3,236 research outputs found
Highly-Optimized Radar-Based Gesture Recognition System with Depthwise Expansion Module
The increasing integration of technology in our daily lives demands the development of
more convenient human–computer interaction (HCI) methods. Most of the current hand-based HCI
strategies exhibit various limitations, e.g., sensibility to variable lighting conditions and limitations
on the operating environment. Further, the deployment of such systems is often not performed
in resource-constrained contexts. Inspired by the MobileNetV1 deep learning network, this paper
presents a novel hand gesture recognition system based on frequency-modulated continuous wave
(FMCW) radar, exhibiting a higher recognition accuracy in comparison to the state-of-the-art systems.
First of all, the paper introduces a method to simplify radar preprocessing while preserving the main
information of the performed gestures. Then, a deep neural classifier with the novel Depthwise
Expansion Module based on the depthwise separable convolutions is presented. The introduced
classifier is optimized and deployed on the Coral Edge TPU board. The system defines and adopts
eight different hand gestures performed by five users, offering a classification accuracy of 98.13%
while operating in a low-power and resource-constrained environment.Electronic Components and Systems for European
Leadership Joint Undertaking under grant agreement No. 826655 (Tempo).European Union’s Horizon 2020 research and innovation programme and
Belgium, France, Germany, Switzerland, and the NetherlandsLodz University of Technology
Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node
Binary Neural Networks (BNNs) have been shown to be robust to random
bit-level noise, making aggressive voltage scaling attractive as a power-saving
technique for both logic and SRAMs. In this work, we introduce the first fully
programmable IoT end-node system-on-chip (SoC) capable of executing
software-defined, hardware-accelerated BNNs at ultra-low voltage. Our SoC
exploits a hybrid memory scheme where error-vulnerable SRAMs are complemented
by reliable standard-cell memories to safely store critical data under
aggressive voltage scaling. On a prototype in 22nm FDX technology, we
demonstrate that both the logic and SRAM voltage can be dropped to 0.5Vwithout
any accuracy penalty on a BNN trained for the CIFAR-10 dataset, improving
energy efficiency by 2.2X w.r.t. nominal conditions. Furthermore, we show that
the supply voltage can be dropped to 0.42V (50% of nominal) while keeping more
than99% of the nominal accuracy (with a bit error rate ~1/1000). In this
operating point, our prototype performs 4Gop/s (15.4Inference/s on the CIFAR-10
dataset) by computing up to 13binary ops per pJ, achieving 22.8 Inference/s/mW
while keeping within a peak power envelope of 674uW - low enough to enable
always-on operation in ultra-low power smart cameras, long-lifetime
environmental sensors, and insect-sized pico-drones.Comment: Submitted to ISICAS2020 journal special issu
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HARD: Hybrid Adaptive Resource Discovery for Jungle Computing
In recent years, Jungle Computing has emerged as a distributed computing paradigm based on simultaneous combination of various hierarchical and distributed computing environments which are composed by large number of heterogeneous resources. In such a computing environment, the resources and the underlying computation and communication infrastructures are highly-hierarchical and heterogeneous. This creates a lot of difficulty and complexity for finding the proper resources in a precise way in order to run a particular job on the system efficiently. This paper proposes Hybrid Adaptive Resource Discovery (HARD), a novel efficient and highly scalable resource-discovery approach which is built upon a virtual hierarchical overlay based on self-organization and self-adaptation of processing resources in the system, where the computing resources are organized into distributed hierarchies according to a proposed hierarchical multi-layered resource description model. The proposed approach supports distributed query processing within and across hierarchical layers by deploying various distributed resource discovery services and functionalities in the system which are implemented using different adapted algorithms and mechanisms in each level of hierarchy. The proposed approach addresses the requirements for resource discovery in Jungle Computing environments such as high-hierarchy, high-heterogeneity, high-scalability and dynamicity. Simulation results show significant scalability and efficiency of the proposed approach over highly heterogeneous, hierarchical and dynamic computing environments
Meta-heuristic algorithms in car engine design: a literature survey
Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system
A Multi-Floor Arrayed Waveguide Grating Based Architecture with Grid Topology for Datacenter Networks
This paper proposes a grid topology based passive optical interconnect (POI) architecture that is composed of multiple floors of arrayed waveguide grating routers (AWGRs) to offer high connectivity and scalability for datacenter networks. In the proposed POI signal only needs to pass one AWGR, and thus can avoid the crosstalk accumulation and cascaded filtering effects, which exist in many existing POI architectures based on cascaded AWGRs. Meanwhile, due to high connectivity, the proposed grid topology based POI also has the potential advantage of high reliability. Simulation results validate the network performance. With a proper node degree, the proposed grid topology can achieve acceptable blocking probability. Besides, steady performance is kept when the number of floors increases, indicating good scalability of the proposed POI
Dynamic Graph Attention for Anomaly Detection in Heterogeneous Sensor Networks
In the era of digital transformation, systems monitored by the Industrial
Internet of Things (IIoTs) generate large amounts of Multivariate Time Series
(MTS) data through heterogeneous sensor networks. While this data facilitates
condition monitoring and anomaly detection, the increasing complexity and
interdependencies within the sensor network pose significant challenges for
anomaly detection. Despite progress in this field, much of the focus has been
on point anomalies and contextual anomalies, with lesser attention paid to
collective anomalies. A less addressed but common variant of collective
anomalies is when the abnormal collective behavior is caused by shifts in
interrelationships within the system. This can be due to abnormal environmental
conditions like overheating, improper operational settings resulting from
cyber-physical attacks, or system-level faults. To address these challenges,
this paper proposes DyGATAD (Dynamic Graph Attention for Anomaly Detection), a
graph-based anomaly detection framework that leverages the attention mechanism
to construct a continuous graph representation of multivariate time series by
inferring dynamic edges between time series. DyGATAD incorporates an operating
condition-aware reconstruction combined with a topology-based anomaly score,
thereby enhancing the detection ability of relationship shifts. We evaluate the
performance of DyGATAD using both a synthetic dataset with controlled varying
fault severity levels and an industrial-scale multiphase flow facility
benchmark featuring various fault types with different detection difficulties.
Our proposed approach demonstrated superior performance in collective anomaly
detection for sensor networks, showing particular strength in early-stage fault
detection, even in the case of faults with minimal severity.Comment: 15 pages, 7 figure
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