امروزه شبكه‌هاي جهاني وب تبديل به يكي از ابزارهاي مورد نياز بشر شده‌اند كه توسط كاربران بسياري در سراسر جهان مورد استفاده قرار مي‌گيرند. مساله‌اي كه در اين حوزه وجود دارد گستردگي بسيار زياد اينترنت و مطالب آن است كه اين گستردگي روز به روز و با سرعت بسيار زياد در حال افزايش است. اكنون يكي از مشكلاتي كه پديد مي‌آيد، اتلاف وقت كاربران براي دستيابي به كالا ها و خدمات مورد نياز آن‌ها است و ممكن است در بسياري مواقع كالا يا خدمت مورد نياز خود را پيدا نكنند، در نتيجه ارائه و پيشنهاد كالا يا خدمات مناسب به كاربران در زمينه هاي مختلف مطابق با نيازها آن‌ها امري بسيار حياتي محسوب مي گردد و يكي از روش هاي بسيار پركاربرد براي اين مساله استفاده از سيستم هاي توصيه‌گر مي‌باشد. ?سيستم‌هاي توصيه‌گر به منظورجلوگيري از اتلاف وقت كاربران، محصولاتي را به آنها پيشنهاد مي‌كنند كه به احتمال زياد مورد علاقه آن‌ها هستند و هنوز آن‌ها را نديده‌اند. الگوريتم پالايش گروهي به عنوان يكي از معروف ترين و پركاربرد ترين الگوريتم‌ها براي پياده سازي يك سيستم توصيه‌گر شناخته مي‌شود. سيستم‌هاي مبتني بر اين الگوريتم بر اساس سوابق جستجو و ابراز علاقه‌مندي كاربر به كالا‌ها و خدمات مختلف و با توجه اطلاعات دريافتي از ديگر كاربران با علاقه‌مندي‌هاي مشابه به كاربر هدف پيشنهادات جديدي را ارائه مي‌كنند. سيستم‌هاي توصيه‌گر اغلب با چالش مهمي به نام تنكي داده‌ها مواجه هستند، زيرا ماتريس‌هاي تعامل كاربر–اقلام در پياده‌سازي‌هاي واقعي معمولاً بيش از ??? تنك هستند. اين مسئله تأثير منفي بر دقت و كارايي توصيه‌ها دارد، به‌ويژه براي كاربران جديد و محصولات خاص يا كم‌تعامل. در حالي كه روش‌هاي موجود تلاش مي‌كنند با افزودن اطلاعات جانبي يا تغيير در طراحي سيستم اثر تنكي داده را كاهش دهند، اغلب مشكل اصلي يعني كمبود داده‌هاي تعاملي را ناديده مي‌گيرند. در اين پژوهش، يك چارچوب جديد معرفي مي‌شود كه از يك مدل ديفيوژني براي توليد امتيازدهي‌هاي مصنوعي با كيفيت بالا در سيستم‌هاي توصيه‌گر استفاده مي‌كند. به طور مشخص، از يك مدل ديفيوژني طراحي‌شده براي داده‌هاي جدولي استفاده مي‌كنيم تا توزيع مشترك و پيچيده‌ي سه‌تايي‌هاي كاربر–اقلام–امتياز را ياد بگيرد و سپس امتيازهاي مصنوعي توليد كند كه از نظر آماري سازگار هستند. براي تضمين كيفيت امتيازهاي توليدشده، يك روش نوآورانه براي شناسايي نويز بر اساس تحليل الگوهاي رفتاري پيشنهاد مي‌كنيم. اين روش امتيازهايي را كه با ترجيحات كاربران و ويژگي‌هاي آيتم‌ها همخواني ندارند، شناسايي كرده و حذف مي‌كند. براي ارزيابي كارايي چارچوب پيشنهادي، از مدل‌هاي پالايش گروهي سنتي و پالايش گروهي مبتني بر شبكه‌هاي عصبي استفاده شده است. آزمايش‌ها روي دو مجموعه‌داده واقعي با سطوح مختلف تنكي داده (با نگهداشت داده از 5% تا ???%) انجام شد و بهبودهاي قابل‌توجهي را نشان داد. به طور خاص، پالايش گروهي سنتي مي‌تواند خطاي RMSE را تا ??% كاهش دهد و نيز پوشش امتيازدهي را تا ?? % افزايش دهد. همچنين، پالايش گروهي عصبي پاسخ‌هاي دقيق‌تر و ظريف‌تري ارائه مي‌دهد و زماني بيشترين كارايي را دارد كه نسبت افزايش داده پايين باشد. اين موضوع نشان مي‌دهد كه مدل‌هاي مختلف به انواع متفاوتي از افزايش داده نياز دارند.   

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

In recent years, intelligent video surveillance systems have played a vital role in enhancing safety and security across various environments. This thesis presents the design and implementation of a comprehensive real-time detection and alerting system capable of identifying specific hazardous objects, including various weapons (pistols, swords, knives), incidents (accidents, fire, smoke), and violence. The proposed method is based on the utilization of two distinct object detection models founded on the YOLO11 (Nano version) architecture. The primary model is used for detecting general objects such as humans and vehicles, while the secondary model—trained on a customized dataset—focuses on the precise identification of hazardous items. This custom dataset was collected, labeled, and integrated using Roboflow tools. The software architecture consists of a processing core based on PyTorch (with CPU or GPU execution capabilities) and OpenCV. To manage concurrent processes, a threading-based system is employed to decouple video processing from the user interface. An advanced Graphical User Interface (GUI) has been developed using the PyQt6 library, allowing for the adjustment of various parameters such as confidence thresholds, input modes (webcam, video/image files), and the display of system logs. A key feature of the system is the multi-frame verification mechanism designed to prevent false alarms. Furthermore, upon threat detection, an immediate audio alert system (including sirens and voice messages) is activated, and the final output video is merged with the original audio and saved. Experimental results demonstrate that the system is capable of identifying hazardous objects in various scenarios with high accuracy and speed, providing a real-time response.   

   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.

The primary aim of thisresearch is to design and implement an intelligent and efficient framework forintrusion detection in Internet of Things (IoT) devices using a novel FederatedLearning (FL) approach. With the rapid growth of IoT applications in domainssuch as healthcare, industry, agriculture, and smart cities, a massive amountof data is generated by connected devices. Ensuring the security and privacy ofthis data has become a critical challenge. Traditional centralized intrusiondetection systems (IDSs) are no longer suitable due to their high communicationoverhead, limited device resources, and hardware heterogeneity. To address these challenges,this thesis introduces a new framework called ASA (Adaptive Smart Agent). ASAemploys an adaptive agent layer that monitors device resources and dynamicallyclusters IoT devices based on their computational power, memory capacity, andbandwidth. For each cluster, an appropriately scaled learning model isassigned. The training process is performed locally on devices, and only modelupdates are transmitted to the central server, thereby reducing communicationcosts and preserving user privacy.Experimentalevaluations on benchmark IoT datasets demonstrate that ASA significantlyoutperforms conventional FL-based methods in terms of detection accuracy,communication efficiency, and participation fairness. It effectively mitigatescritical issues such as device dropouts, non-IID data distribution, and networkinstability, while maintaining robustness and stability in heterogeneousenvironments.Theresults highlight that the proposed ASA framework enhances the accuracy andscalability of IoT intrusion detection systems while ensuringprivacy-preserving distributed learning. Future work can focus on acceleratingmodel convergence, improving fault tolerance, and integrating ASA with edge andfog computing infrastructures to enable real-world deployment in large-scaleIoT ecosystems.  

   The present work evaluated the lycopene recovery from industrial tomato waste using cellulase enzyme along with ethanol and alkaline treatments. Central composite design (CCD) was applied to optimize the enzymatic step. An optimal lycopene yield of ??about 51.09 µg g-1 was obtained under the cellulose-to-tomato peel ratios of 4.25 mL g-1, the temperature of 48.25 ?C, and hydrolysis duration of 152.81 min. A combination of the enzyme-ethanol treatment (at 60°C for 5 s) before solvent extraction resulted in a very small increase in lycopene yield while a combination of the enzyme and alkaline treatments (45% KOH, 10% sample weight at 55°C for 30 min) enhanced significantly the lycopene recovery yield by 249.6%. In lycopene extracted under optimal conditions, the highest antioxidant activity against H2O2 as an oxidizing agent was shown at about 75.4% in the case of the enzyme-alkaline treatment.   

   Common signals recorded from neural activity in the brain, such as EEG or MEG, are non-stationary signals. Therefore, time-frequency analysis tools are well suited to analyze such signals. Over the years, time-frequency analysis has found numerous applications in computational neuroscience, from diagnosing diseases such as schizophrenia in adults to understanding autism spectrum disorders in children and identifying seizures in infants. With advances in computational resources and various types of imaging tools, time-frequency analysis is on the verge of becoming one of the most important mainstream tools in the field of computational neuroscience. We want to examine the methods of examining the properties of brain networks using time-frequency representation tools. In this thesis, time-frequency based phase synchronization methods are introduced and analyzed. Time-frequency distribution includes WVD, Rihaczek, spectrogram, EMBD, ... methods, which are briefly reviewed. Using time-frequency representations, we can access the time-varying properties of brain networks. The results presented in this thesis show that time-frequency-based phase synchrony is a good measure for examining the effective connections of brain networks, which allows us to use powerful analytical tools to investigate and better understand brain networks and their dynamic behavior over time. The results of this evaluation show that phase synchronization methods based on time-frequency representation can have applications in analyzing brain networks, including studying brain signals of infants and adults, diagnosing diseases such as epilepsy, and studying and helping to diagnose diseases such as Parkinson's and autism, which can lead to better and more effective treatment  

 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.  

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  

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%.

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).   

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 >  

  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.

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.   

The stock market in general is very unpredictable in nature. Many factors may play a role in determining the price of a particular stock, such as market trends, supply and demand ratios, the global economy, public sentiment, sensitive financial information, earnings announcements, historical prices, and many more. The challenge of accurate forecasting But, with the help of new technologies such as data mining and machine learning, we can analyze big data and create an accurate forecasting model that avoids some human errors. In this work, the closing prices of specific stocks are predicted from sample data using a supervised machine learning algorithm. Specifically, a Recurrent Neural Network (RNN) algorithm is used on stock time series data. The predicted closing prices are checked against the actual closing price. In this research, we investigate the problem of stock market forecasting using Recurrent Neural Network (RNN) with short-term memory (LSTM). The purpose of this research is to investigate the feasibility and performance of LSTM in stock market forecasting. We optimize the LSTM model by testing different configurations, for example, the number of neurons in the hidden layers and the number of samples, respectively. We have used historical stock price data collected from the Yahoo Finance database to train our model. Nevertheless, based on the forecasting results of the LSTM model, we used it to predict the stock value in the coming days for the final stock price. The results show that the LSTM model with 4 layers has a higher accuracy and the lowest error (about 0.1771, 2101 and 0.1617) compared to the rest of the layers for prediction.   

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.

In this project, a microstrip antenna is presented for use in the RFEH system. In the first chapter, different EH methods are introduced. In the second and third chapters, the structure and components of the RFEH system (antenna, impedance matching circuit, and rectifier are introduced) in the fourth chapter, theory. And the final antenna design technique is discussed, which includes the introduction of the Koch curve, which is used to increase the effective length of the slot. In chapter five, at first, a microstrip antenna with a rectangular patch with a full plate is considered as the initial antenna.

   An epileptic seizure is recognized as a neurological disorder caused by transient and unex- pected disturbance resulting from the excessive synchronous activity of the neurons in the brain. Analy- sis of epileptic seizures derived from long-term recordings of electroencephalogram (EEG) is cumbersome and time consuming for a neurologist. Therefore, introducing an automated detection system surrogate the neurologist involvement all time and speed up the treatment procedure. Methods: This study introduces a matrix determinant of EEG as a significant feature for recognition of epileptic seizures. Initially, artifact-free filtered EEG time series was arranged sequentially to form a square matrix of order, namely 13, 16, 23, and 32 and determinant was estimated. Assumed that the total elements in the square matrix represent a typical segmentation length. The experiment was conducted using EEG database obtained from the University of Bonn and Ramaiah Medical College and Hospital (RMCH). In total, eleven classification problems among non-epileptic group and epileptic EEG were com- posed to examine temporal dynamics of brain activity in different states of the epileptic activity. Next, the extracted feature was classified using support vector machine (SVM), K-nearest neighbor (K-NN), multi- layer perceptron (MLP) classifiers with 10-fold cross-validation. Results: Experimental results revealed the highest classification accuracy of 99.45% (using University of Bonn) and 97.56% (using RMCH). Between normal and epileptic EEG. In addition, other classification prob- lems and matrix orders showed better results using all the classifiers. Further, descriptive analysis, his- togram plot in polar coordinates and the bivariate histogram analysis was performed. In conclusion, ma- trix determinant found to be a potential biomarker for the real-time detection of epileptic seizure with minimal computational complexity

     افزايشانتقال حرارت همواره يكي از موضوع­هاي موردعلاقه پژوهشگران بوده است و پديده جرياندوفازي جوشش در كانال‌هاي عمودي به دليل بالا بودن ضريب انتقال حرارت و استفاده دررآكتورهاي هسته­اي از اهميت ويژه­اي برخوردار است. در اين رآكتورها در صورت دفعنامناسب حرارت و افزايش دماي ديوار، قلب رآكتور دچار حادثه شده و اصطلاحاً شارحرارتي بحراني يا در نوع جريان مادون سرد جوشش انحراف از جوشش هسته­اي رخ داده كهتلاش­ به منظور پيش­بيني و بهبود آن به منظور جلوگيري از آسيب­هاي ناشي از رخ دادناين پديده بسيار ضروري به نظر مي­رسد. در پايان نامه حاضر جريان جوشش مادون سرد آبو فروسيال (نانوسيال مغناطيسي) در يك لوله­ي عمودي به طول 2 متر و قطر 15.4 ميلي­متر،تحت شار حرارتي ثابت روي ديوار به صورت دائم، در حالت دو بعدي با استفاده از نرمافزار Ansys Fluent به صورت عددي موردبررسي قرار گرفته است. براي شبيه­سازي مدل جريان دو فازي از مدل اويلرين، براي مدل­سازيفرآيند جوشش روي ديوار از مدل RPIو براي مدل­سازي آشفتگي از مدل SST k-?

  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.

   Abstract Today, pollution of aquatic ecosystems with a wide range of organic and inorganic pollutants has become one of the biggest human challenges, so that research studies examining techniques and the use of effective attractants in reducing and Or the elimination of some of these harmful pollutants, including arsenic, has many study priorities. The aim of this study was to optimize the adsorption conditions of arsenic heavy metal from water using magnetite-chitosan composite nanosorbent. The advantage of using chitosan coating is higher adsorption capacity and higher stability of adsorbent nanoparticles and its easy separation from aqueous solution. In this study, first magnetite nanoparticles were synthesized by co-precipitation method of iron (II) and (III) chloride ions and the obtained nanoparticles were first identified by FT-IR spectrum, then UV-Visible spectrum, TEM, XRD, VSM. , DLS, Zeta potential were obtained. After reviewing the results and obtaining the result, carboxymethylcytosans were sonochemically bonded to the surface of the modified magnetite nanoparticles and the surface of the magnetite nanoparticles was coated with carboxymethylcytosane. Magnetite-chitosan composite nanoparticles were taken by material analyzers, first FT-IR, UV-Visible, XRD which showed the presence of magnetite with chitosan coating, then TEM, VSM images, magnetic saturation change of nanoparticles after coating Delivery, DLS particle size distribution and Zeta potential surface load were investigated. Then, the adsorption test was performed using Design Expert software and the experiment was designed with Taguchi model from four factors: adsorbent dose, pH, temperature and time, and arsenic solution with an initial concentration of 1 ppm. Residual arsenic concentration was measured by ICP-Mass and statistical operations were performed on the test results and the effect of various factors on the adsorption efficiency was shown as contours so that the adsorption efficiency increased with increasing adsorbent dose and with increasing pH of The adsorption efficiency increased from 3.5 to 8.5 at a certain dose and the increase in temperature and time also increased the adsorption. Dose was 0.0300 g and pH = 3.5, contact time was 1440 min and temperature was 35 ° C. The optimal absorption point with 99.3% yield was obtained at a dose of 0.0283 g and pH = 6.4, and the contact time was 976 min and the temperature was C38, and the validation test showed a value of 99.4%. The weight ratio of chitosan to magnetite was synthesized in four different ratios of 1: 1, 2: 1, 3: 1 and 4: 1, and in optimal adsorption conditions, a test was performed. . Using the linear, nonlinear Langmuir equations and then the linear and nonlinear Friendlich isotherms, the isotherm that was consistent with the adsorption process and showed the highest correlation coefficient was the linear Langmuir and Friendlich isotherms, which obtained the maximum absorption capacity from the equation. The magnetite-chitosan composite was calculated to show 313.57 mg / g. According to the obtained results, the synthesized nanocomposite can be used for water filtration and arsenic adsorption under optimal conditions.

A wireless sensor network consists of sensor nodes located in geographic areas whose job is to monitorPhenomena such as humidity, temperature, vibration, etc. Since wireless sensor networks are networks that are used to monitor and control the environment, data transfer in these networks is based on data. Is done and routing protocols in these networks must be data based.

   With advances in microelectromechanical systems (MEMS), researchers have now become interested in the systems operating based on inertial signals. In fact, inertial signals have proven useful in different areas due to advances in their manufacturing technology, availability, and inexpensiveness as well as the development of powerful processing methods such as deep learning techniques. Handwritten character recognition (HCR) is among such areas. This paper aimed to design, implement, and evaluate a novel system for the recognition of handwritten Farsi characters extracted from an inertial pen. For this purpose, a wireless inertial pen was designed. Its motion trajectory was then determined by combining the signals of its angular velocity and acceleration and using the concepts of navigation systems such as quaternion in order to estimate the position signals of characters. A convolutional neural network (CNN) was also employed to facilitate the extraction of high-level features and classification of characters. The position signal was also extracted as an image used for model learning to enhance the classifier efficiency. The experimental results indicated the CNN-6 architecture outperformed the other CNN-n architectures in terms of character classification accuracy. According to the evaluation of the proposed method through test data, character recognition accuracies of Farsi letters and numbers were reported 91.06% and 94.52%, respectively. In comparison with the previous systems, the proposed method managed to improve the recognition of handwritten Farsi characters.

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RFID 

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

and tried to improve the error  

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   

  This study experimentally investigates heat transfer inside a micro exchanger using some active smooth and coiled inserts. The obtained results were then simulated using computational fluid dynamics (CFD) techniques. A 1mm ID micro exchanger was equipped with some inserts either smooth or coiled. The length of the inserts were 6, 9, 12 cm (equal to the length of the micro exchanger) with the cross sectional radius of 0.26 mm. According to resistance of the inserts, different voltages are applied and the relevant electrical powers are transferred to working fluid. The working fluid is silicon oil warmed up contacting with the surface when flowing through the micro exchanger. Amounts of pressure and temperature were measured in inlet and outlet of the micro exchanger under different operating conditions. These operating conditions were three different flow rates 0.5, 1.5, and 2.5 ml/min and three different voltages 2, 3, and 4 V, which were applied to the micro exchanger with different inserts, as mentioned above. Four criteria employed to compare the effect of different inserts and operating conditions are performance, enhancement in efficiency, frictional losses, and thermal-hydraulic performance coefficient. In all the conditions, the data were compared to the micro exchanger equipped with the shortest smooth insert under the minimum flow rate and applied voltage. The experimental results obtained by smooth inserts demonstrated that by increasing the length of the insert the performance, enhancement in efficiency, and frictional losses were increased. Increasing the rate of thermal performance was considerably higher than frictional losses, and therefore, the thermal-hydraulic performance coefficient, as consequent of heat transfer and pressure drop, significantly increased. The relevant amount of the above mentioned coefficient for the micro exchanger equipped with the 12 cm smooth insert and 3 V applied voltage were 1.5, 2.7, ad 3.5 respectively for 0.5, 1.5, and 2.5 ml/min. In the case of coiled inserts, the thermal-hydraulic performance coefficient were significantly higher than smooth inserts due to increased frictional losses. These amounts were respectively 8.2, 22, 23.5 for 0.5, 1.5, and 2.5 ml/min. In the lowest flow rate, 0.5 ml/min, the micro exchanger equipped with the 9 cm insert demonstrated an acceptable thermal-hydraulic performance coefficient, while for higher flow rates the best performance were obtained for 6 cm insert. Under the higher flow rates, the induced turbulences lead to a better heat transfer in short distances. Increasing the length of the inserts induces additional frictional losses which lead to lower thermal-hydraulic performance coefficient. In order to figure out the heat transfer mechanism, A CFD simulation were conducted for all the relevant experimental conditions. The results were reported via different velocity and temperature vectors and contours. In addition, using the predicted amount of temperature and pressure drop in the micro exchanger, the thermal-hydraulic performance coefficients were calculated and compared to experimental data. The comparisons show a good agreement between the experimental and modeling results, the maximum amount of the error were 18.4% .   Keywords: micro exchanger, active inserts, frictional losses, thermal-hydraulic performance coefficient, CFD simulation  

  :Abstract Every communication network is assumed as a set of interference channels. Therefore, interference channels can be considered as the most important part of the wireless communication system. Each interference channel has included some transceivers, in which all transmitters send signals to their corresponding receivers. In order to maximize the transmission data rate, one common way is to increase the transmitter's power but this fails to be functional in interference channels because it increases the interference power at the receivers as well. Recently alongside all applicable methods to reduce the interference effect at the receivers, such as (TDMA),(FDMA),(SDMA),(CDMA), a method known as Interference Alignment (IA) has been developed. In IA method it is assumed that received signal su  aces in each receiver is projected into two independent linear su  aces with equal dimensions. By this way interference signals from interfering devices are grouped (normally half-size) and desirable signals from each transmitter are placed in an independent linear interference su  ace. Requardless of many advanteges of this method, it is not very functional because it is drastically dependent on the channel state informations (CSI). For this reason this method needs to be improved to a desirable function level. Thus, In this thesis a reconfigurable antenna, which enhances interference alignment with no access to CSI in transmitters, is introduced. It has made the IA to an applicable method. In this way, the need to use the MIMO antenna's and joint processing decreases

Neonates and infants are the most likely group to present with congestive cardiac failule, one of the most cause of mortality in newborns, so that, one out of 125 are born ech year with some form of heart failure. This cardiac failure ranges from mild to severe in terms of severity.In has been well established that the early diagnosis of cardiac disease in the embryyonic period may prevent the cardiac failures after birth and consequently decrease the rate of mortality in newborns.Recording heart electrical activity by electrocardiogram is commonly used to detect abnormal arrythmias. How ever the non-invasive extraction of fetal ECG   from maternal abdominal is challenging, since the fECG signals are aften corrupted by electrical noise from other sources such as maternal heart activity, uterine contractions and respiration, in addition to instrumental noise.Many approaches has been presented to non-invasively extract fECG, However their performance are not entirely satisfactory. Among all exising methods Blind Sourse Seperation have been of great interest to researchers for many applications. In this thesis, we use the method of time-frequency based blind source separation method for the separation of Convolutive Mixtures. We also use the Compressive sensing and the fast independent component analysis method to reduce the complexity of the signal.We apply our proposed method to the database in the 2013 Physionet Challenge and compare the results with existing methods using Compressive Sensing and Independent Component Analysis, which yielded a Mean squar Error of 171.65 and a time of 7.85 minutesKeywords: Fetel Electrocardiogram signal, Blind Source Seperation, Time-frequency analysis, Convolutive Mixtures, fast independent component analysis, Compressive sensing, Discet Wavelet Transform

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  AbstractWith attention to the development of new sciences, in particular electronic science and telecommunication, radio waves (radio communications), the size and physical size of high-capacity components will be smaller and smaller in integrated circuits, and these changes also affect the unwanted (noise) signals in the structure. As a result, we need high-performance filters to eliminate these unwanted waves. There are many errors in the integrated circuits due to the high frequencies, and also have unusual dimensions and size. Using the microstrip technique is very suitable for using a substrate (dielectric) substrate. For use in microwave integrated circuit circuits. In this dissertation, a low-pass microprocessor filter with very small size, a wide bandwidth and good return loss are presented.After designing and using the ADS (Advanced Design System) software and comparing the simulated and measured results, the values are acceptable.

Abstract  In recent yerars, new structures have emerged in the field of electromagnetism and microwave, which are known as Metamaterial. These structures are not present in nature and these are artificially created, and have interesting properties such as negative electrical and magnetic permeability coefficients, resulting in a negative or near-zero refractive index. However, the Metamaterial have high losses, difficulty building and bulky structures. Therefore, a different category of these two-dimensional materials, which are known to be Metasurface, are noticed more. As an example of the use of these artificial structures, it is possible to improve the radiation characteristics of antennas in the presence of a Metasurface Given the refractive index of the material with the electrical and magnetic parameters, we know that if one of the electrical or magnetic coefficients is zero at a specified frequency, it will result in zero refractive index of material and, therefore, will result in the direction of the emission waves from the material , Is closer to the normal vector of the surface, which is used to improve the efficiency and antenna gain. in addition, the proper design of the Metasurface can increase the bandwidth or minimize the antenna. In this thesis, the design and simulation of the optimal Metasurfase to increase the gain and directivity of the antenna in the microwave and terahertz band are discussed.

Design and Simulation of Microstrip Patch Antenna Array for Wireless Power Transmissio  

Mutual Coupling Reduction in Microstrip Patch Antenna Array  

Displacment Sensor using Microstrip patch Antenna with Split Ring Resonator  

Abstract The Wilkinson power dividers are one of the most ubiquitous passive circuit elements in RF/Microwave applications. They are widely used in antenna arrays, balanced amplifiers, mixers, frequency multipliers etc. in this thesis, a compact microstrip four-way filtering power divider is presented. The structure of the proposed device includes an inline filter has generalized Chebyshev response, where frequency dependent coupling method is used to generate transmission zeros at finite frequencies. A detailed design procedure is shown to determine the initial parameters of all the characteristic impedance and electrical length. Therefore, by the outspread inline filter, the configuration of four-way filtering power divider is achieved. Three isolation resistors are used to satisfy both transmission and isolation property. A porotype is designed and fabricated on a substrate with the relative dielectric constant =3.38, the thickness of h=0.7874mm and the dielectric loss tangent of tan ?=0.0022. after the full wave simulations and optimization by using AWR environment, the dimensions of the prototype is obtained and the overall size is about   55.5×40 .The measured result show 5% bandwidth centered at 2.5 GH with low in-band return loss, high-frequency selectivity, good matching and good isolation between outputs.

در اين پژوهش طراحي كوپلرهاي جهتي با پهناي باند زياد و ابعاد كوچك و در حوضه شبكه هاي بي‌سيمي به عنوان هدف قرار داده شده. در اين پژوهش نخست تحقيقات گسترده اي در زمينه المانهاي مايكرواستريپ و پاسخ فركانسي آنها صورت گرفت تا به اطلاعات كافي براي كاربرد اين آلمانها در طراحي يك كوپلر پهن باند دست يابيم. سپس به كمك اطلاعات به دست آمده در اين مرحله خطوط تزويج شده مايكرواستريپ براي ارائه طرح اوليه در يك بازه فركانسي نسبتا وسيع بكار برده شد. در مرحله بعد تلفات بازگشتي و ضريب ايزولاسيون كوپلر با استفاده از خطوط شاخه اي خميده شده و استاب هاي تكرار پذير متقارن بهينه سازي شد. ضمن رسيدن به پاسخ فركانسي مناسب و بهينه، حفظ سادگي ساختار نيز در بهينه سازي مورد توجه قرار گرفته است. در پايان چند طرح مناسب براي كوپلر جهتي رسيديم.

  Over the past several years, different control methods have been presented to avoid hyper synchronization of neurons in various papers, Because this abnormal synchrony disturbs the function of the brain and causes the diseases Like epilepsy and Parkinson. The primary treatment of disease   is drug therapy. if drug   treatment is unsuccessful, other therapies are there such as surgery and stimulation of the damaged area. in the surgical procedure, the damaged area of the brain is destroyed , which will have many side effects. In the direct stimulation technique, due to permanent stimulation of the damaged area, surgical problems can be reversed. Since stimulating methods of feedback and non- feedback are mathematical equations and have no biological background,   tried to use astrocytic stimulant as a biological stimulant. Several studies have shown that the effect of astrocytes on the absorption or release of glutamate in the synaptic space, the storage of potassium ion, and the regulation of extracellular pH is necessary to maintain the correct level of irritability in the CNS. The impairment of this function can lead to an increase in the stimulation of the neurons and their synchronization with each other. The disorder of this function can lead to an increase in the stimulation of the neurons and their synchronization with another. Therefore, astrocytes can apply appropriate control feedback to regulate neuronal activity. In this study, we tried to investigate the biological stimulus ability   induced by the behavioral dynamics of astrocytes using analog circuit design. Because astrocytes play a major role in neurological interactions in the neurons synchronization and desynchronization , the above stimulant advantage can be considered as the biological basis based on the interaction between neurons and astrocytes. To simulate the behavior of neurons in diseases caused by super-synchronization of neurons, the biological network of the neuronal population is considered, and the suggested stimulatory ability is investigate in their desynchronization. Considering the many applications that digital systems have, the design of analog circuits continues to apply where digital equivalents can not perform their tasks well. For example, in applications that require very low power, such as medicine, implantable elements, and portable and lightweight devices, digital circuits may not be appropriate. Also, all the signals in the real world are analog, and these analog signals will never be substitutable. For this reasons, the proposed system   is designed with analog circuits and simulated in the HSpice software.Key words: synchronization, stimulation, astrocyte, neuronal population, analog circuit design.  

Cross Field Antennas (CFA) are a new generation of the antennas that have the major advantages of conventional antennas. Therefore, awareness raising and knowing enough of the performance these antennas can have a significant effect on the advancement and development of telecommunications applications. One of the important reasons to use CFA antennas is very small-size of these antennas,  which can easily be installed on the roof of the buildings. In this thesis, cross-field antennas have analyzed using the quasi-static method. First, in order to reduce the error of calculations in the use of this method, different symmetric conductors were considered. It was observed that the elements to equal dimensions or with the dimensions ratio of one have the minimum probability of error. The using this method and considering the elements with the equal dimensions, the charge density was investigated in asymmetric conductors. It was also shown that too many elements do not have much effect on the results, while the time of the calculation increased and the speed of the process reduced. Then, CFA antenna was analyzed in two static and dynamic states to assume the elements with the dimensions ratio of one using the quasi-static method. The results showed to achieve the ratio of the electric to magnetic fields equal to 337 ohms and efficient radiation; it is necessary that the E-plate excitation amplitudes are twice the D-plate excitation amplitudes in this antenna. The D-plate irradiation phase is about 90 degrees phase-lag to the E-plate irradiation phase, also. Finally, the effect of change each of the physical parameters of the antenna was investigated on the ratio amount of electric to magnetic fields. Finally, the effect of changes each of the physical parameters of the antenna was investigated on the ratio of electric to magnetic fields and was indicated that each parameters a significant impact on this ratio. Whereas, the relationship between the magnitude and the phase of sources must again be determined.

   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.

تعيين نقاط بحراني در شبكه هاي هايبريدي و توسعه مسيرهاي اضافي در شبكه جهت بهبود عملكرد و افزايش ظرفيت و پايداري با ملاحظه به توانايي گره ها

جداسازي سيگنال قلب جنين با استفاده از جداسازي كور منابع بر اساس آناليزهاي زمان فركانس

فيلتر ميان نگذر جذبي و انواع آن

بيماري پاركينسون يكي از بيماري­هاي شايع عصبي است. اين بيماري با مشكلات حركتي براي بيماران همراه مي­باشد كه موجب عدم توانايي كاركردن و ديگر پيامدها مي­باشد. در اين پايان­نامه، سعي شده تصاوير مربوط به دست نوشته افرادي كه تست پاركينسون داده­اند به صورت اتوماتيك توسط روش­هاي پردازش تصاوير بررسي شوند و بيمارها و غير بيمار ها با متد­هاي پردازش ماشين و يادگيري ماشين تفكيك شوند. ويژگي­هاي الگوي باينري محلي و چندي­كردن فاز محلي براي اولين بار در مسئله­ي طبقه­بندي افراد سالم و بيمار پاركينسون بكار برده مي­شوند و پارامترهاي دقت شناسايي،   دقت،فراخواني وF-score   ارزيابي مي­شوند. روش پيشنهادي شامل سه قسمت است: پيش پردازش، استخراج ويژگي­ و كلاس بندي. در بخش پيش پردازش، نرمال سازي، قطعه­بندي مبتني بر عمليات ريخت­شناسي و فيلتر مات بر روي تصوير انجام مي­گردد. سپس، در بخش استخراج ويژگي براي تصوير، دست خط و خط چاپي از هم جدا شده و سپس با هم مقايسه مي­شوند تا ويژگي­هاي مربوط به آن به دست آيد. براي مشخص كردن نقاط متناظر روي دست خط و خط چاپي از اختلاف دو تصوير و همچنين ميانگين­گيري استفاده شده است. در ادامه، ويژگي­هاي بدست آمده كه مبتني بر اطلاعات آماري تصوير مي­باشد، بدست مي­آيد. در مرحله­ي بعد سه طبقه­بند مختلف ماشين بردار پشتيبان، نايو بيز و كا نزديك­ترين همسايه به منظور دسته بندي افراد سالم و بيمار پاركينسون بكار گرفته شده است. براي ارزيابي روش پيشنهادي و مقايسه با روش­هاي پيشين، از مجموعه داده Hand PD استفاده شده و از 90 درصد داده­ها براي آموزش و از 10 درصد براي تست استفاده كرده­ايم. نتايج به­دست آمده نشان مي­دهد كه بهترين الگوريتم در بين طبقه­بندها نايو بيز بوده است كه دقت اين روش براي طبقه­بندي افراد سالم و بيمار با بدست آوردن اطلاعات آماري   تصاوير، برابر با 32/85 است . همچنين در ادامه تاثير بكارگيري دو توصيفگر الگوي باينري محلي و الگوي چندي­ساز فاز محلي، بررسي شده است كه طبقه­بند نايوبيز بيشترين دقت را براي الگوي باينري محلي برابر با مقدار 77/87 و براي الگوي چندي­ساز فاز محلي برابر با 59/85   نتيجه داده است. در مجموع نتيجه­  hy  hy;ي بدست آمده از روش پيشنهاد شده نشان مي­دهد كه اين روش نسبت به روش­هاي اخير 9 درصد افزايش در دقت تشخيص داشته­است.   

Design of an ultra-wideband low noise amplifier with desirable linearity

  Wilkinson Power Divider using Low Pass Filter with compact size and Harmonics Suppression

Design, Simulation and Fabrication of microstrip ultra- wide band (UWB) band pass filter (BPF) with triple notched using stepped impedance resonator.

  Design of Compact Quad-Band Bandpass Filter with Simple Structure and Controllable Frequencies Using Single Rectangular Ring Resonator

رابطه كرنش – تغيير مكان خطي است-2 معادلات ورق بر اسا س تئوري تغير شكل برشي مرتبه اول است.

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Compact ultra-wide stopband lowpassfilter using modified rectangular resonatorloaded by stub  

طراحي يك تقويت كننده كم نويز و بهينه سازي تابع نويز توسط الگوريتم هاي بهينه سازي در متلب

In recent years, studies on microelectromechanical systems have shown that there are considerable opportunities for microsensors based on mechanism like piezoelectric. Microelectromechanical systems objective in miniaturization, multiplicity, creation and integration of microelectronic systems. Today microelectromechanical systems have numerous application in vehicles, military industries, medicine, sensors and actuators. New technologies have led to increase in use of piezoelectric materials. One of the recent changes is using piezoelectric materials as sensors and microsensorS in microsystems. Piezoelectric are a category of smart materials which can be used in electric energy harvesting from environment. Using these materials as sensors has grown popular due to lack of need for an external source. These materials can be used frequently in strain gauges, active biomedical materials, switches, pressure and vibration sensors, accelerometers, flow detectors and vibration and ultrasonic controllers. In the present thesis, speed sensor design and manufacture using piezoelectric polymer and wind energy harvesting via these materials are studied. Piezoelectric polymer is used as cantilever beam inside the low-speed wind tunnel. Standard DC circuit is used to convert piezoelectric alternating voltage to direct voltage using an operational amplifier too boost the piezoelectric output voltage. By performing various tests on the sensor, the relationship between piezoelectric output voltage and wind speed was obtained. The result of sensor tests indicated this relationship can be approximated with a first degree function. The amount of harvested energy from wind is also tested in different speeds. Peak power and power density were obtained with speed of 15m/s and optimal resistance of 2200 as 7.38 mW and 134.2 mW/cm3 respectively. Finally, the produced voltage and harvested energy can be used for wireless sensor networks, sensors and also for place in which changing batteries are not possible.