Regenerating codes provide an efficient way to recover data at failed nodes in distributed storage systems. It has been shown that regenerating codes can be designed to minimize the per-node storage (called MSR) or minimize the communication overhead for regeneration (called MBR). In this work, we propose new encoding schemes for error-correcting MSR and MBR codes that generalize our earlier results on error-correcting regenerating codes. General encoding schemes for product-matrix MSR and MBR codes are derived such that the encoder based on Reed-Solomon (RS) codes is no longer limited to the Vandermonde matrix proposed earlier. Furthermore, MSR codes and MBR codes with the least update complexity can be found. A decoding scheme is proposed that utilizes RS codes to perform data reconstruction for MSR codes. The proposed decoding scheme has better error correction capability and incurs least number of node accesses when errors are present. A new decoding scheme is also proposed for MBR codes that is more capable and can correct more error-patterns. Simulation results are presented that exhibit the superior performance of the proposed schemes.

Today's large-scale distributed storage systems are commonly built using commodity software and hardware. As a result, crash-stop and Byzantine failures in such systems become more and more prevalent. In the literature, regenerating codes have been shown to be a more efficient way to disperse information across multiple storage nodes and recover from crash-stop failures. In this paper, we propose a novel decoding design of product-matrix constructed regenerating codes in conjunction with integrity check that allows exact regeneration of failed nodes and data reconstruction in the presence of Byzantine failures. A progressive decoding mechanism is incorporated in both procedures to leverage computation performed thus far. Unlike previous works, our new regenerating code decoding has the advantage that its building blocks, such as Reed-Solomon codes and standard cryptographic hash functions, are relatively well-understood because of their widespread applications. The fault tolerance and security properties of the proposed schemes are also analyzed. In addition, the performance of the proposed schemes, in terms of the average number of access nodes and the reconstruction failure probability versus the node failure probability, are also evaluated by Monte Carlo simulations.

Regenerating codes provide an efficient way to recover data at failed nodes in distributed storage systems. It has been shown that regenerating codes can be designed to minimize the per-node storage (called MSR) or minimize the communication overhead for regeneration (called MBR). In this work, we propose a new encoding scheme for [n, d] error-correcting MSR codes that generalizes our earlier work on error-correcting regenerating codes. We show that by choosing a suitable diagonal matrix, any generator matrix of the [n, α] Reed-Solomon (RS) code can be integrated into the encoding matrix. Hence, MSR codes with the least update complexity can be found. An efficient decoding scheme is also proposed that utilizes the [n, α] RS code to perform data reconstruction. The proposed decoding scheme has better error correction capability and incurs the least number of node accesses when errors are present.

In this work, we proposed a reliability-based enhancement for the decoding of convolutional tail-biting codes (CTBC) from the observations that the decoding does not have to start from the beginning of the received vector, and that the reliability of the received vector can be used to determine a good starting position of the decoding process. Simulations show that our reliability-based enhancement can be used together with existing decoding algorithms of the CTBC to improve either their error rate or complexity.

Data fusion, in which collected data are fused before they are sent to the base station, is usually implemented over the wireless sensor network. Since a sensor is typically placed in locations that are accessible to malicious attackers, information assurance of the data fusion process is very important. A witness-based approach has been proposed to verify the fusion data. In this approach, the base station receives the fusion data and ``votes'' on the data from a randomly chosen sensor node. The vote comes from other sensor nodes, called ``witnesses,'' to confirm the correctness of the fusion data. Since the base station receives the vote through the chosen node, this node could forge the vote if it is compromised.. This work improves the witness-based approach using a direct voting mechanism, such that the proposed scheme performs better in terms of assurance, overhead and delay. The witness node transmits the vote directly to the base station. Forgery does not pose a problem in this scheme. Moreover, fewer bits are necessary to represent the vote, significantly reducing the power consumption. Performance analysis and simulation results indicate that the proposed approach has a 40-times lower overhead than the witness-based approach.

This work proposes a novel fault-tolerant classification system based on distributed detection and two-dimensional channel coding. A rule is then derived to reduce the search space such that the optimal code matrix can be found. Simulation results reveal that the proposed scheme has higher classification reliability and better capability of fault tolerance than previous methods. Moreover, a code matrix using repetition codes is presented. The proposed scheme with the repetition code has a lower memory requirement at each sensor and higher detection flexibility than that with the optimal code matrix while only having a slightly lower performance. Finally, an asymptotic performance analysis is provided for the proposed scheme.

An antenna selection method based on channel state information (CSI) is proposed for an MIMO-OFDM system. In this method, the receiver of the system exploits selection combing to demodulate received signals and estimates the CSI of each pair of transmit and receive antennas. For each subchannel of the system, the transmit antenna in the pair with the maximum magnitude of subchannel response is selected to carry a data sym- bol. The transmitter then is informed of the selection through a feedback channel. As a result, the probability that the magnitude of the subchannel response is small becomes much lower. Since most symbol errors occur while the magnitude of the subchannel response is small, the symbol error rate (SER) of the system can be significantly reduced. The SER expressions for BPSK and M-QAM are provided. Moreover, we combine this method with full-rate full-diversity space-frequency codes. Performance of the space-frequency code is dramatically improved.

In this paper, we present a broadband wireless communication system. This system is designed using updated techniques. These techniques consist of space-time coding, turbo codes, and OFDM. Moreover, a differential detection scheme is also included such that neither channel estimation nor channel equalization is required. Soft output of the detection is derived for turbo decoding. At a throughput of 2 bit per symbol, BER of the system can reach 10/sup -5/ while E/sub b//N/sub 0/ is less than 10.5 dB.

Existing eigenstructure-based direct multichannel blind image restoration techniques include nullspace-based and direct deconvolver estimation techniques. The nullspace-based approach can be formulated as an optimization problem. We show that this formulation implies a new subspace-based approach that uses matrix operations. This new approach has the same advantages as the nullspace-based one but requires less computational complexity. Under some mild conditions, its complexity is equal to that of the FFT. Furthermore, the relation among the nullspace-based approach, the direct deconvolver estimation and the new subspace-based approach is studied.

We have previously developed an algorithm and sufficient conditions for exact multichannel blind image restoration. In this paper, we use the resultant matrix theorem and techniques of algebraic geometry to prove that the sufficient conditions hold generically given three blurred versions of the same image and some restrictions on the size of the original image. Moreover, the extension to multichannel blind n-dimensional signal restoration is described.

Wavelet techniques have achieved a tremendous success in image data compression. In designing wavelet coding algorithms, the choice of wavelet systems is of great importance for compression performance. We design the entire class of antisymmetric biorthogonal coiflet systems, whose filterbanks have even lengths and are linear phase. We show that one of the novel filterbanks achieves noticeably better rate-distortion performance than several state-of-the-art filterbanks in image coding.

Liu and Xu (see ibid., vol.43, no.11, p.2714-23, 1995) proposed a blind symbol estimation algorithm in digital communications. If all the channels are linear time-invariant finite impulse response (LTI FIR) filters, this algorithm can estimate symbols exactly when the channels and the input symbols satisfy some loose restrictions. We extend this algorithm to two dimensions, and in particular, to multichannel (noise-free) blind image restoration. Assuming linear FIR blur functions, given at least three blurred versions of the same image, and loose restrictions (different from those of Liu and Xu) on the original image and on the blur functions, the original image can be exactly recovered, up to a scalar multiplier.