In petroleum production processes, stable emulsions are extracted along with crude oil. The stability of these emulsions, primarily due to the presence of naturally occurring surface-active compounds in crude oil, leads to various operational and environmental challenges. Therefore, the effective separation of these emulsions is considered one of the critical and essential challenges in the petroleum industry. To provide an effective solution to this challenge, a hyperbranched polymeric demulsifier based on a polyester structure was synthesized in this study via direct polycondensation between a trifunctional polyalkylene glycol as the branching agent and a difunctional isophthalic acid. The hyperbranched structure, with a higher density of end-groups, enables multi-point and faster adsorption at the interface. A series of characterization analyses were performed to confirm the structure and the successful synthesis of the demulsifier, and the results verified the effectiveness of the synthesis process. The performance of the synthesized demulsifier in water-in-oil emulsion separation was evaluated under various operational conditions, specifically demulsifier concentrations ranging from 50 to 100 mg/L, temperatures from 40 to 70°C, and settling times from 10 to 60 minutes. Under the optimal conditions, including concentration of 90 mg/L, temperature of 70°C, and settling time of 50 minutes, demulsification efficiency of approximately 85% was achieved. In addition to its amphiphilic nature, the hyperbranched polymeric demulsifier exhibits acceptable separation performance due to its hyperbranched structure and favorable properties, including an appropriate molecular weight, relatively uniform structure, and high flexibility of the polymer chains. Subsequently, in order to further enhance the performance of the hyperbranched polymeric demulsifier and investigate the effect of magnetic nanoparticles on this system, magnetic graphene oxide nanoparticles were synthesized and incorporated into the system as a modifying agent. Under the previously determined optimal conditions and at a concentration of 0.025wt%, these nanoparticles increased the demulsification efficiency to 95.6%. In addition, the magnetic properties of the nanoparticles enabled their efficient recovery from the system, which not only reduced material consumption and operational costs but also demonstrated the potential for their reuse. Overall, the results of this study demonstrate that the synthesis of a hyperbranched polymeric demulsifier and its modification with magnetic graphene oxide nanoparticles provide a novel and innovative approach for the demulsification of water-in-oil emulsions. Furthermore, due to the high potential of this system in emulsion separation, the findings of this research can be utilized for industrial applications and further research studies in the petroleum industry.   

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.   

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.  

   Approximate computing is a promising approach for high-performance, and low-energy computation in inherently error-tolerant applications. This study proposes an approximate adder comprising a constant-truncation block in the least significant part and several non-overlapping summation blocks in the more significant parts of the adder. The carry-in of each block is supplied using the most significant bit of one of the input operands from the earlier block. In the most significant block, two more-precise approaches are used to generate candidate values for the carry-in. The final value of the carry-in for this block is selected based on the values of the input operands. In fact, the proposed approximate adder is input-aware, and dynamically adjusts its operation in one or two cycles to improve accuracy while limiting the average delay. The experimental results indicate that the proposed adder has a better quality-effort tradeoff than state-of-the-art approximate adders. Different configurations of the proposed adder improve delay, energy, and the energy-delay product (EDP) by 78%, 72% and 87% respectively, when compared to state-of-the-art approximate adders, all without any loss in accuracy. Additionally, the efficiency of the proposed adder is confirmed in both image dithering and stock price prediction through regression.

   نفت خام سنگين به دليل ويسكوزيته بالا، محتواي بالاي آسفالتين و رزين و دشواري در فرآورش همواره يكي از چالش‌هاي اصلي صنايع پالايشي محسوب مي‌شود. يكي از رويكردهاي نوين براي بهبود فرآورش اين برش‌ها، بهره‌گيري از پديده كاويتاسيون و انرژي آزادشده از انفجار حباب‌ها به‌عنوان منبعي براي تغيير خواص نفت سنگين است. در اين تحقيق، شبيه‌سازي عددي پديده كاويتاسيون در يك اجكتور در مقياس آزمايشگاهي با استفاده از ديناميك سيالات محاسباتي انجام شد. هندسه اجكتور در نرم‌افزار انسيس ديزاين مدلر طراحي و شبكه‌بندي آن در انسيس مشينگ انجام شد. شبيه‌سازي جريان سه‌فازي (آب، بخار و نفت سنگين) و پديده كاويتاسيون با استفاده از مدل جريان مخلوط در انسيس فلوئنت انجام گرديد. شرايط مرزي شامل فشار ورودي آب Pa 2.000.000 و دماي   K298 و فشار ورودي نفت سنگين Pa80000 و دماي   K353 بود، در حالي كه فشار خروجي برابر با Pa 101325 تعيين شد. دبي ورودي نفت در شرايط مرزي فوق به ترتيب Kg/s4709171/0 بود و حداكثر سرعت در گلوگاه اجكتور m/s 39/ 63 گزارش شد. داده‌هاي به‌دست‌آمده از فلوئنت شامل فشار و حجم بخار توليدي (m³/s 104×33/7) به نرم‌افزار متلب منتقل شد و با بهره‌گيري از معادله ريلي پلست، ديناميك فروپاشي حباب‌ها و انرژي آزادشده از انفجار آن‌ها محاسبه گرديد. دما و فشار حباب حين فروپاشي به ترتيب،   K98/4722 و bar 2827 انرژي آزادشده از يك حباب در اين فرآيند J 10-10 833× /1بود كه به عنوان بار حرارتي به جريان نفت سنگين اعمال شد. تحليل نتايج نشان داد دانسيته نفت پس از كاويتاسيون از Kg/m³1/903 به 1/880 كاهش يافت و ويسكوزيته ازKg/m·s 2467/0 به 0754/0 كاهش يافت، كه بيانگر تغيير قابل توجه در خواص ترموديناميكي نفت سنگين است. بر اساس نتايج به‌دست‌آمده، بهره‌گيري از كاويتاسيون و طراحي بهينه اجكتور مي‌تواند رويكردي مؤثر براي بهبود فرآيندهاي شكست مولكول ها و سبك‌سازي برش‌هاي سنگين نفت باشد. چارچوب روش‌شناسي ارائه‌شده، امكان تحليل همزمان هيدروديناميكي، ديناميكي و اثرگذاري انرژي آزادشده از كاويتاسيون بر نفت را فراهم مي‌آورد و مي‌تواند مبناي توسعه تحقيقات آينده در بهينه‌سازي فرآيندهاي پالايشي قرار گيرد.

   Epilepsy is a common neurological disorder characterized by recurrent seizures. Research indicates that approximately 30% of epilepsy patients are resistant to pharmaceutical treatments or surgical interventions. Abnormal brain activity, known as the pre-ictal state, typically begins a few minutes before a seizure occurs. Electroencephalography (EEG) is a practical technique for recording brain electrical activity and aiding in the diagnosis of epilepsy. Seizure prediction and assistance for epilepsy patients remain significant challenges in preventing seizure-related complications and improving the quality of life for individuals affected by this condition. Accurate prediction of the onset of the pre-ictal state can help reduce the adverse effects of seizures for patients and their caregivers by providing timely care. The objective of this thesis is to develop a system that enhances evaluation metrics for seizure prediction using deep learning methods. In this study, the CHB-MIT dataset, comprising scalp EEG signals, has been utilized, and the proposed method was evaluated on 24 patients from this dataset. To predict seizures, deep learning-based models and knowledge distillation techniques were employed for model compression, aiming to reduce time and hardware costs and enable real-time application of the network. The teacher model, designed as a patient-independent framework with 22 channels and preprocessed mel-spectrogram inputs, employs a 3D convolutional neural network. This model achieved an accuracy of 87.52%, sensitivity of 88.82%, specificity of 85.97%, and an F1 score of 86.56%. Subsequently, the knowledge distillation technique was applied. By utilizing this approach and employing a single electrode, we identified two electrodes (Electrode 20 and Electrode 22) with superior performance compared to others. The proposed method, for Electrode 20, achieved accuracy84.56%, sensitivity86.76%, specificity82.77%, and F1-score values of 83.63%, and for Electrode 22, achieved accuracy84.30%, sensitivity86.45%, specificity82.93%, and F1-score values of 83.35%, enabling seizure prediction 30 minutes before onset. The results obtained from our proposed method were compared with advanced seizure prediction techniques. The proposed method demonstrated superior performance in terms of accuracy, sensitivity, specificity, and F1 score, highlighting its effectiveness in seizure prediction.

 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.

Abstract Today, with the growing complexity of digital circuits and the increasing compactness of manufacturing technologies, the likelihood of failure during both the manufacturing process and the operation of digital circuits has risen. As a result, these products require testing both during production and operation to ensure proper performance. Therefore, producing a high-quality test with a minimal number of test vectors and in the shortest time is crucial. In this thesis, a simulation-based test generation method for combinational circuits is proposed. This method utilizes an approximate criterion called approximate critical path tracing with parallel patterns to evaluate the effectiveness of candidate test vectors. This criterion is based on the traditional critical path tracing method, but introduces an approximate approach for backtracking in fan-outs. By allowing some inaccuracies in the results, this method reduces the complexity of fault simulation compared to traditional methods. The parallel pattern critical path tracing method leverages both fault-level and pattern-level parallelism, resulting in significantly higher simulation speed compared to traditional methods for determining fault coverage. Applying the parallel patterns approximate critical path tracing algorithm to ISCAS85, ISCAS89, and ITC99 benchmark circuits shows that, in over 98% of the circuits, the results strongly correlate with the exact fault coverage index. Additionally, compared to the parallel pattern fault simulation method, this approach is more than 500 times faster. The proposed test generation method, which utilizes the criterion derived from the parallel pattern approximate critical path tracing algorithm to identify effective test vectors, is capable of producing high-quality test vectors quickly. Evaluations on benchmark circuits demonstrate that this method is over 15 times faster than the one using parallel pattern fault simulation, with only a 1% average increase in the number of test vectors.    Key words: Digital circuits testing, Fault coverage, Simulation-based test-pattern generation, Approximate fault coverage index, Critical path tracing.   

     Medical imaging plays an important role in clinical diagnosis and treatment. Medical imaging is a way to show the anatomical structures of the body with the help of X-rays, which is obtained from computed tomography and magnetic resonance imaging. But often this type of photography is more suitable for physiological function than anatomy. With the development of computer and imaging technology, medical imaging has greatly affected the medical field. Since the quality of medical imaging has had a great impact on disease diagnosis, medical image processing has become one of the most important clinical applications that store and retrieve images for the future, which are prerequisites for accurate storage of these images. Bones are solid organs in the human body that protect many important organs such as the brain, heart, lungs and other internal organs. The human body has 206 bones of different shapes, sizes and sizes. The largest bone is the femur, and the smallest is the ossicle. A common problem in humans is "bone fracture". A bone fracture can be caused by an accident or any other case where a lot of pressure is applied to the bone. There are different types of bone fractures: oblique, compound, comminuted, spiral, girrin's stick1 and transverse. Compared to other methods, X-ray imaging provides precise details of bones and less details of tissue and muscle, which makes it easier to detect fractures

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  

Accurate and timely diagnosis of brain tumors is essential for effective treatment of this disease. The choice of treatment method depends on the level of the tumor at the time of diagnosis, the type of pathology and its grade. Glioma brain tumor is the most common type of primary brain tumor and all of them originate from glial cells that surround neurons. In diagnosing this type of disease, computer-aided diagnosis methods have helped neurologists in various ways. Recent works in this field have led to improved efficiency with the emergence of the concept of deep learning. Computer-aided recognition systems approaches include preprocessing, segmentation, feature analysis (feature extraction, feature selection, and feature verification) and >   In this thesis, methods based on image processing, machine learning, and deep learning have been used to identify glioma brain tumors and >The dataset used in this research is Brats2018, which includes 210 MRI images of high-grade glioma tumors and 75 images of low-grade glioma tumors. In the first proposed method, image segmentation was done using the combination of K-Means clustering algorithm and Coyote optimization algorithm, as well as different feature extraction methods, including texture feature extraction using local binary patterns method and deep feature extraction. From the trained neural network and the pre-trained VGG16 network, among the mentioned methods, the extraction of deep features resulting from the precise adjustment of the pre-trained VGG16 network brought the best results. This proposed method reached an acceptable accuracy of 99%. In the second proposed method, the clustering centers were optimized using the coronavirus algorithm, and the previous feature extraction methods were also implemented for this method, and the best performance related to feature extraction was through a trained neural network. be In this process, we reached 99.80% accuracy. Keywords: Glioma Brain Tumors, Clustering, Coyote Meta-heuristic Algorithm, Coronavirus meta-heuristic algorithm   

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).   

   Due to the growing use of the tunnels and underground structures, and the high importance of these structures, the methods of supporting and maintaining the stability of them have also grown into a high importance subject. One of the usual methods for supporting tunnels is using shotcrete in the walls and crown of the tunnel in order to reduce the displacements which are caused by surrounding rocks. this study focuses on investigating plastic concrete as a flexible layer in the walls and crown of the tunnel. It is expected that using plastic concrete as a flexible layer could reduce some of the displacements caused by surrounding rocks and thus have a positive effect on the stability and seismic performance of the tunnel. This concentrates on the effects of using the plastic concrete as a flexible layer around the tunnel on the stability and seismic behavior of the tunnel by modelling a tunnel in the ABAQUS and comparison the results of the two different conditions, one with the plastic concrete and the other one without the plastic concrete. The results of this research shows 14.76 percent raise in bearing capacity of the tunnel when using plastic concrete and also a 17.76 percent raise in energy absorption. Studying the results from the dynamic analysis of the model shows that by using plastic concrete the displacement and acceleration of the tunnel will reduce by 33.48 and 19.75 percent respectively. By comparison the results of the stress produced in the body of tunnel in two conditions we can see that the plastic concrete layer causes a 36.91 percent reduction in stress level and also the strain will reduce by 22.14 percent as well. As it is illustrated by results, the plastic concrete causes a better performance in seismic behavior of the tunnel and also provides a better condition for its stability. Key words: tunnel, soft rock mass, flexible support, plastic concrete, seismic behavior

Abstract The term cryptocurrency is an emerging topic in today's world, which has created a revolution in our vision in the field of investment and has caused changes in the world's financial systems. Cryptocurrency is a digital currency that uses blockchain technology with secure encryption. Every change can have advantages and disadvantages, cryptocurrencies are no exception to this rule, and along with their advantages, they can also have disadvantages for the economy of any society, so that due to the decentralization of these currencies, traditional monetary systems and the capital market of each They can influence a society. Therefore, due to the importance of the issue, the need to understand public opinion and analyze people's opinions in this regard increases. To understand the opinions and views of people about different topics, you can take help from social networks because they are a rich source of opinions. The Twitter social network is one of the main platforms where users discuss various topics, therefore, in the shortest time and with the lowest cost, the opinion of the community can be measured on this social network. Twitter Sentiment Analysis (TSA) is a field that analyzes the sentiment expressed in tweets. Considering that most of TSA's research efforts on cryptocurrencies are focused on English language, the purpose of this research is to investigate the opinions of Iranian users on the Twitter social network about cryptocurrencies and provide the best model for >  

Network Functions Virtualization of architecture means providing various network services without the need for hardware and not depending on it. Network Functions Virtualization is a new field in the network, with the help of which hardware devices can be implemented in virtual and software form. Network Functions Virtualization improves network functions such as: proxies, firewalls, load balancing, etc. In other words, using virtualization technology, this architecture is able to convert hardware devices into software modules known as virtual network functions and provide the desired service to the user. Providing the service requested by the user in the network is done by a sequence of virtual network functions, which are known as service functions chain. One of the main challenges in the development of network functions virtualization architecture is the allocation of resources to the requested network services in network infrastructures based on network functions virtualization, this challenge is called network function virtualization resource allocation problem. Therefore, in this research, the problem of allocating resources to virtual network functions in Network Functions Virtualization architecture has been solved by using mathematical programming techniques. In this research, a multi-objective mixed integer linear programming model is presented for the problem of resource allocation to virtual network functions. In this model, constraints related to the resource capacity of nodes and connections and delay constraints are desired. Also, the objective functions in this research are: maximum flows accepted in the network, reduction of resource costs of nodes (including: the number of CPU cores and the amount of memory), reduction capital costs, reduction operational costs and checking execution time. These constraints and objective functions are expressed precisely and explicitly by mathematical functions. The proposed mathematical model is implemented and solved with the Cplex solver. To evaluate the proposed mathematical model, several different topology are considered. The optimal cost is evaluated under changing parameters such as the length of service functions chain, the number of flows, the length of flows, the amount of resources of nodes, the number of nodes and the number of virtual network functions. And finally, the increase in execution time is checked by changing the number of nodes and the number of virtual network functions. The numerical results of this research show the effectiveness of the model in resources allocation to virtual network functions. Keywords: Network Functions Virtualization architecture, Virtualized Network Functions, Resource allocation, Mathematical programming, Mixed integer linear programming   

  Long-ShortTerm Memory neural networks have high computational complexity, resulting inlong execution times. Hardwareimplementation is one of the proposed solutions to this challenge. However, thepower, delay, and area are serious challenges in the hardware design process. Asuitable solution would be to replace the approximate circuits with exactcircuits. The purpose of this study is to evaluate the efficiency of long-shortterm memory networks using approximate computing units. However, because themultiplication operator is used extensively in the network structure, we turnedour attention to replacing the multipliers. For this purpose, we replaced allthe multipliers in the EvoApprox8b approximate library instead of the exactmultiplier in the network structure for forecasting stock market signals in thedatasets of Apple, Microsoft, and IBM companies, and we examined theperformance of the network. From the simulation results, it was found thatreplacing the approximate multiplier can cause a decrease of 205.8 µw in power,0.64 ns in delay, 236 µm2 in area, and 366.5 J in PDP, to predictthe close signal in the Apple stock market dataset. The substitution effect inpredicting the High signal in the Microsoft stock market dataset was in theform of a decrease of 170 µm2 in area, 38.8 µw in power and anincrease of 17.7 J in PDP, and the substitution effect in predicting the Lowsignal for IBM was in the form of a decrease of 47.1 µw in power, 432.646 J wasobserved in PDP and 87 µm2 in area. In the second part of study,with the help of the inherent hardware criteria of the approximate multipliers,we designed a predictive model to predict the suitability of the multiplier forreplacement in the network hardware structure. Therefore, a binaryclassification problem was defined. Next, using feature-selection algorithms,we determined the number of entries in the desired model. Our proposed model isa Linear Discriminant classifier, which can predict the performance of anapproximate multiplier from the EvoApprox8b library in the LSTM networkhardware structure using the Mean_AED, Correct, and Var_ED inherent errorcriteria with 99% accuracy.

   چكيده:   بيان مسئله:معماري به عنوان بستري كه مخاطب آن انسان است بايد جنبه هاي متفاوت از نيازهاي روحي و رواني انسان را پوشش دهد.در اين ميان كالبد به عنوان عامل شكل دهنده به معماري نقش مهمي در پاسخگويي به اين نيازها دارد به ويژه در بناهاي مسكوني كه انسان در ارتباط دائمي با آن قرار دارد.كالبد به عنوان حد فاصل فضاي بيروني و دروني ماهيتي دوجانبه دارد   به اين معنا كه علاوه بر تاثيري كه بر فضاي هاي داخلي مي گذارد، نمايشي از چهره بنا در شهر است. نماي ساختمان نمود اين چهره در فضاي شهري است. اين عنصر به عنوان حد فاصلي ميان بيرون و درون بنا مي‌تواند موجب برقراري ارتباط ميان اين دو شود.اين ارتباط در صورت شناخت عوامل موثر در چگونگي برقراري آن ميتواند موجب طراحي صحيح و جانمايي درست فضاهاي داخلي شود و نيز تاثير مفيدي روي فضاهاي شهري داشته باشد .در نهايت مسئله اصلي پايان نامه حاضر دريافت چگونگي ارتباط ميان بيرون و درون،كشف الگوهايي در ارتباط با اين موضوع و طراحي آپارتمان مسكوني بر اساس اين الگوهاست.از اين رو سوال اصلي اين پايان نامه به اين صورت است:چگونه مي‌توان در طراحي جداره آپارتمان‌هاي مسكوني در كرمانشاه،ارتباط درون و بيرون را بهينه نمود؟ پيشينه پژوهش: اين پايان نامه با بررسي ضرورت مسئله در جهت كشف چگونگي ارتباط ميان بيرون و درون   از طريق منابع كتابخانه اي در حوزه معماري و طراحي شهري   به ارائه پيشينه موضوع پرداخته است به اين ترتيب كه در ابتدا با جستجو در منابع كتابخانه‌اي حول موضوع وكلمات كليدي،در بخش اول به مفاهيم مربوط به ويژگي هاي درون و بيرون و ارتباط ميان آنها و دربخش بعدي به مسائل مربوط به جداره و اهميت آن پرداخته شده است. روش پژوهش: روش پژوهش اين پايان نامه تركيبي از نمونه موردي و كيفي است.ابتدا با در نظر گرفتن معيار‌هاي استخراج شده از منابع مكتوب در جهت كشف چگونگي برقراري ارتباط ميان درون و بيرون از طريق جداره و شناسايي عوامل موثر بر اين ارتباط ،خانه هايي از بافت مياني كرمانشاه به عنوان نمونه موردي مورد بررسي قرارگرفته است و داده‌ها با استفاده از روش مصاحبه عميق با ساكنين و حضور در خانه به همراه مشاهده و برداشت كروكي از فضاها به دست آمده است. سپس به منظور   دستيابي به الگوهاي خاص در طراحي جداره،به تبيين روش پژوهش پايان نامه از طريق شناخت كالبد خانه وتحليل اتفاقات و رويدادهاي مرتبط با بيرون ازخانه پرداخته شده است و به كمك رويكرد نظريه‌ي زمينه ايي و روش استدلال منطقي معيار‌هاي كالبدي موثر درارتباط با بيرون شناسايي شده است. نتايج: نتايج در قالب جدولي به عنوان عوامل كمك‌كننده به ارتباط بيرون ودرون ارائه شده است. اين عوامل شامل فضاهاي همنشين با جداره،عناصر معماري و عوامل طبيعي است كه به صورت راهكارهايي در طراحي آپارتمان مسكوني مورد استفاده قرار گرفته است. اين راهكارهاي كالبدي مي‌تواند به منظور افزايش كيفيت فضايي در آپارتمان‌هاي مسكوني مورد توجه طراحان قرار بگيرد و نيز مي تواند در تدوين استانداردهاي طراحي جداره آپارتمان‌هاي مسكوني كاربردي باشد.    كليد واژه ها:بيرون،درون،نما، طراحي،آپارتمان مسكوني،

   چكيده سكونتگاه يا به تعبير بهتر خانه به عنوان مكاني كه كه وابستگي بي‌قيد و شرط با انسان‌ها دارد،مفهومي ماندگار و جاودانه است،كه از ابتدا تغييرات بسياري را متحمل شده است. اين تغييرات كه به واسطه‌ي تفكر انسان در جريان زندگي او بوجود آمده است،گاه ادامه‌ي شرايط پيشين و تداوم آن است و گاه حاصل دگرگوني مفاهيم پيشين است؛ اما در هرحال،چه تداوم و چه دگرگوني حاصل شود، بايد چرايي آنها را جويا شده و علت آن مشخص شود. به دنبال همين موضوع و به منظور روشن شدن مسئله‌ي اصلي،عنصر بام به عنوان فضاي مورد توجه پايان نامه پيش رو،در شرايط كنوني،در ساختمان‌هاي امروزي، و همچنين شرايط گذشته اين فضا،مورد بررسي و مقايسه قرار گرفته است تا در نهايت لزوم توجه به اين فضا به عنوان مسئله‌ي اين پايان نامه مشخص شود. در ادامه و با توجه به محوريت فضاي بام و مشخص شدن مسئله ي مورد نظر،هدفي كه در انجام پايان نامه دنبال مي‌شود، توجه به طراحي اين فضا در آپارتمان‌هاي مسكوني است كه به دنبال آن توجه به ساير موارد   نظير ورود طبيعت به فضاي معماري،توجه به طراحي فضاي باز و ساير موضوعات را نيز در نظر دارد؛ همچنين رفع بخشي از نيازهاي رواني انسان در طراحي اين فضا و در نظر گرفتن انسان به عنوان طرف ديگر اين موضوع در كنار توجه به شرايط متفاوت يك عنصر معماري در ادوار مختلف مي‌تواند از اهداف ديگر اين پايان نامه تلقي شود. راهبردي كه در خصوص جمع آوري اطلاعات در اين پايان نامه استفاده خواهد شد،استدلال منطقي است و داده‌ها از طريق جستجو در منابع مكتوب،مشاهده و تحليل نمونه‌هاي موردي در قالب تصاوير جمع‌آوري خواهد شد؛ به اين معنا كه به دنبال يافتن جواب سوالات از طريق منابع كتابخانه‌اي و دسته‌بندي اين اطلاعات درمراحل مختلف دركنار يافتن ويژگي‌هاي فضاي بام از طريق منابع مكتوب و نمونه‌هاي موردي و در ادامه مقايسه نتايج قسمت هاي مختلف اين پايان نامه با يكديگر،نتايج كلي تري به دست خواهد آمد. در نهايت نتايجي كه از اين پايان نامه به دست خواهد آمد،چون از جنس راهكارهاي طراحانه در فضاي بام و پاسخگوي به چهار دسته از نيازهاي رواني بدست آمده در بخش‌هاي ابتدايي اين پايان نامه است و در قالب نمودار‌هايي درانتهاي بخش‌هاي مربوطه قرار گرفته است،در قالب جزئيات در فضاهاي بام يا در ساير فضاهاي معماري قابل اجرا است. به طور كلي نتايج حاصل از پايان نامه پيش رو،قابليت اجرا در يك آپارتمان مسكوني در بستر مورد نظر كه شهر كرمانشاه است را داراست اما چون اين پايان نامه حول محور فضاي بام است و توجه به فضاهاي باز و حضور فضاي سبز در معماري را نيز به دنبال دارد مي‌تواند تا حدي در خصوص طراحي ساير بناها،با كاربري هاي مختلف،نيز مورد استفاده قرار گيرد. كليد واژه‌ها: آپارتمان مسكوني،طراحي بام، فضاي زندگي

   Individual differences originate from one's personality, which is the most crucial factor affecting one's decisions and choices in life. In recent years, automatic personality detection from text has attracted the attention of most researchers due to its applications like recognition of qualified managers, job selection, selection of academic courses, and online businesses. However, most current methodologies focus on the statistical features of text and ignore the semantic relations and information. These writings and texts are a way to translate internal thinking and feeling in a manner that is understandable to others. The research aims to automatically predict personality from text using language modeling and deep learning. This research presents a deep learning algorithm, Convolutional Neural Networks, through two approaches for personality prediction from two benchmark datasets: Essay and MyPersonality. In the first approach, whole text is utilized for modeling, training and evaluation; However, in the second approach, key phrases derived by a multipartite graph are used. In order to extract features, three techniques of SentenceTransformer, Longformer and short-time Fourier Transform are presented and applied for the first time in the personality prediction research.

  With the rapid growth of the economy and the expansion of the stock market, analyzing and forecasting the stock price and comparing various price forecasting methods, and analyzing the trend of the stock market are more necessary and at the same time popular.The stock market is difficult to predict due to its volatile nature.There are no rules for predicting what will happen to stocks in the future.Accurate forecasting is a big challenge because market trends are always changing depending on many factors. In this research, the goal is to analyze the price and trend of the total index and stocks using machine learning techniques.This work includes two approaches. In the first approach, using the historical data of oil, gold, dollar, some other foreign indices, shares of some large stock exchange companies inside Iran, stock indices, and technical indices extracted from them between 11/13/2012 and 05/21/ 2022, was shown that with the help of artificial intelligence and machine learning algorithm (MLP), it is possible to find the factors and indicators that affect the total index of the Tehran stock market and try to better predict prices with the help of them and machine learning algorithms.The results indicate that the LSTM network with two recurrent layers and the optimal time step is a suitable and time-consuming but high-accuracy network for price and time series forecasting, which has the best results with minimal error compared to other machine learning methods such as nearest neighbor. followedIn the second approach, using some machine learning algorithms and technical indicators and past price information of a specific stock (steel from 03/11/2007 to 08/30/2022), the goal was to analyze the stock trend and check the buying and selling signals that With the help of the Bollinger Band indicator and a buy and sell risk factor, you can find the right time signals for the predicted data.

Climate-based design is important from two points of view. The first step is to increase the quality of thermal comfort and the second step is to save energy consumption for thermal control of buildings. In the design of buildings, much attention should be paid to the indoor environment, because people spend 80-90% of their time indoors, and their comfort, health, and productivity are directly affected by the indoor environment quality (IEQ). Thermal comfort has been identified as the most influential parameter on IEQ among other visual, acoustic, etc. comfort parameters. Since energy consumption is high to establish comfort conditions in buildings, windows, which are integral elements of building facades, can be used to achieve internal thermal comfort. In general, the society is clearly interested in reducing energy consumption, mainly In buildings, it is informed through the proper use of daylight and natural ventilation. The general purpose of this research is to use daylight to minimize the amount of artificial light and reduce electricity consumption and reduce HVAC costs. Using windows or skylights to let in natural light and regulate temperature is one construction strategy that can save costs for homeowners and businesses. Since windows in hot seasons can enter a lot of radiant energy into the building, which leads to an increase in cooling energy, it is important to create a shade on this window in order to prevent the sun from penetrating into the building throughout the year. For this reason, awnings are used to control The amount of sunlight and the reduction of input energy are used in climates with the number of hot days throughout the year. Using a canopy, either as a part of the building or as a separate part of the building, can be effective in reducing the temperature inside, creating pleasant air and using natural light. In this thesis, focusing on window and canopy geometry parameters in order to better control the sun's radiation to improve lighting and natural ventilation, we have tried to reach the optimal percentage of windows on each front of the building in Khorramabad city by comparing different geometries. It is hoped that The results of this research should be placed as a strategy in the initial steps of designing a practice guide for architects.  

   ABSTRACT Audio conversion aims to change one or more aspects of the speech signal while preserving the speech structure of the signal. One of the subcategories of voice conversion is voice conversion. Voice conversion is a technique to transform the identity of the hidden speaker in the source speech waveform while preserving the linguistic information. The goal of the voice conversion system is to create a conversion function, which converts the same speech features of the language from both source and target speakers. By placing the corresponding features of the target speaker with the corresponding features of the source speaker's speech, and reconstructing these features into the speech wave, voice conversion occurs. Most of the topics of voice conversion revolve around learning the corresponding characteristics of the source and target speakers. In this research, it has been tried to convert the speech wave of the source speaker by separating the signal of the source speaker and the target into the same time segments and convert it into a two-dimensional Mel Spectrum matrix (using the MelGAN vocoder), it prepare the input data and train the network Created, i  ired by Cycle GAN, this transformation function. The MelGAN vocoder has been used to synthesize (transform) the waveform into Mel Spectrum and vice versa (speech waveform). Also, in this research, the data of the voice imitation challenge of 2018 was used. The challenge [50], held every two years, attempts to improve the quality of voice imitation by providing data (in the 2018 series, non-parallel data). In the end, two subjective and objective (realistic) methods have been used to evaluate the final transformation function trained in this research. Existing objective evaluation criteria for voice conversion (VC) are not always relevant to human perception. Therefore, training VC models with such metrics may not effectively improve the naturalness and similarity of the converted speech. In this project, evaluation models based on deep learning have been used to predict human ratings from transformed speech. We adopt convolutional and recurrent neural network models to develop a mean opinion score (MOS) predictor, called MOSnet. Also, the MCD criterion has been used for objective evaluation. Despite years of research, voice imitation systems, and the progress of the transformation function learning process, using different types of neural networks, still have deficiencies in accurately imitating a target speaker spectrally and prosodically and at the same time maintaining speech quality.

  Convolutional neural network has been used in the cuckoo filter for the named data network. First, this cuckoo filter has been made two-dimensional, then a neural network has been used for training. The purpose of this training and learning method is to extract the features of the inserted data and use those features during the data search, which ultimately improves the search speed.

  امروزه با افزايش ارتباط بين فناوريهاي رايانهاي و حوزه پزشكي، واسطهاي مغز و كامپيوترتأثير مهمي در زمينههاي مختلف از جمله تشخيص فعاليت تصور حركت، بازشناسي احساسات، تشخيص بيماري صرع، امتياز بندي سطح خواب و باركاري ذهني دارند. تشخيص تصور حركت يكي از تكنيكهاي مبتني بر واسط مغز و كامپيوتر است. اين تكنولوژي با پردازش سيگنالهاي مغز و استخراج الگو از يكي از مهمترين سيگنالها در تشخيص اين نوع EEG تأثير به سزايي بر مطالعه ذهن وكاركردهاي آن دارد. سيگنال ،MI سيگنالهاي فعاليت است. اين پژوهش به طراحي، پياده سازي و ارزيابي يك روش جديد براي تشخيص تصور حزكت دست انسان مي پردازد. مغز انسان اين قابليت را دارد كه از طريق ارتباط بين نواحي و اثرگذاري بر يكديگر منجر به فعاليت هاي شناختي شود. به عبارت ديگر مغز از نواحي مختلفي تشكيل شده است كه هر كدام از آن ها يا به طور جداگانه يا تعاملي منجر به اجراي وظايف مختلف توسط انسان مي شود. اين روش جديد، از قابليت ذكر شده جهت ارزيابي عملكرد مغز و تشخيص تصور حركت دست انسان استفاده كرده است. در اين تحقيق تلاش شده است كه اطلاعات دقيق تر و كامل تري جهت ارزيابي مدل پيشنهادي، استفاده شود. به اين منظور براي تحليل سيگنال هاي حاصل از تصور حركت، از رويكرد مسئله معكوس به كار برده شد كه به اطلاعات آناتوميكي مغز حين تصور حركت دست EEG دسترسي دارد. در اين راستا از بازسازي منبع سه بعدي كه شامل مراحل مدل سازي فضاي منبع، مدل پيشرو و مسئله معكوس است، به كار برده شد. باتوجه به اطلاعات به دست آمده، از اتصال مؤثر (يكي از سه نوع اتصال بين نواحي مغز) مبتني بر مدل سازي علّي پويا استفاده گرديد كه گراف مربوط به نواحي مرتبط با تصور حركت طراحي و پياده سازي شود. نواحي مؤثر به كمك بازسازي منبع به دست آمده است. اين نوع مدل سازي بهتر مي تواند اتصالات جهت دار و علّي بين نواحي مغز و نقش مؤثر فعاليت نورون هاي قشر مغز را در ايجاد و اجراي تصور حركت تفسير كند. به دليل اينكه اطلاعات حاصل از گراف مدل سازي علّي پويا يك ماتريس مجاورت از مقادير اتصال مؤثر بين نواحي، ناشي از تعامل و اثر گذاري قشرهاي مغز است براي سهولت در استخراج ويژگي هاي سطح بالاي تصور حركت، از تكنيك شبكه عصبي كانولوشن گراف گونه جهت طبقه بندي نوع تصور حركت به كار برده شد. اين شبكه عصبي از طريق ماتريس مجاورت، جهت بين نواحي را تشخيص مي دهد و اطلاعات يال و رئوس گراف را براي استخراج ويژگي به كار مي برد. نتايج اين روش دقت بالاتري را در مقايسه با 5 و 10 لايه جهت تشخيص (GCN_ نشان داده است كه اجراي شبكه عصبي با 15 لايه ي كانولوشني ( 15 0% است. در مقايسه با / 0% و 99 / نوع تصور حركت را داشته است. دقت حاصل براي تصور حركت دست راست و چپ به ترتيب 95 پژوهش هاي پيشين نيز، روش پيشنهادي توانسته است دقت تشخيص را افزايش دهد.

سرطان ريه شايع ترين سرطان در دنيا است. در اين پژوهش با استفاده از مدل يادگيري عميق توانستيم سرطان ريه را به دو دسته تومور و سالم طبقه بندي كنيم.

Pilates includes set of motions, Focusing on the simultaneous useof mind and body, to increase the body's resistance,. uses gravity, body weight andspecial devices. If for any reason a person intends to perform these movementsat home without a trainer, there are various commercial software that play therole of trainer; But these softwares do not have a guide or software monitor togive the user proper feedback on the correctness of the movements. Thisdissertation addresses the issue of the correctness of the user's Pilatesmovements by providing an approach based on image processing techniques.In this study, computer analysis of 6 main Pilates movements in theabsence of a Pilates instructor has been performed. To do this research, asuitable data set is needed first. To collect data, 20 main body joints wereextracted from deep video in each 3D frame using a Kinect sensor. The resultingdata set contains 300 records that are collected in a fixed location fromdifferent users. The proposed method consists of four steps. First, thethree-dimensional coordinates of the 20 main joints are extracted from theinput. In the second step, the required preprocessors include calculating the 4main body angles, namely the angles of the knees and elbows in each frame,applying the Savitzky Golay filter, and the PiecewiseAggregate Approximation. In the third step, various functions were proposed to calculatethe data distance, which are Dynamic TimeWarping, Hausdorff,fast Dynamic Time Warping, and providean improved distance function based on fast Dynamic Time Warping. In the fourth stage, learning and >After >  

  One of the methods for stabilizing earth slopes or excavated ditches is soil nailing, which has been used around the world for about fourteen decades, and many advances have been made in the use of this method. Has been. However, the use of this method in cold regions is more limited than other places with normal temperature conditions due to lack of sufficient studies and lack of necessary information about the response of the nailed earthen wall due to the experience of freezing and thawing cycles. Therefore, in the present study, in order toachieve a comprehensive plan for the proper design of nailed earthen walls in cold regions, using ABAQUS finite element software, numerical modeling of a nailed earthen wall in Brunswick, Maine The United States has made this region one of the coldest regions. The purpose of this study was to numerically investigate the effect of freezing phenomenon on the behavior of nailed earthen wall, to investigate the stresses caused by freezing activity in the nails that strengthen the earthen body, the amount of pressure on the top wall and the amount of body displacement. Soil is the result of experiencing the freezing process. The results obtained in this study are validated based on the results of field operations performed in Brunswick (Duchesne 2003). Finally, in orderto achieve more comprehensive results, the effect of different nailing conditions inside the soil body under the .mentioned conditions has been investigated

  Abstract Images are the most important and widely used digital data used in computer systems. A digital image is made up of a set of objects or areas, so one of the efficient techniques for extracting features from images with respect to their constituent objects is the image segmentation technique, which delimits objects or areas. Highlights the image with high accuracy due to its texture and features. Using image segmentation, image pixels are placed next to each other in specific areas due to common features and generally similarity to each other. Multi-level image thresholding is one of the most popular and at the same time the simplest and most efficient methods of image segmentation. The most important issue in this method is the selection of the value of the relevant thresholds. In such a way that by determining the appropriate thresholds, the desired image can be more accurately zoned. Atsu method is one of the thresholding methods that has a good performance in determining two-level thresholds, but when increasing the number of thresholds, Atsu performance decreases in terms of time and segmentation accuracy. Therefore, it is combined with optimization algorithms to achieve better performance in terms of time and segmentation accuracy. In this research, an improved Grasshopper optimization algorithm is also proposed to increase the accuracy of finding answers and increasing the accuracy of segmentation, as well as to increase image quality. In this method, Atsu evaluation function is proposed for the image segmentation process in optimization algorithms. According to experiments and results, the improved Grasshopper algorithm is performs better compared to the optimization algorithms for Grasshopper, whale, firefly and bee colony. Keywords: Image segmentation, Improved Grasshopper Optimization Algorithm, Grasshopper Optimization Algorithm, Firefly optimization algorithm, Artificial Bee colony algorithm and Whale optimization algorithm. Abstract Images are the most important and widely used digital data used in computer systems. A digital image is made up of a set of objects or areas, so one of the efficient techniques for extracting features from images with respect to their constituent objects is the image segmentation technique, which delimits objects or areas. Highlights the image with high accuracy due to its texture and features. Using image segmentation, image pixels are placed next to each other in specific areas due to common features and generally similarity to each other. Multi-level image thresholding is one of the most popular and at the same time the simplest and most efficient methods of image segmentation. The most important issue in this method is the selection of the value of the relevant thresholds. In such a way that by determining the appropriate thresholds, the desired image can be more accurately zoned. Atsu method is one of the thresholding methods that has a good performance in determining two-level thresholds, but when increasing the number of thresholds, Atsu performance decreases in terms of time and segmentation accuracy. Therefore, it is combined with optimization algorithms to achieve better performance in terms of time and segmentation accuracy. In this research, an improved Grasshopper optimization algorithm is also proposed to increase the accuracy of finding answers and increasing the accuracy of segmentation, as well as to increase image quality. In this method, Atsu evaluation function is proposed for the image segmentation process in optimization algorithms. According to experiments and results, the improved Grasshopper algorithm is performs better compared to the optimization algorithms for Grasshopper, whale, firefly and bee colony. Keywords:

Approximatecomputing   are a promising technique for reducingpower consumption or improving circuit delay, with the help of which a suitable trade-offcan be achieved between power consumption, delay, and accuracy in circuitoutput. In this work, we propose approximate multiplication circuits withdifferent bit widths and the effect of using 8-bit multiplication in imageprocessing algorithms such as: Gaussian filter smoothing algorithm, ContrastStretching algorithm, edge detection algorithm with sobel-filter, andmultiplication of images algorithm. The proposed approximate base multiplier isa 4-bit multiplier that divides the circuit into two parts to reduce circuitdelay, the lower part is free of carry, and the upper part has a 2-bit carrychain independent of the lower part. To expand the circuit and produce 8-, 16-,and 32-bit multiplier circuits, we use a 4-bit base multiplier, and we useseven techniques for final accumulation and summation. The adder used for thefinal summation is an accurate adder and an approximate adder available withdifferent configurations to have accuracy at different levels. The results of approximate multiplier implementation show that theproposed 4-bit multiplier has a maximum of 11.55%, 11.75%, 7.99%, 45.64%,53.21%, 68.57%, 82.91% and 94.63% improvement in parameters Mean Error Distance(MED), Mean Normalized Error Distance (NMED), Mean Relative Error Distance(MRED), Power Consumption, Area, Delay, Power-Delay Product (PDP), and Energy-DelayProduct (EDP), respectively, compared to existing 4-bitmultiplication. In the proposed 4-bit multiplier, 72.81%, 74.35%, 83.33%, 95.46%and 99.24% improvement in parameters Power Consumption,Area,Delay, Power-Delay Product (PDP), and Energy-Delay Product (EDP), respectively, compared to accurate wallacetree multiplier. The results of using 8-bit multipliersin the mentioned image processing algorithms also show the acceptable qualityof the processed images.

پيشرفت فنّاوري­هاي ساخت تراشه­هاي سيليكوني و دستيابي به ابعاد نانومتري، امكان ساخت سيستم­هاي الكترونيكي بزرگ بر روي يك تراشه را فراهم نموده است. اين تراشه­هاي جديد با چالش­هاي جديدي نيز مواجه هستند و ممكن است در هر زماني در محيط كار دچار اشكال شوند. اين امر نياز به روش­هاي خودآزمايي دوره­اي در محيط كار را بيشتر كرده است. استفاده از روش­هاي خودآزمايي سخت­افزاري به علت ويژگي­هاي آزمون تصادفي نمي­تواند به تنهايي كافي باشد و براي رسيدن به سطح كيفيت مناسب براي آزمون پردازنده، بايستي از روش­هاي خودآزمايي نرم­افزاري نيز بهره برد. يكي از مهمترين مراحل در فرآيند آزمون يك تراشه، توليد بردارهاي آزمون كارا براي آزمون آن تراشه است. توليد بردارهاي آزمون با استفاد از روش­هاي قطعي توليد آزمون بسيار زمانبر است. علاوه بر اين محدوديت­هاي زماني و عملكردي در پردازنده­ها، باعث مي­شود كه توليد آزمون­هاي نرم­افزاري براي پردازنده با استفاده از روش­هاي قطعي توليد بردار آزمون، ناممكن و يا حداقل سخت و ناكارا باشد. استفاده از روش­هاي توليد آزمون مبتني بر شبيه­سازي به دليل غلبه بر اين محدوديت­ها، مي تواند يك جايگزين مناسب باشد. در روش­هاي مبتني بر شبيه­سازي، تعدادي الگوي آزمون به صورت كاملاً تصادفي و يا با استفاده از روش­هاي فرا ابتكاري توليد مي­شود. سپس اين بردارهاي آزمون بر اساس شاخص پوشش شكال، مقايسه شده و بهترين آنها انتخاب مي­شوند. در اين روش­ها، محاسبه­ي شاخص پوشش اشكال بردار آزمون زمانبر است. مي­توان به­جاي شاخص دقيق و زمانبر پوشش اشكال، از يك شاخص تقريبي و سريع براي ارزيابي و انتخاب بردارهاي آزمون استفاده نمود.   در اين راستا، در اين پايان­نامه يك شاخص تقريبي به نام APXD پيشنهاد شده است كه تقريبي مناسب از تعداد اشكال­هاي شناسايي شده توسط يك الگوي آزمون ارائه مي­كند. با تكيه­ بر اين شاخص، يك روش توليد آزمون مبتني بر شبيه­سازي به نام APXD_TG نيز در اين پايان­نامه پيشنهاد شده است و با استفاده از آن براي برخي از اجزاي يك پردازنده، آزمون نرم­افزاري توليد شده است. نتايج ارزيابي­هاي ما نشان مي­دهد كه شاخص APXD سرعت و دقت مناسب داشته و جايگزين مناسبي براي شاخص پوشش اشكال است. علاوه بر اين نتايج ارزيابي­ها نشان مي­دهند كه استفاده از شاخص APXD به جاي شاخص پوشش اشكال، ضمن حفظ كيفيت آزمون، زمان توليد آزمون را به نحو قابل توجهي كاهش مي­دهد. شاخص پيشنهادي بسيار سريع­تر از شاخص پوشش است. به طور ميانگين نسبت به روش موازي ?? برابر سريع­تر و نسبت به روش سريال 696.9 برابر سريع­تر است. لذا استفاده از آن در بخش ارزيابي بردارهاي آزمون كانديد، تسريع قابل توجهي در الگوريتم‌هاي توليد آزمون مبتني بر شبيه­سازي به وجود مي­آورد.   

  In this paper, the vulnerability of air distribution transformers has been studied qualitatively and quantitatively (analytically). In the qualitative evaluation, according to the trajectories observed in previous earthquakes, the modes of failure of the transformer and their probable cause have been studied according to the evil of their executive environment in the country. Quantitative evaluation with dynamic analysis of a post sample The air has been affected by the acceleration of various earthquakes and the study of internal forces created in critical components. Based on qualitative studies, it was found that the most important weakness in air transformers is the lack of poor resistance of the lille, the lack of lateral restraint at the connection of the platform holding them to the base. These can cause trans (or overturning) of the transformer during an earthquake. The quantitative assessment also shows the vulnerability of the transformer if the retaining platform is not restrained to the base posts. Finally, solutions and suggestions for improving existing posts are provided. Keywords: Transformer bases, interactions of connected bases, seismic improvement, seismic resilience, seismic performance, earthquake damage

  Abstract: Today, due to the limited fossil fuels, environmental pollution, the transition from this fuels to renewable energy is inevitable. Among the renewable energies, solar and wind energy have received more attention due to better accessibility and higher capacity. Wind turbines with vertical and horizontal axis are used to exploit the wind power, which are more commonly used with horizontal axis wind turbines due to higher power extraction. The generators used in these turbines are also divided into two categories: constant speed and variable speed. Variable speed generators are more commonly used due to lower mechanical stress and higher efficiency. Among variable speed generators, DFIG is particularly important because of its unique advantages. These features include four-zone Active and Reactive Power Control, optimum performance at variable wind speeds, lower converter costs and reduced power losses and more. Today, due to the special structure of wind generators and the way they are controlled and connected to the grid, as well as issues such as variable wind speeds and uncertainties, the use of these generators faces particular problems. These include power generation control, maximum power point tracking, optimum performance at voltage and current unbalanced conditions. The wind turbine studied in this study is a doubly feed induction generator (DFIG) in which the stator is directly connected to the grid and the rotor coil is powered by a frequency converter consisting of two AC-DC converters based on a two-way IGBT controller and a DC link. Rotor-side converter with variable frequency injector plays the role of compensator for mechanical frequency difference with grid frequency. The grid side converter function is control of DC link voltage and in some cases provide reactive power. In this study, the performance and control design of a doubly fed induction generator is first evaluated in balance and then the DFIG generator is analyzed while the grid voltage is in unbalanced state. In a doubly fed induction generator, the unbalanced grid voltage causes the stator current, the rotor current to the converter, the current to the converter, torque and flux to be unbalanced. In this study, the stator and rotor currents are balanced by separating the positive and negative components in the unbalanced state of the grid voltage and negative sequence compensation. In the next step, the voltage of point of common coupling will be unbalanced because of the interruption of one of the three-phase load phases, using the static synchronous compensator (STATCOM) with the Decoupled Double Synchronous Reference Frame method (DDSRF) will be balanced and stabilized. In this study, MATLAB simulator software, which is a powerful software in this field, was used to analyze the system and model under study. By comparing the waveforms of the stator current, rotor, grid side converter and PCC point voltage obtained from the proposed control scheme and comparing it with the absence of control on the system, it can be concluded that the proposed design can guarantee the performance of doubly fed induction generator in different conditions. key words: doubly fed induction generator – unbalance voltage - negative sequence – current of stator - static synchronous compensator -   decoupled double synchronous reference frame

 design interactive IoT smart toy based on Persian language

   It is very time consuming to use deterministic methods for test generation as they use backtrack. The simulation-based test generation methods only analyze the circuit in forward path and this has made them popular. Random Test Generation Methods, which are among simulation-based methods, need a short time for test generation, but the number of test vectors produced in random methods is high. A suitable solution to reduce the number of these vectors is through using the Fault Coverage Index to evaluate the competency of test vectors and trimming test vectors that are inadequate. But calculating the Fault Coverage Index for each test vector requires a fault simulation that is a time consuming process. Also, the genetic algorithm can reach a very compact test set because of the optimized search it performs over a large space of test vectors. But this method, which is simulation based, again requires the time consuming simulation of fault as it uses the fault coverage index as a fitness function. The main purpose of this thesis is to reduce the test generation time in simulation-based methods by maintaining their quality for combinational circuits. The idea behind this thesis is to study the competency of test vectors using a new index based on Probabilistic  ystem that is fast and low-cost to calculate. To evaluate the accuracy of the proposed competency index, the concept of statistical correlation was used. The results showed that there is a correlation between the proposed competency index and the Fault Coverage Index for all circuits and the correlation was greater than 0.7 for 6 circuits out of 10 ISCAS85 circuits, which indicates high correlation.   The results of using the proposed competency index in simulation-based test generation methods showed that the basic method of trimming test vectors can be accelerated to 86% on average by maintaining the quality of test generation and the basic method of test generation based on genetic algorithm can be accelerated to 49.85% on average with an additional test vector.

Abstract The advancement of technology and the Internet has led to an ever-increasing growth of databases, especially images, which has led to the search for the desired image and its recovery from the massive amount of databases. searching for images from the past has been an important research topic and several methods have been proposed, including methods for image retrieval based on text, the text-based retrieval method is a basic method and performs searches using the keywords defined for each image, given that the method of text search was a time consuming and costly method. attempts toward other methods and techniques, namely, image retrieval based on content, were made using descriptors of structural elements or low-level features of the image, ie, color, texture and shape, so that we can look at the search image. in this research, we have tried to describe the structural elements of SED and compare it with other descriptors and algorithms that are implemented in this implemented project and to achieve a higher degree of accuracy. by researching and investigating methods and descriptors of structural elements that utilize low-level features of color and texture, the proposed combination method is presented using structural elements and color difference histograms. on the other hand, considering that changing the size of images is an important issue and accessing the image with different sizes is considered an important issue, so the results of different methods of extracting features in 128× 128, 64× 64, 32× 32, 16× 16 and 8×

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چكيدهبا توجه به افزايش ترافيك و نياز به پاسخ گويي سريع به درخواست­ها دسته­بندي بسته­ها به يك تكنولوژي مهم و يك چالش در عملكرد مسيرياب­ها تبديل شده است، بخصوص در زمان همگام سازي تصميم گيري خود با سرعت تبادل داده­ها اين موضوع بيشتر نمود پيدا مي كند، يعني سرعت جست و جوي فيلدها با سرعت لينك­هاي انتقال برابر باشد و تا زماني كه سرعت شبكه­ها ثابت نشود كار روي دسته­بندي بسته­ها اهميت خود را حفظ مي­كند. افزايش روزافزون داده­هاي انتقالي و پويا بودن آنها باعث شده راه حل­ها و معماري­هاي سخت­افزاري يا نرم­افزاري متعددي براي اين موضوع ارائه شود. الگوريتم­هاي نرم­افزاري با وجود توسعه­پذيري بالايي كه فراهم مي­كنند اما از سرعت پائيني برخوردارند از طرف ديگر راه­حل­هاي سخت­افزاري سرعت خوبي دارند ولي هزينه بالا و قابليت توسعه­پذيري كمي دارند. از اين رو ارائه روشي براي ايجاد مصالحه بين سخت­افزار و نرم­افزار مورد توجه محققان قرار گرفته است. طبقه بندي بسته­ها يك جستجوي چند فيلدي با سرعت لينك ا انجام مي­دهد.در اين تحقيق ، به منظور رفع مشكلاتي كه در بالا ذكر شد از دو فيلتر بلوم و خارج قسمت استفاده شد و به منظور انطباق روش جستجو با بسته هاي ارسالي در تعداد فيلدهاي موجود در معماري نوين SDN، اين تعداد به 15 فيلد سرايند افزايش يافت. در نهايت با استفاده از ابزارهاي در دسترس از جمله   Intel Platform Power Estimation Tool (IPPET)   معيارهاي مورد نظر براي بررسي قابليت هاي روش ارائه شده استفاده گرديد و از نتايج حاصل از دو فيلتر برتري فيلتر بلوم نسبت به فيلتر خارج قسمت دربرخي معيارها اثبات گرديد به اين صورت كه در مورد زمان مصرفي، سرعت انجام الگوريتم، توان عملياتي و انرژي مصرفي فيلتر بلوم عملكرد بهتري داشته ولي در موارد حافظه مصرفي و نرخ خطاي مثبت فيلتر خارج قسمت عملكرد بهتري دارد.

  DN is a new paradigm in computer networks based on global view provided form separation of data plane and control plane. This separation is possible by means of an API between the switches and the controllers such as OpenFlow. Logical centralized in SDN by global view of the network can help to improve network management, load balancing, routing and security. Logically centralized controller allows SDN load balancer to allocate the new incoming flow to the best possible server, efficiently. SDN load balancers mostly operate on L4 in OSI model and decide based on the L2/4 headers and this conditions cause limitations in implementation of networks when Back-end servers are not replica. In this case a data base is needed to store mapping between content and controller and with each incoming flow to the Frontend load balancer the controller allocates that flow to the server containing the request content. To implement the L7 load balancer (application layer) there are traditional methods such as Delayed Binding and TCP Socket Migration and this project discuss the implementation of Delayed Binding based on SDN concepts and also the best server should be chose regarding to the network global view, traffic load and response time of the Back-end server that contains request content. The implementation of this method is done by using a virtual switch named Open vSwitch in a virtual machine monitor or hypervisor and Floodlight controller and the results of the implementation has been shown in this project. The average improvement of response time in comparison with three other algorithms, the L  RT, Round Robin and Random selection methods are 19.58%, 33.94% and 57.41% respectively. Furthermore, the average improvement of throughput in comparison with three other algorithms are 16.52%, 29.72%, and 58.27%, respectively.

  نفت خام كه از چاه استخراج مي‌گردد در واحد بهره برداري طي چند مرحله با افت فشار مواجه مي شود. در نهايت گاز همراه از آن جدا شده و در صورت عدم وجود واحدي جهت جمع آوري ميعانات گازي سوخته مي شود. اين ميعانات گازي، كه از تركيبات هيدروكربني با ارزشي تشكيل شده اند، در صورت بازيافت و جمع آوري باعث افزايش در آمدهاي حاصله خواهند شد. هيدروكربن هاي تشكيل دهنده ميعانات گازي عمدتا شامل اتان و هيدروكربن هاي سنگين تر مانند پروپان، بوتان و ساير هيدروكربورهاي سنگين، كه بنزين طبيعي نيز ناميده مي شوند، مي باشند. البته درصد هر كدام از اين مواد در ميعانات گازي، بستگي به نوع مخزن، محل آن، عمق مخزن و عوامل ديگر دارد. نفت خام استخراج شده از چاه به دليل اينكه از اعماق زمين به بالا آمده‌ است، در طول مسير بالا آمدن، با خود مقداري شن و ماسه و آب شور را به همراه دارد. از اين رو، قبل از ارسال اين نفت به پالايشگاه‌ ها، جامدات، آب، و گاز همراه با آن در محل هايي كه به مجموعه تأسيسات سرِچاهي شناخته مي‌شوند، توسط دستگاه‌ هايي به نام جداكننده، از نفت جدا مي‌گردند. با توجه به وجود ميعانات گازي در گاز خروجي از تفكيك گرهاي واحد بهره برداري نفت شهر و ارزش اقتصادي بالاي آن جداكردن اين تركيبات بسيار حائز اهميت است. از اين رو پيدا كردن روشي براي جداكردن اين تركيبات سنگين با صرف كمترين انرژي و هزينه از نظر اقتصادي بسيار حائز اهميت مي باشد.ما در اين تحقيق سعي داريم از روشي استفاده كنيم كه مقدار اين جداسازي را با حداقل امكانات به حداكثر رسانده و از دستگاهي با نام لوله ي گردابه اي يا vortex tube استفاده مي كنيم.دستگاه vortex tube يا لوله ي گردابه اي يك دستگاه مكانيكي بسيار ساده فاقد اجزاي متحرك مي باشد و قابليت تنظيم دما را داراست كه جرياني از يك سيال فشرده را در دما و فشار معين از طريق نازل به صورت مماسي دريافت مي كند و شامل يك ورودي و دو خروجي مي باشد كه سيال از قسمت ورودي وارد دستگاه شده و دچار چرخش شده و از دو خروجي سرد و گرم خارج مي شود. سيال گرم در حاشيه ي لوله حركت مي كند و سيال سرد در مركز مي باشد. اغلب در جامعه ي علمي سيال هاي مورد استفاده در vortex tube گازها يا بخارها گزارش شده است اما برانو و Hajdik به طور مستقل از لحاظ تجربي ثابت كرده اند كه مايع ها مي توانند به عنوان بخشي از سيال كار مورد استفاده قرار گيرند و بخش ديگر گاز باشد.

  Low density polyethylene is one of the most expensive raw materials of plastic that isproduced in the high temperature and pressure and by free radical polymerization ofsupercritical ethylene. the very important problem that possible occurs for a tubular reactorproducing low density polyethylene, the fouling of polymer in the inner layer is formed.fouling may be defined as the accumulation of undesirable polymer at the reactor surface thatincrease the resistance to heat transmission and heat transfer rate of ethylene andpolyethylene mixed with coolant flow in jacket of reactor is reduced thus reduces the amountof production. the main priority of this thesis presents a model for the analysis of low densitypolyethylene production by taking fouling on the temperature of reactor inner walls. Themathematical model based on heat transfer equations formed, and to solve the equations of it,the matlab and trial and error method is used. Finally, the inlet and outlet water temperatureof the jacket, the temperature of the inner wall, the conversion of monomers to polymers, thethickness of fouling that formed in the walls and resistance of fouling was calculated for eachtube. Due to the two phase mixture of ethylene and polyethylene formed neare the inner wallfouling is formed. Energy balance on different parts of the reactor and its jacket, was used tocalculate heat transfer and wall temperature. by temperature and pressure near the wall andusing the SRK equation of state, equilibrium data are calculated. The results of this sectionindicate that almost all the flow is formed in the form of a single phase and only in the innerregion where the temperature is sufficiently low, the two phase flow is formed. Therefore inthe reactor pressure,the internal wall temperature is the main parameter for the production offouling. The results of model calculations and its use in optimization of model show thatcooling water temperature is more effective than its mass flow rate in quantity and productquality comparing these results with real data as well as validating the model used.

In this study, the graphene oxid/polyacrylonitrile nanofibers were prepared by electrospinning process. The effect of addition of graphene oxide on mechanical properties and removal of heavy metals from aqueous solution were investigated. Graphene oxide (GO) functionalized by tannic acid (GO-TA) in order to remove two kind of the most hazardous heavy metals from aqueous solution. A novel electrospun polyacrylonitrile / modified graphene oxide nanofibrous adsorbent was favorably developed by an electrospinning process. Mechanical property measurements show a 60% enhancement in tensile strength compared to pure polyacrylonitrile nanofibers. Also the nanocamposite is able to remove lead and cadmium from aqueous solution 97% and 94%, respectively.

ساخت واصلاح غشا نانو كامپوزيتي براي جداسازي   محلول اتانول /آب بافرايند تراوش تبخيري

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.   

Nowadays, in the various industries, studies on the use of magnetic water are under way. In this study, by constructing the magnetic water production device, the authors of the results on the water hardness were investigated. By applying magnetic and electromagnetic fields on water, its structure has been changed. In experiments conducted with two different channels of microchannel, the output water after the passage of filter paper for the separation of calcium carbonate has been studied and the results show that factors such as discharge flow, magnetic field intensity and electromagnetic field on the water properties It has direct effect, and less water can be gotten softer water. With magnetic water, without adding chemicals to the water, the sediments on the surfaces in contact with previously formed water are thinned and the formation of new sediment is also prevented.  

  Energy consumption in Computer networks in recent years, due to the notable grow of the users and demanding of multimedia services have been increased. To preserve the environment, decreasing of energy consumption has been attended, specifically. Energy consumption is investigated from different aspects. In a network, different protocols have been defined which affect on energy consumption. Energy consumption in a protocol is defined based on the generated load on link and necessary time to transfer  the generated load. TCP is a protocol that assures a flow will arrive the destination surely. Therefore, generates a notable volume of the  load because of the  acknowledge  acket  which increase  the load on a related link.In this thesis, energy consumption is investigated  from the software point of view and is tried to decrease the number of acknowledge packets to improve the energy consumption beside of reliability control. The achieved  energy efficiency  improvement in this work is   12.09%. The proposed approach in this work may cause the decreasing of throughput in online networks like VOIP wich can be ignored  generally.