oppurtunity to observe the neural activity in the brain. In fMRI tests, a blood oxygen level dependent (BOLD) signal is measured. Dynamic causal modeling is a Bayesian framework for inferring latent neural states from measured brain activity. DCM is increasingly used in the analysis of a wide range of neuro imaging and electrophysiological data. One of the methods of functinal magnetic resonance imaging is functional magnetic resonance imaging at rest. The idea that cerebral blood flow can reflect neural activity is the basis of all imaging techniques today. rs-fMRI imaging is used to describe areas of the brain that are correlated with time signals. Spectral Dynamic causal modelling (sp DCM) is introduced to estimate the intrinsic effective connectivity of rs-fMRI data. spDCM estimate the effective connectivity that results in a functional connectivity. spDCM reliably estimates the intrinsic efffective connectivity in the absence of an external stimulus. Spectral DCM models a range of endogenous activities that reproduce the observed functional connectivity fMRI at rest. The aim of this thesis is to investigate both DCM methods for inferring latent neural states from measured brain activity and spDCM methods for estimating the intrinsic effective connectivity of rs-fMRI data.

  In this dissertation, the designs of antennas with two bands and two polarizations have been studied. In this study, three different and new designs in the field of multiple antennas are presented. The simulations for all three antennas were examined in ansys hfss software and the antennas were evaluated and compared with each other.The structure of the designed antennas consists of an FR4 substrate and two or four 50 ohm power lines and an improved DGS (incomplete ground plate). The first proposed structure is a square antenna with dual patch. In Figure 4-10, this antenna is made using FR4 substrate with a thickness of less than 1 mm and a dielectric constant of 4.4 and a loss tangent of 0.022, and the overall dimensions of the substrate are 32 × 32 mm2. This structure is powered by a 50 ohm microstrip power line. According to the theory, patch antennas are located at the bottom of the substrate, the defective ground plate or DGS, and at the top of the substrate, the radiation patch and the microstrip power line. Where the measured bandwidth was 4.2 GHz equal to 2.86 GHz (3.59-6.53 GHz). And the measured bandwidth is 9 GHz equal to 3.08 GHz (7.05 - 10.13 GHz).For the second simulation, by creating a circular patch with a thickness of 10 mm, a suitable and practical bandwidth can be obtained from the antenna. At 3.5 GHz, the impedance bandwidth is approximately equal to 2.7 GHz, and at 10 GHz, the impedance bandwidth is equal to 4 GHz, which is It is considered a desirable band.To design the third design in the final antenna structure shown in Figure (32-4), the structure has an impedance bandwidth of 5.076 (3.5030 - 8.5790) GHz and 4.935 (11.587 - 16.522) GHz. The proposed antenna configuration creates a circular polarization with a phase difference of 90 degrees, which has many applications in the industry.

  Electromyography (EMG) is a widely used diagnostic tool in clinical physiology which is used by physicians to accurately diagnose neuromuscular disorders in patients, particularly myopathy. In this research, continuous wavelet transform method and convolutional neural network were used to diagnose myopathy from EMG signals. The data analyzed in this study included two groups of healthy (20 signals) and myopathy (44 signals). The continuous wavelet transform was performed to decompose each signal after preprocessing operations on them. Then the scalogram was extracted and used as an input image to the convolutional neural network. The neural network structure used in this research composed seven layers which were taught by 70% of the total data. The final accuracy of this model in detecting myopathy from EMG signal was 89.06%.

  Abstract Getting the body by robot fingers despite the constraints of the topics studied by many researchers. In studies conducted so far, investigations of disturbance with the aim of bringing the body to a new stable condition have been considered. In this research, we study the garsping model in three dimensional space, as well as the related topics, first, the kinematic and dynamic modeling of the fingers and the object and the integration of these equations using the adaptive control method. In addition to the usual kinematic and dynamical studies, this paper examines the speed and position of the robot's motion in order to maintain the desired position of the arms and body under its control. The simulation results by MATLAB software have shown that the model reference adaptive control method with disturbance is capable of creating stability for the speed and displacement of the robot arm fingers

   In today's information age, it is a prerequisite that we have reliable information before making any decision. In this regard, location-based social networks have become an important program in location-based social networks as an effective way to help users find attractive places and recommend points of interest. Recently, they have gained a lot of popularity. Adding a location dimension to these networks makes their information closer to reality by creating a bridge between virtual social networks and the real world. The purpose of creating these networks is to provide location-related services; By allowing users to share experiences and visited locations with other users in different geographical locations. Location-based social networks are rich resources for data mining and information discovery by obtaining and updating the information of their users around the world. Recommended systems are also a special type of intelligent systems that take advantage of users' past rankings. Collaborative filtering is one of the most common approaches used for recommendation systems, although this method can sometimes present challenges such as cold start. Cold start occurs due to data scatter and is based on the fact that most users only connect to a small number of possible locations and the recommendation system for ranking some items or new users lacks data; Not available or only a small amount of data available. Solving this problem can greatly improve the user experience and trust in recommender systems. In this dissertation, we try to use machine learning and deep learning algorithms to provide a spatial recommendation system with a participatory filtering approach. Therefore, by implementing the torsional neural network algorithm on Yelp data set and presenting experimental results, we show that the proposed method can perform better than other related methods. Keywords: recommendation system, collaborative filtering, location recommendation, location-based social networks, deep learning, convolutional neural network

  Abstract Today, the security debate is considered an important and challenging issue. Older tools such as usernames and passwords alone are not responsive and reliable. That is why, day by day, in many areas, we need tools to identify individuals based on vital signs. With the advent of biometric knowledge, common methods of authentication in biometric systems have changed. Recently, the use of electrical brain signals (EEG) in biometric systems has been considered by researchers as an attractive and practical branch of research because it has two main advantages: First, this signal must be recorded from a living person in a normal mental state. Second, the EEG signal, unlike many other biometrics, is the result of a set of internal and cortical events in the brain that make it impossible to mimic. In this study, a data set with two different stimuli (relaxation and concentration) has been used that in the first period of time people are in a state of relaxation and in the second period of time people are in a state of concentration. An electrode is used to process and record EEG signals, then the analog signals are converted into digital signals. In this research, EEG data set with 109 topics has been used. In order to improve the performance of the authentication system in this study, instead of extracting features and selecting optimal features, deep features have been used. The results of our experiments on Albasri database with 99% accuracy indicate that using deep features and neural network algorithm Convolution using the genetic algorithm (GPCNN) is significantly improved over other electrical signal-based authentication systems of the brain, and shows a clear vision of the practical and commercial use of brain electrical signals in future authentication systems.

In today's world, developments are happening very fast and civil engineering plays an important role in providing the basis for the current developments in the world. Concrete and concrete technology have also undergone significant improvements in line with these changes. Reactive Powder Concrete (RPC) is a new type of high-strength concrete (UHPC) and a cement-based material developed through microstructural engineering. This type of concrete was first introduced in France in the 90's by Richard P, Cheyrezy M and has been able to eliminate many of the weaknesses of conventional high strength concretes and much research has been done on this subject in the world of science and and It is also being done. During our research and studies, new angles of this type of concrete attracted our attention and we decided that part of our future research on improving the mechanical properties and improving the quality of reactive powdered concrete and the other part, trying to Addressing weaknesses and shortcomings that have not been addressed to date. We intend to replace the additives with suitable and economical properties and available in the country as an alternative to cement and green reactive powder concrete with a new mechanism and concretes with excellent properties and higher environmental compatibility to produced. In order to increase the final quality of the produced reactive powdered concrete, additional tests were performed to identify the properties of aggregates, modify the granulation of materials and also to perform super-lubricants tests in order to identify materials and materials to control their properties. In this research, two types of heat treatment and standard, as well as in two projects, the effect of internal processing and also the effect of using burnt oil and comparison with standard mold oil with fresh and hardened concrete tests have been done. By using the optimal percentage of microsilica, metakaolin and natural zeolite pozzolans and steel and polypropylene fibers, along with the use of sand and quartz powder as a substitute for part of the sand, we were able to reduce cement consumption by up to 25% and also the percentage of microsilica use. In order to make the design economical, it has been minimized and at the same time produced concrete with very suitable properties and with increasing resistance compared to the control sample, with a compressive strength of up to 173.2 MPa, a tensile strength of 13.1 MPa with a water absorption of 0.9% produced.   

Abstract In recent years, microfluidic technology has been considered by many researchers in various fields of biology, chemistry and medical engineering due to its many advantages such as reducing the sample size, producing less waste, saving time and money. One of the main parts of laboratory processes is cell isolation. In laboratories, different methods are used to isolate or count cells; One of the main disadvantages of these methods is the high volume of the prototype, and the process of testing by these methods requires a lot of time and money. The use of dielectric force has become one of the most popular manipulation methods in microsystems due to its favorable effects such as laboratory scale, simplicity of instrument, ability to induce positive and negative forces and, most importantly, ineffective particle structure. Today, with technologies such as on-chip laboratories and dimensional shrinkage (nano and micro), all of these experiments can be performed in less time, with greater accuracy, and with smaller sample sizes. With the development of on-chip laboratory technology, the tendency to use this technology for cell isolation for cell counting or diagnostic applications in micro-dimensions has increased. Since the electrophoresis force allows us to use laboratory technologies on the chip to count or separate, and has the ability to shrink in micro and nano dimensions, more attention has been paid. What is discussed in this dissertation is the effect of different electrode shapes, different frequencies, fluid properties, channel dimensions, electrode dimensions and electrode spacing in applying dielectric force in cell separation. Since the main purpose is to isolate biological cells and these cells are strongly dependent on temperature, after comparing electric fields, field gradient, fluid conductivity and dielectric force, the effect of changes in these elements on temperature should be investigated. The sensitivity of biological cells to temperature is such that if the temperature rises above a certain value, these cells will die. Then the effect of changes in voltage and fluid conductivity is investigated and finally its effect on the process of separation of living cells from non-living cells.

AbstractThe increasing number of private cars on the tra  ortation routes causes a heavy traffic load. In many countries, high occupancy vehicles (HOVs) have been developed to reduce the traffic load on special lines. Also, only buses, police vehicles, fire trucks, emergency vehicles, and personal vehicles with capacities to carry more than one passenger are allowed to use these lines. Another issue in monitoring the tra  ortation and traffic of vehicles is the observance of driving rules within the vehicle compartment. These rules include the drivers' use of seat belts while driving, and the accurate and automatic detection of these rules is of particular importance. In this paper, we propose a method based on deep learning models for simultaneous detection of the occupants and the status of driver's seat belt. In this method, first, the windshield is detected using the YOLOv5s network. Then, we determine the presence of a person in the passenger compartment using the front seat passenger detector model. Finally, using the deep learning-based image >Keywords: Car occupant detection, Seat belt status detection, Automated tra  ort images analysis, deep learning, transfer learning, YOLOv5, ResNet34, TPP,   , PMT   

   Abstract   The global prevalence of lung cancer in recent years has affected many countries around the world and has had devastating effects on the international community. Due to the very important features of this type of cancer, including its lack of recognition in the early stages of the disease, the similarity of lung nodules with blood vessels, etc., and as a result, misdiagnosis of them instead of each other and high mortality rate has led to The most important and best way to control and control lung cancer, based on the consensus of doctors and global health organizations, early detection and termination of its transmission to other organs of the body with the rapid start of effective treatments for people with this type Be cancer. Therefore, accurate, fast and accessible diagnosis is the best tool to achieve this important. In this regard, in the present dissertation, an intelligent machine for diagnosing lung cancer has been presented and implemented using CT scan images and deep learning. Also, in order to evaluate the proposed smart machine, MATLAB software has been used and the necessary results have been extracted, and the obtained results show the very high accuracy of the proposed smart machine. The use of transfer learning theory, especially the Resnet18 network, in the diagnosis of lung cancer has not yet been used and is an innovative aspect of this design. key words: Lung cancer, lung nodules, lung scans, image processing, deep learning, transfer learning, Convolutional neural network.

چكيده مغز انسان پيچيده ترين سيستم هستي است كه از ويژگي هاي منحصر به فردي برخوردار است. طي چند دهه اخير ، پژوهشگران سعي داشته اند تا با الهام گرفتن از مغز و نحوه عملكرد آن در پردازش اطلاعات ، سيستم هايي مشابه آن ايجاد كنند . يكي از ويژگي هاي مهم مغز توانايي خود تعميري و قابليت تحمل خطاست ؛ به طوري كه در صورت‌ آسيب ديدن چند سيناپس ، به دليل وجود سلول هايي به نام آستروسيت و از طريق سيناپس هاي سالم باقي مانده ،   سيستم عملكرد خود را در حد قابل قبولي حفظ مي كند . از آن جا كه سلول هاي عصبي در محيطي تصادفي فعاليت مي كنند ، وجود نويز يك مسئله غير قابل اجتناب است . به همين دليل در اين پروژه براي اولين بار به بررسي تاثير نويز در شبكه آستروسيت – نورون خود تعمير پرداخته شده است . با وجود اعمال نويز به جريان نورون ها ، سيستم هم چنان از توانايي خود تعميري برخوردار است و از طريق فيدبك غير مستقيم از سلول آستروسيت اثرات مخرب سيناپس هاي آسيب ديده را تا حدودي جبران مي كند . نتايج نشان مي دهند كه با وجود حضور نويز در جريان نورون ها و اعمال 80 درصد خطا به شبكه ، سلول آستروسيت هم چنان سعي در جبران خطا دارد و تا حد امكان فركانس هدف را حفظ مي نمايد .   

Abstract Congenital heart disease can cause heart failure or even mortality in 1 out of 125 newborns and infants, each year.   This cardiac failure severity can vary from mild to serious. It has been well-known that the early diagnosis of cardiac disease in the embryonic period may avert the cardiac failures and reduce the chance of mortality in newborns, accordingly. Therefore, it is of great importance to diagnose the condition and treat it, early.    Congenital heart defects can appear in the early stages of pregnancy, when the foetus’s heart is being formed and can affect the heart or its function. Generally, the heart failures may be caused due to genetic syndrome, inherited disorders, or environmental factors such as infection or consumables.    To examine the foetus’s heart condition, the heart’s electrical activities are commonly recorded by electrocardiogram (ECG) and examined by the cardiologist. An ECG is a non-invasive diagnostic method that records the variations in the heart electrical potentials. The non-invasive extraction of fetal ECG (fECG) from maternal abdominal is a demanding difficult task,   as the signal-to-noise ratio (SNR)   is very low and fECG signals are often blended with other signals such as; maternal heart activity,   the respiration, the uterine contractions, and also instrumental noise in the surroundings.    Previous research has already investigated the non-invasive extraction of fECG in the past four decades, but their performances are not yet satisfactory.   esides, there is   till a need for fECG extraction from multi-channel recordings. The main aim is to distinguish the fECG morphology with the highest possible accuracy.    This thesis is a review dissertation on previous research conducted, their outcomes, advantages, disadvantages, using adaptive filtering and discrete wavelet transform based methods, which have been less utilized in the context.    Keywords: Fetal electrocardiogram, Maternal electrocardiogram, Adaptive filter, Discrete wavelet transform. Abstract Congenital heart disease can cause heart failure or even mortality in 1 out of 125 newborns and infants, each year.   This cardiac failure severity can vary from mild to serious. It has been well-known that the early diagnosis of cardiac disease in the embryonic period may avert the cardiac failures and reduce the chance of mortality in newborns, accordingly. Therefore, it is of great importance to diagnose the condition and treat it, early.    Congenital heart defects can appear in the early stages of pregnancy, when the foetus’s heart is being formed and can affect the heart or its function. Generally, the heart failures may be caused due to genetic syndrome, inherited disorders, or environmental factors such as infection or consumables.    To examine the foetus’s heart condition, the heart’s electrical activities are commonly recorded by electrocardiogram (ECG) and examined by the cardiologist. An ECG is a non-invasive diagnostic method that records the variations in the heart electrical potentials. The non-invasive extraction of fetal ECG (fECG) from maternal abdominal is a demanding difficult task,   as the signal-to-noise ratio (SNR)   is very low and fECG signals are often blended with other signals such as; maternal heart activity,   the respiration, the uterine contractions, and also instrumental noise in the surroundings.   

Abstract This dissertation investigates and simulates the process of forging the open mold in cold condition and the billet with a round cross section. The billet is placed horizontally on the lower V-shaped mold and is subjected to compressive force with the upper mold which is flat. The billet is long, so the deformation is assumed to be a plate strain. The bottom mold is fixed and the top mold moves down. For a given upper template displacement, a deformation model is proposed in which it is assumed that the original center moves downward and the free deformation regions are formed as arcs with the new center. Based on the principle of incompressibility, a relationship is obtained between the two radii of the free deformation regions. Based on the symmetry in the figure, the deformed areas are divided into three parts, in which the free diagram is drawn using the force balance method and the selection of the appropriate element. By writing the equations of governance equilibrium and the equation of yield, a differential equation is created which, by solving it, will give a relation for calculating the forming force at each decrease in height. Using the equilibrium equations of incompressibility, the radii of the free arcs are calculated based on force minimization. Next, using Deform software, the process in question is simulated and the values ??of the radii and the contact width of the deformed section are changed, as well as the required forming force curve according to the press rate. The geometric values of the deformed cross section resulting from the analysis and simulation of the process are compared. The geometric values of the deformed cross section resulting from the analysis and simulation of the process are compared. ... Geometric quantities deformed sections of analysis and process simulation were compared with each other. ... «نتايج كامل» بار نشد امتحان مجدد درحال تلاش مجدد… درحال تلاش مجدد… Also, the shaping force curve obtained from this software is compared with the analytical results. Comparison of the results has shown that the analytical and simulation values are well matched. The results show that increasing the low mold angle increases the forming force Also, increasing the friction constant will increase the forming force. In other words, at a zero degree angle of the mold, the friction constant has no effect on the forming force, but at other angles, the friction constant affected the forming force.The results show that increasing the low mold angle increases the forming force.   

Solution to increase and decrease the radiation of radio signals in buildings

مقدمه: مغز انسان يك سلسله مراتب پيچيده ازماژول‌ها است كه بطور پويا در مقياس‌هاي ميكرو، مزو و ماكرو با يكديگر در تعاملهستند. نواحي مجزاي آناتوميكي كه به طور همزمان در حال نوسان و پويايي بوده و ازلحاظ عملكردي با يكديگر در ارتباط هستند، شبكه‌هاي حالت استراحت(RSN) ناميدهمي‌شوند. اين شبكه‌هاي مغزي را مي‌توان با استفاده از تصويربرداري تشديد مغناطيسيعملكردي حالت استراحت بدست آورد. آگاهي از اختلال در عملكرد اين شبكه‌ها، منجر بهشناسايي طيف وسيعي از بيماري‌هاي مغزي از جمله صرع، بيماري‌هاي آلزايمر و اوتيسم،افسردگي و اسكيزوفرني مي‌شود. رفتار پوياي اين شبكه‌ها از نظر زماني پيچيده است. پيچيدگيزماني RSNها ممكن است يك نشانگر مبتني بر تصويربرداري از عملكرد مغز در سلامت و بيماريباشد. هدف: باتوجه به اصلاحات انجام شده در آنتروپي حدود نسبت به آنتروپي نمونه، هدف از اين پايان‌نامه،بررسي پيچيدگي زماني RSNها با استفاده از معيار آنتروپيحدود چندمقياس است. از اهداف ديگر اين پايان‌نامه،بررسي ارتباط پيچيدگي زماني RSNها با شناخت مراتب بالاتر است. مواد و روش‌ها: منحني‌هاي ميله‌هاي خطايسايه‌دار و نمودارهاي جعبه‌اي مربوط به مقادير آنتروپي حدود چند مقياس را برايدادگان 240 فرد از مجموعه داده‌ي HCP   رسم كرديم. از طرفي به منظوربررسي ارتباط پيچيدگي زماني RSNها با شناخت مراتب بالا، ما 50 متغيررفتاري كه در شناخت مراتب بالاتر دخيل هستند، انتخاب كرده و رگرسيون شبكه‌ي الاستيكرا به آنها اعمال كرديم. يافته‌ها: مي‌توان دريافت كه شبكه شنوايي داراي بيشترين مقدار آنتروپيو شبكه حالت پيش‌فرض داراي كمترين مقدار است. اين در حالي است كه در منحني‌هايمربوط به آنتروپي چندمقياس نمونه، نتايجي معكوس حاصل مي‌شود. از طرفي متغير مربوطبه اندازه‌گيري توانايي‌هاي استدلال و برنامه‌ريزي، بزرگترين (مثبت) ضريب رگرسيونرا شامل مي‌شود. نتايج: بنابراين، مي‌توان نتيجه گرفت كه معيار آنتروپيحدود چندمقياس به عنوان معياري براي رابطه‌ي معكوس ميان پيچيدگي و مقدار آنتروپيشناخته مي‌شود. از طرفي اين معيار، مقادير كوچكتري از آنتروپي را ارائه داده و دامنه‌يتغييرات كوچكتري در اين مقادير دارد. همچنين، متغير مربوط به اندازه‌گيري توانايي‌هاياستدلال و برنامه‌ريزي در ميان 50 متغير رفتاري، بزرگترين (مثبت) ضريب رگرسيون را داراست.  

and tried to improve the error  

Unfortunately, nowadays many patients suffer from abnormalities in their nervous system. For some of those patients, brain surgery is the only chance of survivale. In that case, the surgeon needs a clear and high-resolution (temporally and spatially) picture of the brain which shows the locations of the brain abnormalities. Lack of such picture may lead to unsuccessful brain operation by the surgeon and removal of healthy brain tissues (instead of brain tumors). The mostly used techniques for brain imaging are: EEG and fMRI. EEG imaging has a good time resolution, but it has a poor spatial resolution while fMRI imaging has a good spatial resolution, but it has a poor time resolation. Therefore, combination of EEG and fMRI can be a powerful non-invasive imaging tool for providing both spatial and time resolution. Obtaining EEG and fMRI data simultaneously is a non-invasive approach to study and investigate the electrophysiological and hemodynamic aspects of the brain functions. Despite the time-varying nature of both measurements, their relationship is usually to be time-invariant. In general, the combination of EEG and fMRI involves two important challenges: firstly, simultaneous data acquisition, secondly data integration. In this study, the main focus is on the data integration. The motivation of this research is to increase the accuracy of existing methods by introducing a new method for combining EEG and fMRI in which the maximum information is retained. In this regard, the Dynamic Regional Phase Synchrony (DRePS) criterion and methods for measuring phase synchrony in multichannel EEG signals are deployed. The methodology was applied to a data set composed of behavioral, EEG, and fMRI data acquired from human subjects performing a perceptual decision making task. The data set is publicly available at https://osf.io   under a Data Use Agreement. The proposed methodology can be used as a neuroimaging tool for studying epilepsy as well as neuro-vascular coupling and cognitive studies. It can also be deployed to get better constrain solutions of the inverse problem of source localization of  EEG  activity.

  شبيه­سازي جريان­هاي چند فازي و مطالعه نشست ذرات

Abstract With the advancement of radar technology due to electronic warfare, radar systems are capable of measuring high-precision targets over long ranges as they are increasingly being used. Radar protection is more important than radar itself. In radar design, the transmitter and receiver position the transmitter and receiver in one place, but they separate the sender and receiver to protect the accuracy of accuracy. Passive radar is a kind of radar used to detect and detect targets using an unknown antenna. Passive radar can be combined with array antennas to preserve more radar security. In this project, a phase array antenna is used to design passive radar. When a number of antennas are located at a distance from each other, they have the potential to continue to operate in the system alone, in other words, some of the antenna may disappear or have a problem, but the rest of the antenna can continue to function with less efficiency. Many parameters have a role in the design of passive radar using phase array antennas. Depending on this radar, this radar has a stationary or mobile location and is used in different applications such as: military, aerial, imagery, photography. The most important part of this thesis is the number of elements in the array. In this design, all-directional antennas (omni-directional) are or can be used with better performance and more complex antennas. All antennas must be synchronous (synchronous), one of the major challenges in this design. At the same time the antenna is complex in a geographic area. To synchronize the antennas using global positioning systems (GPS). To achieve the desired phase, a source antenna with zero phase is used for proper shape shaping elements that have a suitable pattern in one direction according to the desired phase. Keywords:  oscillators,  eam  haping,  antenna  array,  avigation,  assive  radar   

زمين لرزه يكي از بلاياي طبيعي است كه جوامع بشري همواره با آن روبرو هستند.   برخي نواحي روي كره زمين به دليل نزديك بودن با مرز هاي صفحه هاي پوسته زمين، داراي لرزه خيزي بيشتري نسبت به بقيه مناطق هستند. ايران نيز كشوري است كه به دليل قرار گيري روي كمربند آلپايد داراي لرزه خيزي بالايي است. زلزله 21 آبان   ازگله يكي از زلزله هاي مهم در منطقه زاگرس مي‌باشد كه خسارت هاي جاني و مالي بسياري را به بار آورد، از اين رو مطالعه و بحث در مورد اين زلزله از اهميت زيادي برخوردار است. در اين متن، مطالعات و نتايج آنها كه در مورد ساختگاه شهر سرپل ذهاب انجام گرفته مورد بحث قرار ميگيرند. مطالعاتي از جمله: پروفيل سرعت موج برشي براي نقاط مختلف شهر بدست آمده است. در برخي نقاط به صورت آرايه‌اي با تركيب سه روش F-K ، روش SPAC و روش HVTFA و در بقيه نقاط به صورت تك نقطه اي با استفاده از روش HVTFA و با كمك گرفتن از نتايج آرايه‌اي بدست آمده اند. نتايج برخي نقاط با نتايج حاصل از نزديكترين نقاطي كه توسط پژوهشگاه بين المللي زلزله شناسي و مهندسي زلزله با روش ژئوسايزميك انكساري بدست آمده بودند مقايسه شده‌اند. نقشه هاي مربوط به ريز پهنه بندي فركانسي شهر، تيپ بندي نوع خاك بر اساس نظريه vs30   و نيز نقشه هاي مربوط به ساختار لايه هاي زير زمين از جمله نقشه هاي عمق سنگ بستر هوازده و سالم مهندسي و همچنين نقشه ميانگين سرعت لايه هاي رسوبي روي سنگ بستر كه با استفاده از اطلاعات بيش از 80 برداشتي كه انجام شده، بدست آمده و نقشه آنها ارائه شده است.

  اندازه گيري بار شناختي مغز با استفاده از روش هاي اندازه گيري همزماني فاز سيگنال EEG

Displacment Sensor using Microstrip patch Antenna with Split Ring Resonator  

Climate change and increased harvesting of water resources cause environmental problems and major changes in lakes and wetlands. In recent years, this has caused many lakes to face a complete drought. The Bakhtegan and Tash Lakes is one of the most important environmental assets and the second largest lake in the country is almost dried up. In this study, to determine the process of drying the lake, using the LandSat satellite images and image processing by ILWIS and ArcMap software, the time series of water surface of the Bakhtegan Lake were prepared monthly for a thirty year period. Then, the equation was extracted between the annual average area of the lake and the annual discharge of the Khor River at the site of the Paul Khan Hydrometric Station. According to the fifth report (AR5) of the IPCC, in order to assess the effects of climate change the Outputs of the General Climate Model (GCM) on the three the sub catchment of Droudezan, Sivand and Hasanabad (Korr), have been downscaled. Among the selected models, after review, the BCC-CSM 1.1 model was used as the preferred model in each of the three sub catchment for downscaling in order to produce more appropriate results. The results of downscaling in accordance AR5 in the form of the average of four scenarios indicate a decline in precipitation by 2050 compared to the long-term average for the Droodzene sub catchment of 23 mm, Sivand   ub catchment 12-mm and Hassan Abad (Khor) sub catchment is 10 mm. During this period, the average long-term temperature is increased for the Droudezn sub catchment is 1.05 degrees Celsius, Sivand sub catchment 1.06 degrees Celsius and Hassan Abad 0.6 degrees Celsius. In order to apply the effects of climate change on the amount of runoff entering the lake, the water resource planning model of Bakhtegan watershed was created and calibrated in WEAP software. By modifying the discharge, for conformed to the climate change conditions (rainfall reduction), predicted the annual forecast of the runoff Khor River in the Pul Khan site by 2050, and finally, using the governing equation between the annual average area of the lake and the annual yield of the Khor River, The area of the lake in the period from 2020 to 2050 is projected to be annual average. By the year 2050, the average annual of the lake will be 20% of the past area. The largest predicted area of the lake is related to the Rcp60 scenario in 2038 with a value of 568 square kilometers (equivalent to 40 percent of the lake area), and the least is the Rcp85 scenario in 2038 with a value of 77 square kilometers (equivalent to 7 percent of the lake area).Water resources management is very important in preventing environmental crises and losing national resources. Climate change or any other risk can be controlled by correct recognition of the phenomenon, and short-term and long-term planning, control adverse consequences and minimize losses. The results of this research can be used to manage and plan water resources in the Bakhtegan Basin, in allocating the water necessary to maintain the Lake Bakhtegan and Tashk as a national capital.

   Activation of oncogenes is one of the most critical factors which results in cancer. In this study, activation of oncogenes due to the mutation is studied and then the effect of mutant cells on the neighborhood cells is investigated. We have combined evolutionary equations of oncogenes with replicator equations in order to study the model precisely. We interpret the formation process of mutant cells and immune cells function against them, then we model the immune function with differential equations. We model the interaction between cancerous and healthy cells using the concept of evolutionary game theory. System dynamics are examined by employing replicator equations as well as control theory notions. We categorize the system into separate cases depending on its parameter values.   For cases in which the system tends to converge to undesired equilibrium points, stem-cell injection is utilized as a therapeutic suggestion. The effect of inserted stem cells on the model is considered by reforming the replicator equations as well as adding some new parameters to the system. We analyze the system by categorizing it into different scenarios based on amounts of its parameters.   In each case, the interaction rates’ values are suggested in a way that the equilibrium points of the replicator dynamics are located on an appropriate region of the state space. Based on the suggested interaction rates, it is proved that the system doesn’t have any undesirable interior equilibrium point as well. Therefore, the system is converged to a desired equilibrium points, i.e., a point with no or a scanty level of cancerous cells. The simulation results show the effectiveness of the suggestions in investigation of cancer emergent and matastasis, the rule of immune system against it and a therapeutic suggestion to elimination of the cancerous cell in different scenarios.

Design and feasibility analysis a micro device for micro particle separation magnetophoresis

  Each part of the blood provides important information for diagnosis and treatment. Mineral-rich blood plasma biomarkers, including: proteins, metabolites and Etc. To access this information must first be separated from the blood plasma. Separate the plasma from the blood stages, which are the blood has different biological properties such as derivatives with platelet and white blood cells and red blood combined [27]. The classic method for separating plasma from blood, by sedimentation [28].The classic method for separating plasma from blood, is deposition method [28] based on the law of gravity and density differences in blood separation is performed, but due to the low speed centrifuges to speed up this method of precipitate action used be. This method is currently used in many laboratories. The use of centrifuges in addition to being expensive and require professional training, may cause degradation of the target (plasma proteins and metabolites) is also. With the advent and development of MEMS, plasma separation methods in the field of micro is considered. The benefits of micro-scale separation can be compared to the classic method: use a small amount of blood, portability and low price point make microdevice. In this thesis microdevice to separate the plasma from the whole blood-based MEMS technology is presented. The advantage of this method compared to other separation methods is that active and passive separation takes place, and this makes the advantages of both methods have together. The proposed method measures different from other methods of separating the samples were gathered and tried to optimize it. Among the advantages of this method to other methods this method is that continuity errors such as human error is significantly reduced.

 synapses are the essential part for neural network that transmit the information between neurons. The biological neuron transmits electrical or chemical signal between neurons. When the pre-synapse receives a spike, it releases the neurotransmitters and cause to active receptors in the post-synapse. After receive enough neurotransmitters from pre-synapses, the post-neuron reaches threshold voltage, the action potential is occurred in post-neuron. So, with this way the information transmit between neurons  The synapse electrical circuit is the most important parts in neuromorphic that transmit the information between neurons. In this paper we propose a modified Differential Pair Integrator (DPI) synapse circuit that can remove problems of previous synapse circuits by producing standard first-order differential equation. The proposed circuit works in sub-threshold region with low power consumption and has biological time constant   in low supply voltage (1Volt). This circuit can tune the gain and time constant with three separated controlling voltages and can play a crucial role for learning neural codes and encoding spatiotemporal spike patterns linearly in biological frequency range. This circuit has good performance and advantages in comparison with different synapse circuits, so it is suitable for applications in reconfigurable neuromorphic devices which implement biologically brain circuits. The proposed circuit is designed at 0.35µm standard CMOS technology and simulated by Hspice software.   Keywords: neuron; synapse;DPI synapse circuit; sub-threshold; low power;