INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2023-07-05T08:18:47+00:00 Editorial Office Open Journal Systems Design and Implementation of P300 Brain-Controlled Wheelchair with a Developed Wireless DA Converter 2023-07-05T08:18:47+00:00 Zizhu Li Boning Li Wenping Luo Jianting Cao <p>This article presents a P300 brain-controlled wheelchair system utilizing a wireless Digital-to-Analog converter for signal transmission. The wireless Digital-to-Analog converter addresses issues with device connectivity and simplifies signal transmission, removing the need for complex serial port protocols. A support vector machine model is trained to extract the P300 component from the Electroencephalogram signal. A P300 stimulator is designed to elicit the P300 component response, with subjects controlling the wheelchair's movement by looking at randomly flickering white circles. Experimental validation is conducted on a modified wheelchair, demonstrating the effectiveness and reliability of the proposed method.</p> 2023-08-01T00:00:00+00:00 Copyright (c) 2023 Zizhu Li, Boning Li, Wenping Luo, Jianting Cao iEEG Signal Data Augmentation in Convolutional Neural Networks for Epileptic Focus Localization 2023-06-29T15:25:14+00:00 Ruixuan Chen Linfeng Sui Mo Xia Jianting Cao <p>Epileptic focus localization plays a crucial role in the diagnosis and treatment of epilepsy. Convolutional Neural Networks (CNNs) have exhibited promising outcomes in automatically detecting epileptic focus through the analysis of intracranial electroencephalogram (iEEG) signals. However, the limited availability of labeled iEEG dataset, which require specialist annotations, has constrained the effectiveness of CNNs. In this study, data augmentation techniques, including time shifting, amplitude scaling and noise addition, were employed to enhance the diversity and information content of the data. These techniques aimed to enable machine learning models to extract features from various aspects of iEEG data, thereby improving the accuracy of the models. Three deep learning models, namely DeepConvNet, ShallowConvNet and EEGNet, were introduced for the identification of epileptic foci. To evaluate the proposed methods, the Bern-Barcelona iEEG dataset was utilized. The experimental results demonstrated that the augmented dataset, formed by applying the three data augmentation techniques, achieved higher accuracies across all three deep learning models compared to the original dataset. This finding underscores the feasibility and efficacy of the proposed data augmentation and feature extraction methods in automated epilepsy detection.</p> 2023-07-27T00:00:00+00:00 Copyright (c) 2023 Ruixuan Chen, Linfeng Sui, Mo Xia, Jianting Cao LLVM-IR Instruction Latency Estimation Using Deep Neural Networks for a Software–Hardware Interface for Multi-Many-Cores 2023-06-16T10:18:44+00:00 Hiro Mikami Seira Iwai Masato Edahiro <div> <div>This study presents a method for estimating the latency of each LLVM-IR instruction to enable effective parallelization in model-based development. In recent embedded systems, such as in-vehicle electronic control, multi-many-core processors are utilized for the hardware, and model-based development for software. In the design of these systems, the degree of parallelism in the software and accuracy of performance estimation in the early design stages of the model-based development can be improved by estimating the performance of the blocks in the models and utilizing the estimate for parallelization. Research is therefore being performed on a software performance estimation technique that uses IEEE2804-2019 hardware feature description called Software-Hardware Interface for Multi-many-core (SHIM). In SHIM, each LLVM-IR instruction is associated with an execution cycle of the target processor. Several types of assembly instruction sequences are generated for the target processor from a given LLVM-IR instruction; thus, it is not easy to estimate the number of execution cycles. In this study, we propose a method that uses deep neural networks to estimate execution cycles for each LLVM-IR instruction. It can be observed that our method obtains a better estimation of LLVM-IR instruction latency compared with previous methods in experiments using the Raspberry Pi3 Model B+.</div> </div> 2023-07-08T00:00:00+00:00 Copyright (c) 2023 Hiro Mikami, Seira Iwai, Masato Edahiro Comparison of Stereo Matching Algorithms for the Development of Disparity Map 2023-02-20T20:45:49+00:00 Hamid Fsian Vahid Mohammadi Pierre Gouton Saeid Minaei <p><span style="font-weight: 400;">Stereo Matching is one of the classical problems in computer vision for the extraction of 3D information but still controversial for accuracy and processing costs. The use of matching techniques and cost functions is crucial in the development of the disparity map. This paper presents a comparative study of six different stereo matching algorithms including Block Matching (BM), Block Matching with Dynamic Programming (BMDP), Belief Propagation (BP), Gradient Feature Matching (GF), Histogram of Oriented Gradient (HOG) and Fixed-Window Aggregated Cost (FWAC). In addition, three cost functions, namely, Mean Squared Error (MSE), Sum of Absolute Differences (SAD), and Normalized Cross-Correlation (NCC) were utilized and compared. The stereo images used in this study were obtained from the Middlebury Stereo Datasets provided with perfect and imperfect calibrations. It was observed that the selection of matching function is quite important and also depends on the image properties. Results showed that the BP algorithm in most cases provided better results achieving accuracies over 95%. Accordingly, BP algorithm is highly recommended based on rapidity and performance and for applications with the need of detection of small details, HOG is advised.</span></p> 2023-03-31T00:00:00+00:00 Copyright (c) 2023 Hamid Fsian, Vahid Mohammadi, Pierre Gouton , Saeid Minaei An Unprecedented View of Quantum Computers 2023-01-30T01:04:01+00:00 Author: Jeffrey H. Boyd <p><span style="font-weight: 400;">Every discussion of quantum computing starts with wave-particle duality, to explain how qubits differ from bits. But what if wave-particle duality were wrong? How would we explain quantum computing then? A little-known science called the Theory of Elementary Waves (TEW) says that quantum particles follow zero-energy waves backwards. Wave-particle duality cannot be true if waves and particles travel in opposite directions. This article proposes the first-ever TEW theory of quantum circuits. Elementary waves emanate from measuring devices and travel backwards through the circuits, whereas qubits move forwards through the wires and gates following those waves backwards. Quantum computers are known to be reversible. After we present that way-of-thinking, we will explain some of the evidence that TEW is valid. There is a mountain of empirical evidence from outside information technology. TEW is a maverick theory, out-of-step with the consensus about how quantum computers work. At first TEW sounds counterintuitive. Its advantage is Occam’s Razor: we present a simpler explanation of quantum circuits. We will present the quantum computer equivalent of saying that before the box is open Schrödinger’s cat is already dead or alive, but not both. Observing the cat simply tells us what was already true before we looked.</span></p> 2023-03-06T00:00:00+00:00 Copyright (c) 2023 Author: Jeffrey H. Boyd Viscous Stability Criterion for Hydrodynamic Differential Rotation 2022-12-31T14:02:06+00:00 Zinab M. Maatoug Hana N. Albibas Bashir W. Sharif Ali M. Awin <p>Viscous stability criterion in a thin layer on a rotating sphere is studied. The case when the fluid is inviscid was explained by Watson in 1981, the work was motivated by the idea suggested by Drazin and Ried in their celebrated text "Hydrodynamic Stability"; here we will investigate the model worked out by B. Sherif and C. Jones in the year 2005, and show the necessary condition for instability which depends on the energy that is provided by the shear motion of the fluid in spherical thin layer. </p> 2023-01-16T00:00:00+00:00 Copyright (c) 2023 Zinab M. Maatoug, Hana N. Albibas, Bashir W. Sharif , Ali M. Awin