An underactuated robotic finger is proposed and studied for capturing non-cooperative objects, featuring an underactuated rigid-mechanism that can brake residual motion of non-cooperative targets. The finger consists of three joints driven by two actuators, and a set of one-input-two-output planetary gears is introduced to realize force transmission by using the ring gear and the planetary carrier to drive the two joints. The rotation of the ring gear and the planetary carrier are governed jointly by the inputs and the external force. In the process of capturing a moving target, the proposed robotic finger can effectively limit the impact force peak due to collision with the target. Moreover, the recovery time of the joint can be adjusted by changing the input torque of the motor, which can be set to achieve optimal recovery process for different tasks. A prototype has been built to study the proposed design of the robotic finger, and experimental results have confirmed its desirable characteristics.

For iterative mean-square error (MSE) quantizers with alphabet size M=2Kusing low-density generator-matrix (LDGM) code constructions, an efficient recovery algorithm is proposed, which adjusts the priors used in belief propagation (BP) to limit the impact of previous non-ideal decimation steps. Based on an analysis of the BP process under ideal or non-ideal decimation, the algorithm first estimates the conditional probability distributions describing the effect of non-ideal decimation, then adjusts the priors to make the distributions match the ideal situation. As shown in simulation results, the recovery algorithm can improve quantization performance greatly, reducing the shaping loss to as low as 0.012 dB, while the increase in computational complexity is modest thanks to the use of FFT techniques.

The superior shaping performance of low-density generation matrix (LDGM) codes makes them important in the design of capacity-approaching dirty paper coding (DPC) schemes. One possible LDGM-based DPC scheme is the nested LDGM-LDPC code proposed by Wainwright and Martinian, which has been shown to be capacity-achieving under optimal encoding and decoding. In this paper, considering the Gaussian DPC problem limited to a binary alphabet, we propose practical encoding and decoding algorithms for the nested LDGM-LDPC code utilizing ideas from the fast LDPC encoding algorithm to satisfy the parity constraints, and optimize the degree distributions of the code through density evolution. Simulation results show that the proposed scheme can indeed closely approach the alphabet-constrained DPC capacity.

The closure of the set of entropy functions associated with n discrete variables, ¿¿¿¿¿¿n*, is a convex cone in (2n- 1)-dimensional space, but its full characterization remains an open problem. In this paper, we map ¿¿¿n* to an n-dimensional region ¿¿n* by averaging the joint entropies with the same number of variables, and show that the simpler ¿¿n* can be characterized solely by the Shannon-type information inequalities.

Approaching the 1.53-dB shaping gain limit in mean-squared error (MSE) quantization of Rnis important in a number of problems, notably dirty-paper coding. Unlike the traditional method of trellis-coded quantization (TCQ), in this paper we propose quantization codebooks constructed from binary low-density generation-matrix (LDGM) codes and from two such codes combined with Gray mapping. The quantization algorithm is based on belief propagation, and it uses a novel adaptive decimation procedure to do the guessing necessary for convergence. Simulation results show that the proposed code can achieve a shaping gain of 1.477 dB, or 0.056 dB from the limit, which is significantly better than the 1.40 dB previously achieved with TCQ.

In this paper, we propose a link model for TD-SCDMA systems for use by a system-level simulator to estimate block error rates (BLER) without doing time-consuming physical-layer simulations. Our model averages the different qualities of the physical channels using the mutual information between the coded bits at the transmitter and the post-rate-dematching soft information at the receiver, resulting in higher accuracy compared to existing models that average e.g. the raw bit error rate (BER) and that do not take rate-matching into account. Our model also supports the large range of possible coding rates and code block sizes in a generic way, greatly reducing the amount of simulation data that have to be included in the model when many different services are used. Simulations show that the accuracy of our model stays within 0.2 dB for Turbo coding and 0.5 dB for convolutional coding, even in Rayleigh fading channels.