visualsensor networks (VSNs) represent distributed embedded systems with tight constraints on sensing, processing, memory, communications and power consumption. VSNs are expected to scale up in the number of nodes, b...
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ISBN:
(纸本)9781538633441
visualsensor networks (VSNs) represent distributed embedded systems with tight constraints on sensing, processing, memory, communications and power consumption. VSNs are expected to scale up in the number of nodes, be required to offer more complex functionality, a higher degree of flexibility and increased autonomy. The engineering of such VSNs capable of (self-)adapting on the application and platform levels poses a formidable challenge. In this paper, we introduce a novel design approach for visual sensor nodes which is founded on computational self-awareness. Computational self-awareness maintains knowledge about the system's state and environment with models and then uses this knowledge to reason about and adapt behaviours. We discuss the concept of computational self-awareness and present our novel design approach that is centred on a reference architecture for individual VSN nodes, but can be naturally extended to networks. We present the VSN node implementation with its platform architecture and resource adaptivity and report on preliminary implementation results of a Zynq-based VSN node prototype.
visualsensor networks (VSNs) represent distributed embedded systems with tight constraints on sensing, processing, memory, communications and power consumption. VSNs are expected to scale up in the number of nodes, b...
详细信息
visualsensor networks (VSNs) represent distributed embedded systems with tight constraints on sensing, processing, memory, communications and power consumption. VSNs are expected to scale up in the number of nodes, be required to offer more complex functionality, a higher degree of flexibility and increased autonomy. The engineering of such VSNs capable of (self-)adapting on the application and platform levels poses a formidable challenge. In this paper, we introduce a novel design approach for visual sensor nodes which is founded on computational self-awareness. Computational self-awareness maintains knowledge about the system's state and environment with models and then uses this knowledge to reason about and adapt behaviours. We discuss the concept of computational self-awareness and present our novel design approach that is centred on a reference architecture for individual VSN nodes, but can be naturally extended to networks. We present the VSN node implementation with its platform architecture and resource adaptivity and report on preliminary implementation results of a Zynq-based VSN node prototype.
The Discrete Wavelet Transform (DWT) is extensively used for image coding due to its excellent energy compaction property and its ability to simultaneously analyze images in space-frequency domains. However, conventio...
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ISBN:
(纸本)9781538630044
The Discrete Wavelet Transform (DWT) is extensively used for image coding due to its excellent energy compaction property and its ability to simultaneously analyze images in space-frequency domains. However, conventional methods of computing the DWT coefficients of an image require large amounts of memory, thus making them unsuitable for memory-constraint low-cost portable devices. In this paper we propose a novel low memory approach named Segmented Fractional Wavelet Filter SFrWF to compute the DWT of high resolution images on low-memory devices. Evaluation results show that the SFrWF requires less than 10 kB of RAM for a gray-scale image of size 2048x2048 thus making the SFrWF suitable for low-cost visual sensor nodes.
In this demo we present an energy efficient motion detection mechanism for a high-resolution wireless (battery powered) surveillance camera node. We use Raspberry Pi (RPi) as a camera sensor node to demonstrate two sc...
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ISBN:
(纸本)9781450348607
In this demo we present an energy efficient motion detection mechanism for a high-resolution wireless (battery powered) surveillance camera node. We use Raspberry Pi (RPi) as a camera sensor node to demonstrate two scenarios. In the first scenario, the RPi performs motion detection using Pyroelectric InfraRed (PIR) sensor, while in the latter we use an external low-power node to keep the RPi in low power mode and at the same time detect motion using PIR sensor. The main contributions of this demo are the power management implementation, a surveillance application and the evaluation of power consumption between the two scenarios.
Wireless visualsensor Network (WVSN) is an emerging field which combines image sensor, on board computation unit, communication component and energy source. Compared to the traditional wireless sensor network, which ...
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ISBN:
(纸本)9780819491299
Wireless visualsensor Network (WVSN) is an emerging field which combines image sensor, on board computation unit, communication component and energy source. Compared to the traditional wireless sensor network, which operates on one dimensional data, such as temperature, pressure values etc., WVSN operates on two dimensional data (images) which requires higher processing power and communication bandwidth. Normally, WVSNs are deployed in areas where installation of wired solutions is not feasible. The energy budget in these networks is limited to the batteries, because of the wireless nature of the application. Due to the limited availability of energy, the processing at visual sensor nodes (VSN) and communication from VSN to server should consume as low energy as possible. Transmission of raw images wirelessly consumes a lot of energy and requires higher communication bandwidth. Data compression methods reduce data efficiently and hence will be effective in reducing communication cost in WVSN. In this paper, we have compared the compression efficiency and complexity of six well known bi-level image compression methods. The focus is to determine the compression algorithms which can efficiently compress bi-level images and their computational complexity is suitable for computational platform used in WVSNs. These results can be used as a road map for selection of compression methods for different sets of constraints in WVSN.
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