In today's world, we are surrounded by variety of computer vision applications e.g. medical imaging, bio-metrics, security, surveillance and robotics. Most of these applications require real time processing of a s...
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ISBN:
(纸本)9781538608739
In today's world, we are surrounded by variety of computer vision applications e.g. medical imaging, bio-metrics, security, surveillance and robotics. Most of these applications require real time processing of a single image or sequence of images. This real time image/video processing requires high computational power and specialized hardware architecture and can't be achieved using general purpose CPUs. In this paper, a FPGA based generic canny edge detector is introduced. Edge detection is one of the basic steps in image processing, image analysis, image pattern recognition, and computer vision. We have implemented a re-sizable canny edge detector IP on programmablelogic (PL) of PYNQ-Platform. The IP is integrated with HDMI input/output blocks and can process 1080p input video stream at 60 frames per second. As mentioned the canny edge detection IP is scalable with respect to frame size i.e. depending on the input frame size, the hardware architecture can be scaled up or down by changing the template parameters. The offloading of canny edge detection from PS to PL causes the CPU usage to drop from about 100% to 0%. Moreover, hardware based edge detector runs about 14 times faster than the software based edge detector running on Cortex-A9 ARM processor.
The convolutional neural network (CNN) is a state-of-the-art model that can achieve significantly high accuracy in many machine-learning tasks. Recently, for further developing the practical applications of CNNs, effi...
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ISBN:
(纸本)9781509061839
The convolutional neural network (CNN) is a state-of-the-art model that can achieve significantly high accuracy in many machine-learning tasks. Recently, for further developing the practical applications of CNNs, efficient hardware platforms for accelerating CNN have been throughly studied. A binarized neural network has been reported to minimize the multipliers, which consume a large amount of resources, with a minimal decrease in accuracy. In this study, we analyzed the optimal performance of CNN implemented on an fieldprogrammable gate array (FPGA) considering its logic resources and a memory bandwidth, using multiple types of parallelisms such as kernels, pixels, and channels both in conventional and binarized CNNs. As a result, it became clear that all the parallelisms are required for the binarized neural network to obtain the best performance of 8.38 TOPS.
Traditional processor design approaches using CISC and RISC philosophies suffer from low performance. One of alternative approaches to improve system performance is instruction level parallelism (ILP). Among the proce...
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Software Defined Networking (SDN) is the key technology for combining networking and Cloud solutions to provide novel applications. SDN offers a number of advantages as the existing resources can be virtualized and or...
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ISBN:
(纸本)9781538616291
Software Defined Networking (SDN) is the key technology for combining networking and Cloud solutions to provide novel applications. SDN offers a number of advantages as the existing resources can be virtualized and orchestrated to provide new services to the end users. Such a technology should be accompanied by powerful mechanisms that ensure the end-to-end quality of service at high levels, thus, enabling support for complex applications that satisfy end users needs. In this paper, we propose an intelligent mechanism that agglomerates the benefits of SDNs with real-time 'Big Data' forecasting analytics. The proposed mechanism, as part of the SDN controller, supports predictive intelligence by monitoring a set of network performance parameters, forecasting their future values, and deriving indications on potential service quality violations. By treating the performance measurements as time-series, our mechanism employs a novel ensemble forecasting methodology to estimate their future values. Such predictions are fed to a Type-2 Fuzzy logic system to deliver, in real-time, decisions related to service quality violations. Such decisions proactively assist the SDN controller for providing the best possible orchestration of the virtualized resources. We evaluate the proposed mechanism w.r.t. precision and recall metrics over synthetic data.
A novel approach is present to obtain coverage data of both embedded software and fieldprogrammable Gate Array (FPGA) program running in the System-on-programmable-Chip (SoPC) system. This approach integrates technol...
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A novel approach is present to obtain coverage data of both embedded software and fieldprogrammable Gate Array (FPGA) program running in the System-on-programmable-Chip (SoPC) system. This approach integrates technologies of program instrumentation and traditional digital simulation, and improves the verification process by hardware acceleration. In order to verify the efficiency of the proposed approach, we have implemented it to a SoPC system which is employed for aviation applications. Statement and branch coverage of both FPGA program as well as embedded software have been obtained simultaneously during the verification. According to the case study, the proposed approach for coverage probing has excellent performance in SoPC verification.
While formal semantics of theoretical languages underlying SQL have been provided in the past, they all made simplifying assumptions ranging from changes in the syntax to omitting bag semantics and nulls. This situati...
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Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost. They are particularly well suited to reconfigurable logic devices, w...
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OpenCL pipes offer a powerful construct for synthesizing multikernel FPGA applications with inter-kernel communication dependencies. The communication discipline between the FPGA kernels is restricted to producer-cons...
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In this paper, operating data of lathes is analyzed with regard to the specifications of the ISO 13849-1. The Weibull analysis is based on machine running times of 1519 multi spindle automatic lathes. The analyzed saf...
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Direct connectivity between vehicles will let a variety of new applications become a reality, from safety to traffic management and infotainment. Different wireless access technologies can enable this kind of connecti...
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ISBN:
(纸本)9781509064847
Direct connectivity between vehicles will let a variety of new applications become a reality, from safety to traffic management and infotainment. Different wireless access technologies can enable this kind of connections, from mobile-fi IEEE 802.11p in the US and ETSI G5 in Europe, to the more recent 3GPP long term evolution (LTE) for vehicle-to-vehicle (V2V) communications. To integrate the capabilities and performance of these radio access technologies, light emitting diodes (LEDs) operating in the front and rear vehicles' lights, can introduce a further degree of connectivity through the so called visible light communication (VLC), by setting up direct links between vehicles in visibility and between vehicles and the road side (such as traffic lights and variable message panels). This paper investigates the performance of vehicular visible light networks (VVLNs) in terms of message delivery rate when full-duplex (FD) capabilities are exploited. Specifically, instead of considering carrier sensing multiple access with collision avoidance (CSMA/CA) as foreseen by the IEEE 802.15.7 standard for VLC, we here propose a CSMA with collision detection (CSMA/CD) protocol exploiting the reverse link made available by the concurrent use of LEDs as transmitters and photodiodes as receivers for an immediate feedback during decoding. Results show an increase of up to 10% in the delivered data thanks to FD in realistic urban scenarios and give guidelines to network designers.
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