There have been many matching pursuit algorithms (MPAs) which handle the sparse signal recovery problem, called compressed sensing (CS). In the MPAs, the correlation step makes a dominant computational complexity. In this paper, we propose a new fast correlation method for the MPA when we use partial Fourier sensing matrices and partial Hadamard sensing matrices which are widely used as the sensing matrix in CS. The proposed correlation method can be applied to almost all MPAs without causing any degradation of their recovery performance. Also, the proposed correlation method can reduce the computational complexity of the MPAs well even though there are restrictions depending on a used MPA and parameters.

Quantity-based event reliability protocols have been proposed for reliable event detection in wireless sensor networks. They support the event reliability by achieving the desired number of data packets successfully transmitted from sensor nodes sensing an event to a sink by controlling the transport process. However, since many data collisions and buffer overflows frequently happen due to data congestions on limited data delivery paths from an event to a sink, the quantity-based event reliability protocols are hard to achieve the desired number due to lost data packets. Thus, this letter proposes a Quality-based Event Reliability Protocol (QERP) utilizing a property that the data packets from sensor nodes have different Contribution Degree (CD) values for event detection according to their environmental conditions. QERP selects sensor nodes to forward their data packets according to CD, and differentially transports the data packets by CD-based buffer management and load balancing.

The conventional generalized spatial modulation (GSM) simultaneously activates multiple transmit antennas in order to improve the spectral efficiency of the original SM. In this letter, to lessen the hardware burden of the multiple RF chains, we provide a new scheme that is designed by combining the GSM scheme using only two active antennas with quaternary quasi-orthogonal sequences of a length of two. Compared with the other SM schemes, the proposed scheme has significant benefits in average error performances and/or their hardware complexities of the RF systems.

To construct good DNA codes based on biologically motivated constraints, it is important that they have a large minimum Hamming distance and the number of GC-content is kept constant. Also, maximizing the number of codewords in a DNA code is required for given code length, minimum Hamming distance, and number of GC-content. In most previous works on the construction of DNA codes, quaternary constant weight codes were directly used because the alphabet of DNA strands is quaternary. In this paper, we propose new coding theoretic constructions of DNA codes based on the binary Hadamard matrix from a binary sequence with ideal autocorrelation. The proposed DNA codes have a greater number of codewords than or the equal number to existing DNA codes constructed from quaternary constant weight codes. In addition, it is numerically shown that for the case of codes with length 8 or 16, the number of codewords in the proposed DNA code sets is the largest with respect to the minimum reverse complementary Hamming distances, compared to all previously known results.

In this letter, we propose a multi-stage decoding scheme with post-processing for low-density parity-check (LDPC) codes, which remedies the rapid performance degradation in the high signal-to-noise ratio (SNR) range known as error floor. In the proposed scheme, the unsuccessfully decoded words of the previous decoding stage are re-decoded by manipulating the received log-likelihood ratios (LLRs) of the properly selected variable nodes. Two effective criteria for selecting the probably erroneous variable nodes are also presented. Numerical results show that the proposed scheme can correct most of the unsuccessfully decoded words of the first stage having oscillatory behavior, which are regarded as a main cause of the error floor.

Energy-efficient tracking of continuous objects such as fluids, gases, and wild fires is one of the important challenging issues in wireless sensor networks. Many studies have focused on electing fewer nodes to report the boundary information of continuous objects for energy saving. However, this approach of using few reporting packets is very sensitive to packet loss. Many applications based on continuous objects tracking require timely and precise boundary information due to the danger posed by the objects. When transmission of reporting packets fails, applications are unable to track the boundary reliably and a delay is imposed to recover. The transmission failure can fatally degrade application performance. Thus, it is necessary to consider just-in-time recovery for reliable continuous object tracking. Nevertheless, most schemes did not consider the reliable tracking to handle the situation that packet loss happen. Recently, a scheme called I-COD with retransmission was proposed to recover lost packets but it leads to increasing both the energy consumption and the tracking latency owing to the retransmission. Thus, we propose a reliable tracking scheme that uses fast recovery with the redundant boundary information to track continuous objects in real-time and energy-efficiently. In the proposed scheme, neighbor nodes of boundary nodes gather the boundary information in duplicate and report the redundant boundary information. Then the sink node can recover the lost packets fast by using the redundant boundary information. The proposed scheme provides the reliable tracking with low latency and no retransmissions. In addition, the proposed scheme saves the energy by electing fewer nodes to report the boundary information and performing the recovery without retransmissions. Our simulation results show that the proposed scheme provides the energy-efficient and reliable tracking in real-time for the continuous objects.

In this paper, to provide scalability and mobility of sinks and events, we propose a new independent structure-based routing protocol which exploits a k-level Independent Grid Structure (IGS) for data dissemination from sources to sinks. Beside the k-level IGS, the proposed protocol does not to construct any additional routing structure irrespective of the number and the movement of both sinks and events. The proposed protocol also allows query or report packets to be sent to only a few grid headers in the k-level IGS and provides aggregation and multicasting of report packets by the k-level IGS. Simulation results show that the proposed protocol is superior to other protocols regarding to energy-efficiency.

Hidden Singer is a television program in Korea. In the show, the original singer and four imitating singers sing a song in hiding behind a screen. The audience and TV viewers attempt to guess who the original singer is by listening to the singing voices. Usually, there are few correct answers from the audience, because the imitators are well trained and highly skilled. We propose a computerized system for distinguishing the original singer from the imitating singers. During the training phase, the system learns only the original singer's song because it is the one the audience has heard before. During the testing phase, the songs of five candidates are provided to the system and the system then determines the original singer. The system uses a 1-class authentication method, in which only a subject model is made. The subject model is used for measuring similarities between the candidate songs. In this problem, unlike other existing studies that require artist identification, we cannot utilize multi-class classifiers and supervised learning because songs of the imitators and the labels are not provided during the training phase. Therefore, we evaluate the performances of several 1-class learning algorithms to choose which one is more efficient in distinguishing an original singer from among highly skilled imitators. The experiment results show that the proposed system using the autoencoder performs better (63.33%) than other 1-class learning algorithms: Gaussian mixture model (GMM) (50%) and one class support vector machines (OCSVM) (26.67%). We also conduct a human contest to compare the performance of the proposed system with human perception. The accuracy of the proposed system is found to be better (63.33%) than the average accuracy of human perception (33.48%).