Recent studies revealed that hand gesture-based interfaces can complement therapies for individuals with upper motor impairments and reduce the need of traditional rehabilitation sessions through hospital visits. Unfortunately, existing gesture-based interfaces have been developed without considering the physical limitations of users with motor impairments. An analytic approach was presented in our previous work to convert existing gesture-based interfaces designed for able-bodied individuals to be usable by individuals with quadriplegia using the Laban Theory of Movement. This paper extends the previous work by including gesture variability analysis (based on Uncontrolled Manifolds theory) and robotic execution. A WAM robotic arm was used to mimic gesture trajectories and a physical metric was empirically obtained to evaluate the physical effort of each gesture. At last, an integration method was presented to determine the accessible gesture set based on both the stability and empirical robot execution. For all the gesture classes, the accessible gestures were found to lie within 31% of the optimality of stability and work, respectively.

In this paper, the existence of capacity-achieving codes for memoryless binary-input output-symmetric (MBIOS) channels under maximum-likelihood (ML) decoding with bounded graphical complexity is investigated. Graphical complexity of a code is defined as the number of edges in the graphical representation of the code per information bit and is proportional to the decoding complexity per information bit per iteration under iterative decoding. Irregular repeat-accumulate (IRA) codes are studied first. Utilizing the asymptotic average weight distribution (AAWD) of these codes and invoking Divsalar's bound on the binary-input additive white Gaussian noise (BIAWGN) channel, it is shown that simple nonsystematic IRA ensembles outperform systematic IRA and regular low-density parity-check (LDPC) ensembles with the same graphical complexity, and are at most 0.124 dB away from the Shannon limit. However, a conclusive result as to whether these nonsystematic IRA codes can really achieve capacity cannot be reached. Motivated by this inconclusive result, a new family of codes is proposed, called low-density parity-check and generator matrix (LDPC-GM) codes, which are serially concatenated codes with an outer LDPC code and an inner low-density generator matrix (LDGM) code. It is shown that these codes can achieve capacity on any MBIOS channel using ML decoding and also achieve capacity on any BEC using belief propagation (BP) decoding, both with bounded graphical complexity. Moreover, it is shown that, under certain conditions, these capacity-achieving codes have linearly increasing minimum distances and achieve the asymptotic Gilbert–Varshamov bound for all rates.

We propose a high quality and imperceptible reversible data hiding method in this paper. We divide the cover image into 5 by 5 blocks with non-overlapping boundaries, shrink them, and generate back to 5 by 5 blocks by interpolation technique. If the absolute value of the difference between the interpolation value and the original gray level is less than a threshold in the middle of the four 3 by 3 overlapping blocks of each 5 by 5 block. We may embed one bit of the secret data in this position. We apply PRNG to shuffle the hidden data before embedding. Simply follow the embedding steps to extract the embedded data where the location is embedded then shuffle back the order to find the hidden data. The experiment results demonstrate our method is quite imperceptible with high quality and can obtain the original cover image after extracting all hidden secret data.

The performance of punctured low-definition parity-check (LDPC) codes under maximum-likelihood (ML) decoding is studied in this correspondence via deriving and analyzing their average weight distributions (AWDs) and the corresponding asymptotic growth rate of the AWDs. In particular, it is proved that capacity-achieving codes of any rate and for any memoryless binary-input output-symmetric (MBIOS) channel under ML decoding can be constructed by puncturing some original LDPC code with small enough rate. Moreover, it is shown that the gap to capacity of all the punctured codes can be the same as the original code with a small enough rate. Conditions under which puncturing results in no rate loss with asymptotically high probability are also given in the process. These results show high potential for puncturing to be used in designing capacity-achieving codes, and in rate-compatible coding under any MBIOS channel.

Embedded systems such as mobile phones widely adopt heterogeneous dual-core processors to improve the performance and reduce the cost and power consumption. Considering mobile devices that are usually battery-operated, one of the critical issues for such an embedded system is software design which must fully utilize the computation power and minimizes the power consumption. A number of power consumption models were proposed to facilitate designers to evaluate and diagnose the power consumption of the software. Unfortunately, previous studies only investigated single-core processors, but have not yet developed power consumption models for heterogeneous dual-core processors. In this paper, a power consumption model for the dual-core processor based on TI OMAP5912 is proposed for evaluating the power consumption of embedded software. This tool not only estimates the energy consumed by embedded software but also analyzes the energy distributions of the software on each processor core. With the aid of this tool, designers could diagnose the power consumption of dual-core embedded software efficiently and easily.

In this paper, the average input-parity weight enumerator (AIPWE) of regular/irregular repeat-accumulate (RA) ensembles is derived, by viewing an RA code as a serial concatenation of an outer low-density generator matrix (LDGM) code and an inner accumulator. The exact average weight distribution (AWD) of the systematic and nonsystematic versions of the RA ensembles are then obtained from their AIPWE's. We further derive the asymptotic growth exponent of the AWD's, which are then used to bound the ensemble performance under maximum likelihood (ML) decoding. It is shown that simple nonsystematic regular RA ensembles outperform systematic regular RA and regular low-density parity-check (LDPC) ensembles and have distance spectrum which closely resembles that of the random ensemble.

The problem of joint data detection and frequency/phase estimation is considered in this paper. The traditional belief regarding exact generalized-likelihood-based joint detection and estimation is that its complexity is exponential in the sequence length N. This belief is justified due to the memory imposed on the transmitted sequence by the lack of knowledge of the auxiliary channel parameters. In this paper, we show that the exact solution can be performed with O(N/sup 4/) worst case complexity regardless of the operating signal-to-noise ratio. The concepts used in the proof of the polynomial complexity result are also utilized to evaluate tight performance bounds on the exact and a family of approximate algorithms.

The problem of joint data detection and frequency/phase estimation is considered in this work. The traditional belief regarding exact generalized-likelihood-based joint detection and estimation is that its complexity is exponential in the sequence length N. This belief is justified due to the memory imposed on the transmitted sequence by the lack of knowledge of the auxiliary channel parameters. In this paper, we show that the exact solution can be performed with O(N/sup 5/) worst-case complexity regardless of the operating signal-to-noise ratio. In addition, based on the exact solution, we propose an approximate algorithm with O(N/sup 3/) complexity and demonstrate its performance through simulations.

The problem of optimal decoding of a trellis coded sequence transmitted over a frequency nonselective, time-selective fading channel is considered in this paper. In particular, the case where the channel state information (CSI) is unknown to the receiver, thus Viterbi's algorithm (VA) can not be employed to find the maximum a posteriori probability sequence detection (MAPSqD) solution with linear complexity in sequence length N. It is proved that for two-state trellis, the exact solution can indeed be obtained with only polynomial complexity in N for any signal-to-noise ratio.