7391513
Asymptotic performance of hybrid-selection/maximal-ratio combining over fading channels
In this letter, we study the asymptotic performance of hybrid-selection/maximal-ratio combining (HS/MRC) and postdetection HS/equal-gain combining (HS/EGC) over generalized fading channels for large average signal-to-noise ratios (ASNRs). By evaluating the asymptotic moment generating function of the HS/MRC output SNR at high ASNR, we derive the diversity and coding gains for HS/MRC for a large class of modulation formats and versatile fading conditions, including different types of fading channels and nonidentical SNR statistics across diversity branches. Our analytical results reveal that the diversity gains of HS/MRC and HS/EGC are equivalent to that of MRC, and the difference in the coding gains for different modulation formats is manifested in terms of a modulation factor defined in this letter. Some new analytical results about effects of the number of combined branches for HS/MRC and noncoherent combining loss of HS/EGC are also provided.
13893124
Lifetime Maximization of Sensor Networks Under Connectivity and k-Coverage Constraints
In this paper, we study the fundamental limits of a wireless sensor network's lifetime under connectivity and k-coverage constraints. We consider a wireless sensor network with n sensors deployed independently and uniformly in a square field of unit area. Each sensor is active with probability p, independently from others, and each active sensor can sense a disc area with radius rs. Moreover, considering the inherent irregularity of a sensor's sensing range caused by time-varying environments, we model the sensing radius rs as a random variable with mean r0 and variance r$_{\rm 0}^{\rm 2}$σ$_{s}^{\rm 2}$. Two active sensors can communicate with each other if and only if the distance between them is smaller than or equal to the communication radius rc. 
 
The key contributions of this paper are: (1) we introduce a new definition of a wireless sensor network's lifetime from a novel probabilistic perspective, called ω-lifetime (0 ≤ ω ≤ 1). It is defined as the expectation of the time interval during which the probability of guaranteeing connectivity and k-coverage simultaneously is at least ω; and (2) based on the analysis results, we propose a near-optimal scheduling algorithm, called PIS (Pre-planned Independent Sleeping), to achieve the network's maximum ω-lifetime, which is validated by simulation results, and present a possible implementation of the PIS scheme in the distributed manner.
750827
Outage mutual information of space-time MIMO channels
We derive analytical expressions for the probability density function (pdf) of the random mutual information between transmitted and received vector signals of a random space-time independent and identically distributed (i.i.d.) multiple-input multiple-output (MIMO) channel, assuming that the transmitted signals from the multiple antennas are Gaussian i.i.d.. We show that this pdf can be well approximated by a Gaussian distribution, and such a Gaussian approximation is based on expressions for the given pdfs mean and variance that we derive. We prove that at high signal-to-noise ratio (SNR), every factor of 2 increase in SNR leads to an increase in outage rate in the amount of min(M,N) bits, where M and N denote the number of transmit and receive antennas, respectively. A simple expression for the moment generating function (MGF) of the mutual information pdf is also provided, based on which we establish normality of the pdf, when both M and N are large, and the SNR is large.
32491059
Speech Driven MPEG-4 Based Face Animation via Neural Network
In this paper, some clustering and machine learning methods are combined together to learn the correspondence between speech acoustic and MPEG-4 based face animation parameters. The features of audio and image sequences can be extracted from the large recorded audio-visual database. The face animation parameter (FAP) sequences can be computed and then clustered to FAP patterns. An artificial neural network (ANN) was trained to map the linear predictive coefficients (LPC) and some prosodic features of an individual's natural speech to FAP patterns. The performance of our system shows that the proposed learning algorithm is suitable, which can greatly improve the realism of real time face animation during speech.
236203
New Techniques for DNA Sequence Classification
DNA sequence classification is the activity of determining whether or not an unlabeled sequence S belongs to an existing class C. This paper proposes two new techniques for DNA sequence classification. The first technique works by comparing the unlabeled sequence S with a group of active motifs discovered from the elements of C and by distinction with elements outside of C. The second technique generates and matches gapped fingerprints of S with elements of C. Experimental results obtained by running these algorithms on long and well conserved Alu sequences demonstrate the good performance of the presented methods compared with FASTA. When applied to less conserved and relatively short functional sites such as splice-junctions, a variation of the second technique combining fingerprinting with consensus sequence analysis gives better results than the current classifiers employing text compression and machine learning algorithms.
18764674
Image information restoration based on long-range correlation
A new class of image information restoration algorithms, virtually different from traditional techniques, is proposed. In comparison with other approaches, our methods use not only the information in local areas, but also that in the remote regions of the image. The methods originate from the idea that there exists abundant long-range correlation within natural images and the human vision system, composed of our eyes and brains, can sufficiently utilize such types of information redundancy to implement the functions of image interpretation, representation, restoration, enhancement and error concealment. Our general approach can be summarized as five basic steps: fetching, searching, matching, competing and recovering. The experimental results on several practical applications show that our methods perform substantially better than many other state-of-the-art methods.
46267544
Neighboring Feature Clustering
In spectral datasets, such as those consisting of MR spectral data derived from MS lesions, neighboring features tend to be highly correlated, suggesting the data lie on some low-dimensional space. Naturally, finding such low-dimensional space is of interest. Based on this real-life problem, this paper extracts an abstract problem, neighboring feature clustering (NFC). Noticeably different from traditional clustering schemes where the order of features doesn't matter, NFC requires that a cluster consist of neighboring features, that is features that are adjacent in the original feature ordering. NFC is then reduced to a piece-wise linear approximation problem. We use minimum description length (MDL) method to solve this reduced problem. The algorithm we proposed works well on synthetic datasets. NFC is an abstract problem. With minor changes, it can be applied to other fields where the problem of finding piece-wise neighboring groupings in a set of unlabeled data arises.
14687819
On finite precision implementation of low density parity check codes decoder
In this paper, we analyze the finite precision effects on the decoding performance of Gallager's low density parity check (LDPC) codes and develop optimal finite word lengths of variables as far as the tradeoffs between the performance and hardware complexity are concerned. We have found that 4 bits and 6 bits are adequate for representing the received data and extrinsic information, respectively. Simulation results indicate that the quantization scheme we have developed for the LDPC decoder is effective in approximating the infinite precision implementation.