Shadow puppet play is a representative Chinese intangible cultural heritage, which has a history of more than two thousand years. However, with the popularity of digital media, this traditional art form has become desolate. "Reconstruction" is an interactive digital artwork inspired by the production and performance of Chinese shadow puppet. The scenes and characters are designed based on the art style of shadow puppet. The participant's motion is captured with a Kinect and used to control the motion of the character.

In this study, the authors focus on pulse modulation waveform estimation based on a sequence of intercepted pulses from the same radar emitter. In general, modulation waveform estimators first perform pulse alignment in time and frequency separately, and then accumulate the aligned pulses to estimate the waveform. However, commonly used alignment methods may lead to considerable alignment errors which are difficult to detect and compensate, especially under low signal‐to‐noise ratio (SNR) conditions, resulting in non‐ideal effects of accumulation. This study proposes a robust and nearly optimal modulation waveform estimation algorithm. The new algorithm first aligns pulses in time and frequency jointly via the cross‐ambiguity function to avoid the transfer and accumulation of alignment errors. After that, an iterative maximum‐likelihood estimator is invoked to achieve the waveform estimation. Theoretical analysis and extensive experiments show that the proposed algorithm has much smaller alignment errors and better modulation waveform and modulation parameter estimation ability than competing methods at low SNRs, and can approach the ideal case. Moreover, this algorithm does not make any assumption on the type of modulation and is computationally efficient, thus having broad applications.

The paper considers the problem of reconstructing blocks-sparse signals. A new algorithm, called synthesized multitask compressive sensing (SMCS), is proposed. In contrast to existing methods that rely on the availability of the sparsity structure information, the SMCS algorithm resorts to the multitask compressive sensing (MCS) technique for signal recovery. The SMCS algorithm synthesizes new compressive sensing (CS) tasks via circular-shifting operations and utilizes the minimum description length (MDL) principle to determine the proper set of the synthesized CS tasks for signal reconstruction. An outstanding advantage of SMCS is that it can achieve good signal reconstruction performance without using prior information on the block-sparsity structure. Simulations corroborate the theoretical developments.

This paper proposes a method for estimating the direction of arrival (DOA) of multi-path signals for an underdetermined case, where the number of incident signals exceeds the number of sensors. Some DOA estimation methods have already been proposed for multi-path signals. However, most of them cannot be used for an underdetermined case. To deal with this problem, we propose a novel DOA estimation method which combines independent component analysis (ICA) and sparse reconstruction algorithm. It can estimate DOA of M ×(M - 1) incident signals when the number of sensors is M. The simulation results demonstrate the promising performance of the proposed algorithm compared to several state of the art methods.