Nbp 2.0: Updated Next Bar Predictor, An Improved Algorithmic Music Generator,
2022
Don Mariano Marcos Memorial State University
Nbp 2.0: Updated Next Bar Predictor, An Improved Algorithmic Music Generator, Belinda M. Dungan, Proceso L. Fernandez Jr
Electronics, Computer, and Communications Engineering Faculty Publications
Deep neural network advancements have enabled machines to produce melodies emulating human-composed music. However, the implementation of such machines is costly in terms of resources. In this paper, we present NBP 2.0, a refinement of the previous model next bar predictor (NBP) with two notable improvements: first, transforming each training instance to anchor all the notes to its musical scale, and second, changing the model architecture itself. NBP 2.0 maintained its straightforward and lightweight implementation, which is an advantage over the baseline models. Improvements were assessed using quantitative and qualitative metrics and, based on the results, the improvements from these …
Evaluation Of Automated Eye Blink Artefact Removal Using Stacked Dense Autoencoder,
2022
Technological University Dublin, Ireland
Evaluation Of Automated Eye Blink Artefact Removal Using Stacked Dense Autoencoder, Matthew Rigney
Dissertations
The presence of artefacts in Electroencephalograph (EEG) signals can have a considerable impact on the information they portray. In this comparative study, the automated removal of eye blink artefacts using the constrained latent representation of a stacked dense autoencoders (SDAE) and comparing its ability to that of the manual independent component analysis (ICA) approach was evaluated. A comparative evaluation of 5 stacked dense autoencoder architectures lead to a chosen architecture for which the ability to automatically detect and remove eye blink artefacts were both statistically and humanistically evaluated. The ability of the stacked dense autoencoder was statistically evaluated with the …
Scrolling Vs Paging: Reading Performance And Preference Of Reading Modes In Long-Form Online News,
2022
Technological University Dublin, Ireland
Scrolling Vs Paging: Reading Performance And Preference Of Reading Modes In Long-Form Online News, Richard Herlihy
Dissertations
This study explores the impact of scrolling and dynamic pagination in long-form online documents on reader performance and reader experience. Previous research has produced mixed results, indicating no difference between modes, or a positive effect favouring scrolling. Recent advances in web standards have enabled simpler, dynamic, performant methods of pagination to tailor content responsively to any screen, meriting renewed study in this area. This paper uses one such method to load subsequent online news pages instantly without buffering. In an online browser experiment with 38 participants, an increase in reading speed in the scrolling mode was found at a level …
A Simple Dual-Band Quasi-Yagi Antenna With Defected Ground Structures,
2022
TÜBİTAK
A Simple Dual-Band Quasi-Yagi Antenna With Defected Ground Structures, Göksel Turan, Hayretti̇n Odabaşi
Turkish Journal of Electrical Engineering and Computer Sciences
In this article, a dual-band compact quasi-Yagi antenna with defected ground structure (DGS) is proposed. The proposed antenna has a simple feeding mechanism consists of a microstrip and transmission line. Half of the driver and director elements are printed on the opposite side of the substrate to ensure good coupling between the antenna elements and achieve a stable radiation pattern. The ground plane is modified with one rectangular slot below the microstrip line to form dual-band operation. Also rectangular slots placed on the sides of the ground plane to improve the matching. The proposed antenna works at $f_{1}=3.35$ and $f_{2}=6.15$ …
Dynamic Instance-Wise Decision-Making For Machine Learning,
2022
University at Albany, State University of New York
Dynamic Instance-Wise Decision-Making For Machine Learning, Yasitha Warahena Liyanage
Legacy Theses & Dissertations (2009 - 2024)
In a typical supervised machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. However, using a different subset of features that are most informative for each test instance individually may improve not only the quality of prediction but also the overall interpretability of the model. To this end, in this dissertation, we study the problem of optimizing the trade-off between instance-level sparsity and the quality of prediction using a dynamic instance-wise decision-making approach. Specifically, this approach sequentially reviews features one at a time for each data instance given …
Design And Analysis Of Marangoni-Driven Robotic Surfers,
2022
Michigan Technological University
Design And Analysis Of Marangoni-Driven Robotic Surfers, Mitchel L. Timm
Dissertations, Master's Theses and Master's Reports
We designed and experimentally studied the dynamics of two robotic systems that surf along the water-air interface. The robots were self-propelled by means of creating and maintaining a surface tension gradient resulting from an asymmetric release of isopropyl alcohol (IPA). The imbalance in the distribution of surface tension surrounding the robots generates a propulsive force commonly referred to as Marangoni propulsion. First, we considered a single surfer, which was custom-made with novel control mechanisms that allow for both forward motion and steering to be remotely adjusted solely through the manipulation of local surface stresses. We analyzed the performance of this …
Lpvit: A Transformer Based Model For Pcb Image Classification And Defect Detection,
2022
Hangzhou Normal University
Lpvit: A Transformer Based Model For Pcb Image Classification And Defect Detection, Kang An, Yanping Zhang
Computer Science Faculty Scholarship
PCB (printed circuit board) is an extremely important component of all electronic products, which has greatly facilitated human life. Meanwhile, tons of PCBs in the waste streams become a waste of resources, which puts the recycling and reuse of PCBs in urgent need. In the manufacturing and recycling of electronic products, the classification of PCBs, recognition of sub-components, and defect detection have been the key technology. Traditional manual detection and classification are subjective and rely on individuals’ experience. With the development of artificial intelligence, lots of research efforts have been dedicated to the automated detection and recognition of PCBs. In …
Optimized Cancer Detection On Various Magnified Histopathological Colon Imagesbased On Dwt Features And Fcm Clustering,
2022
TÜBİTAK
Optimized Cancer Detection On Various Magnified Histopathological Colon Imagesbased On Dwt Features And Fcm Clustering, Tina Babu, Tripty Singh, Deepa Gupta, Shahin Hameed
Turkish Journal of Electrical Engineering and Computer Sciences
Due to the morphological characteristics and other biological aspects in histopathological images, the computerized diagnosis of colon cancer in histopathology images has gained popularity. The images acquired using the histopathology microscope may differ for greater visibility by magnifications. This causes a change in morphological traits leading to intra and inter-observer variability. An automatic colon cancer diagnosis system for various magnification is therefore crucial. This work proposes a magnification independent segmentation approach based on the connected component area and double density dual tree DWT (discrete wavelet transform) coefficients are derived from the segmented region. The derived features are reduced further shortened …
Distributed Wireless Sensor Node Localization Based On Penguin Searchoptimization,
2022
TÜBİTAK
Distributed Wireless Sensor Node Localization Based On Penguin Searchoptimization, Md Al Shayokh, Soo Young Shin
Turkish Journal of Electrical Engineering and Computer Sciences
Wireless sensor networks (WSNs) have become popular for sensing areas-of-interest and performing assigned tasks based on information on the location of sensor devices. Localization in WSNs is aimed at designating distinct geographical information to the inordinate nodes within a search area. Biologically inspired algorithms are being applied extensively in WSN localization to determine inordinate nodes more precisely while consuming minimal computation time. An optimization algorithm belonging to the metaheuristic class and named penguin search optimization (PeSOA) is presented in this paper. It utilizes the hunting approaches in a collaborative manner to determine the inordinate nodes within an area of interest. …
Cnn Based Sensor Fusion Method For Real-Time Autonomous Robotics Systems,
2022
TÜBİTAK
Cnn Based Sensor Fusion Method For Real-Time Autonomous Robotics Systems, Berat Yildiz, Aki̇f Durdu, Ahmet Kayabaşi, Mehmet Duramaz
Turkish Journal of Electrical Engineering and Computer Sciences
Autonomous robotic systems (ARS) serve in many areas of daily life. The sensors have critical importance for these systems. The sensor data obtained from the environment should be as accurate and reliable as possible and correctly interpreted by the autonomous robot. Since sensors have advantages and disadvantages over each other they should be used together to reduce errors. In this study, Convolutional Neural Network (CNN) based sensor fusion was applied to ARS to contribute the autonomous driving. In a real-time application, a camera and LIDAR sensor were tested with these networks. The novelty of this work is that the uniquely …
Fft Enabled Ecc For Wsn Nodes Without Hardware Multiplier Support,
2022
TÜBİTAK
Fft Enabled Ecc For Wsn Nodes Without Hardware Multiplier Support, Utku Gülen, Selçuk Baktir
Turkish Journal of Electrical Engineering and Computer Sciences
ECC is a popular cryptographic algorithm for key distribution in wireless sensor networks where power efficiency is desirable. A power efficient implementation of ECC without using hardware multiplier support was proposed earlier for wireless sensor nodes. The proposed implementation utilized the number theoretic transform to carry operands to the frequency domain, and conducted Montgomery multiplication, in addition to other finite field operations, in that domain. With this work, we perform in the frequency domain only polynomial multiplication and use the fast Fourier transform to carry operands between the time and frequency domains. Our ECC implementation over $GF((2^{13}-1)^{13})$ on the MSP430 …
Using Vertical Areas In Finite Set Model Predictive Control Of A Three-Level Inverter Aimed At Computation Reduction,
2022
TÜBİTAK
Using Vertical Areas In Finite Set Model Predictive Control Of A Three-Level Inverter Aimed At Computation Reduction, Alireza Jaafari, Alireza Davari, Cristian Garcia, Jose Rodriguez
Turkish Journal of Electrical Engineering and Computer Sciences
In power electronics applications, finite set model predictive control (FS-MPC) has proven to be a viable strategy. However, due to the high processing power required, using this technology in multilevel converters is difficult. This strategy, which is based on predicting the behavior of the system for all conceivable states, has an issue with a numerous of possible switching states. A recent and useful strategy for dealing with the problem is the limiting of calculations based on triangle regions. Despite its success, this method has several limitations, including the computation required to locate the right triangle and the boundary modes. In …
An Approach For Performance Prediction Of Saturated Brushed Permanent Magnetdirect Current (Dc) Motor From Physical Dimensions,
2022
TÜBİTAK
An Approach For Performance Prediction Of Saturated Brushed Permanent Magnetdirect Current (Dc) Motor From Physical Dimensions, Rasul Tarvirdilu, Reza Zeinali, Hulusi̇ Bülent Ertan
Turkish Journal of Electrical Engineering and Computer Sciences
An analytical approach for performance prediction of saturated brushed permanent magnet direct current (DC) motors is proposed in this paper. In case of a heavy saturation in the stator back core of electrical machines, some flux completes its path through the surrounding air, and the conventional equivalent circuit cannot be used anymore. This issue has not been addressed in the literature. The importance of considering the effect of the flux penetrating the surrounding air is shown in this paper using finite element simulations and experimental results, and an analytical approach is proposed to consider this effect on magnet operating point …
Application Of Long Short-Term Memory (Lstm) Neural Network Based On Deeplearning For Electricity Energy Consumption Forecasting, Mehmet Bi̇lgi̇li̇, Ni̇yazi̇ Arslan, Ali̇i̇hsan Şekerteki̇n, Abdulkadi̇r Yaşar
Turkish Journal of Electrical Engineering and Computer Sciences
Electricity is the most substantial energy form that significantly affects the development of modern life, work efficiency, quality of life, production, and competitiveness of the society in the ever-growing global world. In this respect, forecasting accurate electricity energy consumption (EEC) is fairly essential for any country?s energy consumption planning and management regarding its growth. In this study, four time-series methods; long short-term memory (LSTM) neural network, adaptive neuro-fuzzy inference system (ANFIS) with subtractive clustering (SC), ANFIS with fuzzy cmeans (FCM), and ANFIS with grid partition (GP) were implemented for the short-term one-day ahead EEC prediction. Root mean square error (RMSE), …
A Futuristic Approach To Generate Random Bit Sequence Using Dynamic Perturbedchaotic System,
2022
TÜBİTAK
A Futuristic Approach To Generate Random Bit Sequence Using Dynamic Perturbedchaotic System, Sathya Krishnamoorthi, Premalatha Jayapaul, Vani Rajasekar, Rajesh Kumar Dhanaraj, Celestine Iwendi
Turkish Journal of Electrical Engineering and Computer Sciences
Most of the web applications require security which in turn requires random numbers. Pseudo-random numbers are required with good statistical properties and efficiency. Use of chaotic map to dynamically perturb another chaotic map that generates the random bit output is introduced in this work. Perturbance is introduced to improvise the chaotic behaviour of a base map and increase the periodicity. PRNG with this architecture is devised to generate random bit sequence from initial keyspace. The statistical properties of newly constructed PRNG are tested with NIST SP 800-22 statistical test suite and were shown to have good randomness. To ensure its …
Motion-Aware Vehicle Detection In Driving Videos,
2022
TÜBİTAK
Motion-Aware Vehicle Detection In Driving Videos, Mehmet Kiliçarslan, Tansu Temel
Turkish Journal of Electrical Engineering and Computer Sciences
This paper focuses on vehicle detection based on motion features in driving videos. Long-term motion information can assist in driving scenarios since driving is a complicated and dynamic process. The proposed method is a deep learning based model which processes motion frame image. This image merges both spatial (frame) and temporal (motion) information. Hence, the model jointly detects vehicles and their motion from a single image. The trained model on Toyota Motor Europe Motorway Dataset reaches 83% mean average precision (mAP). Our experiments demonstrate that the proposed method has a higher mAP than a tracking-based model. The proposed method runs …
A Novel Energy Consumption Model For Autonomous Mobile Robot,
2022
TÜBİTAK
A Novel Energy Consumption Model For Autonomous Mobile Robot, Gürkan Gürgöze, İbrahi̇m Türkoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
In this study, a novel predictive energy consumption model has been developed to facilitate the development of tasks based on efficient energy consumption strategies in mobile robot systems. For the proposed energy consumption model, an advanced mathematical system model that takes into account all parameters during the motion of the mobile robot is created. The parameters of inclination, load, dynamic friction, wheel slip and speed-torque saturation limit, which are often neglected in existing models, are especially used in our model. Thus, the effects of unexpected disruptors on energy consumption in the real world environment are also taken into account. As …
Automatically Classifying Familiar Web Users From Eye-Tracking Data:A Machine Learning Approach,
2022
TÜBİTAK
Automatically Classifying Familiar Web Users From Eye-Tracking Data:A Machine Learning Approach, Meli̇h Öder, Şükrü Eraslan, Yeli̇z Yesi̇lada
Turkish Journal of Electrical Engineering and Computer Sciences
Eye-tracking studies typically collect enormous amount of data encoding rich information about user behaviours and characteristics on the web. Eye-tracking data has been proved to be useful for usability and accessibility testing and for developing adaptive systems. The main objective of our work is to mine eye-tracking data with machine learning algorithms to automatically detect users' characteristics. In this paper, we focus on exploring different machine learning algorithms to automatically classify whether users are familiar or not with a web page. We present our work with an eye-tracking data of 81 participants on six web pages. Our results show that …
Stability Regions In Time Delayed Two-Area Lfc System Enhanced By Evs,
2022
TÜBİTAK
Stability Regions In Time Delayed Two-Area Lfc System Enhanced By Evs, Ausnain Naveed, Şahi̇n Sönmez, Saffet Ayasun
Turkish Journal of Electrical Engineering and Computer Sciences
With the extensive usage of open communication networks, time delays have become a great concern in load frequency control (LFC) systems since such inevitable large delays weaken the controller performance and even may lead to instabilities. Electric vehicles (EVs) have a potential tool in the frequency regulation. The integration of a large number of EVs via an aggregator amplifies the adverse effects of time delays on the stability and controller design of LFC systems. This paper investigates the impacts of the EVs aggregator with communication time delay on the stability. Primarily, a graphical method characterizing stability boundary locus is implemented. …
Temporal Bagging: A New Method For Time-Based Ensemble Learning,
2022
TÜBİTAK
Temporal Bagging: A New Method For Time-Based Ensemble Learning, Göksu Tüysüzoğlu, Derya Bi̇rant, Volkan Kiranoğlu
Turkish Journal of Electrical Engineering and Computer Sciences
One of the main problems associated with the bagging technique in ensemble learning is its random sample selection in which all samples are treated with the same chance of being selected. However, in time-varying dynamic systems, the samples in the training set have not equal importance, where the recent samples contain more useful and accurate information than the former ones. To overcome this problem, this paper proposes a new time-based ensemble learning method, called temporal bagging (T-Bagging). The significant advantage of our method is that it assigns larger weights to more recent samples with respect to older ones, so it …
