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Öğe A compact coplanar waveguide fed UWB antenna for energy harvesting applications(IEEE, 2021) Jameel, Maryam S.; Mezaal, Yaqeen Sabah; Atilla, Doğu Çağdaş; Hussein, EhabA new monopole antenna design fed by the Coplanar Wave Guide (CPW) is proposed for energy harvesting applications using FR4 substrate. The antenna presented here functions with an impedance bandwidth of 10 GHz from 3.27 to 13.27 GHz. Dimensions in total are 27*25 mm2 . The antenna was designed with the help of the CST Microwave Studio 3D EM software simulation and analysis. It may be used for ultrawideband (UWB) and other radio systems.Öğe A survey on intrusion detection system in ad hoc networks based on machine learning(IEEE, 2021) Abbood, Zainab Ali; Atilla, Doğu Çağdaş; Aydın, Çağatay; Mahmoud, Mahmoud ShukerThis advanced research survey aims to perform intrusion detection and routing in ad hoc networks in wireless MANET networks using machine learning techniques. The MANETs are composed of several ad-hoc nodes that are randomly or deterministically distributed for communication and acquisition and to forward the data to the gateway for enhanced communication securely. MANETs are used in many applications such as in health care for communication; in utilities such as industries to monitor equipment and detect any malfunction during regular production activity. In general, MANETs take measurements of the desired application and send this information to a gateway, whereby the user can interpret the information to achieve the desired purpose. The main importance of MANETs in intrusion detection is that they can be trained to detect intrusion and real-time attacks in the CIC-IDS 2019 dataset. MANETs routing protocols are designed to establish routes between the source and destination nodes. What these routing protocols do is that they decompose the network into more manageable pieces and provide ways of sharing information among its neighbors first and then throughout the whole network. The landscape of exciting libraries and techniques is constantly evolving, and so are the possibilities and options for experiments. Implementing the framework in python helps in reducing syntactic complexity, increases performance compared to implementations in scripting languages, and provides memory safety.Öğe Car-like robot path planning based on voronoi and Q-learning algorithms(ICEET, 2021) Alhassow, Mustafa Mohammed; Ata, Oğuz; Atilla, Doğu ÇağdaşThis paper discusses a differential path planning issue for the mobile robot depending on Voronoi diagram (VD) and Q-learning algorithms (QL). The issues with re-arranging paths in a dynamic environment with obstructions are treated as an issue of looking for the best route between the start and target stage. Since the car-like robot is differentially system and its mathematically involved with the inward state of impediment because of that some modification will be embedded like the orientation method. This is a unique instance of a solitary vehicle, which just goes ahead at a consistent speed and can just turn left also, right. Voronoi diagram presents the world encompassing brilliant specialists for robots, computer games, and military issues, so improving the dependability, of arranging the environment using it will help and decrease Q learning calculations by re-updating the Q-table, according to the new state, also decreasing both existence intricacy, relies upon earlier information that came from the environment. The work arrangement was tested for a 2D environment. The result of the proposed work showed better performance in time, speed, and length of the path also it can be utilized as an alternate style of guides. A comparison with other related works is performed and the result of these comparisons showed that our work provides a good trajectory with performance.Öğe Decision support system for operational, financial, performance and risk indicators of maturity models over cloud-based software(IEEE, 2021) Şen, Neriman; Atilla, Doğu Çağdaş; Karan, OğuzThe safe continuation of ordinary and critical processes defined in information systems and the operational, financial and security management of hardware and services that change with their priorities require dynamic information technology management. With automated inventory collection studies, it is possible to bring edge assets closer to local end server data sources. However, in cloud-based architectures, a dynamic computing framework solution can be presented by using intelligent assets. Intelligent assets' insight the data at the source with artificial intelligence algorithms and improved response times with the selection of the data to be sent to business intelligence dashboards, managerial significance - with added value - can be mapped - information is important in maturity leveling studies. Positioning the agent to the asset and sending selective data over the service can provide powerful advantages in optimum use of bandwidth. In this way, continuous data flow can be provided, on the other hand, by combining maturity models, it can be transformed into consistent - automated repeatable information. Cost is an important factor in all technology investments. In infrastructures where all assets can communicate, dynamically performing cost and risk management, showing cost losses, adding to the information security risk management methodology will provide convenience to those who manage the system in terms of controllability.Öğe Design continues mode inverse class-f power amplifier(Institute of Electrical and Electronics Engineers Inc., 2023) Atilla, Doğu Çağdaş; Alsaedi, Mustafa Oudah Hani; Alsaadi, Hajir Adil JasimThis paper outlines the design, simulation, and testing of a continuous mode inverse class-F (CMICF) power amplifier (PA) specifically optimized for GSM applications. The comprehensive design methodology encompasses DC analysis, optimum load impedance calculation, stability circuit analysis, and input and output matching circuit design. The amplifier is built using Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT), and it introduces a novel technique for designing the output matching network (OMN). This technique involves extracting parasitic elements from the power device using a tuning method and employs a graphical approach with the Smith chart utility to obtain the second and third harmonics of the output signal. The resulting design achieves high efficiency with a reduced component count, simplifying the overall design process and reducing costs. The design work was facilitated using Keysight ADS software. Operating in the frequency range of 1.5 GHz to 2 GHz, the amplifier demonstrates a maximum output power of approximately 40 dBm and a power gain of 16 dB, with a power-added efficiency exceeding 70%.Öğe Design of adaptive controller for regulating the voltage by a dynamic voltage restorer DVR(Institute of Electrical and Electronics Engineers Inc., 2019) Abdulazeez, Saud N.; Atilla, Doğu Çağdaş; Aydın, ÇağataySpeedy changes in the source of power could influence the loads performance like (1) semiconductor invention factories (2) paper grindery (3) food treatment factories and (4) automotive installation factories. The prevalent troubles in the power sources are the decreasing of voltage (sags) or the increasing of voltage (swells) this could be caused by (1) upsets emerge in the transmission arrangement, (2) collapse of approach feeders and (3) fuse or breaker procedure. Voltage decreasing (sags) of Ten percent standing for five to ten cycles can cause an expensive loads damage. To relieve the low quality problems of power equipping, the modifier of voltage source could be connected by transmission lines serial which put as compensators. Which are called Dynamic Voltage Restorer (DVR). This research is suggesting a new blueprint for DVR controller by applying an adaptive neuro-fuzzy logic. As well as, the theory of instantaneous power is applied to estimate the voltage phase because of the great accuracy and low calculation for this theory. However, the results of simulation indicate the application of real-time on the suggested controller is powerful and potential comparison with traditional controllers which investigated previously. © 2019 IEEE.Öğe Design of compact UWB antenna based on FSSIR feeder(2023) Jameel, Maryam S.; Mezaal, Yaqeen S.; Atilla, Doğu ÇağdaşThis study describes a new ultra-wideband slotted patch antenna. The antenna is mounted on an FR-4 substrate with a size of 27.2x14x1.5 mm(3) and a permittivity of 4.3. A slotted patch resonator, four-stage stepped impedance resonators (FSSIR) feeder, and a reduced ground plane are features of our new antenna design. The input reflection values are 31, 30, and 32 dB at resonant frequencies of 3.3, 5.75, and 7.1 GHz, respectively, with a bandwidth range of 2.91-9.13 GHz, as determined by the simulated S11 response. The antenna was designed using the CST Microwave Studio simulator. The consequences of S11 simulations and actual measurements coincide rather well.Öğe Development of high accuracy classifier for the speaker recognition system(Hindawi, 2021) Al-Hassani, Raghad Tariq; Atilla, Doğu Çağdaş; Aydın, ÇağataySpeech signal is enriched with plenty of features used for biometrical recognition and other applications like gender and emotional recognition. Channel conditions manifested by background noise and reverberation are the main challenges causing feature shifts in the test and training data. In this paper, a hybrid speaker identification model for consistent speech features and high recognition accuracy is made. Features using Mel frequency spectrum coefficients (MFCC) have been improved by incorporating a pitch frequency coefficient from speech time domain analysis. In order to enhance noise immunity, we proposed a single hidden layer feed-forward neural network (FFNN) tuned by an optimized particle swarm optimization (OPSO) algorithm. The proposed model is tested using 10-fold cross-validation over different levels of Adaptive White Gaussian Noise (AWGN) (0-50 dB). A recognition accuracy of 97.83% was obtained from the proposed model in clean voice environments. However, a noisy channel is realized with lesser impact on the proposed model as compared with other baseline classifiers such as plain-FFNN, random forest (RF), -nearest neighbour (KNN), and support vector machine (SVM).Öğe Efficient routing discovery algorithm in manet(Altınbaş Üniversitesi, 2020) Alani, Bakr Kamal Jasim; Aydın, Çağatay; Atilla, Doğu ÇağdaşThere is a recent wireless technology called Mobile Ad-hoc Network (MANET) with a vast range of applications. MANET without infrastructure leads to routing faces challenges. A broadcasting technique is utilized in a MANET to find a route in on-demand routing protocols. Establishment and regular maintenance of a route represent the important challenge issues. Therefore, nodes require to control the broadcast packets among themselves. This situation leads to broadcast storm problem, which increases link breakage, reduce the duration and decreases the overall performance of the network. The commonly ideal protocol of MANET is reactive routing protocols, due to less control overhead and scalability. However, due to the mobility of the nodes, there is a frequent link breakages they are continually suffers and a new reactive routing protocol is proposed Aware Ad-Hoc On-demand Distance Vector routing protocol - Power and Time Direction Predication (AODVPTD) aim to handle the diminish the connection breakages and get a steady route in Ad-Hoc networks. AODVPTD evidence the route discovery and route reply depending on the power, time of the taking an interest hubs and their headings. In addition, the proposed AODV-PTD algorithm reduced the network overhead. Network reenactment version 2.35 (NS2.35) was utilized for looking at the proposed calculation with AODV routing protocol in terms end-to-end delay, average throughput, and packet delivery ratio.Öğe Enhancement of the performance of MANET using machine learning approach based on SDNs(Elsevier GmbH, 2023) Abbood, Zainab Ali; Atilla, Doğu Çağdaş; Aydın, ÇağatayDeep learning (DL) is a subdivision of machine learning (ML) that employs numerous algorithms, each of which provides various explanations of the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to the potential wireless sensor network (WSN) to provide a small-cost solution to real-world contact challenges. But the lifespan in this network is restricted lifespan. Therefore, the wireless sensor network (WSN) is more vulnerable to battery consumption. On the other hand, routing packets in a Wireless Sensor Network (WSN) is a challenging task, according to the limited resources available on the nodes of these networks, especially their energy sources. The use of Machine Learning (ML) techniques in a Software-Defined Network (SDN) topology has shown good potential for solving such a complex task. However, existing techniques emphasize finding the shortest paths to deliver the packets, which can overload certain nodes in the network, depending on their positioning. In this study, a new method is proposed to extend the lifetime of the WSN by balancing the loading on the nodes, using a Deep Reinforcement Learning (DRL) approach. By emphasizing the lifetime of the network, the proposed method has been able to discover and use alternative routes to deliver the packets, avoiding the use of nodes with low energy. Hence, the average number of hops the packets travel through has been increased, but the time required for the first node to exhaust its energy has been significantly increased.Öğe Human activity detection using smart wearable sensing devices with feed forward neural networks and PSO(2023) Al Hassani, Raghad Tariq; Atilla, Doğu ÇağdaşHospitals must continually monitor their patients’ actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behavior is challenging, since it is hard to forecast how individuals will think or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems that apply artificial intelligence (AI) applications referred to as “categories”. Because motion sensors, such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors are presented and used, and the classification technique is based on distinguishing characteristics. This study focuses on the age-old problem of tracking turbulence in motion to improve detection precision. We trained the model, considering that detection accuracy is limited by factors such as the subject’s mass, velocity, and gait style. This is performed by employing an experimental dataset. When we used the sophisticated technique of particle swarm optimization (PSO) in combination with a four-stage forward neural network (4SFNN) to forecast four different types of turbulent motion, we observed that the total prediction accuracy was 98.615% accurate.Öğe Intrusion detection system through deep learning in routing manet networks(Tech Science Press, 2023) Abbood, Zainab Ali; Atilla, Doğu Çağdaş; Aydın, ÇağatayDeep learning (DL) is a subdivision of machine learning (ML) that employs numerous algorithms, each of which provides various explanations of the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to MANET’s potential to provide small-cost solutions for real-world contact challenges, decentralized management, and restricted bandwidth, MANETs are more vulnerable to security threats. When protecting MANETs from attack, encryption and authentication schemes have their limits. However, deep learning (DL) approaches in intrusion detection systems (IDS) can adapt to the changing environment of MANETs and allow a system to make intrusion decisions while learning about its mobility in the environment. IDSs are a secondary defiance system for mobile ad-hoc networks vs. attacks since they monitor network traffic and report anything unusual. Recently, many scientists have employed deep neural networks (DNNs) to address intrusion detection concerns. This paper used MANET to recognize complex patterns by focusing on security standards through efficiency determination and identifying malicious nodes, and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network (CBPNN), Feedforward-Neural-Network (FNN), and Cascading-Back-Propagation-Neural- Network (CBPNN) (FFNN). In addition to Convolutional-Neural-Network (CNN), these primary forms of deep neural network (DNN) building designs are widely used to improve the performance of intrusion detection systems (IDS) and the use of IDS in conjunction with machine learning (ML). Furthermore, machine learning (ML) techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to different environments. Compared with another current model, The proposed model has better average receiving packet (ARP) and end-to-end (E2E) performance. The results have been obtained from CBP, FFNN and CNN 74%, 82% and 85%, respectively, by the time (27, 18, and 17 s).Öğe Libyan program for integration and development web system performance assessment using neural network(Altınbaş Üniversitesi, 2018) Aborisha, Gaffala Isaa; Atilla, Doğu Çağdaş; Aydın, ÇağatayThe Libyan Integration and Development Program (LPFIAD) is an Internet-based service that supports students to achieve educational opportunities in both graduates and students. This program was presented by the Libyan government to ease the efforts of students and ensure reasonable opportunities among them. This study is biased to a broad survey to gather people’s opinions about the system (to measure satisfaction with the use of these services). The poll is designed to make candidates express their comments in depth after facing this system. The poll is designed to be online using an online survey platform from SurveyMonkey. Once the candidate is logged on to the questionnaire page, he/she will begin to answer a total of 46 questions and have already been categorized. Once the answers have been obtained, we have collected all the data including the candidate IDs and their comments, and we have applied the analysis to this data to evaluate the said system. The front neural Feeding Network (FFNN) is used in this study to learn from the candidates ‘ answers and then submit the evaluation decision on a predetermined basis. Data is analyzed using the Matlab program and the review results recorded by( Lpfiad) by 56%.Öğe Miniaturized coplanar waveguide-fed UWB antenna for wireless applications(MDPI, 2023) Jameel, Maryam S.; Mezaal, Yaqeen S.; Atilla, Doğu ÇağdaşThis study presents a compact ultra-wideband (UWB) antenna fed by a coplanar waveguide (CPW) with huge bandwidth for the demands of modern wireless communities. To overcome some technical limitations of the employed substrate and UWB antenna design, a slotted patch resonator was used to create and simulate this antenna based on Locked-Key topology. It has been printed on a 1.5 mm-thick FR4 substrate with a dielectric constant of 4.4. A feeder with characteristic impedances of 50 Ω has been employed. A CST electromagnetic simulator has been employed to simulate and analyze the antenna design. It is operated within the UWB spectrum with a bandwidth of 10.354 GHz, spanning 3.581 to 14 GHz. The overall surface area is 27 × 25 mm2. The gain and maximum efficiency within UWB are better than 3 dBi and 82%, respectively. The antenna is fabricated, and the simulated results are correlated with the measured ones. Finally, the equivalent circuit models for the antenna and rectifier circuit are simulated and measured.Öğe Multi-agents path planning for a mobile robot in a dynamic warehouse environment(Springer Science and Business Media Deutschland, 2023) Alhassow, Mustafa Mohammed; Ata, Oğuz; Atilla, Doğu ÇağdaşRoute planning in robotic systems is a critical and complex task in any environment. Robotic systems allow multiple robots to accomplish multiple goals simultaneously. Many mobile service robots are now used in warehouses to reduce operating and overhead costs. Large warehouses may have multiple robots to handle a large number of tasks. Route planning means finding the best route, i.e. the route without collisions. Optimizing both parameters can be a daunting task. By properly addressing the problem of route planning between robots, we can improve the efficiency of the operation of the entire warehouse. At the beginning, every robot will navigate to its desired goal by funding the optimal route without collisions with other robots. In this work, a relative study with the notable route plan was presented. The proposed intelligent approach was presented for a multi-robot system that finds the best collision-free path in the warehouse and processes the storage box. This paper proposes a sensible variety metric for multi-robotic structures to intelligently become aware of goals and take the best minimum paths to attain them without encountering collisions. Using an intelligent variety metric to discover the route that we want to reach our goal. The proposed planning path are similar to different works including A *, RNN, PRM and heuristics. Three exclusive times of the warehouse have been taken into consideration to carry out experiments with parameters including route length, common route, and elapsed time. Experiments with 800 pods and sixteen robots have said overall performance enhancements of as much as 2.3%, common route length, and elapsed time of 11%.Öğe Obstacle avoidance capability for multi-target path planning in different styles of search(Tech Science Press, 2024) Alhassow, Mustafa Mohammed; Ata, Oğuz; Atilla, Doğu ÇağdaşThis study investigates robot path planning for multiple agents, focusing on the critical requirement that agents can pursue concurrent pathways without collisions. Each agent is assigned a task within the environment to reach a designated destination. When the map or goal changes unexpectedly, particularly in dynamic and unknown environments, it can lead to potential failures or performance degradation in various ways. Additionally, priority inheritance plays a significant role in path planning and can impact performance. This study proposes a Conflict- Based Search (CBS) approach, introducing a unique hierarchical searchmechanism for planning paths formultiple robots. The study aims to enhance flexibility in adapting to different environments. Three scenarioswere tested, and the accuracy of the proposed algorithm was validated. In the first scenario, path planning was applied in unknown environments, both stationary and mobile, yielding excellent results in terms of time to arrival and path length,with a time of 2.3 s. In the second scenario, the algorithmwas applied to complex environments containing sharp corners and unknown obstacles, resulting in a time of 2.6 s, with the algorithm also performing well in terms of path length. In the final scenario, themulti-objective algorithmwas tested in awarehouse environment containing fixed,mobile, andmulti-targeted obstacles, achieving a result of up to 100.4 s. Based on the results and comparisons with previous work, the proposed method was found to be highly effective, efficient, and suitable for various environments.Öğe Performance study of four equalization techniques over a wireless communication channel(2020) Elasyri, Anwar; Atilla, Doğu Çağdaş; Aydın, ÇağatayEqualization techniques are commonly used in low to moderate data transmission over time varying wireless mobile channels. Their use is mainly to alleviate the detrimental effect of intersymbol interference brought about by the multipath phenomena of such channels. The wireless multipath channel is time varying which makes the use of adaptive equalization a necessity to cope with channel variations and consequently to overcome resulting intersymbol interference effects, restore the transmitted symbols, and extract the information being sent. The first part of this paper presents a comparison study of the performances of four representative methods of adaptive equalization. Linear and nonlinear structures each with both LMS and RLS adaptation algorithms are used in the investigation. In the second part of the paper, a computer simulation is carried out to investigate the relationship between the number of propagation paths of the wireless mobile channel and the number of taps in the incorporated adaptive equalizer. Four equalizer structures are used in this investigation which is based on Simulink software package to simulate a communication system incorporating these equalizers. The bit error rate (BER) performance is used as a measure for establishing this dependency.Öğe Producing secure multimodal biometric descriptors using artificial neural networks(Wiley, 2021) Atilla, Doğu Çağdaş; Alzuhairi, Raghad Saeed Hasan; Aydın, CağatayWith the rapidly growing use of biometric authentication systems, the security of these systems and the privacy of users have attracted significant attention in recent years. Multi-modal biometrics have been able to improve the accuracy of the system but require additional bandwidth to exchange the data. Fragile watermarking has been used to allow the transmission of both biometric templates using the amount of data required to transmit one of them, that is, the cover image, while securing these templates against attacks. Despite the high accuracy of these systems, communicating such templates imposes risks towards the privacy of the users. In this study, a new method is proposed to generate fixed-size descriptors for the face and fingerprint templates, including the timestamp of the transmission and a unique system identifier. The inclusion of the timestamp enables the system to detect and deny replay attacks, while the unique system identifier maintains the privacy of the users. The experiments conducted to evaluate the proposed method have shown that the proposed method has been able to achieve these features while maintaining high recognition rates, 99.41% and 99.32%, similar to the use of the entire biometric templates in the matching stage.Öğe RFT based efficient broadband chebyshev filter design of (K and Ka) range(Institute of Electrical and Electronics Engineers Inc., 2023) Almusali, Raqia S.; Atilla, Doğu ÇağdaşA stepped impedance bandpass Chebyshev filter design covers the K and Ka bands which includes three of 5G RF2 bands n257, n258, and n261 via real frequency technique (RFT) is studied, synthesized and simulated in this work. RFT is a complex math work, for that reason MATLAB employed for generating polynomials needed for this technique, ADS utilized for designing and simulating the BP-filter for verifying the resulted gain of the circuit. The characteristics of the filter listed in details in this paper, the characteristics of the final design after optimization are fl, f2 are 22.22, and 29.5 GHz respectively, Insertion Loss (IL) is set as 0.5dB. The achieved attenuation reached -55, the initial and final number of order before and after the optimization are n=3 and n=8 respectively. RFT technique yielded an accurate synthesized values, small size, and sharp slop.Öğe Size reduction technique of microstrip patch antenna using saw teeth slots(Ieee, 2018) Saadi, Mustafa A.; Aydın, Çağatay; Atilla, Doğu ÇağdaşSince the demand of the mobile devices is increasing, when the mobility is concerned, the miniaturization techniques of circuits, circuit components and antennas are become popular beside reducing the size of the devices. However, it should be noted that, the size reduction should not affect the operating frequencies of the mobile device. In this paper, a practical approach is presented to reduce the size of a planar microstrip patch antenna to achieve lower frequencies. which is based on cutting the antenna into slots in shape of saw teeth.
