Source Symbol Purging-Based Distributed Conditional Arithmetic Coding

A distributed arithmetic coding algorithm based on source symbol purging and using the context model is proposed to solve the asymmetric Slepian–Wolf problem. The proposed scheme is to make better use of both the correlation between adjacent symbols in the source sequence and the correlation between the corresponding symbols of the source and the side information sequences to improve the coding performance of the source. Since the encoder purges a part of symbols from the source sequence, a shorter codeword length can be obtained. Those purged symbols are still used as the context of the subsequent symbols to be encoded. An improved calculation method for the posterior probability is also proposed based on the purging feature, such that the decoder can utilize the correlation within the source sequence to improve the decoding performance. In addition, this scheme achieves better error performance at the decoder by adding a forbidden symbol in the encoding process. The simulation results show that the encoding complexity and the minimum code rate required for lossless decoding are lower than that of the traditional distributed arithmetic coding. When the internal correlation strength of the source is strong, compared with other DSC schemes, the proposed scheme exhibits a better decoding performance under the same code rate.     

Side Information Generation Scheme Based on Coefficient Matrix Improvement Model in Transform Domain Distributed Video Coding

In order to effectively improve the quality of side information in distributed video coding, we propose a side information generation scheme based on a coefficient matrix improvement model. The discrete cosine transform coefficient bands of the Wyner–Ziv frame at the encoder side are divided into entropy coding coefficient bands and distributed video coding coefficient bands, and then the coefficients of entropy coding coefficient bands are sampled, which are divided into sampled coefficients and unsampled coefficients. For sampled coefficients, an adaptive arithmetic encoder is used for lossless compression. For unsampled coefficients and the coefficients of distributed video coding coefficient bands, the low density parity check accumulate encoder is used to calculate the parity bits, which are stored in the buffer and transmitted in small amount upon decoder request. At the decoder side, the optical flow method is used to generate the initial side information, and the initial side information is improved according to the sampled coefficients by using the coefficient matrix improvement model. The experimental results demonstrate that the proposed side information generation scheme based on the coefficient matrix improvement model can effectively improve the quality of side information, and the quality of the generated side information is improved by about 0.2–0.4 dB, thereby improving the overall performance of the distributed video coding system.     

Quantum benchmark for storage and transmission of coherent states

We consider the storage and transmission of a Gaussian distributed set of coherent states of continuous variable systems. We prove a limit on the average fidelity achievable when the states are transmitted or stored by a classical channel, i.e., a measure and repreparation scheme which sends or stores classical information only. The obtained bound is tight and serves as a benchmark which has to be surpassed by quantum channels in order to outperform any classical strategy. The success in experimental demonstrations of quantum memories as well as quantum teleportation has to be judged on this footing.     

Zero-Delay Joint Source Channel Coding for a Bivariate Gaussian Source over the Broadcast Channel with One-Bit ADC Front Ends

In this work, we consider the zero-delay transmission of bivariate Gaussian sources over a Gaussian broadcast channel with one-bit analog-to-digital converter (ADC) front ends. An outer bound on the conditional distortion region is derived. Focusing on the minimization of the average distortion, two types of methods are proposed to design nonparametric mappings. The first one is based on the joint optimization between the encoder and decoder with the use of an iterative algorithm. In the second method, we derive the necessary conditions to develop the optimal encoder numerically. Using these necessary conditions, an algorithm based on gradient descent search is designed. Subsequently, the characteristics of the optimized encoding mapping structure are discussed, and inspired by which, several parametric mappings are proposed. Numerical results show that the proposed parametric mappings outperform the uncoded scheme and previous parametric mappings for broadcast channels with infinite resolution ADC front ends. The nonparametric mappings succeed in outperforming the parametric mappings. The causes for the differences between the performances of two nonparametric mappings are analyzed. The average distortions of the parametric and nonparametric mappings proposed here are close to the bound for the cases with one-bit ADC front ends in low channel signal-to-noise ratio regions.     

New Result on the Feedback Capacity of the Action-Dependent Dirty Paper Wiretap Channel

The Gaussian wiretap channel with noncausal state interference available at the transmitter, which is also called the dirty paper wiretap channel (DP-WTC), has been extensively studied in the literature. Recently, it has been shown that taking actions on the corrupted state interference of the DP-WTC (also called the action-dependent DP-WTC) helps to increase the secrecy capacity of the DP-WTC. Subsequently, it has been shown that channel feedback further increases the secrecy capacity of the action-dependent DP-WTC (AD-DP-WTC), and a sub-optimal feedback scheme is proposed for this feedback model. In this paper, a two-step hybrid scheme and a corresponding new lower bound on the secrecy capacity of the AD-DP-WTC with noiseless feedback are proposed. The proposed new lower bound is shown to be optimal (achieving the secrecy capacity) and tighter than the existing one in the literature for some cases, and the results of this paper are further explained via numerical examples.     

A Pattern Dictionary Method for Anomaly Detection

In this paper, we propose a compression-based anomaly detection method for time series and sequence data using a pattern dictionary. The proposed method is capable of learning complex patterns in a training data sequence, using these learned patterns to detect potentially anomalous patterns in a test data sequence. The proposed pattern dictionary method uses a measure of complexity of the test sequence as an anomaly score that can be used to perform stand-alone anomaly detection. We also show that when combined with a universal source coder, the proposed pattern dictionary yields a powerful atypicality detector that is equally applicable to anomaly detection. The pattern dictionary-based atypicality detector uses an anomaly score defined as the difference between the complexity of the test sequence data encoded by the trained pattern dictionary (typical) encoder and the universal (atypical) encoder, respectively. We consider two complexity measures: the number of parsed phrases in the sequence, and the length of the encoded sequence (codelength). Specializing to a particular type of universal encoder, the Tree-Structured Lempel–Ziv (LZ78), we obtain a novel non-asymptotic upper bound, in terms of the Lambert W function, on the number of distinct phrases resulting from the LZ78 parser. This non-asymptotic bound determines the range of anomaly score. As a concrete application, we illustrate the pattern dictionary framework for constructing a baseline of health against which anomalous deviations can be detected.     

Entanglement enhanced bit rate over multiple uses of a lossy bosonic channel with memory

We present a study of the achievable rates for classical information transmission via a lossy bosonic channel with memory, using homodyne detection. A comparison with the memoryless case shows that the presence of memory enhances the bit rate if information is encoded in collective states, i.e., states which are entangled over different uses of the channel.     

On the compression of information of a classical source with the use of side quantum and classical information

The problem of the compression of classical information when a receiver has access only to side quantum states associated with classical states of a source, which are not available directly, is examined. For the receiver to be able to reconstruct the entire information of the source, a certain additional amount of side classical information is required. A bound on the minimum necessary amount of side classical information has been obtained by simple means.     

Low-Complexity Chase Decoding of Reed–Solomon Codes Using Channel Evaluation

A novel time-varying channel adaptive low-complexity chase (LCC) algorithm with low redundancy is proposed, where only the necessary number of test vectors (TVs) are generated and key equations are calculated according to the channel evaluation to reduce the decoding complexity. The algorithm evaluates the error symbol numbers by counting the number of unreliable bits of the received code sequence and dynamically adjusts the decoding parameters, which can reduce a large number of redundant calculations in the decoding process. We provide a simplified multiplicity assignment (MA) scheme and its architecture. Moreover, a multi-functional block that can implement polynomial selection, Chien search and the Forney algorithm (PCF) is provided. On this basis, a high-efficiency LCC decoder with adaptive error-correcting capability is proposed. Compared with the state-of-the-art LCC (TV = 16) decoding, the number of TVs of our decoder was reduced by 50.4% without loss of the frame error rate (FER) performance. The hardware implementation results show that the proposed decoder achieved 81.6% reduced average latency and 150% increased throughput compared to the state-of-the-art LCC decoder.     

Determinant of Wave Operator as a Measure for Resonance and Bound States in One-Dimensional Potential Scattering System

The determinant of the wave operator or its transposed operator (source operator) works as a measure for resonance and bound states in one-dimensional potential scattering system. This fact is based on an identity proved here: the determinant of the wave or source operator equals to the transmission coefficient, which represents the field amplitude in the forward side of the scatterer. Utilizing this measure, resonance and bound states in one-dimensional system are properly assigned without solving wave equation. In future, local enhancement of optical near-field in three-dimensional system, i.e., local plasmon resonance will be treated, generalizing the present method.     

无线多径信道中基于时间反演的物理层安全传输机制

宽带无线通信用户大多处在复杂的环境中,其时变多径传播和开放特性将严重影响通信系统的性能.针对物理层安全研究中的窃听信道问题,提出了一种适用于宽带无线多径信道的联合时间反演技术和发端人工噪声的物理层安全传输机制.首先,在一个典型窃听信道模型中采用时间反演技术,利用其时空聚焦性来提高信息在传输过程中的安全性;其次,由于时间反演的时空聚焦性,信息在聚焦点附近容易被窃听,通过在发送端加入人工噪声来扰乱窃听用户对保密信息的窃听,由于合法用户采用零空间人工噪声法,人工噪声对合法用户没有影响.理论分析和仿真结果表明,与已有物理层安全机制相比,所提机制可以有效地提高系统的保密信干噪比和可达保密速率,降低合法用户的误比特率,系统的保密性能得到提升.     

A novel multipriority reservation protocol for plastic optical fiber access network

In this paper, a novel multipriority reservation protocol for plastic optical fiber access network based on optical code division multiplexing access (OCDMA) technology is proposed. Conventional OCDMA system only allows finite units to transmit and access simultaneously according to the number of channels. The protocol is proposed to resolve this problem. By using the reservation scheme and a distributed arbitration algorithm, channel collision and destination conflict can be avoided. The protocol can efficiently support the transmission of multimedia messages that require the different time-delays. At the same time, each optical network unit is equipped with a fixed optical encoder/decoder that is always tuned to channel for control and the tunable optical encoder/decoder that is tuned to any of channel for data. The network throughput and average delay have been investigated by numerical analysis and simulation experiments. It is shown that the multipriority reservation protocol in this POF access net     

一种用于塑料光纤接入网的多优先级控制协议设计

提出一种基于光码分多址(OCDMA)技术的塑料光纤接入网的多优先级控制协议.由于受传输误码率的限制,常规的光码分多址系统只允许有限个用户传输数据或接入网络,为了解决用有限的码字来实现多于码字数目的多媒体工作站间的信息通信,这种多优先级的控制协议通过使用预约和随机分配算法可以有效地解决信道冲突和目的地址的冲突问题, 该协议可以支持多媒体信息传输中各类信息不同的时间延迟要求.另外,在该接入网中,每个光网络单元均设有两套光编码器和光解码器,一套用于控制信道,另一套用于各个数据信道.利用本文提出的协议对塑料光纤接入网的网络吞吐量和平均延时时间进行了数值分析和仿真实验,结果证明了这种多优先级控制协议的有效性和高效性.     

Statistical mechanical approach to lossy data compression: Theory and practice

The encoder and decoder for lossy data compression of binary memoryless sources are developed on the basis of a specific-type nonmonotonic perceptron. Statistical mechanical analysis indicates that the potential ability of the perceptron-based code saturates the theoretically achievable limit in most cases although exactly performing the compression is computationally difficult. To resolve this difficulty, we provide a computationally tractable approximation algorithm using belief propagation (BP), which is a current standard algorithm of probabilistic inference. Introducing several approximations and heuristics, the BP-based algorithm exhibits performance that is close to the achievable limit in a practical time scale in optimal cases.     

基于光码分多址技术的塑料光纤接入网探讨

本文通过对多信道光接入网复用和多址接入方式的分析,提出一种基于光码分多址(OCDMA)技术的塑料光纤接入网结构,同时给出了适合于多媒体信息业务传输的控制协议以及低成本的硬件实现方案,为实现光接入网的最终目标即光纤到办公室(FTTO)或光纤到家庭(FTTH)提供了一条有效的途径.     

Trade-offs between Error Exponents and Excess-Rate Exponents of Typical Slepian–Wolf Codes

Typical random codes (TRCs) in a communication scenario of source coding with side information in the decoder is the main subject of this work. We study the semi-deterministic code ensemble, which is a certain variant of the ordinary random binning code ensemble. In this code ensemble, the relatively small type classes of the source are deterministically partitioned into the available bins in a one-to-one manner. As a consequence, the error probability decreases dramatically. The random binning error exponent and the error exponent of the TRCs are derived and proved to be equal to one another in a few important special cases. We show that the performance under optimal decoding can be attained also by certain universal decoders, e.g., the stochastic likelihood decoder with an empirical entropy metric. Moreover, we discuss the trade-offs between the error exponent and the excess-rate exponent for the typical random semi-deterministic code and characterize its optimal rate function. We show that for any pair of correlated information sources, both error and excess-rate probabilities exponential vanish when the blocklength tends to infinity.     

Performance of run-length limited (4, 18) code for optical storage systems

In this paper, the authors investigate the performance of recently presented run-length limited (4, 18) code for high density optical storage systems. The construction of the code is described simply. The code has code rate R = 1/3 and density ratio (DR) = 1.67. The bit error rate (BER) performance for decision feedback equalizer (DFE) and partial response maximum likelihood (PRML) detector are simulated, considering signal-to-noise ratio (SNR) and optical channel jitter. The result shows that the performance of the code is acceptable. The encoder and decoder of the code are implemented by complex programmable logic device (CPLD) chip and the hardware resources required for encoder and decoder arelow.     

A Manifold Learning Perspective on Representation Learning: Learning Decoder and Representations without an Encoder

Autoencoders are commonly used in representation learning. They consist of an encoder and a decoder, which provide a straightforward method to map n-dimensional data in input space to a lower m-dimensional representation space and back. The decoder itself defines an m-dimensional manifold in input space. Inspired by manifold learning, we showed that the decoder can be trained on its own by learning the representations of the training samples along with the decoder weights using gradient descent. A sum-of-squares loss then corresponds to optimizing the manifold to have the smallest Euclidean distance to the training samples, and similarly for other loss functions. We derived expressions for the number of samples needed to specify the encoder and decoder and showed that the decoder generally requires much fewer training samples to be well-specified compared to the encoder. We discuss the training of autoencoders in this perspective and relate it to previous work in the field that uses noisy training examples and other types of regularization. On the natural image data sets MNIST and CIFAR10, we demonstrated that the decoder is much better suited to learn a low-dimensional representation, especially when trained on small data sets. Using simulated gene regulatory data, we further showed that the decoder alone leads to better generalization and meaningful representations. Our approach of training the decoder alone facilitates representation learning even on small data sets and can lead to improved training of autoencoders. We hope that the simple analyses presented will also contribute to an improved conceptual understanding of representation learning.     

基于光纤布拉格光栅编解码器的直接序列扩频跳频光码分多址系统

提出了一种混合直扩跳频光纤码分多址（DS／FH－OCDMA）系统的实现方法，该系统采用跳频素数码作为扩频码，运用了光纤布拉格光栅序列作为编解码器，并在解码器两端安置了双硬限幅器以提高系统的性能，理论分析和仿真结果表明，该系统具有大用户容量，高功率效率和低误码率等方面的优点。     

Self-consistent field theory of collisions: Orbital equations with asymptotic sources and self-averaged potentials

The self-consistent field theory of collisions is formulated, incorporating the unique dynamics generated by the self-averaged potentials. The bound state Hartree–Fock approach is extended for the first time to scattering states, by properly resolving the principal difficulties of non-integrable continuum orbitals and imposing complex asymptotic conditions. The recently developed asymptotic source theory provides the natural theoretical basis, as the asymptotic conditions are completely transferred to the source terms and the new scattering function is made fullyintegrable. The scattering solutions can then be directly expressed in terms of bound state HF configurations, establishing the relationship between the bound and scattering state solutions. Alternatively, the integrable spin orbitals are generated by constructing the individual orbital equations that contain asymptotic sources and self-averaged potentials. However, the orbital energies are not determined by the equations, and a special channel energy fixing procedure is developed to secure the solutions. It is also shown that the variational construction of the orbital equations has intrinsic ambiguities that are generally associated with the self-consistent approach. On the other hand, when a small subset of open channels is included in the source term, the solutions are only partiallyintegrable, but the individual open channels can then be treated more simply by properly selecting the orbital energies. The configuration mixing and channel coupling are then necessary to complete the solution. The new theory improves the earlier continuum HF model.