Computer Sciences Commons^™

Presenting A Method To Detect Intrusion In Iot Through Private Blockchain, Rezvan Mahmoudie, Saeed Parsa, Amir Masoud Rahmani

Turkish Journal of Electrical Engineering and Computer Sciences

Blockchain (BC) has been used as a new solution to overcome security and privacy challenges in the Internet of Things (IoT). However, recent studies have indicated that the BC has a limited scalability and is computationally costly. Also, it has significant overhead and delay in the network, which is not suitable to the nature of IoT. This article aims at implementing BC in the IoT context for smart home management, as the integration of these two technologies ensures the IoT's security and privacy. Therefore, we proposed an overlay network in private BC to optimize its compatibility with IoT by increasing …

Improving The Performance Of Industrial Mixers That Are Used In Agricultural Technologies Via Chaotic Systems And Artificial Intelligence Techniques, Onur Kalayci, İhsan Pehli̇van, Selçuk Coşkun

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, it is aimed to show how important to apply chaotic systems and Fuzzy Logic artificial intelligence technique to increase the production performance of industrial mixers used in agriculture in terms of important criteria such as product quality, homogeneity, time, and energy saving by using. A PLC (Programmable Logic Controller) controlled mixer whose all functions can be controlled by the HMI (Human Machine Interface) operator panel is designed and manufactured for experimental studies. Water, leonardite and potassium hydroxide (KOH) mixture components are mixed in a newly designed mixer in three different ways by using traditional, chaos, and artificial …

Rtpltool: A Software Tool For Path Loss Modeling In 5g Outdoor Systems, Ci̇han Bariş Findik, Özlem Özgün

Turkish Journal of Electrical Engineering and Computer Sciences

The increase in the required bandwidth along with the global growth of existing wireless communication systems is one of the major reasons why research and industry communities are exploring 5G and millimeter-wave frequencies. The advantages of millimeter wave frequencies for 5G applications are a wide range of accessible and unlicensed spectrum, the use of small antennas in RF applications with increasing frequency, and low losses due to the interference effects compared to the currently used frequency bands. However, due to some computational challenges especially at millimeter waves (i.e. in FR2 frequency band), it is necessary to develop efficient software tools …

Design And Implementation Of A Low Cost And Portable Tactile Stimulator, Coşkun Kazma, Vecdi̇ Emre Levent, Merve Çardak, Ni̇zametti̇n Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

When central nervous system has a problem, somatic area I and II respond to stimulation differently. Therefore, it is possible to identify some of the central nervous diseases when somatosensory on the fingertip is stimulated and responses are recorded and analyzed. We designed a system to stimulate the mechanoreceptors on fingertips. It is composed of a mechanical system for fingertip stimulation, an embedded controller, a control computer, and a software to control overall operation. During test, mechanoreceptors are stimulated according to the test protocols. Individuals' answers are recorded to be evaluated by the developed software. In this study, several design …

Model-Based Deep Learning For Computational Imaging, Xiaojian Xu

McKelvey School of Engineering Graduate Student Theses & Dissertations

This dissertation addresses model-based deep learning for computational imaging. The motivation of our work is driven by the increasing interests in the combination of imaging model, which provides data-consistency guarantees to the observed measurements, and deep learning, which provides advanced prior modeling driven by data. Following this idea, we develop multiple algorithms by integrating the classical model-based optimization and modern deep learning to enable efficient and reliable imaging. We demonstrate the performance of our algorithms by validating their performance on various imaging applications and providing rigorous theoretical analysis.

The dissertation evaluates and extends three general frameworks, plug-and-play priors (PnP), regularized …

28 Ghz Patch Antenna Array With Reduced Mutual Coupling For 5g Communications Systems, Rahabu F. Mwang’Amba, Hashimu Uledi Iddi

Tanzania Journal of Engineering and Technology (TJET)

A 28 GHz patch antenna array with reduced mutual coupling for 5G communication systems is presented in this paper. Two elements antenna array was simulated with a periodic boundary to represent an infinity array. The antenna array is attached with a pair of the coupled directional coupler with a coupling value of -3.47 dB, and transmission lengths of 3.40 mm and 7.62 mm depending on the antenna array's magnitude and phase coefficient were designed and simulated. A reduced mutual coupling of -31.86 dB compared to -10.75 dB for an array without a decoupling network was observed. The wide scanning angle …

Applied Deep Learning: Case Studies In Computer Vision And Natural Language Processing, Md Reshad Ul Hoque

Electrical & Computer Engineering Theses & Dissertations

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover …

Emotion Detection Using An Ensemble Model Trained With Physiological Signals And Inferred Arousal-Valence States, Matthew Nathanael Gray

Electrical & Computer Engineering Theses & Dissertations

Affective computing is an exciting and transformative field that is gaining in popularity among psychologists, statisticians, and computer scientists. The ability of a machine to infer human emotion and mood, i.e. affective states, has the potential to greatly improve human-machine interaction in our increasingly digital world. In this work, an ensemble model methodology for detecting human emotions across multiple subjects is outlined. The Continuously Annotated Signals of Emotion (CASE) dataset, which is a dataset of physiological signals labeled with discrete emotions from video stimuli as well as subject-reported continuous emotions, arousal and valence, from the circumplex model, is used for …

Distributed Learning With Automated Stepsizes, Benjamin Liggett

All Theses

Stepsizes for optimization problems play a crucial role in algorithm convergence, where the stepsize must undergo tedious manual tuning to obtain near-optimal convergence. Recently, an adaptive method for automating stepsizes was proposed for centralized optimization. However, this method is not directly applicable to decentralized optimization because it allows for heterogeneous agent stepsizes. Furthermore, directly using consensus between agent stepsizes to mitigate stepsize heterogeneity can decrease performance and even lead to divergence.

This thesis proposes an algorithm to remedy the tedious manual tuning of stepsizes in decentralized optimization. Our proposed algorithm automates the stepsize and uses dynamic consensus between agents’ stepsizes …

Feature Analysis Of Indus Valley And Dravidian Language Scripts With Similarity Matrices, Sarat Sasank Barla, Sai Surya Sanjay Alamuru, Peter Revesz

School of Computing: Conference and Workshop Papers

This paper investigates the similarity between the Indus Valley script and the Kannada, Malayalam, Tamil, and Telugu scripts that are used to write Dravidian languages. The closeness of these scripts is determined by applying a feature analysis of each sign of these scripts and creating similarity matrices that describe the similarity of any pair of signs from two different scripts. The feature list that we use for the analysis of these Dravidian language-related scripts includes six new features beyond the thirteen features that were used for the study of Minoan Linear A and related scripts by Revesz. These new features …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Constraint-Aware, Scalable, And Efficient Algorithms For Multi-Chip Power Module Layout Optimization, Imam Al Razi

Graduate Theses and Dissertations

Moving towards an electrified world requires ultra high-density power converters. Electric vehicles, electrified aerospace, data centers, etc. are just a few fields among wide application areas of power electronic systems, where high-density power converters are essential. As a critical part of these power converters, power semiconductor modules and their layout optimization has been identified as a crucial step in achieving the maximum performance and density for wide bandgap technologies (i.e., GaN and SiC). New packaging technologies are also introduced to produce reliable and efficient multichip power module (MCPM) designs to push the current limits. The complexity of the emerging MCPM …

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air drag …

Computational Models To Detect Radiation In Urban Environments: An Application Of Signal Processing Techniques And Neural Networks To Radiation Data Analysis, Jose Nicolas Gachancipa

Beyond: Undergraduate Research Journal

Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This article presents a computational model …

Combining Solution Reuse And Bound Tightening For Efficient Analysis Of Evolving Systems, Clay Stevens, Hamid Bagheri

School of Computing: Conference and Workshop Papers

Software engineers have long employed formal verification to ensure the safety and validity of their system designs. As the system changes—often via predictable, domain-specific operations—their models must also change, requiring system designers to repeatedly execute the same formal verification on similar system models. State-of-the-art formal verification techniques can be expensive at scale, the cost of which is multiplied by repeated analysis. This paper presents a novel analysis technique—implemented in a tool called SoRBoT—which can automatically determine domain-specific optimizations that can dramatically reduce the cost of repeatedly analyzing evolving systems. Different from all prior approaches, which focus on either tightening the …

Classifying Toe Walking Gait Patterns Among Children Diagnosed With Idiopathic Toe Walking Using Wearable Sensors And Machine Learning Algorithms, Rahul Soangra, Yuxin Wen, Hualin Yang, Marybeth Grant-Beuttler

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait abnormality in which children’s toes touch at initial contact and demonstrate limited or no heel contact throughout the gait cycle. Toe walking results in poor balance, increased risk of falling, and developmental delays among children. Identifying toe walking steps during walking can facilitate targeted intervention among children diagnosed with ITW. With recent advances in wearable sensing, communication technologies, and machine learning, new avenues of managing toe walking behavior among children are feasible. In this study, we investigate the capabilities of Machine Learning (ML) algorithms in identifying initial foot contact (heel strike versus toe …

Machine Learning Approach For Classifying Power Outage In Secondary Electric Distribution Network, Stephan Mgaya, Hellen Maziku

Tanzania Journal of Engineering and Technology (TJET)

Power outage is the problem that hinders social and economic development especially for developing countries like Tanzania. Frequent power outages damage electric equipment, and negatively affect the industrial production process. Power outages cannot be completely eradicated due to uncontrolled cause like natural calamities but technical challenges can be managed and hence reducing power outages. The existing manual methods used to locate power outage like customer calls is inefficient and time consuming. On the other hand, modern method like the Advanced Metering Infrastructure (AMI) still faces a challenge in effectively classifying power line outage due to the nature of imbalanced datasets. …

Big Data Analytics Framework For Effective Higher Education Institutions, George Matto

Tanzania Journal of Engineering and Technology (TJET)

There has been an increased dependency on Information and Communication Technologies (ICTs) in undertaking various activities in Higher Education Institutions (HEIs) ecosystems. Because of that, huge volumes of data have increasingly been generated. There have been, for instance, considerable amounts of data generated through electronic platforms involved in students’ admission and registration process, students’ academic records management, teaching and learning data, curriculum related data, and several other administrative data. Analysis of data generated from these platforms stands to give students, lecturers, HEIs Management, policy makers and implementers, and other stakeholders useful insights that would help in improving HEIs’ effectiveness. Unfortunately, …

An Iot Based Worker Safety Helmet Using Cloud Computing Technology, Yuthriya Iddi Khamis, Mahmoud Alawi, Waheed Muhammad Sanya

Tanzania Journal of Engineering and Technology (TJET)

Miner safety and security is a major challenge around the world due to the exposure to toxic gases that are frequently released in underground mines. Miners' health is adversely affected primarily by toxic gases, which endanger the workers' lives. Furthermore, human sensory abilities do not detect these dangerous gases. As a result, this paper proposes a safety monitoring system that includes a temperature sensor, humidity sensor, and gas sensors to detect harmful gases and alert miners to those harmful gases using the smart helmet they wear. These gases are transmitted to the control station via the cloud using Internet of …

Control Implemented On Quantum Computers: Effects Of Noise, Nondeterminism, And Entanglement, Kip Nieman, Keshav Kasturi Rangan, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Quantum computing has advanced in recent years to the point that there are now some quantum computers and quantum simulators available to the public for use. In addition, quantum computing is beginning to receive attention within the process systems engineering community for directions such as machine learning and optimization. A logical next step for its evaluation within process systems engineering is for control, specifically for computing control actions to be applied to process systems. In this work, we provide some initial studies regarding the implementation of control on quantum computers, including the implementation of a single-input/single-output proportional control law on …

Robust Sensor Design For The Novel Reduced Models Of The Mead-Marcus Sandwich Beam Equation, Ahmet Aydin

Masters Theses & Specialist Projects

Novel space-discretized Finite Differences-based model reductions are proposed for the partial differential equations (PDE) model of a multi-layer Mead-Marcus-type beam with (i) hinged-hinged and (ii) clamped-free boundary conditions. The PDE model describes transverse vibrations for a sandwich beam whose alternating outer elastic layers constrain viscoelastic core layers, which allow transverse shear. The major goal of this project is to design a single boundary sensor, placed at the tip of the beam, to control the overall dynamics on the beam.

For (i), it is first shown that the PDE model is exactly observable by the so-called nonharmonic Fourier series approach. However, …

Breast Cancer-Caps: A Breast Cancer Screening System Based On Capsule Network Utilizing The Multiview Breast Thermal Infrared Images, Devanshu Tiwari, Manish Dixit, Kamlesh Gupta

Turkish Journal of Electrical Engineering and Computer Sciences

This paper proposed an accurate and fully automated breast cancer early screening system called the "Breast Cancer-Caps". The capsule network is used in this approach for the cancer detection in breast utilizing the thermal infrared images for the first time. This capsule network is trained with the help of Dynamic as well as Static breast thermal images dataset consisting of left, right, frontal views along with a new multiview thermal images. These multiview breast thermal images are fabricated by concatenating the conventional left, frontal and right view breast thermal images. The other current and popular deep transfer learning models such …

Comparative Study Of A Bidirectional Multi-Phase Multiinput Converter For Electric Vehicles, Furkan Akar, Murat Kale, Sebahatti̇n Yalçin, Gözde Taş

Turkish Journal of Electrical Engineering and Computer Sciences

Multiinput converters allow to create hybrid energy systems in electric vehicles with a reduced part count. In addition, interleaved structures help to build efficient converters with several possible benefits, such as low current ripple and high power density. This paper proposes utilizing a multiphase multiinput converter (MPMIC), which concentrates the aforementioned advantages and presents a comprehensive comparison with its single-phase version, called single phase multiinput converter (SPMIC). After analysing their steady-state characteristics, SPMIC and MPMIC are designed considering same conditions. Then, two laboratory prototypes rated at 2.5kW output power are implemented to validate the analysis. Finally, these prototypes are compared …

A Novel Hybrid Algorithm For Morphological Analysis: Artificial Neural-Net-Xmor, Ayla Kayabaş, Ahmet Ercan Topcu, Özkan Kiliç

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, we present a novel algorithm that combines a rule-based approach and an artificial neural network-based approach in morphological analysis. The usage of hybrid models including both techniques is evaluated for performance improvements. The proposed hybrid algorithm is based on the idea of the dynamic generation of an artificial neural network according to two-level phonological rules. In this study, the combination of linguistic parsing, a neural network-based error correction model, and statistical filtering is utilized to increase the coverage of pure morphological analysis. We experimented hybrid algorithm applying rule-based and long short-term memory-based (LSTM-based) techniques, and the results …

A Concept For Weighting Sentiment Phrase Using Deterministic Solution Of Algebraic Equations, Maryam Jalali, Morteza Zahedi, Abdolali Basiri

Turkish Journal of Electrical Engineering and Computer Sciences

Many text mining methods have used statistical information as text and language-independent procedures that are not deterministic. On the other hand, grammatical structure-based methods are limited to use in a certain language and text. We aim to suggest an algorithmic algebraic equation in a deterministic and nonprobabilistic way while maintaining the advantage of language independence. We propose a mathematical approach that transforms text and labels into a set of dumb equations. By solving the equations, each word is assigned a weight that can reflect the semantic information of that word, then we use the proposed algorithm to build a novel …

Degree-Based Random Walk Approach For Graph Embedding, Sarmad N. Mohammed, Semra Gündüç

Turkish Journal of Electrical Engineering and Computer Sciences

Graph embedding, representing local and global neighbourhood information by numerical vectors, is a crucial part of the mathematical modeling of a wide range of real-world systems. Among the embedding algorithms, random walk-based algorithms have proven to be very successful. These algorithms collect information by creating numerous random walks with a predefined number of steps. Creating random walks is the most demanding part of the embedding process. The computation demand increases with the size of the network. Moreover, for real-world networks, considering all nodes on the same footing, the abundance of low-degree nodes creates an imbalanced data problem. In this work, …

Generating Ad Creatives Using Deep Learning For Search Advertising, Kevser Nur Çoğalmiş, Ahmet Bulut

Turkish Journal of Electrical Engineering and Computer Sciences

We generated advertisement creatives programmatically using deep neural networks. A landing page contains relevant text data, which can be used for generating advertisement creatives, i.e. ads. We treated the ad generation task as a text summarization problem and built a sequence to sequence model. In order to assess the validity of our approach, we conducted experiments on four datasets. Our empirical results showed that our model generated relevant ads on a template-based dataset with moderate hyperparameters. Training the model with more content increased the performance of the model, which we attributed to rigorous hyperparameter tune-up. The choice of word embedding …

Prediction Of Broken Rotor Bar In Induction Motor Using Spectral Entropy Features And Tlbo Optimized Svm, Sudip Halder, Sunil Bhat, Bimal Dora

Turkish Journal of Electrical Engineering and Computer Sciences

The information of the fault frequency characteristics is of great importance for all associated fault diag nostics. This requires a high-resolution spectrum analysis to achieve efficient monitoring of machinery faults, especially while diagnosing rotor bar breakage under light load conditions, because the fault frequencies almost overlap with the fundamental. In this context, rather than looking for frequencies associated with rotor faults, several frequency bands are observed separately in terms of the entropy contained within these bands. First, the motor current signal has been divided into several frequency bands using the continuous wavelet transform (CWT), and the spectral entropy is calculated …

Harnessing Confidence For Report Aggregation In Crowdsourcing Environments, Hadeel Alhosaini, Xianzhi Wang, Lina Yao, Zhong Yang, Farookh Hussain, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Crowdsourcing is an effective means of accomplishing human intelligence tasks by leveraging the collective wisdom of crowds. Given reports of various accuracy degrees from workers, it is important to make wise use of these reports to derive accurate task results. Intuitively, a task result derived from a sufficient number of reports bears lower uncertainty, and higher uncertainty otherwise. Existing report aggregation research, however, has largely neglected the above uncertainty issue. In this regard, we propose a novel report aggregation framework that defines and incorporates a new confidence measure to quantify the uncertainty associated with tasks and workers, thereby enhancing result …

Actor-Critic Reinforcement Learning For Bidding In Bilateral Negotiation, Furkan Arslan, Reyhan Aydoğan

Turkish Journal of Electrical Engineering and Computer Sciences

Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is a conflict of interests. Instead of designing a hand-crafted decision-making module, this work proposes a novel bidding strategy adopting an actor-critic reinforcement learning approach, which learns what to offer in a bilateral negotiation. An entropy reinforcement learning framework called Soft Actor-Critic (SAC) is applied to the bidding problem, and a self-play approach is employed to train the model. Our model learns to produce the target utility of …