作者:
Gong, DNHe, YTsinghua Univ
Dept Elect Engn State Key Lab Microwave & Digital Commun Beijing 100084 Peoples R China
There are huge bit-level operations in shape coding algorithms for MPEG-4. These bit-level operations make it difficult to implement real-time MPEG-4 coding in the general-purpose processor without any hardware suppor...
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ISBN:
(纸本)0819437034
There are huge bit-level operations in shape coding algorithms for MPEG-4. These bit-level operations make it difficult to implement real-time MPEG-4 coding in the general-purpose processor without any hardware support. This paper first gives the analysis of computation complexity in MPEG-4 shape coding algorithms. Then a dedicated VLSI architecture to accelerate shape coding, which is called Shape Engine, is proposed. Shape Engine is composed of three dedicated but flexible processors element (PE) namely PB-PE, Filtering-PE and CAE-PE. The combination of RISC core and Shape Engine leads to a great speed up over pure software implementation of MPEG-4 shape coding.
FastSLAM algorithm is commonly used in Unmanned Ground Vehicles (UGVs) recently. One of the main problems under research is the computation cost of this probabilistic algorithm. Since the speed of the UGV is limited b...
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ISBN:
(纸本)9781728109336
FastSLAM algorithm is commonly used in Unmanned Ground Vehicles (UGVs) recently. One of the main problems under research is the computation cost of this probabilistic algorithm. Since the speed of the UGV is limited by the latency of the algorithm, the computation complexity and its effect on the step time of the FastSLAM needs to be investigated. The present work addresses the effects of the number of particles and number of map features on the computation complexity of the FastSLAM algorithm. The study included the prediction, the observation, data association and resampling phase's complexities. Also, the correlation between the uncertainty of the UGV location and the number of particles was addressed. The simulation study was validated experimentally using hardware in the loop (HIL) setup. The analysis showed that when there is a prior knowledge of the average number of map features, an optimum number of particle filters could be set for that UGV in the given environment while maintaining an improved performance of the algorithm.
Inverse design of complex nanophotonic devices is a very computation-consuming task. Deep-learning-based approaches can facilitate this process. However, due to the lack of solid knowledge about the underlying complex...
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Inverse design of complex nanophotonic devices is a very computation-consuming task. Deep-learning-based approaches can facilitate this process. However, due to the lack of solid knowledge about the underlying complexity of the input-output relation for a selected class of nanostructures, it is common to select an over-parameterized neural network (NN) for modeling this relation. We present a novel pruning method based on removing weak nodes and connections in the original NN to simplify the input-output relation without imposing significant error. In addition to reducing the model complexitycomputations, the pruned network can be used to find valuable insight into the physics of device operation. To show the efficacy of our approach, we use it for modeling and inverse design of two classes of nanostructures with different complexities.
Bit loading is a powerful technique to enhance the performance of multi-carrier systems, In this paper, a bit loading scheme with low computation complexity is addressed in all technical details. The algorithm uses th...
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Bit loading is a powerful technique to enhance the performance of multi-carrier systems, In this paper, a bit loading scheme with low computation complexity is addressed in all technical details. The algorithm uses the measured signal-to-noise-ratio of each subcarrier directly to start the bit-by-bit loading procedure. The results show a good bit error rate (BER) performance and a low computation complexity compared to other bit loading algorithms.
The optimal order in fractional Fourier transform (FrFT) can be used to estimate chromatic dispersion (CD) and nonlinearity in an optical fiber transmission system. In this paper, we propose a novel method to estimate...
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ISBN:
(数字)9781510617469
ISBN:
(纸本)9781510617469;9781510617452
The optimal order in fractional Fourier transform (FrFT) can be used to estimate chromatic dispersion (CD) and nonlinearity in an optical fiber transmission system. In this paper, we propose a novel method to estimate CD with lower computation complexity in fractional domain. The computation complexity can be reduced by 10 3 times with the same measurement accuracy compared with one step method when the number of samples is 8192 and search step is 0.0001. The correctness of the novel method for optimal order searching is proved by chirp parameter estimation for linear frequency modulation (LFM) signals. The measurement relative error is only 0.02%. For CD estimation, the maximum estimation error ratio is 0.338% and 0.564% for 28GBaud quadrature phase-shift keying (QPSK) and 16 quadrature amplitude modulation (16QAM) optical fiber transmission systems over 100 km similar to 2000 km SSMF.
Convolutional neural network(CNN)has been widely adopted in many tasks. Its inference process is usually applied on edge devices where the computing resources and power consumption are *** present, the performance of ...
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Convolutional neural network(CNN)has been widely adopted in many tasks. Its inference process is usually applied on edge devices where the computing resources and power consumption are *** present, the performance of general processors cannot meet the requirement for CNN models with high computation complexity and large number of parameters. Field-programmable gate array(FPGA)-based custom computing architecture is a promising solution to further enhance the CNN inference *** software/hardware co-design can effectively reduce the computing overhead, and improve the inference performance while ensuring accuracy. In this paper, the mainstream methods of CNN structure design, hardwareoriented model compression and FPGA-based custom architecture design are summarized, and the improvement of CNN inference performance is demonstrated through an example. Challenges and possible research directions in the future are concluded to foster research efforts in this domain.
After passing a systematic bit through a turbo encoder, the encoding process will introduce some extent of correlation between a systematic bit and its associated parity bits. However, this correlation is neglected in...
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After passing a systematic bit through a turbo encoder, the encoding process will introduce some extent of correlation between a systematic bit and its associated parity bits. However, this correlation is neglected in the subsequent turbo decoding process so as to reduce its computational complexity. In this paper, we try to explore the feasibility of modeling the bit-level stochastic correlation for the iterative turbo decoding. By properly adjusting the parameter of the correlation model, we can approximate various degrees of the underlying correlation within the received codewords. Reduction in bit error rate (BER) then may benefit from a more accurate capture of the correlation information at the cost of requiring only a small additional computation complexity. Experimental results indicate that incorporating the correlation model into the turbo decoding process can achieve better BER performance than that of conventional turbo decoders over AWGN channels. (C) 2006 Elsevier B.V. All rights reserved.
In recent years, Small Satellite Networks (SSNs) are attracting increasing attention due to its economical prospects and advantages in high bandwidth and low latency. More and more companies and organizations are plan...
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In recent years, Small Satellite Networks (SSNs) are attracting increasing attention due to its economical prospects and advantages in high bandwidth and low latency. More and more companies and organizations are planning to construct large-scale SSNs to provide global services. In this way, the traffic pattern is more complicated because of random transmission requirements and stochastic packet generations/arrivals, and routing faces more challenges due to network scale and limited resource in small satellites. Traditional satellite routing algorithms, which attempt to exploit the predictable satellite's trajectory with time-discrete graph models, cannot handle these challenges. In this paper, we propose a novel Temporal Netgrid Model (TNM) to portray the time-varying topology of large-scale SSNs. In TNM, the whole space is divided into small cubes (i.e., netgrids) and then, satellites can be located by netgrids instead of coordinates. By doing so, we can construct a network topology for random traffic routing. Furthermore, an efficient Netgrid-based Shortest Path Routing (NSR) algorithm is proposed based on TNM;NSR attempts to find the optimal path from source netgrid to any other reachable netgrids. In this way, the routing complexity is significantly reduced. We also develop a Large-scale Satellite Network Simulator (LSNS) to validate our study. The results show that NSR achieves a significant reduction in computational complexity as well as near-optimal routing performance in terms of end-to-end delay and packet drop rate in scenarios of large-scale SSNs compared with existing satellite routing algorithms.
File storing as well as retrieving is the most important security-related research area in cloud. Based on our literature survey, many researchers have developed several searchable encryption protocols. Among the seve...
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File storing as well as retrieving is the most important security-related research area in cloud. Based on our literature survey, many researchers have developed several searchable encryption protocols. Among the several protocols, only minimum number of protocols satisfies the efficient file retrieval from the cloud. Previous research papers support the file encryption based on the keyword, secret key and random number. But in this research paper, we have proposed attribute-based encryption based on the hierarchical manner. The collections of files are encrypted under one common access layer. Compared to earlier researches, our attribute-based hierarchical file encryption scheme supports very less computation as well as storage complexity. We have constructed a new index model named data vector (DV) tree using crossover genetic algorithm which is a component of the soft computing. The DV tree is built based on the term frequency, inverse document frequency and attributes of the file. We have also developed a new key generation, encryption and decryption function for efficient retrieval of files towards the data users.
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