Deep neuralnetworks (DNN) have become a significant applications in both cloud-server and edge devices. Meanwhile, the growing number of DNNs on those platforms raises the need to execute multiple DNNs on the same de...
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processing-In-Memory(PIM) has emerged as a high-performance and energy-efficient computing paradigm for accelerating convolutional neuralnetwork (CNN) applications. Resistive random access memory (ReRAM) has been wid...
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processing-In-Memory(PIM) has emerged as a high-performance and energy-efficient computing paradigm for accelerating convolutional neuralnetwork (CNN) applications. Resistive random access memory (ReRAM) has been widely used in PIM architectures due to its extremely high efficiency for accelerating matrix-vector multiplications through analog computing. However, because CNN training usually requires high-precision computation in the backward propagation (BP) stage, the limited precision of analog PIM accelerators impedes their adoption in CNN training. In this article, we propose ReHy, a hybrid PIM accelerator to support CNN training in ReRAM arrays. It is composed of Analog PIM (APIM) and Digital PIM (DPIM) modules. ReHy uses APIM to accelerate the feed-forward propagation (FP) stage for high performance, and DPIM to process the BP stage for high accuracy. We exploit the capability of ReRAM for Boolean logic operations to design the DPIM architecture. Particularly, we design floating-point multiplication and addition operators to support matrix multiplications in ReRAM arrays. We also propose a performance model to offload high-precision matrix multiplications to DPIM according to the data parallelism. Experimental results show that ReHy can speed up CNN training by 48.8x and 2.4x, and reduce energy consumption by 35.1x and 2.33x, compared with CPU/GPU architectures (baseline) and the state-of-the-art FloatPIM, respectively.
An adaptive fast nonsingular integral terminal sliding mode control scheme is proposed for the distributed formation control problem of multiple unmanned aerial vehicles (UAVs) affected by actuator faults and external...
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An adaptive fast nonsingular integral terminal sliding mode control scheme is proposed for the distributed formation control problem of multiple unmanned aerial vehicles (UAVs) affected by actuator faults and external disturbances. In order to achieve a good cooperative tracking performance of UAVs during mission execution, the adverse effect of the lumped disturbances composed of actuator failures, wind and vortex disturbances is estimated by the cerebellar model articulation neuralnetwork, meanwhile the tangent function is introduced to eliminate the chattering of the sliding mode. Then the analysis of superior convergence performance of the fast nonsingular integral terminal sliding manifold is implemented, and the finite time stability of the closed-loop formation flight control system is proved. Finally, the simulation experiments verify that the proposed approach is superior to the nonsingular integral terminal sliding mode control for the formation control system of faulty multiple UAVs.
Sensing internal bodily signals, or interoception, is fundamental to maintain life. However, interoception should not be viewed as an isolated domain, as it interacts with exteroception, cognition and action to ensure...
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Sensing internal bodily signals, or interoception, is fundamental to maintain life. However, interoception should not be viewed as an isolated domain, as it interacts with exteroception, cognition and action to ensure the integrity of the organism. Focusing on cardiac, respiratory and gastric rhythms, we review evidence that interoception is anatomically and functionally intertwined with the processing of signals from the external environment. Interactions arise at all stages, from the peripheral transduction of interoceptive signals to sensory processing and cortical integration, in a network that extends beyond core interoceptive regions. Interoceptive rhythms contribute to functions ranging from perceptual detection up to sense of self, or conversely compete with external inputs. Renewed interest in interoception revives long-standing issues on how the brain integrates and coordinates information in distributed regions, by means of oscillatory synchrony, predictive coding or multisensory integration. Considering interoception and exteroception in the same framework paves the way for biological modes of information processing specific to living organisms. Engelen et al. review in animals and humans how the CNS senses cardiac, respiratory and gastric rhythmic activity, and detail the range of cognitive functions impacted, from perceptual detection up to the sense of self.
In recent years, with the rapid development of computer vision and deep learning technology, real-time object recognition has become more and more important in various application fields. This research is devoted to d...
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Image registration is a prerequisite for remote sensing image fusion and classification, and registration accuracy affects the performance of these tasks. However, there are significant nonlinear radiation differences...
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Assessment of the pavement condition plays a significant role in pavement maintenance and driving comfort enhancement. Current evaluation methods primarily employ manual weights according to the geometric appearance o...
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Assessment of the pavement condition plays a significant role in pavement maintenance and driving comfort enhancement. Current evaluation methods primarily employ manual weights according to the geometric appearance of the distress, which makes it difficult to assess its depth or impact on passengers' experience. This paper proposes a data fusion-based method for pavement distress evaluation, which comprehensively considers the joint effect of distress physical appearance and the corresponding impact on riding comfort. A deep convolutional neuralnetwork was employed to automatically detect and locate the pavement distress using image data. A wavelet transform was applied to extract the acceleration effectuated by the defects in the frequency domain using vibration data. Finally, a comfort evaluation index was constructed based on the results of image and vibration data fusion. Furthermore, a mobile vehicle-mounted collective system was designed for rapid evaluation of the pavement distress, which integrated multiple distributed accelerometers, an industrial camera, and a graphics processing unit. The results demonstrated the stability and efficiency of the proposed approach, making it a potential tool to comprehensively evaluate the condition of pavement distress.
Detecting Denial of Service (DoS) and distributed Denial of Service (DDoS) attacks remains a critical challenge in cybersecurity. This research introduces a hybrid deep learning model combining Gated Recurrent Units (...
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Subjective feelings of emotion differ in the extent to which some might focus on pleasure or displeasure and some might emphasize their arousal in the reports of emotional experience. In this paper, we examine whether...
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Subjective feelings of emotion differ in the extent to which some might focus on pleasure or displeasure and some might emphasize their arousal in the reports of emotional experience. In this paper, we examine whether functional brain connectivity is modulated by individual differences in the subjective feelings of emotion. We adopt the public available DEAP dataset and utilize the unsupervised clustering method to delineate reports of emotional valence and arousal profiles in the data. Results provide evidence of two subgroups: one group consistently rates high in arousal and low in valence regarding the emotional stimulus, whereas the other group rates high in valence and low in arousal. The two groups further differ in their minimum spanning tree characteristics derived from EEG signals. Specifically, people more emphasize valence experience recruit broadly distributed brain areas than those who focus on arousal. Together, these findings provide new insights to understand individual differences in emotional experience and suggest the distinct underlying neuralprocessing mechanisms. (c) 2022 Elsevier B.V. All rights reserved.
Dynamic Spectrum Access (DSA) is a critical technology for Cognitive Wireless Sensor network (CWSN). The main challenge of DSA is how Secondary Users (SUs) can quickly and accurately identify vacant spectrum, while en...
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