Memristor, also known as Resistive Random Access Memory (ReRAM), enables in-memory computing by processing data directly within storage locations, thus mitigating the data transfer bottleneck between the processor and...
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ISBN:
(数字)9798350380101
ISBN:
(纸本)9798350380118
Memristor, also known as Resistive Random Access Memory (ReRAM), enables in-memory computing by processing data directly within storage locations, thus mitigating the data transfer bottleneck between the processor and memory in high-speed applications. However, existing methods for implementing addition of two n-bit operands within ReRAM/memristor crossbar structures do not ensure minimal device utilization or minimal ReRAM/memristor read-write cycles. In this paper, we propose a novel approach for implementing n-bit addition in ReRAM crossbars to optimize the usage of the ReRAM device and minimize read-write cycles. We develop the ReRAM Sequence Graph (ReSG) to efficiently represent adder functionality and systematically allocate ReRAM devices to ReSG nodes. Furthermore, we present a scheduling mechanism for implementing ReSG nodes within ReRAM crossbars, accelerating the execution of the addition operation. Experimental evaluations show that our method surpasses current approaches, achieving better device utilization and reduced read-write cycles.
Prostate cancer is a prevalent form of malignancy impacting a substantial male population and ranks among the primary contributors to cancer-related fatalities globally. The utilization of magnetic resonance imaging (...
Prostate cancer is a prevalent form of malignancy impacting a substantial male population and ranks among the primary contributors to cancer-related fatalities globally. The utilization of magnetic resonance imaging (MRI) scans for prostate cancer detection has presented significant difficulties. This proposed, explores the use of machine learning algorithms for prostate cancer detection using MRI scans and addresses the challenge mentioned above. The proposed ensemble models employ a combination of Machine Learning (ML) Algorithms, including Support Vector Machine (SVM), AdaBoost, Decision Tree (DT), and Random Forest (RF) to improve accuracy detection. The results of our rigorous evaluation process revealed that the ensemble model achieved an outstanding accuracy rate of 96% in classifying prostate cancer into Significant and Non-Significant. By comparing our results to existing studies, we have demonstrated that the ensemble model-based method is on par with or even surpasses various techniques used in previous research efforts.
The computer-based testing (CBT) platforms for conducting mass-driven examinations over computer networks in order to eliminate certain challenges such as delay in marking, misplacement of scripts, impersonation, moni...
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Partial discharge localization in power transformers is of utmost importance, requiring an effective evaluation method to identify the location of such events precisely. Antenna placement poses challenges within power...
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With developing countries, energy demand has been rapid over the years. Bangladesh contributes just 5% of the entire energy ratio in a proportion of renewable energy, and desire to 10% of the year 2025. Bangladesh is ...
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The proposed work aims at 5G base station antenna communication applications. The Reconfigurable Antenna is built with an idea of parasitic loaded elements in antenna. The proposed design has a zig-zag S-shape structu...
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ISBN:
(数字)9798350376753
ISBN:
(纸本)9798350376760
The proposed work aims at 5G base station antenna communication applications. The Reconfigurable Antenna is built with an idea of parasitic loaded elements in antenna. The proposed design has a zig-zag S-shape structure with a driven Monopole antenna located along the central axis and a parasitic structure enfolding around the monopole. Reconfiguration is accomplished by adjusting the PIN diodes bias voltages. The 2.44-9.46 GHz frequency range i.e., the Ultra-Wide-Band frequency spectrum is used at which this reconfigurable antenna is designed to operate. This paper has three radiation pattern which are obtained by two PIN diodes. Beam-steering capability in the elevation plane is achieved i.e., θ (0°-180°). The outcome shows the design’s potential and its wide range concurrent applications for base station antennas.
Plasma dynamics is the behavior exhibited by two or more charged species with respect to electric or magnetic fields. In high-performance computing (HPC) applications, it requires all these factors: the accuracy of pa...
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ISBN:
(数字)9798331517892
ISBN:
(纸本)9798331517908
Plasma dynamics is the behavior exhibited by two or more charged species with respect to electric or magnetic fields. In high-performance computing (HPC) applications, it requires all these factors: the accuracy of parallel implementations, effective inter-process communication, and scalability with respect to workload. This paper points out the limitations in the current approaches to the plasma dynamics problems, and discusses the use of MPI continuation tasks and of its performance enhancement with OpenMP methods. Within the framework of the Vlasov-Poisson system, we develop theory of MPI continuation and describe techniques optimal for its use, which allows to efficiently combine communication with computation, which is quite a difficult task in most of the cases, especially in the case of multidimensional simulations. The results allow better insights on how to increase the level of parallelism and reduce the time to compute, which in turn fosters the formulation of more effective high-performance strategies and also the understanding of the parallelism in plasma simulations using the MPI standard.
Many attempts have been made at estimating discrete emotions (calmness, anxiety, boredom, surprise, anger) and continuous emotional measures commonly used in psychology, namely ‘valence’ (The pleasantness of the emo...
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Diabetic Retinopathy (DR) is a medical condition that can occur as a complication of diabetes and is characterised by blood leakage into the retinal tissues. The condition initially manifests as no symptoms or minor v...
Diabetic Retinopathy (DR) is a medical condition that can occur as a complication of diabetes and is characterised by blood leakage into the retinal tissues. The condition initially manifests as no symptoms or minor vision problems, which can progress to severe vision loss. DR is classified into five stages: Normal, Mild, Moderate, Severe, and PDR (Proliferative Diabetic Retinopathy). Detecting DR early is crucial to avoid severe complications, including complete blindness. The presence of certain characteristics, including soft exudates, hard exudates, hemorrhages, and microaneurysms in the affected eye, may indicate the presence of diabetic retinopathy. Early detection and prevention measures can control DR and prevent further damage to the retina. Diagnosis of diabetic retinopathy involves monitoring and assessment of fundus images. However, manual detection of DR is a time consuming process and is highly dependent on the expertise of ophthalmologists. It is also challenging for ophthalmologists to detect DR in its early stages from fundus ***, there is a need for an automated diagnosis system capable of extracting features from fundus images and classifying DR into different stages. In this regard, we propose a multi-headed CNN and vision transformer based model capable of accurately detecting and classifying all early stages of diabetic retinopathy as early as possible. Our proposed model leverages the CNN’s ability to extract local spatial features and the vision transformer’s ability to extract global contextual information. By combining these local and global features, a rich feature representation is created, which is then used to detect and classify all stages of DR.
Education is distinctive for Visually Impaired and Blind students compared to regular students. This research aims to optimize the creation of tactile flashcards. Images gathered are first processed using instance seg...
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