Second-order sliding mode observer for multiple kinetic rates estimation in bioprocesses

Specific kinetic rates are key variables regarding metabolic activity in bioprocesses. They are non-linear functions of concentrations and operating conditions and therefore of difficult access for process control. In this paper, a multiple kinetic rates observer based on second-order sliding mode ideas is proposed. The main difference with other proposals is that smooth estimates are achieved in finite-time without adding additional dynamics. The resulting estimator is robust against uncertainty in the model of the estimated variables. Experimental results from continuous fermentation of S. cerevisiae are presented, where microbial specific growth rate and net ethanol production rate are estimated.     

Second-order stochastic fluid models with fluid-dependent flow rates

In this paper, the analysis of second-order stochastic fluid models, where the fluid rate is dependent on the fluid level, is addressed. The boundary conditions are presented for the fluid models under consideration, which have extended previous work with only reflecting barrier assumptions. To obtain the transient solution of the fluid dynamics, a finite difference solution method is presented, which confirms to the boundary conditions and satisfies the normalization condition at the same time. With our approach, the modeling power of second-order fluid models is directly extended to include the case with fluid-dependent rates. As an application example, a statistical multiplexing problem is analyzed with our proposed method.     

A note on random coding bounds for classical-quantum channels

A modified derivation of achievability results in classical-quantum channel coding theory is proposed, which has, in our opinion, two main benefits over previously used methods: it allows to (i) follow in a simple and clear way how binary hypothesis testing relates to channel coding achievability results, and (ii) derive in a unified way all previously known random coding achievability bounds on error exponents for classical and classical-quantum channels.     

A data embedded video coding scheme for error-prone channels

We propose a novel data embedding scheme for fractional-pixel based video coding algorithms such as H.263 and MPEG-2. By modifying the motion estimation procedure at fractional-pixel precision, two bits data can be embedded in a motion vector (MV) for an inter-mode coded macroblock (MB). For half-pixel precision motion estimation, the resulted bitstream is compatible with the current video coding standards. Performance of the proposed data embedding scheme in terms of compression efficiency is also studied. As an application example of the proposed data embedding scheme, an error-resilient video coding scheme is also presented where some redundant information, which is used to protect MVs and coding modes of MBs in one frame, is embedded into the MVs in the next frame. When errors occur in one group of blocks (GOB), the decoder can exactly recover the MVs of the corrupted GOB if the next frame can be received correctly. Simulations show that the proposed scheme has better performance than standard H.263 coding scheme for transmission over error-prone channels     

Multiterminal source coding for multiview images under wireless fading channels

This paper addresses the problem of wireless transmission of a captured scene from multiple cameras, which do not communicate among each other, to a joint decoder. Correlation among different camera views calls for distributed source coding for efficient multiview image compression. The fact that cameras are placed within a short range of each other results in a high level of interference, multipath fading, and noise effects during communications. We develop a novel two-camera system, that employs multiterminal source coding and complete complementary data spreading, so that while the former technique exploits the statistical correlation between camera views, and performs joint compression to reduce transmission rates, the spreading technique will protect transmitted data by mitigating the effects of wireless fading channels. Our results indicate that the proposed system is competitive when compared to two independently JPEG encoded streams at low to medium transmission rates.     

不同信道条件下MIMO空时编码技术的特性分析

为了分析多输入多输出（ MIMO）空时编码技术在不同信道条件下的性能优劣,研究了无线信道的衰落统计特性,对比了在Rayleigh信道、Rician信道和Nakagami信道条件下,正交频分复用空时块编码（ STBC-OFDM）、垂直空时分层码（ V-BLAST）及空时格型码（ STTC）的系统误码率。通过仿真分析结果表明：STBC-OFDM系统在衰减指数为5的Nakagami信道条件,系统误码率最低,Rician信道次之,Rayleigh信道最高,而且当系统误码率为10－3时,其所需的信噪比（ SNR）分别为7,9,11 dB;但对于V-BLAST和STTC系统,随着信噪比的增加,不同的信道条件对系统误码率的影响各有优劣。     

Position-based coding and convex splitting for private communication over quantum channels

The classical-input quantum-output (cq) wiretap channel is a communication model involving a classical sender X, a legitimate quantum receiver B, and a quantum eavesdropper E. The goal of a private communication protocol that uses such a channel is for the sender X to transmit a message in such a way that the legitimate receiver B can decode it reliably, while the eavesdropper E learns essentially nothing about which message was transmitted. The \(\varepsilon \)-one-shot private capacity of a cq wiretap channel is equal to the maximum number of bits that can be transmitted over the channel, such that the privacy error is no larger than \(\varepsilon \in (0,1)\). The present paper provides a lower bound on the \(\varepsilon \)-one-shot private classical capacity, by exploiting the recently developed techniques of Anshu, Devabathini, Jain, and Warsi, called position-based coding and convex splitting. The lower bound is equal to a difference of the hypothesis testing mutual information between X and B and the “alternate” smooth max-information between X and E. The one-shot lower bound then leads to a non-trivial lower bound on the second-order coding rate for private classical communication over a memoryless cq wiretap channel.     

Polar quantum channel coding with optical multi-qubit entangling gates for capacity-achieving channels

We demonstrate a fashion of quantum channel combining and splitting, called polar quantum channel coding, to generate a quantum bit (qubit) sequence that achieves the symmetric capacity for any given binary input discrete quantum channels. The present capacity is achievable subject to input of arbitrary qubits with equal probability. The polarizing quantum channels can be well-conditioned for quantum error-correction coding, which transmits partially quantum data through some channels at rate one with the symmetric capacity near one but at rate zero through others.     

Nakagami-m信道下多播中继系统网络编码性能分析

在Nakagami-m衰落信道下,分析多播中继系统中网络编码的中断概率性能.分别采用复数域网络编码(complex fiel dnetwork coding,CFNC)和放大转发网络编码(amplify-and-forward network coding,AFNC)两种传输策略,推导了高信噪比时的中断概率近似表达式,得到两种传输策略下的系统分集增益均为2m,并通过仿真结果加以验证.仿真结果还表明,CFNC在系统中断概率、和速率和误符号率性能上均优于AFNC和无中继直接传输策略.     

Minimum cost multiple multicast network coding with quantized rates

In this paper, we consider multiple multicast sessions with intra-session network coding where rates over all links are integer multiples of a basic rate. Although having quantized rates over communication links is quite common, conventional minimum cost network coding problem cannot generally result in quantized solutions. In this research, the problem of finding minimum cost transmission for multiple multicast sessions with network coding is addressed. It is assumed that the rate of coded packet injection at every link of each session takes quantized values. First, this problem is formulated as a mixed integer linear programming problem, and then it is proved that this problem is strongly NP-hard on general graphs. In order to obtain an exact solution for the problem, an effective and efficient scheme based on Benders decomposition is developed. Using this scheme the problem is decomposed into a master integer programming problem and several linear programming sub-problems. The efficiency of the proposed scheme is subsequently evaluated by numerical results on random networks.     

Perceptual coding of narrow-band audio signals at low rates

This paper describes a coding paradigm using coding tools based on the characteristics of the human hearing system so as to accommodate a wide range of narrow-band audio inputs without annoying artifacts at low rates (down to 8 kb/s). The narrow-band perceptual audio coder (NPAC) employs a variety of algorithms to account for the perceptually irrelevant parts of the input signal in addition to statistical redundancies. The new algorithms used in the NPAC coder include a perceptual error measure in training the codebooks and selecting the best codewords which takes into account the audible parts of the quantization noise, a perception-based bit-allocation algorithm and a new predictive scheme to vector quantize the scale factors. The NPAC coder delivers acceptable quality without annoying artifacts for most narrow-band audio signals at around 1 bit/sample. Informal subjective tests have shown that the NPAC coder outperforms a commercial low-rate music coder operating at 8 kb/s.     

Neural coding and decoding: communication channels and quantization.

We present a novel analytical approach for studying neural encoding. As a first step we model a neural sensory system as a communication channel. Using the method of typical sequence in this context, we show that a coding scheme is an almost bijective relation between equivalence classes of stimulus/response pairs. The analysis allows a quantitative determination of the type of information encoded in neural activity patterns and, at the same time, identification of the code with which that information is represented. Due to the high dimensionality of the sets involved, such a relation is extremely difficult to quantify. To circumvent this problem, and to use whatever limited data set is available most efficiently, we use another technique from information theory--quantization. We quantize the neural responses to a reproduction set of small finite size. Among many possible quantizations, we choose one which preserves as much of the informativeness of the original stimulus/response relation as possible, through the use of an information-based distortion function. This method allows us to study coarse but highly informative approximations of a coding scheme model, and then to refine them automatically when more data become available.     

A coding theorem for multiple access channels using a maximum likelihood list decoding scheme

Bounds on the probability of error for list decoding, in which the receiver lists L messages rather than one after receiving a message, have been obtained by Elias, Ebert, and Shannon, Gallager and Berlekemp. In the present paper the problem of list decoding has been considered for a multiple access channel. The paper contains an upper bound on the probability of error for list decoding in this case.     

Second-order conditions for local controllability

This paper presents second-order (depending on the 2-jet) sufficient conditions for small time local controllability of affine smooth control systems, based on a very simple observation that can also lead to higher-order conditions; a second-order obstruction for a certain class of systems is also discussed, as is the gap between the obstruction and the sufficient condition. The new results are used for deciding local controllability of systems for which a direct application of the usual criteria is not conclusive.     

Integrating Factors for Second-order ODEs

A systematic algorithm for building integrating factors of the form μ(x, y), μ(x, y^′) or μ(y, y^′) for second-order ODEs is presented. The algorithm can determine the existence and explicit form of the integrating factors themselves without solving any differential equations, except for a linear ODE in one subcase of the μ (x, y) problem. Examples of ODEs not having point symmetries are shown to be solvable using this algorithm. The scheme was implemented in Maple, in the framework of the ODEtools package and its ODE-solver. A comparison between this implementation and other computer algebra ODE-solvers in tackling non-linear examples from Kamke's book is shown.     

Textual image compression at low bit rates based on region-of-interest coding

In this paper, we deal with those applications of textual image compression where high compression ratio and maintaining or improving the visual quality and readability of the compressed images are of main concern. In textual images, most of the information exists in the edge regions; therefore, the compression problem can be studied in the framework of region-of-interest (ROI) coding. In this paper, the Set Partitioning in Hierarchical Trees (SPIHT) coder is used in the framework of ROI coding along with some image enhancement techniques in order to remove the leakage effect which occurs in the wavelet-based low-bit-rate compression. We evaluated the compression performance of the proposed method with respect to some qualitative and quantitative measures. The qualitative measures include the averaged mean opinion scores (MOS) curve along with demonstrating some outputs in different conditions. The quantitative measures include two proposed modified PSNR measures and the conventional one. Comparing the results of the proposed method with those of three conventional approaches, DjVu, JPEG2000, and SPIHT coding, showed that the proposed compression method considerably outperformed the others especially from the qualitative aspect. The proposed method improved the MOS by 20 and 30 %, in average, for high- and low-contrast textual images, respectively. In terms of the modified and conventional PSNR measures, the proposed method outperformed DjVu and JPEG2000 up to 0.4 dB for high-contrast textual images at low bit rates. In addition, compressing the high contrast images using the proposed ROI technique, compared to without using this technique, improved the average textual PSNR measure up to 0.5 dB, at low bit rates.     

Second-order neural nets for constrained optimization

Analog neural nets for constrained optimization are proposed as an analogue of Newton's algorithm in numerical analysis. The neural model is globally stable and can converge to the constrained stationary points. Nonlinear neurons are introduced into the net, making it possible to solve optimization problems where the variables take discrete values, i.e., combinatorial optimization.     

Second-order Temporal Pooling for Action Recognition

International Journal of Computer Vision - Deep learning models for video-based action recognition usually generate features for short clips (consisting of a few frames); such clip-level features...     

Second-order sliding-mode observer for mechanical systems

The super-twisting second-order sliding-mode algorithm is modified in order to design a velocity observer for uncertain mechanical systems. The finite time convergence of the observer is proved. Thus, the observer can be designed independently of the controller. A discrete version of the observer is considered and the corresponding accuracy is estimated.