Mobile Sensor networks have become a hot topic of research in the last decade. There are a lot of new applications which employ this kind of network architecture. For example, Body Area Network (BAN) is a new applicat...
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ISBN:
(纸本)9781467364263
Mobile Sensor networks have become a hot topic of research in the last decade. There are a lot of new applications which employ this kind of network architecture. For example, Body Area Network (BAN) is a new application where multiple sensors on different body parts simultaneously transmit their data to a monitoring unit. In the case of numerous sensors, the addition of active relays which can act as parity nodes in a bipartite graph enables to obtain redundant information to improve the integrity of the transmission. The network topology is somewhat equivalent to that of a block code. However, in the case of a small number of sensors, this technique becomes less efficient. On the other hand, using Automatic Repeat Request (ARQ) protocol, one can avoid transmission errors. In this paper we propose a protocol to combine the two correction methods mentioned above (relaying and ARQ) for mobile sensor networks for a small number of sensors which operate on quasi-static Rayleigh fading channel. coding error correction is first applied, and then a detection layer will be added to detect errors that were not corrected by the error correction code. Power saving which is crucial in mobile sensor network is greatly improved by our proposed algorithm.
Relay communications channels have been shown to be used to achieve combined diversity and coding gain in wireless communications networks. To enhance the performance of relay network some cooperation and distributed ...
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ISBN:
(纸本)9781479989201
Relay communications channels have been shown to be used to achieve combined diversity and coding gain in wireless communications networks. To enhance the performance of relay network some cooperation and distributed coding schemes have been introduced. In relay networks signals transmitted from one terminal to another through a number of relays. Embedding the distributed turbo code (DTC) technique into the relay channels has been shown to approach the theoretical bound of the capacity of the wireless relay networks. In some applications, the availability of the power required at the source device to satisfy some level of quality at the destination device is not available, like in sensor networks, and in some other applications the power assigned for the source and relay devices are different. In this paper, for the distributed turbo code (DTC) in relay network, we consider a two hop relay network, which consists of one source device, one relay device and one destination device. So, in this paper, the power allocated to each component of the turbo code word, which are generated at both source device and at the relay device, has been investigated and conclusions are drawn for design purposes to enhance the performance of the distributed turbo code (DTC) in relay networks.
Multi-terminal sources coding refers to separate lossy encoding and joint decoding of two or more correlated sources. Based on good output performance it can effectively reduce encoding complexity. With focus on the a...
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ISBN:
(纸本)9780769535227
Multi-terminal sources coding refers to separate lossy encoding and joint decoding of two or more correlated sources. Based on good output performance it can effectively reduce encoding complexity. With focus on the asymmetry case, This paper designs a asymmetry multi-terminal sources audio coding algorithm, then analyses and simulates it. The encouraging simulation results show multi-terminal sources audio coding is feasible, simple and can get higher acoustical effect.
Randomly connected recurrent networks of excitatory groups of neurons can possess a multitude of attractor states. When the internal excitatory synapses of these networks are depressing, the attractor states can be de...
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Randomly connected recurrent networks of excitatory groups of neurons can possess a multitude of attractor states. When the internal excitatory synapses of these networks are depressing, the attractor states can be destabilized with increasing input. This leads to an itinerancy, where with either repeated transient stimuli, or increasing duration of a single stimulus, the network activity advances through sequences of attractor states. We find that the resulting network state, which persists beyond stimulus offset, can encode the number of stimuli presented via a distributed representation of neural activity with non-monotonic tuning curves for most neurons. Increased duration of a single stimulus is encoded via different distributed representations, so unlike an integrator, the network distinguishes separate successive presentations of a short stimulus from a single presentation of a longer stimulus with equal total duration. Moreover, different amplitudes of stimulus cause new, distinct activity patterns, such that changes in stimulus number, duration and amplitude can be distinguished from each other. These properties of the network depend on dynamic depressing synapses, as they disappear if synapses are static. Thus, short-term synaptic depression allows a network to store separately the different dynamic properties of a spatially constant stimulus.
An improved distributed turbo coding （DTC） scheme, namely, systematic-bit-selection DTC, is proposed for a two-hop relay network implementing selective-relaying （SR）. In the scheme, source broadcasts a punctured t...
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An improved distributed turbo coding （DTC） scheme, namely, systematic-bit-selection DTC, is proposed for a two-hop relay network implementing selective-relaying （SR）. In the scheme, source broadcasts a punctured turbo code in order to increase the successful decoding rate of relay. The relay forwards systematic-bit to the destination in case of successful cyclic redundancy check （CRC）. From the two versions of systematic-bit coming from relay and source respectively, the des- tination selects the one with higher signal-to-noise ratio （SNR） for turbo decoding. Simulation re- suits show the improved bit error rate （BER） performance of our scheme.
Instances of sustained stationary sensory input are ubiquitous. However, previous work focused almost exclusively on transient onset responses. This presents a critical challenge for neural theories of conscious-ness,...
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Instances of sustained stationary sensory input are ubiquitous. However, previous work focused almost exclusively on transient onset responses. This presents a critical challenge for neural theories of conscious-ness, which should account for the full temporal extent of experience. To address this question, we use intra-cranial recordings from ten human patients with epilepsy to view diverse images of multiple durations. We reveal that, in sensory regions, despite dramatic changes in activation magnitude, the distributed represen-tation of categories and exemplars remains sustained and stable. In contrast, in frontoparietal regions, we find transient content representation at stimulus onset. Our results highlight the connection between the anatomical and temporal correlates of experience. To the extent perception is sustained, it may rely on sen-sory representations and to the extent perception is discrete, centered on perceptual updating, it may rely on frontoparietal representations.
Designing an active visual system, able to autonomously learn its behavior, implies to make the learning controller independent of an external signal (e.g. the error between the actual and the desired vergence angle) ...
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Designing an active visual system, able to autonomously learn its behavior, implies to make the learning controller independent of an external signal (e.g. the error between the actual and the desired vergence angle) or of perceptual decisions about dispar–ity (e.g. from the response of a previously trained network). The proposed approach is based on a direct use of a computational substrate of modeled V1 complex cells that provide a distributed representation of binocular disparity information. The design strategies of the cortical-like architecture, including uniform coverage in feature space and divisive normalization mechanisms, allow the global energy of the population to effectively mediate the learning process towards the proper motor control. Since the learning controller is based on an intrinsic representation of the visual signal, it comes to overlap and coincide with the system that is learning the behaviour, thus closing at an inner cycle the perception-action loop necessary for learning. Experi–mental tests proved that the control architecture is both able to learn an effective vergence behavior, and to exploit it to fixate static and moving visual targets.
We study distributed coding of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a distributed framework for realizing optimized quantizer that enables encoding CS measurements of correlated...
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ISBN:
(纸本)9781479928941
We study distributed coding of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a distributed framework for realizing optimized quantizer that enables encoding CS measurements of correlated sparse sources followed by joint decoding at a fusion center. The optimality of VQ encoder-decoder pairs is addressed by minimizing the sum of mean-square errors between the sparse sources and their reconstruction vectors at the fusion center. We derive a lower-bound on the end-to-end performance of the studied distributed system, and propose a practical encoder-decoder design through an iterative algorithm.
We propose a new method for low-complexity compression of multispectral images. We develop on a novel approach to coding signals with side information based on recent advances in compressed sensing and universal scala...
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ISBN:
(纸本)9781479999897
We propose a new method for low-complexity compression of multispectral images. We develop on a novel approach to coding signals with side information based on recent advances in compressed sensing and universal scalar quantization. Our approach can be interpreted as a variation of quantized compressed sensing, where the most significant bits are discarded at the encoder and recovered at the decoder from the side information. The image is reconstructed using weighted total variation minimization, incorporating side information in the weights while enforcing consistency with the recovered quantized coefficient values. Our experiments validate our approach and confirm the improvements in rate-distortion performance.
distributed encoding is desired in content preparation workflows in the cloud to reduce turnaround times. Content-adaptive bit allocation at title, chunk, and even frame level, as opposed to using a fixed ladder of co...
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ISBN:
(纸本)9781510851771
distributed encoding is desired in content preparation workflows in the cloud to reduce turnaround times. Content-adaptive bit allocation at title, chunk, and even frame level, as opposed to using a fixed ladder of content-independent bit-rates and resolutions, have been proposed to achieve efficiencies in storage and delivery. Many of these methods tend to be iterative in nature and consume significant additional compute resources over 2-pass Variable bit-rate (VBR) encoders. With live use-cases attempting to use a set of distributed encoders to achieve higher bit-rate savings (both through use of higher compression presets and through content-adaptive bit allocation) and consistent quality, there is a need to limit the increase in computational complexity. In this paper, we propose a non-iterative codec-agnostic approach that employs machine learning techniques to perform consistent quality content-adaptive encoding within the constraints of a maximum bit-rate in a manner that makes it equally suitable for live and on-demand workflows. The method has the ability to take a target subjective rating level and allocate appropriate bits for each group of frames to achieve that. The proposed approach also anticipates automatic selection of the right resolution and frame-rate for a given representation within an ABR set, and content-specific encoding parameters to maximize the bit savings and/or visual quality. Test results are presented over a wide range of content types comparing the performance of the proposed approach against 2-pass VBR methods. Initial results indicate that the proposed approach can recover ~85% of the bit-savings possible with exhaustive techniques. The computational complexity of the proposed approach is only 15-20% of 2-pass VBR encoding.
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