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Öğe 3D Localization and Error Minimization in Underwater Sensor Networks(Assoc Computing Machinery, 2022) Sah, Dinesh Kumar; Nguyen, Tu N.; Kandulna, Manjusha; Cengiz, Korhan; Amgoth, TarachandWireless sensor networks (WSNs) consist of nodes distributed in the region of interest (ROI) that forward collected data to the sink. The node's location plays a vital role in data forwarding to enhance network efficiency by reducing the packet drop rate and energy consumption. WSN scenarios, such as tracking, smart cities, and agriculture applications, require location details to accomplish the objective. Assuming a 3D application space, a combination of received signal strength (RSS) and time of arrival (TOA) can be helpful for reliable range estimation of nodes. Notably, the anchor node can minimize localization error for non-line-of-sight (NLOS) signals. We proposed an error minimization protocol for localization of the sensor node, assuming that the anchor node's location is known prior and can limit the receiving signal in LOS, single, or twice reflection. We start to exploit the sensor node's geometrical relationship and the anchor node for LOS and NLOS signals and address misclassification. We started initially from the erroneous node position, bound its volume in 3D space, and reduced volume with each iteration following the constraint. Our simulation result outperforms the traditional methods on many occasions, such as boundary volume and computational complexity.Öğe 4x-expert systems for early prediction of osteoporosis using multi-model algorithms(Elsevier Sci Ltd, 2021) Prakash, U.; Kottursamy, Kottilingam; Cengiz, Korhan; Kose, Utku; Bui Thanh HungOsteoporosis occurs due to micro-architectural deterioration of the bone tissues with an increased risk of bone fragility, which can cause fractures in the bone without much pressure applied to it. The T-score of a person's bone density report can be used to calculate the difference between BMD to that of healthy bones. Currently, osteoporosis is detected using conventional methods like DXA scans or high computational power requiring FEA tests. Considering individual approaches and mono-prediction techniques leads to omission of micro-fractional prediction parameters. In this paper, we have proposed a 4x-expert system for suspected osteoporosis patients, which is designed using multi model machine learning algorithms for improving prediction and accuracy through the various computational process. The experiment results shows, that the 4x-expert system covers the extensive prediction and accuracy of any suspected bone disorder patients, ranging from 75% to 97%.Öğe An accurate and exact clustering algorithm for next generation sequencing metagenomic sequences(Wiley, 2021) Bhat, Ashaq Hussain; Nguyen, Tu N.; Cengiz, Korhan; Prabhu, PuniethaaClustering algorithms are the essential tools in the target metagenomics, used to perform the taxonomic profiling of microbial communities. In the present study, an algorithmic tool called hash-based exact alignment (HBEA) clustering algorithm is presented, which uses exact pairwise global alignment algorithm to improve the cluster quality and creates a hash table for extraction of cluster representatives. The algorithm is de novo based and uses the general de facto 97% sequence similarity score to cluster the sequences. Our experimental investigation on various types of datasets with distinct parameters and attributes showed that HBEA produces better operational taxonomic unit (OTU) clusters and computational complexity than other algorithms.Öğe Adaptive Swarm Intelligence Algorithms for Wireless Sensor Networks in IoT Preface(Igi Global, 2022) Kottursamy, Kottilingam; Cengiz, Korhan[Abstract Not Available]Öğe Analyzing of Novel Energy-Efficient Clustering Protocols for Wireless Sensor Networks(IEEE, 2017) Cengiz, KorhanWireless Sensor Networks (WSNs) nowadays are used in numerous fields. Direct Communication, Multi-hop Routing and Clustering are main approaches that can be used for communication protocols in Wireless Sensor Networks. In this study, novel clustering algorithms which are designed for WSNs are analyzed and compared. The comparison results are also summarized with a table. Finally, the popular algorithms LEACH and LEACH-F for WSNs are compared with a novel algorithm which is low energy fixed clustering (LEFCA) in terms of network lifetime and energy-efficiency.Öğe ANALYZING THE PERFORMANCE OF PURE LATERATION IN INDOOR ENVIRONMENTS WITH VARIOUS PERFORMANCE METRICS(2020) Cengiz, KorhanNowadays, determining the location of the users and devices in indoorbuildings is promising research topic. Accurate position determination of the usersfor indoor environments is used for numerous applications such as public safety,supermarkets, health care applications, travelling, social networks and tourism.However, global positioning systems created for outdoor localizations cannot beused for indoor positioning systems (IPS) because detecting the exact position of atarget is an issue for IPS. For indoor environments, there are several positioningalgorithms such as lateration, fingerprinting, dead reckoning etc. Lateration is lowcost and easy to deploy when compared to other existing algorithms. Therefore, inthis study, received signal strength based pure lateration that uses synthetic datagenerated from MATLAB is proposed. The performance of pure lateration isinvestigated in terms of several performance metrics such as effect of varyingnumber of the access points (AP), varying dimensions of the measurement area,varying Gaussian Noise power and varying number of test points in the field. Thesimulation of the pure lateration algorithm is conducted in MATLAB. The effect ofthe performance metrics are investigated and discussed in details. According to theresults, accuracy performance of lateration is increased when the number of APsincrease in the area, however this will bring some hardware costs. In addition, whenthe number of test points increases in the field, in other words the step size betweentwo test points decreases in the field the error performance of lateration is alsoenhanced however, this will also cause to computational costs. Finally, enlargingthe measurement area causes to decrease the accuracy performance of lateration asexpected. The main purpose of this study is to obtain the optimum conditions forlateration to provide a solution for real time applications. For future work, the realtime implementations of this study are performed and to improve the accuracyperformance, it is aimed to use a curve fitting idea to the measured values.Öğe Appearance Based Gaze Estimation Using Eye Region Landmarks and Math Approach(Library & Information Center, Nat Dong Hwa Univ, 2020) Cheng, Shichao; Zhang, Bocheng; Li, Jianjun; Tang, Zheng; Cengiz, KorhanThe gaze direction can be defined by the pupil and the center of the eyeball, the latter cannot be observed in the 2D image, which can cause ill-posed problems and cannot achieve highly accurate gaze estimation. Therefore, we try to extract several effective landmarks around the eyeball and iris from the monocular input for gaze estimation. Instead of directly returning the two angles for the pitch and yaw of the eyeball, we return to an intermediate graphical representation, which in turn simplifies the task of 3D gaze estimation. We try to use a novel learning-based method to locate the landmarks of an eyeball with the appearance-based method. Through these high-accuracy feature points, we have proposed a new and effective formula for drawing more accurate gaze directions. As for the individual gaze estimation of independent people, our method is superior to existing model fitting and appearance-based methods.Öğe Application of clustering algorithm in complex landscape farmland synthetic aperture radar image segmentation(Walter De Gruyter Gmbh, 2021) Chen, Zhuoran; Cong, Biao; Hua, Zhenxing; Cengiz, Korhan; Shabaz, MohammadIn synthetic aperture radar (SAR) image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different labels due to speckle noise. This study presents the technique based on organization evolution (OEA) algorithm to improve ISODATA in pixels. This approach effectively filters out the useless local information and successfully introduces the effective information. To verify the accuracy of OEA-ISO data algorithm, the segmentation effect of this algorithm is tested on SAR image and compared with other techniques. The results demonstrate that the OEA-ISO data algorithm is 10.16% more accurate than the WIPFCM algorithm, 23% more accurate than the K-means algorithm, and 27.14% more accurate than the fuzzy C-means algorithm in the light-colored farmland category. It can be seen that the OEA-ISO data algorithm introduces the pixel block strategy, which successfully reduces the noise interference in the image, and the effect is more obvious when the image background is complex.Öğe Application of Internet of Things Framework in Physical Education System(Library & Information Center, Nat Dong Hwa Univ, 2021) Hu, Lixun; Liu, Caili; Cengiz, Korhan; Nallappan, GunasekaranRecently, students engage in physical activities to promote physical, mental, and psychological benefits globally. Many schools employ the physical education system as an integral part of their curriculum to develop their students. This paper presents the main benefits of the physical education system. Further, the pillars of physical education in the modern education system are also analyzed. The usage of the Internet of Things (IoT) framework in the physical education system is also being presented in this research. We also propose a new framework called the IoT-based Physical Activity Recognition (IPAR) model. In this model, physical action recognition is done using data from a single tri-axial accelerometer. The recognized action and medical parameters like accelerometer, oxygen level, pulse rate, and temperature are transferred through the cloud to the physical activity instructor's mobile phone. The proposed physical action recognition model produces an overall accuracy of 95.82%. Further, the overall F-score attained by the proposed IPAR algorithm is 97.83%. Moreover, the overall time complexity of the proposed IPAR algorithm was as low as 53.96ms.Öğe BEPO: A novel binary emperor penguin optimizer for automatic feature selection(Elsevier, 2021) Dhiman, Gaurav; Oliva, Diego; Kaur, Amandeep; Singh, Krishna Kant; Vimal, S.; Sharma, Ashutosh; Cengiz, KorhanEmperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin's huddling behaviour. However, the original version of the EPO will fix issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm. (C) 2020 Elsevier B.V. All rights reserved.Öğe Bi-GISIS KE: Modified key exchange protocol with reusable keys for IoT security(Elsevier, 2021) Seyhan, Kubra; Tu N Nguyen; Akleylek, Sedat; Cengiz, Korhan; Islam, S. K. HafizulWe propose a new bilateral generalization inhomogeneous short integer solution (Bi-GISIS)-based key exchange protocol with reusable key feature for post-quantum IoT security. It is aimed to reduce the time consumption in the key generation of key exchange protocols to be used in IoT devices. To obtain reusable key, we define modified bilateral pasteurization in the random oracle model. By ensuring reusable keys, the same key becomes available in several executions of the proposed protocol. This feature allows efficient usage of reusable keys in resource-constrained IoT architectures. The proposed scheme is suitable for quantum secure key exchange in D2D-aided fog computing environment. A key exchange protocol with improved key management process is constructed for D2D.Öğe Compact C-Slot Microstrip-Fed Planar Antenna for Wireless Devices(IEEE, 2021) Kulkarni, Jayshri Sharad; Cengiz, Korhan; Gharat, SarveshThis paper proposes a novel, compact, multi and wide band microstrip fed planar antenna for fifth generation (5G), Wireless Local Area Network (WLAN) and Wireless Access in the Vehicular Environment (WAVE) applications. The antenna has a degree of proficiency 20x20mm(2) and is scripted on RT duroid Roggers substrate having a thickness of 0.8mm. The proposed antenna is consisted of rectangular radiating element where a small rectangular patch is loaded at the top of rectangular radiating element. A 'C' slot is rotated in anticlockwise direction at an angle of 180 degrees and is anchored along the Y-axis on radiating element so that the proposed antenna operates in 5G, WAVE and WLAN bands. The proposed antenna exhibits a 10 dB fractional impedance bandwidth of 10.77% (3.25 similar to 13.62) GHz and 49.71% (4.67 similar to 17.76) GHz covering a wide bandwidth requirement of 5G New Radio n77, WLAN and WAVE bands. The proposed antenna structure also confirms a good radiation performance like balloon shape polar patterns, gain larger than 2dBi and radiation efficiency greater than 80% throughout the operating frequency bands. Due to the fact that the proposed C-slot antenna has compact nature, excellent scattering and radiation characteristics, it is an attractive contender for WLAN, 5G, and WAVE operations.Öğe Comprehensive Analysis on Least-Squares Lateration for Indoor Positioning Systems(IEEE-Inst Electrical Electronics Engineers Inc, 2021) Cengiz, KorhanIn pursuit of the accomplishment of certain position estimations of targets in outdoor places, finding the locations of the targets in indoor environments has been a significant topic. Exact position estimations of the objects for indoor places have potentials for the enhancement of several emerging Internet-of-Things (IoT) applications, such as smart manufacturing, smart home, public security, social networks, transportation, traveling, marketing applications, and information services lead to a huge demand on the designing of low-cost and high-accuracy localization and navigation solutions. On the other hand, the global positioning system (GPS) technology designed for outdoor positioning applications, is not suitable to indoor positioning systems. Making exact position detection with GPS is a compelling problem for indoor positioning methods. In this study, received signal strength (RSS)-based least-squares triangulation approach that utilizes existing infrastructure, is proposed. By increasing the number of access points (APs) and using line fitting algorithms to the RSS values, the triangulation method improves the certainty of location estimation. The utilization of the existing infrastructure turns the proposed approach into cheaper when compared to existing localization methods which require expensive components. The proposed least-squares lateration algorithm is compared with pure lateration (PL) in terms of accuracy error under different Gaussian noise parameters for varying number of APs and varying dimensions of the measurement area. Usage of the least-square algorithm with line fitting approaches provides significant performance improvements for all cases when it compared with PL.Öğe Cooperative spectrum sensing optimization for cognitive radio in 6 G networks(Pergamon-Elsevier Science Ltd, 2021) Singh, Krishna Kant; Yadav, Piyush; Singh, Akansha; Dhiman, Gaurav; Cengiz, KorhanThe upcoming sixth generation (6 G) systems can meet the high user demands. The existing communication systems are becoming inefficient in meeting the user demands. The multifold growth in the usage of high-definition multimedia applications requires new capabilities. The users are looking forward to high throughput and low latency. The shift from 5 G to 6 G networks is since the 6 G networks are expected to combine the terrestrial, aerial, and maritime communications into a robust network. This will provide the users a faster network with high reliability, accommodation to a larger number of users, and ultra-low latency. However, the limited availability of spectrum is a bottleneck in enhancing the user experience. Therefore, advanced techniques like cognitive radios and cooperative spectrum sensing are critical in the design of future network. The optimal usage and management of the available spectrum is significant for the performance of the network. In this paper, a cooperative spectrum sensing technique using Manta Ray Foraging Algorithm (MRFO) is proposed. The weighting vector at the fusion center is optimized using MRFO. The allocation of the spectrum is done using the optimal weight vector for secondary users. The proposed work aims at finding the maximum probability of detection. Probability of detection is significant in spectrum sensing. The channel needs to be sensed for the presence or absence of primary users. If the detection probability is maximized, then the channel usage efficiency will increase. The proposed method is compared with other state of the art methods. The results show that MRFO can be used efficiently for spectrum sharing by cognitive radios.Öğe Deep Learning-Based Context-Aware Video Content Analysis on IoT Devices(Mdpi, 2022) Gad, Gad; Gad, Eyad; Cengiz, Korhan; Fadlullah, Zubair; Mokhtar, BassemIntegrating machine learning with the Internet of Things (IoT) enables many useful applications. For IoT applications that incorporate video content analysis (VCA), deep learning models are usually used due to their capacity to encode the high-dimensional spatial and temporal representations of videos. However, limited energy and computation resources present a major challenge. Video captioning is one type of VCA that describes a video with a sentence or a set of sentences. This work proposes an IoT-based deep learning-based framework for video captioning that can (1) Mine large open-domain video-to-text datasets to extract video-caption pairs that belong to a particular domain. (2) Preprocess the selected video-caption pairs including reducing the complexity of the captions' language model to improve performance. (3) Propose two deep learning models: A transformer-based model and an LSTM-based model. Hyperparameter tuning is performed to select the best hyperparameters. Models are evaluated in terms of accuracy and inference time on different platforms. The presented framework generates captions in standard sentence templates to facilitate extracting information in later stages of the analysis. The two developed deep learning models offer a trade-off between accuracy and speed. While the transformer-based model yields a high accuracy of 97%, the LSTM-based model achieves near real-time inference.Öğe Design of Gudermannian Neuroswarming to solve the singular Emden-Fowler nonlinear model numerically(Springer, 2021) Sabir, Zulqurnain; Raja, Muhammad Asif Zahoor; Baleanu, Dumitru; Cengiz, Korhan; Shoaib, MuhammadThe current investigation is related to the design of novel integrated neuroswarming heuristic paradigm using Gudermannian artificial neural networks (GANNs) optimized with particle swarm optimization (PSO) aid with active-set (AS) algorithm, i.e., GANN-PSOAS, for solving the nonlinear third-order Emden-Fowler model (NTO-EFM) involving single as well as multiple singularities. The Gudermannian activation function is exploited to construct the GANNs-based differential mapping for NTO-EFMs, and these networks are arbitrary integrated to formulate the fitness function of the system. An objective function is optimized using hybrid heuristics of PSO with AS, i.e., PSOAS, for finding the weights of GANN. The correctness, effectiveness and robustness of the designed GANN-PSOAS are verified through comparison with the exact solutions on three problems of NTO-EFMs. The assessments on statistical observations demonstrate the performance on different measures for the accuracy, consistency and stability of the proposed GANN-PSOAS solver.Öğe Dual Band and Dual Diversity Four-Element MIMO Dipole for 5G Handsets(Mdpi, 2021) Jamshed, Muhammad Ali; Ur-Rehman, Masood; Frnda, Jaroslav; Althuwayb, Ayman A.; Nauman, Ali; Cengiz, KorhanThe increasing popularity of using wireless devices to handle routine tasks has increased the demand for incorporating multiple-input-multiple-output (MIMO) technology to utilize limited bandwidth efficiently. The presence of comparatively large space at the base station (BS) makes it straightforward to exploit the MIMO technology's useful properties. From a mobile handset point of view, and limited space at the mobile handset, complex procedures are required to increase the number of active antenna elements. In this paper, to address such type of issues, a four-element MIMO dual band, dual diversity, dipole antenna has been proposed for 5G-enabled handsets. The proposed antenna design relies on space diversity as well as pattern diversity to provide an acceptable MIMO performance. The proposed dipole antenna simultaneously operates at 3.6 and 4.7 sub-6 GHz bands. The usefulness of the proposed 4x4 MIMO dipole antenna has been verified by comparing the simulated and measured results using a fabricated version of the proposed antenna. A specific absorption rate (SAR) analysis has been carried out using CST Voxel (a heterogeneous biological human head) model, which shows maximum SAR value for 10 g of head tissue is well below the permitted value of 2.0 W/kg. The total efficiency of each antenna element in this structure is -2.88, -3.12, -1.92 and -2.45 dB at 3.6 GHz, while at 4.7 GHz are -1.61, -2.19, -1.72 and -1.18 dB respectively. The isolation, envelope correlation coefficient (ECC) between the adjacent ports and the loss in capacity is below the standard margin, making the structure appropriate for MIMO applications. The effect of handgrip and the housing box on the total antenna efficiency is analyzed, and only 5% variation is observed, which results from careful placement of antenna elements.Öğe Dynamic Polygon Generation for Flexible Pattern Formation in Large-Scale UAV Swarm Networks(IEEE, 2020) Raja, Gunasekaran; Kottursamy, Kottilingam; Theetharappan, Ajay; Cengiz, Korhan; Ganapathisubramaniyan, Aishwarya; Kharel, Rupak; Yu, KepingA UAV swarm network is a network formed by aggregating a large number of UAVs and coordinate them to execute a specific mission, especially in areas where human intervention is not physically possible or economically viable. The process of coordinating and maintaining a UAV swarm network has various phases. The pattern formation phase is one of the important phases and is highly significant in missions where geography is an important aspect of the mission. For the purpose of automating the pattern generation process, this paper proposes the dynamic polygon generation (DPGen) algorithm that can generate convex polygonal pattern with any number of vertices in linear time. The DPGen algorithm generates a pattern dynamically for any number of drones which increases the scalability of the UAV swarm networks, increasing the magnitude of use-cases of the swarm. The DPGen algorithm contains a mechanism to use this algorithm in a decentralized manner while balancing the load on all the UAVs in the network. The usage of DPGen algorithm reduces the network traffic in the UAV swarm network by 78.26% and decreases the power requirement of the leader drone by 74.39%.Öğe EDGF: Empirical dataset generation framework for wireless sensor networks(Elsevier, 2021) Sah, Dinesh Kumar; Cengiz, Korhan; Donta, Praveen Kumar; Inukollu, Venkata N.; Amgoth, TarachandIn wireless sensor networks (WSNs), simulation practices, system models, algorithms, and protocols have been published worldwide based on the assumption of randomness. The applied statistics used for randomness in WSNs are broad, e.g., random deployment, activity tracking, packet generation, etc. Even though authors' adequate formal and informal information and pledge validation of the proposal became challenging, the minuscule information alteration in implementation and validation can reflect the enormous effect on eventual results. In this proposal, we show how the results are affected by the generalized assumption made on randomness. In sensor node deployment, ambiguity arises due to node error-value (epsilon), and its upper bound in the relative position is estimated to understand the delicacy of diminutives changes. Besides, the effect of uniformity in the traffic and participation of scheduling position of nodes is also generalized. We propose an algorithm to generate the unified dataset for the general and some specific applications system models in WSNs. The results produced by our algorithm reflect the pseudo-randomness and can efficiently regenerate through seed value for validation.Öğe An efficient partitioning and placement based fault TSV detection in 3D-IC using deep learning approach(Springer Heidelberg, 2021) Radhakrishnan Nair, Radeep Krishna; Pothiraj, Sivakumar; Radhakrishnan Nair, T. R.; Cengiz, KorhanOver topical eras, three dimensional Integrated Circuit (3D-IC) fabrications have become vital among the researchers and industrial people, owing to its wide range of amenities including smaller intersect lengths, advanced incorporation density, and enhanced performance. Still, fault Through Silicon Via (TSV) detection is a bottleneck, due to poor fabrication processes such as partitioning and placement. Besides, state of the art works have concentrated on redundant TSV allocation instead of detected fault TSV and hence, the area overhead and size of the circuit are increased. To resolve these shortcomings, this paper proposes an Efficient Partitioning and Placement based Fault TSV detection in 3D-IC. The proposed work comprises five processes: Quick cut oriented Partitioning, Multi-Objective based Placement, Deep learning based Fault TSV detection, Re-routing and Adaptive Time Division Multiple Access (TDMA) time slot. Initially, Quick Cut algorithm has been employed to partition the 3D-IC and it is easier for placement process. The placement is executed through Multi-Objective Brain Storm Optimization algorithm that selects the optimal place to position the cells in 3D-IC. The fault TSV in the 3D-IC is detected using the Adam Deep Neural Network algorithm. Further, Adam optimizer has been used to estimate weight for each input and it provides fast performance and better convergence rate compared to the traditional stochastic gradient algorithm. After obtaining the fault TSV, rerouting is performed to reroute the signals transmitted over the defected TSV to the nearby defect free TSV. The Adaptive TDMA algorithm has been used to provide time slot to TSV positioned in each partition. The proposed method has been implemented in MATLABR2017b tool. The results attained from the simulations are propitious in terms of the metrics such as Area, Wirelength, Delay, Run time and Temperature.
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