The Dual Carry Approximate Adder (DCAA) is proposed as an advanced 8-bit approximate adder featuring dual carry-out and carry-in full adders (FAs) along with an Error Reduction Unit (ERU) to enhance accuracy. The 8-bit adder is partitioned into upper...
Disaster memorials have emerged as pivotal approaches for not only commemorating disasters but also imparting lessons to future generations. However, despite discussions on preservation, little involvement has been reflected in the impacts of differe...
Memorial facilities are one of the crucial places where citizens conduct activities facilitating disaster risk reduction (DRR). However, previous studies have primarily focused on the post-construction phase of official memorial facilities, neglectin...
As quantum computing technology continues to develop, the need for research into novel quantum algorithms is growing. However, such algorithms cannot yet be reliably tested on actual quantum hardware, which is still limited in several ways, including...
(1) Background: Neurofeedback training (NFT) has emerged as a promising approach for enhancing cognitive functions and reducing anxiety, yet its specific impact on university student populations requires further investigation. This study aims to exam...
Micro-electro-mechanical system inertial measurement unit (MEMS-IMU), a core component in many navigation systems, directly determines the accuracy of inertial navigation system; however, MEMS-IMU system is often affected by various factors such as e...
The new energy structure needs to balance energy security and dual carbon goals, which has brought major challenges to coal-fired power plants. The pollution reduction and carbon emissions reduction in coal-fired power plants will be a key task in th...
This study presents an active noise control (ANC) algorithm using long short-term memory (LSTM) layers as a type of recurrent neural network. The filtered least-mean-square (FxLMS) algorithm is a widely used ANC algorithm, where the noise in a target...
The non-stationary nature of energy data is a serious challenge for energy forecasting methods. Frequent model updates are necessary to adapt to distribution shifts and avoid performance degradation. However, retraining regression models with lookbac...
In this paper, we present a novel and effective fault detection and diagnosis (FDD) method for a wind energy converter (WEC) system with a nominal power of 15 KW, which is designed to significantly reduce the complexity and computation time and possi...
Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edg...
The municipal solid waste incineration (MSWI) process plays a crucial role in managing the risks associated with waste accumulation and promoting the sustainable development of urban environments. However, unstable operation of the MSWI process can l...
Loads and strains in critical areas play a crucial role in aircraft structural health monitoring, the tracking of individual aircraft lifespans, and the compilation of load spectra. Direct measurement of actual flight loads presents challenges. This...
Electrochemical impedance spectroscopy (EIS) is the golden tool for many emerging biomedical applications that describes the behavior, stability, and long-term durability of physical interfaces in a specific range of frequency. Impedance measurements...
Minimizing the variation in threshold voltage (Vt) of programmed cells is required to the extreme level for realizing multi-level-cells; as many as even 5 bits per cell recently. In this work, a recent program scheme to write the cells from the top,...
In this paper, a deep neural network (DNN)-based nonlinear model predictive controller (NMPC) is demonstrated using real-time experimental implementation. First, the emissions and performance of a 4.5-liter 4-cylinder Cummins diesel engine are modele...
The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going rese...
In this paper, an analytical method based on the series transform and skewed slot structure of rotor is adopted to reduce the cogging torque of the variable flux memory permanent magnet (VFMPM) machine. Firstly, the theory analysis of the cogging tor...
Resistive random-access memory has emerged as a promising non-volatile memory technology, receiving substantial attention due to its potential for high operational performance, low power consumption, temperature robustness, and scalability. Two-dimen...
Alcohol consumption is believed to affect Alzheimer’s disease (AD) risk, but the contributing mechanisms are not well understood. A potential mediator of the proposed alcohol-AD connection is autophagy, a degradation pathway that maintains orga...
Lattice-based post-quantum cryptography relies on fast polynomial multiplication. The Number-Theoretic Transform (NTT) is the key operation that enables this acceleration. To provide high throughput and low latency while keeping the area overhead sma...
Travel planning is a process that allows users to obtain maximum benefit from their time, cost and energy. When planning a route from one place to another, it is an important option to present alternative travel areas on the route. This study propose...
Convolutional Decision Trees (CDTs) are machine learning models utilized as interpretable methods for image segmentation. Their graphical structure enables a relatively simple interpretation of how the tree successively divides the image pixels into...
Recently, deep learning-based classification approaches have made great progress and now dominate a wide range of applications, thanks to their Herculean discriminative feature learning ability. Despite their success, for hyperspectral data analysis,...
Failure monitoring of industrial systems is imperative in order to ensure their reliability and competitiveness. This paper presents an innovative hybrid modelling approach applied to DC electric motors, specifically the Kollmorgen AKM42 servomotor....
In this paper, we propose a partial decoding method with limited memory usage for high-speed thumbnail extraction. The proposed method performs a partial inverse transform and a partial intra prediction in order to reconstruct pixels for intra predic...
Systems of interacting species, such as biological environments or chemical reactions, are often described mathematically by sets of coupled ordinary differential equations. While a large number
Using Spatial Domain Correlation Pattern Recognition (CPR) in Internet-of-Things (IoT)-based applications often faces constraints, like inadequate computational resources and limited memory. To reduce the computation workload of inference due to larg...
The Internet of Things (IoT) has seen a surge in mobile devices with the market and technical expansion. IoT networks provide end-to-end connectivity while keeping minimal latency. To reduce delays, efficient data delivery schemes are required for di...
In this paper, a stochastic quasi-Newton algorithm for nonconvex stochastic optimization is presented. It is derived from a classical modified BFGS formula. The update formula can be extended to the framework of limited memory scheme. Numerical exper...
Malware complexity is rapidly increasing, causing catastrophic impacts on computer systems. Memory dump malware is gaining increased attention due to its ability to expose plaintext passwords or key encryption files. This paper presents an enhanced c...
Objectives: Hearing loss is highly prevalent in older adults and is independently associated with accelerated cognitive decline. Cochlear implants are usually the only effective treatment for people with severe–profound hearing loss, who have t...
Both virtual reality (VR) and aromatherapy have shown significant potential in enhancing the health and well-being of older adults. Aromatherapy has been noted to improve the immersive quality of VR experiences. However, the combined use of these int...
Text summarization namely, automatically generating a short summary of a given document, is a difficult task in natural language processing. Nowadays, deep learning as a new technique has gradually been deployed for text summarization, but there is s...
As the demand for detailed load data descriptions in modern power systems continues to increase, challenges such as high computational complexity in load identification tasks and high hardware requirements for devices have significantly hindered prog...
31 October 2022
The forward head posture of visual display terminal (VDT) users induces various physical and cognitive clinical symptoms. However, few studies have been conducted to identify and solve problems associated with VDT posture. This study aimed to examine...
Co-optimization for memory bank compilation and placement was suggested as a way to improve performance and power and reduce the size of a memory subsystem. First, a multi-configuration SRAM compiler was realized that could generate memory banks with...
Convolutional neural networks (CNNs) play a crucial role in many EdgeAI and TinyML applications, but their implementation usually requires external memory, which degrades the feasibility of such resource-hungry environments. To solve this problem, th...
Besides causing neuronal damage, traumatic brain injury (TBI) is involved in memory reduction, which can be a result of alterations in vasculo-neuronal interactions. Inflammation following TBI is involved in elevation of blood content of fibrinogen (...
The kNN (k-nearest neighbors) classification algorithm is one of the most widely used non-parametric classification methods, however it is limited due to memory consumption related to the size of the dataset, which makes them impractical to apply to...
Most modern CPU architectures are based on the von Neumann principle, where memory and processing units are separate entities. Although processing unit performance has improved over the years, memory capacity has not followed the same trend, creating...
This paper presents a backprojection algorithm (BPA) accelerator implemented on a field-programmable gate array (FPGA) for circular synthetic aperture radar (SAR) systems. Although the BPA offers superior image quality, it requires significantly more...
Aerospace equipages encounter potential radiation footprints through which soft errors occur in the memories onboard. Hence, robustness against radiation with reliability in memory cells is a crucial factor in aerospace electronic systems. This work...
Due to the complex topological relationship, poor data locality, and data racing problems in unstructured CFD computing, how to parallelize the finite volume method algorithms in shared memory to efficiently explore the hardware capabilities of many-...
Split-brain experiments, which have been actively conducted since the twentieth century, have provided a great deal of insight into functional asymmetry and inter-hemispheric interactions. However, how communication between the left and right hemisph...
In-memory computing (IMC) aims to solve the performance gap between CPU and memories introduced by the memory wall. However, it does not address the energy wall problem caused by data transfer over memory hierarchies. This paper proposes the data-loc...
High-caloric diets induce several deleterious alterations in the human body, including the brain. However, information on the effects of these diets on the elderly brain is scarce. Therefore, we studied the effects of 2 months of treatment with high-...
The speed of modern digital systems is severely limited by memory latency (the “Memory Wall” problem). Data exchange between Logic and Memory is also responsible for a large part of the system energy consumption. Logic-in-Memory (LiM) represents an a...
Several studies have identified main changes in T- and B-lymphocyte subsets during chronic HIV infection, but few data exist on how these subsets behave during the initial phase of HIV infection. We enrolled 22 HIV-infected patients during the acute...
Convolutional neural networks (CNNs) have achieved remarkable success in computer vision tasks, driving the rapid development of hardware accelerators. However, memory efficiency remains a key challenge, as conventional accelerators adopt layer-by-la...
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