The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing “power wall” and “memory wall” problems. To resolve those problems, ...
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The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing “power wall” and “memory wall” problems. To resolve those problems, processing-in-memory(PIM) architectures are developed to bring computation logic in or near memory to alleviate the bandwidth limitations during data transmission. NAND-like spintronics memory(NAND-SPIN) is one kind of promising magnetoresistive random-access memory(MRAM) with low write energy and high integration density, and it can be employed to perform efficient in-memory computation operations. In this study, we propose a NAND-SPIN-based PIM architecture for efficient convolutional neural network(CNN) acceleration. A straightforward data mapping scheme is exploited to improve parallelism while reducing data movements. Benefiting from the excellent characteristics of NAND-SPIN and in-memory processing architecture, experimental results show that the proposed approach can achieve ～2.6× speedup and ～1.4× improvement in energy efficiency over state-of-the-art PIM solutions.
A backward wave oscillator with parallel multiple beams and multi-pin slow-wave structure(SWS)operating at the frequency above 500 GHz is studied. Both the cold-cavity dispersion characteristics and CST Particle Studi...
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A backward wave oscillator with parallel multiple beams and multi-pin slow-wave structure(SWS)operating at the frequency above 500 GHz is studied. Both the cold-cavity dispersion characteristics and CST Particle Studio simulation results reveal that there are obvious mode competition problems in this kind of terahertz *** that the structure of the multi-pin SWS is similar to that of two-dimensional photonic crystals, we introduce the defects of photonic crystal with the property of filtering into the SWS to suppress high-order ***, a detailed study of the effect of suppressing higher-order modes is carried out in the process of changing location and arrangement pattern of the point defects. The stable, single-mode operation of the terahertz source is realized. The simulation results show that the ratio of the output peak power of the higher-order modes to that of the fundamental mode is less than 1.9%. Also, the source can provide the output peak power of 44.8 m W at the frequency of 502.2 GHz in the case of low beam voltage of 4.7 kV.
Coconut tree diseases are a serious risk to agricultural yield, particularly in developing countries where conventional farming practices restrict early diagnosis and intervention. Current disease identification metho...
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Billions of people worldwide are affected by vision impairment majorly caused due to age-related degradation and refractive errors. Diabetic Retinopathy(DR) and Macular Hole(MH) are among the most prevalent senescent ...
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The gaming industry is getting more attraction from cloud services providing gaming applications for cooperative multiplayer gaming. Real-time services like cloud gaming are possible by performing necessary process-in...
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In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intellige...
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In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior(human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligentstanding human detection (ISHD) method based on an improved single shot multibox detector to detect thetarget of standing human posture in the scene frame of exam room video surveillance at a specific examinationstage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posturefeature extractor of a standing person, merges prior knowledge, and introduces transfer learning in the trainingstrategy, which greatly reduces the computation amount, improves the detection accuracy, and reduces the trainingdifficulty. The experiment proves that the model proposed in this paper has a better detection ability for the smalland medium-sized standing human body posture in video test scenes on the EMV-2 dataset.
In order to dynamically create a sequence of textual descriptions for images, image description models often make use of the attention mechanism, which involves an automatic focus on different regions within an image....
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This article aims to improve the detection of ASD in children using ET data and advanced ML techniques. ASD, a neurodevelopmental disorder characterized by impairments in social communication, interaction, and repetit...
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This article aims to improve the detection of ASD in children using ET data and advanced ML techniques. ASD, a neurodevelopmental disorder characterized by impairments in social communication, interaction, and repetitive behaviors, typically manifests before the age of three. Early diagnosis is crucial for timely and effective interventions. ET data have revealed distinct gaze patterns in children with ASD, such as diminished attention to social cues and increased fixation on repetitive stimuli. However, current methods of ET data analysis are largely manual and lack scalability for widespread clinical screening. To address these limitations, we propose a novel, scanpath-based ASD detection method that identifies atypical gaze behaviors through dynamic changes in visual attention. We extract four sequential features from scanpaths and analyze variations in feature space across different ASD severity levels low, mild, moderate, and severe using the MultiMatch and DTW similarity metrics. Our analysis reveals that children with ASD exhibit unique and highly individualized gaze patterns when compared to TD children. Notable differences are observed in attention duration and vertical gaze distribution, providing key insights into ASD-related visual behaviors. For classification, we employ a hybrid CNN-RNN model, which significantly outperforms traditional ML methods. The CNN-RNN model achieves an accuracy of 97%, recall of 98.24%, and an F1-score of 97.04% using the feature set (x, d, y). In comparison, models based on GRU and 2-LSTM networks show competitive accuracies of 92% and 90%, respectively. However, RFC and XGBoost models underperform, with accuracies ranging between 70.25 and 80.80%. These findings demonstrate the efficacy of DL approaches, particularly the CNN-RNN hybrid model, in accurately classifying ASD based on ET data, emphasizing their potential to enhance diagnostic accuracy. The proposed scalable method for ASD detection holds promise for improving ea
Surface Electromyographic (sEMG) signals are a promising approach to hand and finger gesture recognition. Most of the sEMG-based hand gesture recognition has developed based on the whole hand gesture, full wavelength,...
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The article focuses on the Bobcat-1 CubeSat mission, particularly its role in evaluating the feasibility of monitoring GNSS-to-GNSS time offsets from low-Earth orbit (LEO). It discusses the mission objectives, accompl...
The article focuses on the Bobcat-1 CubeSat mission, particularly its role in evaluating the feasibility of monitoring GNSS-to-GNSS time offsets from low-Earth orbit (LEO). It discusses the mission objectives, accomplishments, and challenges faced in estimating these parameters, highlighting the significance of XYTO monitoring for full GNSS interoperability. It presents results from data collections conducted by Bobcat-1.
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