CONSTRAINED VITERBI ALGORITHM AND ITS APPLICATION TO ERROR RESILIENT TRANSMISSION OF SPIHT CODED IMAGES

To overcome some drawbacks of Viterbi algorithm （VA）, such as exponential growing complexity of decoding, and its poor performance under bad channel conditions, some available known information must be used as constrained condition and apriori knowledge for decoding. A new constrained VA is proposed by adding con- straint bits directly for conventional codec. Compared with the conventional VA, under the bad channel condi- tion, the proposed scheme can improve the peak signal to noise ratio （PSNR） of the decoding image 2--10 dB by changing the number of constrained bits. Experimental results show that it is an efficient error-controlling way for the transmission of set partitioning in hierarchical trees （SPIHT） coded image.

A Novel Sequential Soft Output Viterbi Algorithm

In order to fully utilize the soft decision ability of the outer decoder in a concatenated system, reliability information (called soft output) from the inner decoder or equalizer is required. In this paper, based on the analysis of typical implementations of soft output VA, a novel algorithm is proposed by utilizing the property of Viterbi algorithm. Compared with the typical implementations, less processing expense is required by the new algorithm for weighting the hard decisions of VA. Meanwhile, simulation results show that, deterioration in performance of this algorithm is usually small for decoding of convolutional code and negligible for equalization.

Combined Viterbi Detector for a Balanced Code in Page Memories

Based on the two path metrics being equal at a merged node in the trellis employed to describe a Viterbi detector for the detection of data encoded with a rate 6:8 balanced binary code in page-oriented optical memories, the combined Viterbi detector scheme is proposed to improve raw biterror rate performance by mitigating the occurrence of a twobit reversing error event in an estimated codeword for the balanced code. The effectiveness of the detection scheme is verified for different data quantizations using Monte Carlo simulations. Key words holographic data storage - balanced code - modulation code - Viterbi algorithm - path metric CLC number TN 911. 21 Foundation item: Supported by National 973 Research Program of China (G1999033006)Biography: Chen Duan-rong (1960-), male, Lecturer, Ph. D candidate, research direction: coding and signal processing for the recording channel of holographic data storage.

Optimal state and branch sequence based parameter estimation of continuous hidden Markov model

A parameter estimation algorithm of the continuous hidden Markov model isintroduced and the rigorous proof of its convergence is also included. The algorithm uses theViterbi algorithm instead of K-means clustering used in the segmental K-means algorithm to determineoptimal state and branch sequences. Based on the optimal sequence, parameters are estimated withmaximum-likelihood as objective functions. Comparisons with the traditional Baum-Welch and segmentalK-means algorithms on various aspects, such as optimal objectives and fundamentals, are made. Allthree algorithms are applied to face recognition. Results indicate that the proposed algorithm canreduce training time with comparable recognition rate and it is least sensitive to the training set.So its average performance exceeds the other two.

Pattern recognition of optimal traffic path based on HMM

In order to alleviate urban traffic congestion and provide fast vehicle paths,a hidden Markov model(HMM)based on multi-feature data of urban regional roads is constructed to solve the problems of low recognition rate and poor instability of traditional model algorithms.At first,the HHM is obtained by training.Then according to dynamic planning principle,the traffic states of intersections are obtained by the Viterbi algorithm.Finally,the optimal path is selected based on the obtained traffic states of intersections.The experiment results show that the proposed method is superior to other algorithms in road unobstruction rate and recognition rate under complex road conditions.

HMM in Predicting Protein Secondary Structure

We introduced a new method---duration Hidden Markov Model (dHMM) to predicate the secondary structure of Protein. In our study, we divide the basic second structure of protein into three parts: H (a-Helix), E (B-sheet) and O (others, include coil and turn). HMM is a kind of probabilistic model which more thinking of the interaction between adjacent amino acids (these interaction were represented by transmit probability), and we use genetic algorithm to determine the model parameters. After improving on the model and fixed on the parameters of the model, we write a program HMMPS. Our example shows that HMM is a nice method for protein secondary structure prediction.

Semantic Region Estimation of Assistant Robot for the Elderly Long-Term Operation in Indoor Environment

In this work, in order to improve spatial recognition abilities for the long-term operation tasks of the assistant robot for the elderly, a novel approach of semantic region estimation is proposed. We define a novel graphbased semantic region descriptions, which are estimated in a dynamically fashion. We propose a two-level update algorithm, namely, Symbols update level and Regions update level. The algorithm firstly adopts particle filter to update weights of the symbols, and then use the Viterbi algorithm to estimate the region the robot stays in based on those weights, optimally. Experimental results demonstrate that our proposed approach can solve problems of the long-term operation and kidnapped robot problem.

Instantaneous frequency estimation of multi-component Chirp signals in noisy environments

A classical time-varying signal, the multi-component Chirp signal has been widely used and the ability to estimate its instantaneous frequency （IF） is very useful. But in noisy environments, it is hard to estimate the 1F of a multi-component Chirp signal accurately. Wigner distribution maxima （WDM） are usually utilized for this estimation. But in practice, estimation bias increases when some points deviate from the true IF in high noise environments. This paper presents a new method of multi-component Chirp signal 1F estimation named Wigner Viterbi fit （WVF）, based on Wigner-Ville distribution （WVD） and the Viterbi algorithm. First, we transform the WVD of the Chirp signal into digital image, and apply the Viterbi algorithm to separate the components and estimate their IF. At last, we establish a linear model to fit the estimation results. Theoretical analysis and simulation results prove that this new method has high precision and better performance than WDM in high noise environments, and better suppression of interference and the edge effect. Compared with WDM, WVF can reduce the mean square error （MSE） by 50% when the signal to noise ration （SNR） is in the range of-15dB to -11dB. WVF is an effective and promising 1F estimation method.

A simple and efficient method for image transmission over inferior channel

The constrained Viterbi algorithm （C-VA） makes use of some prior reliable information to reduce complexity and improve performance of Viterbi algorithm （VA）. However it can only be used in the concatenate code scheme because the detection result of cyclic redundancy check code （CRC） is exploited to provide reliable information. In this paper, a different way is proposed to provide reliable information for C-VA, which is possible to be used in simple codec. Known bits were added to the set partitioning in hierarchical trees （SPIHT） coded image sequence periodically and directly. They were thought to be reliable information for C-VA in the decoder. Experimental results show that the proposed scheme can obtain much better error resilient ability compared with conventional VA under the extremely inferior channel condition if the best desired quality of reconstructed images can be sacrificed.

Design and Implementation of Single Chip WCDMA High Speed Channel Decoder

A memory and driving clock efficient design scheme to achieve WCDMA high-speed channel decoder on a single XILINX’ XVC1000E FPGA chip is presented. Using a modified MAP algorithm, say parallel Sliding Window logarithmic Maximum A Posterior (PSW-log-MAP), the on-chip turbo decoder can decode an information bit by only an average of two clocks per iteration. On the other hand, a high-parallel pipeline Viterbi algorithm is adopted to realize the 256-state convolutional code decoding. The final decoder with an 8×chip-clock (30 72MHz) driving can concurrently process a data rate up to 2 5Mbps of turbo coded sequences and a data rate over 400kbps of convolutional codes. There is no extern memory needed. Test results show that the decoding performance is only 0 2～0 3dB or less lost comparing to float simulation.

QUASI-OPTIMAL T-VVA IN CODED MIMO SYSTEMS

Vector Viterbi Algorithm (VVA) is an optimal decoding algorithm in multiplechannel environments. It can be applied in coded Multi-Input-Multi-Output (MIMO) systems,but its complexity makes its application difficult. This paper proposes adaptive Threshold VVA (T-VVA) for coded MIMO system. By adaptively choosing a threshold according to Signal to Noise Ratio (SNR) of channel, T-VVA only searches a subset of branchesfor path extension. The easy method to decide the threshold is also derived based on Cheroff bound. Simulation results show that the near-optimal decoding results can be obtained with largely reduced complexity.

Hidden Markov Models for Automatic Speech Recognition

In this paper the authors look into the problem of Hidden Markov Models （HMM）： the evaluation, the decoding and the learning problem. The authors have explored an approach to increase the effectiveness of HMM in the speech recognition field. Although hidden Markov modeling has significantly improved the performance of current speech-recognition systems, the general problem of completely fluent speaker-independent speech recognition is still far from being solved. For example, there is no system which is capable of reliably recognizing unconstrained conversational speech. Also, there does not exist a good way to infer the language structure from a limited corpus of spoken sentences statistically. Therefore, the authors want to provide an overview of the theory of HMM, discuss the role of statistical methods, and point out a range of theoretical and practical issues that deserve attention and are necessary to understand so as to further advance research in the field of speech recognition.

Effects of laser phase noise on the performance of optical coherent receivers

Laser phase noise （LPN） plays an important role in optical coherent systems. Based on the algorithm of Viterbi-Viterbi cartier phase estimation （CPE）, the effects of LPN imposed on the coherent receivers are investigated for quadrature phase shift keying （QPSK）, 8 phase shift keying （SPSK） and 16-quadrature amplitude modulation （16-QAM） optical coherent systems, respectively. The simulation results show that the optimal block length in the phase estimation algorithm is a tradeoffbetween LPN and additive white Gaussian noise （AWGN）, and depends on the level of modulation formats. The resolution requirements of analog to digital converter （ADC） in the coherent receivers are independent of LPN or the level of modulation formats. For the bit error rate （BER） of 10-3, the required bit number of ADC is 6, and the gain is marginal for the higher resolution.

Reduced-state noncoherent MLSD method for CPM in aeronautical telemetry

Due to high spectral efficiency and power efficiency, the continuous phase modulation(CPM) technique with constant envelop is widely used in range telemetry. How to improve the bit error rate(BER) performance of CPM and keep a reasonable computational complexity is the key of the entire telemetry system and the focus of research and engineering design. In this paper, a reduced-state noncoherent maximum likelihood sequence detection(MLSD) method for CPM is proposed. In the proposed method, the criterion of noncoherent MLSD is derived for CPM when the carrier phase is unknown. A novel Viterbi algorithm(VA) with modified state vector is designed to simplify the implementation of noncoherent MLSD. Both analysis and numerical results show that the proposed method reduces the computational complexity significantly and does not need accurate carrier phase recovery, which overcomes the shortage of traditional MLSD method. Additionally, the proposed method exceeds the traditional MLSD method when carrier phase deviation exists.

Data recovery with sub-Nyquist sampling:fundamental limit and a detection algorithm

While the Nyquist rate serves as a lower bound to sample a general bandlimited signal with no information loss,the sub-Nyquist rate may also be sufficient for sampling and recovering signals under certain circumstances.Previous works on sub-Nyquist sampling achieved dimensionality reduction mainly by transforming the signal in certain ways.However,the underlying structure of the sub-Nyquist sampled signal has not yet been fully exploited.In this paper,we study the fundamental limit and the method for recovering data from the sub-Nyquist sample sequence of a linearly modulated baseband signal.In this context,the signal is not eligible for dimension reduction,which makes the information loss in sub-Nyquist sampling inevitable and turns the recovery into an under-determined linear problem.The performance limits and data recovery algorithms of two different sub-Nyquist sampling schemes are studied.First,the minimum normalized Euclidean distances for the two sampling schemes are calculated which indicate the performance upper bounds of each sampling scheme.Then,with the constraint of a finite alphabet set of the transmitted symbols,a modified time-variant Viterbi algorithm is presented for efficient data recovery from the sub-Nyquist samples.The simulated bit error rates(BERs)with different sub-Nyquist sampling schemes are compared with both their theoretical limits and their Nyquist sampling counterparts,which validates the excellent performance of the proposed data recovery algorithm.