Rate-distortion speech coding with a minimum discrimination information distortion measure

An information theory approach to the theory and practice of linear predictive coded (LPC) speech compression systems is developed. It is shown that a traditional LPC system can be viewed as a minimum distortion or nearest-neighbor system where the distortion measure is a minimum discrimination information between a speech process model and an observed frame of actual speech. This distortion measure is used in an algorithm for computer-aided design of block source codes subject to a fidelity criterion to obtain a 750-bits/s speech compression system that resembles an LPC system but has a much lower rate, a larger memory requirement, and requires no on-line LPC analysis. Quantitative and informal subjective comparisons are made among our system and LPC systems.     

An application of the minimum discrimination information estimate for detecting Gaussian signals (Corresp.)

The minimum discrimination information estimate is used to compute the log-likelihood ratio, or logarithm of the Radon-Nikodym derivative, when the Gaussian signal has one of two covariance functions.     

Quantising for minimum information loss

A random variable pair consisting of a continuous random vector (the observation or feature) vector and of a discrete random variable (the class) is considered. The authors report on the design of a machine able to accept as input the observation of, and present as output an approximation to, the conditional probability of the classes given the observation. More precisely. They deal with the design of a histogram-type approximation with variable cell size and shape. In this approach, the cells are the Voronoi regions of a nearest neighbour vector quantiser, and the position of code vectors (i.e. the size and the shape of the cells) is designed in such a way that the information loss caused by quantisation is minimised     

基于最小互信息的自适应偏振差分成像方法

偏振信息解析是偏振成像探测涉及的一个重要课题.针对传统的偏振差分成像方法存在的不足,从信息论的角度出发,提出了一种基于最小互信息的自适应偏振差分成像方法.首先,利用偏振信息解析得到的斯托克斯参量图像,计算得到偏振片透光轴与所选参考坐标轴的成任意夹角的出射光强度图像;然后,以归一化互信息为评价指标,寻找具有最大不相关的两...     

Training approach for hidden Markov models

The authors propose a new training approach based on maximum model distance (MMD) for HMMs. MMD uses the entire training set to estimate the parameters of each HMM, while the traditional maximum likelihood (ML) only uses those data labelled for the model. Experimental results showed that significant error reduction can be achieved through the proposed approach. In addition, the relationship between MMD and corrective training was discussed, and we have proved that the corrective training is a special case of the MMD approach     

一种图像的隐藏信息还原方法

提出了一种基于遗传算法的机密信息还原方法.该方法首先确定嵌入扩频序列的长度:然后,采用遗传算法求得嵌入序列的最佳估计:进而,用最佳的估计序列还原出机密信息.实现机密信息的盲还原.该方法己在微机上进行模拟实验,实验结果表明,还原效果较好.     

A combined approach for detecting hidden nodes in 802.11 wireless LANs

Hidden node is a fundamental problem that severely degrades the performance of wireless networks. The problem occurs when nodes that do not hear each other transmit at the same time, which leads to data packet collision. IEEE 802.11 Wireless Local Area Networks (WLANs) tries to solve this problem through the Request to Send/Clear to Send (RTS/CTS) mechanism. However, the mechanism is not wholly successful. The RTS/CTS idea is based on the assumption that all nodes in the vicinity of Access Points will hear CTS packets and consequently defer their transmissions. The shortcoming of RTS/CTS stems from the fact that such packets introduce high overhead if extensively used. In this article, we propose a hybrid approach for detecting hidden nodes in 802.11 WLANs. The approach is mainly based on adaptive learning about collisions in the network. We think that the approach will be useful for controlling the tuning of RTS/CTS threshold and therefore reduce the overhead those packets introduce. Detailed simulation experiments have shown the strength of the proposed approach compared with other approaches.     

Axioms for discrimination information (Corresp.)

Axioms characterizing combinatorial discrimination information are given and extended to characterize arbitrary measure theoretic discrimination information.     

A performance evaluation approach for GSM-based information services

The ever increasing diffusion of mobile communications will lead to massive mobile access to the Internet. The paper focuses on the wireless application protocol (WAP) for providing mobile information services, as envisaged by the personalized access to local information and services for tourists (PALIO) project within the fifth research framework of the European Commission. In particular, we consider a global system for mobile communications (GSM) network where WAP traffic is transported by the short message service (SMS) on specific GSM signaling channels. We develop suitable traffic models for both the WAP downlink traffic and the signaling traffic. An analytical approach is described that allows evaluation of the mean transmission delay of a WAP page. Finally, a downlink traffic scheduling policy is proposed in order to reduce the delay variance so that users experience a more regular traffic behavior (service usability requirement). The theoretical derivations for the GSM-SMS scenario are general and can also be tailored for other mobile communication systems. The study allows dimensioning of both the service and the downlink signaling traffic capacity in order to fulfill quality-of-service requirements for users.     

A digital approach to clock recovery in generalized minimum shiftkeying

A method for clock recovery to be used with generalized minimum-shift-keying (MSK) modulations is presented. Attractive features of the method are that it is suited for digital implementation and that its performance is not affected by the carrier-phase recovery process. Clock reference is extracted by passing the sampled baseband waveform through the cascade of a nonlinearity, followed by a digital differentiator whose average output represents the error signal to be employed in a tracking loop. The performance of this scheme is analyzed by means of simulation in steady-state and in transient conditions. Tracking errors are compared with those attained by the well-known De Buda synchronizer and with the Cramer-Rao lower bound     

Recovering hidden information in audio by genetic algorithm

An audio recovering method of spread-spectrum hidden information is proposed based on genetic algorithm. In this method the embedded sequence length is confh＇med firstly, then the best estimated sequence with the confa＇med length is got by genetic algorithm, finally the confidential message hidden in stego-andio can be recovered. Using this approach, the hidden information can be recovered without any information fi＇om the transmitter. The presented method has been implemented on PC, and the experimental results show that the average recovering correct rate is higher than 90%.     

An approach to minimum cost steel rolling

A metal rolling process is examined, and shown to be an implicit, discrete, multistage, control process with admissible control set dependent only upon the system state. By means of numerical techniques, dynamic programming is applied to this process to generate a closed-loop roll setting policy which is optimal in the sense that it achieves a specified final state at minimum dollar cost. Digital simulation and Monte Carlo techniques are used to compare performance of the optimally controlled mill with that of the the same mill, operating in accord with current rolling practices. Both models are examined using random initial conditions and the process sensors are assumed to contribute noise to the measurement of the system state. An averaging technique is used to decrease the effects of this noise on system performance.     

A semidefinite programming approach to optimal unambiguous discrimination of quantum states

We consider the problem of unambiguous discrimination between a set of linearly independent pure quantum states. We show that the design of the optimal measurement that minimizes the probability of an inconclusive result can be formulated as a semidefinite programming problem. Based on this formulation, we develop a set of necessary and sufficient conditions for an optimal quantum measurement. We show that the optimal measurement can be computed very efficiently in polynomial time by exploiting the many well-known algorithms for solving semidefinite programs, which are guaranteed to converge to the global optimum. Using the general conditions for optimality, we derive necessary and sufficient conditions so that the measurement that results in an equal probability of an inconclusive result for each one of the quantum states is optimal. We refer to this measurement as the equal-probability measurement (EPM). We then show that for any state set, the prior probabilities of the states can be chosen such that the EPM is optimal. Finally, we consider state sets with strong symmetry properties and equal prior probabilities for which the EPM is optimal. We first consider geometrically uniform (GU) state sets that are defined over a group of unitary matrices and are generated by a single generating vector. We then consider compound GU state sets which are generated by a group of unitary matrices using multiple generating vectors, where the generating vectors satisfy a certain (weighted) norm constraint.     

A unified minimum variance spectrum-based approach for simultaneousidentification of both harmonic and stationary random noise fields

The technique presented in this correspondence uses the MV spectrum's convergence properties to identify unknown and arbitrary harmonic signal fields. The correlation sequence for the identified harmonic signal field is then combined with the observed overall correlation sequence to obtain a spectral model of the random noise field     

Frequency tracking using hidden Markov models with amplitude andphase information

It is demonstrated how the hidden Markov model (HMM) frequency tracker can be extended by the addition of amplitude and phase information. The HMM tracker as originally formulated uses a gate of spectral bins from fast Fourier transform (FFT) processing, and associates each cell with a state of the hidden Markov chain. A measurement sequence based on the output of a simple threshold detector forms the input to the HMM tracker. Two extensions to the original tracker are proposed. The first, the HMM/A tracker, incorporates the FFT amplitudes in the cells of the measurement sequence. The second, the HMM/AP tracker, does not use a measurement sequence, but uses instead the FFT amplitude and phase values in all cells within the gate. A comparison of the results obtained in using the three HMM-based trackers with simulated data reveals that the extended trackers outperform the original. An analysis of the effect of parameter mismatch for the three trackers is presented. Their use as detectors is also discussed     

Mutual information and minimum mean-square error in Gaussian channels

This paper deals with arbitrarily distributed finite-power input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the input-output mutual information and the minimum mean-square error (MMSE) achievable by optimal estimation of the input given the output. That is, the derivative of the mutual information (nats) with respect to the signal-to-noise ratio (SNR) is equal to half the MMSE, regardless of the input statistics. This relationship holds for both scalar and vector signals, as well as for discrete-time and continuous-time noncausal MMSE estimation. This fundamental information-theoretic result has an unexpected consequence in continuous-time nonlinear estimation: For any input signal with finite power, the causal filtering MMSE achieved at SNR is equal to the average value of the noncausal smoothing MMSE achieved with a channel whose SNR is chosen uniformly distributed between 0 and SNR.     

A modular approach to the communication protocol and standard for multimedia information: A review

The paper focuses on a modular approach to design of a communication protocol and an MPEG-like standard for multimedia information processing. Generally, the following basic problems can be considered: (i) selection, (ii) composition, and (iii) combinatorial evolution and forecasting. Here, the composition problem is examined. The ZigBee Protocol for wireless sensor networks is studied as an example for the modular design. A generalized MPEG-like standard is considered as a representative example as well. Morphological (modular) system design is used for composition of the elements (components) of the protocol/standard. The solving process is based on Hierarchical Morphological Multicriteria Design (HMMD): (i) multicriteria selection of alternatives for system components, (ii) synthesis of the selected alternatives into a resultant combination. Numerical examples illustrate the design process.