Wireless sensor networks (WSNs) are ubiquitous in a wide range of applications requiring the monitoring of physical and environmental variables, such as target localization and identification. One of these applications is the sensing of ferromagnetic objects. In typical applications, the area to be monitored is typically large compared to the sensing radius of each magnetic sensor. On the other hand, the RF communication radii of WSN nodes are invariably larger than the sensing radii. This makes it economical and efficient to design and implement a sparse network in terms of sensor coverage, in which each point in the monitored area is likely to be covered by at most one sensor. This work aims at investigating the sensing potential and limitations (e.g. in terms of localization accuracy on the order of centimeters) of the Honeywell HMC 1002 2-axis magnetometer used in the context of a sparse magnetic WSN. The effect of environmental variations, such as temperature and power supply fluctuations, magnetic noise, and sensor sensitivity, on the target localization and identification performance of a magnetic WSN is examined based on experimental tests. Signal processing strategies that could enable an alternative to the typical “target present/absent” mode of using magnetic sensors, such as providing successive localization information in time, are discussed.

In this paper, a novel approach for central angle estimation of coherently distributed targets that utilizes electric vector sensors in bistatic MIMO radar is proposed. First, the coherently distributed targets signal model in bistatic MIMO radar that equipped with electric vector sensors is reconstructed. The Hadamard product rotation invariance property of the coherently distributed targets' steering vectors is found to get the initial estimation of direction of departure (DOD). 1-D MUSIC is then used to estimate the accurate central angles of direction of arrival (DOA) and DOD. The proposed method can estimate the central angles of DOA and DOD efficiently and accurately without pairing even in the situation where the angular signal distribution functions are unknown. Our method has better performance than Guo's algorithm. Numerical results verify the improvement and performance of the proposed algorithm.

In this paper, we study a distributed time-reversal space-time block coded single-carrier (D-TR-STBC-SC) system for amplify-and-forward (AF) half-duplex relaying in frequency-selective Rayleigh fading channels. Under the imperfect channel estimation condition, we analyze the mean-square-error (MSE) performance of the optimal and channel-mismatched frequency domain minimum MSE (FD-MMSE) and least square (LS) equalization. Our analysis results show that, unlike the point-to-point communications, the channel-mismatched FD-MMSE equalization of D-TR-STBC-SC relaying network leads to the ceiling effect that the MSE increases as the signal-to-noise ratio (SNR) of relay-to-destination link increases. Decomposing the MSE, it is found that the primary cause of the ceiling effect is the source-to-destination link in the first time-slot, which makes the covariance matrix of noise vector ill-conditioned. In order to resolve the channel-mismatching problems in the equalization process, we develop optimum relay power control strategies by considering practical channel estimations, i.e., training-based LS and linear minimum MSE (LMMSE) channel estimations. It is shown that the optimum power control resolves the trade-off between MSE performance and relay power consumption, and improves the robustness against the channel-mismatching. Finally, we introduce a performance evaluation to demonstrate the performance of channel equalization combined with the proposed power controls in D-TR-STBC-SC relaying network.

Instrumental variable (IV) filters designed for range sidelobe suppression in multiple-input multiple-output (MIMO) radar suffer from Doppler mismatch. This mismatch causes losses in peak response and increases sidelobe levels, which affect the performance of MIMO radar. In this paper, a novel method using the component-code processing prior to the IV filter design for MIMO radar is proposed. It not only compensates for the Doppler effects in the design of IV filter, but also offers more virtual sensors resulting in narrower beams with lower sidelobes. Simulation results are presented to verify the effectiveness of the method.

Association rule mining is a powerful data mining tool, and it can be used to discover unknown patterns from large volumes of data. However, people often have to face the risk of disclosing sensitive information when data is shared with different organizations. The association rule mining techniques may be improperly used to find sensitive patterns which the owner is unwilling to disclose. One of the great challenges in association rule mining is how to protect the confidentiality of sensitive patterns when data is released. Association rule hiding refers to sanitize a database so that certain sensitive association rules cannot be mined out in the released database. In this study, we proposed a new method which hides sensitive rules by removing some items in a database to reduce the support or confidence levels of sensitive rules below specified thresholds. Based on the information of positive border rules and negative border rules contained in transactions, the proposed method chooses suitable candidates for modification aimed at reducing the side effects and the data distortion degree. Comparative experiments on real datasets and synthetic datasets demonstrate that the proposed method can hide sensitive rules with much fewer side effects and database modifications.

This paper proposes a voice activity detection (VAD) algorithm based on an energy related feature of the frequency modulation of harmonics. A multi-resolution spectro-temporal analysis framework, which was developed to extract texture features of the audio signal from its Fourier spectrogram, is used to extract frequency modulation features of the speech signal. The proposed algorithm labels the voice active segments of the speech signal by comparing the energy related feature of the frequency modulation of harmonics with a threshold. Then, the proposed VAD is implemented on one of Texas Instruments (TI) digital signal processor (DSP) platforms for real-time operation. Simulations conducted on the DSP platform demonstrate the proposed VAD performs significantly better than three standard VADs, ITU-T G.729B, ETSI AMR1 and AMR2, in non-stationary noise in terms of the receiver operating characteristic (ROC) curves and the recognition rates from a practical distributed speech recognition (DSR) system.

This letter introduces an Energy-Aware LED Light System (EA-LLS) that provides adequate illumination to users according to the analysis of the sun's position, the user's movement, and various environmental factors, without sun illumination detection sensors. This letter presents research using algorithms and scenarios. We propose an EA-LLS that offers not only On/Off and dimming control, but dimming control through daylight, space, and user behavior analysis.

Most of the current voice conversion methods are conducted based on parallel speech, which is not easily obtained in practice. In this letter, a novel iterative speaker model alignment (ISMA) method is proposed to address this problem. First, the source and target speaker models are each trained from the background model by adopting maximum a posteriori (MAP) algorithm. Then, a novel ISMA method is presented for alignment and transformation of spectral features. Finally, the proposed ISMA approach is further combined with a Gaussian mixture model (GMM) to improve the conversion performance. A series of objective and subjective experiments are carried out on CMU ARCTIC dataset, and the results demonstrate that the proposed method significantly outperforms the state-of-the-art approach.

We propose a new extension to reconfiguration algorithms used to address wavelength defragmentation to enhance the path accommodation efficiency in optical transparent wavelength division multiplexing networks. The proposed algorithm suppresses the number of fibers employed to search for a reconfigurable wavelength channel by combining routes between the target path and the existing path in a reconfigured wavelength channel. This paper targets three main phases in reconfiguration: i) the reconfiguration trigger; ii) redesign of the wavelength path; and iii) migrating the wavelength paths. The proposed and conventional algorithms are analyzed from the viewpoints of the number of fibers, accommodation rate and the number of migrating sequences. Numerical evaluations show that the number of fibers is suppressed by 9%, and that the accommodation efficiency is increased by approximately 5%-8% compared to when reconfiguration is not performed.

Code-based public-key encryption schemes (PKE) are the candidates for post-quantum cryptography, since they are believed to resist the attacks using quantum algorithms. The most famous such schemes are the McEliece encryption and the Niederreiter encryption. In this paper, we present the zero-knowledge (ZK) proof systems for proving statements about data encrypted using these schemes. Specifically, we present a proof of plaintext knowledge for both PKE's, and also a verifiable McEliece PKE. The main ingredients of our constructions are the ZK identification schemes by Stern from Crypto'93 and by Jain, Krenn, Pietrzak, and Tentes from Asiacrypt'12.

Sparse Matrix-Vector Multiplication (SpMxV) is widely used in many high-performance computing applications, including information retrieval, medical imaging, and economic modeling. To eliminate the overhead of zero padding in SpMxV, prior works have focused on partitioning a sparse matrix into row vectors sets (RVS's) or sub-matrices. However, performance was still degraded due to the sparsity pattern of a sparse matrix. In this letter, we propose a heuristics, called recursive merging, which uses a greedy approach to recursively merge those row vectors of nonzeros in a matrix into the RVS's, such that each set included is ensured a local optimal solution. For ten uneven benchmark matrices from the University of Florida Sparse Matrix Collection, our proposed partitioning algorithm is always identified as the method with the highest mean density (over 96%), but with the lowest average relative difference (below 0.07%) over computing powers.

Combining heterogeneous wireless networks that cross licensed and unlicensed spectra is a promising way of supporting the surge in mobile traffic. The unlicensed band is mostly used by wireless LAN (WLAN) nodes which employ carrier sense multiple access/collision avoidance (CSMA/CA). Since the number of WLAN devices and their traffic are increasing, the wireless resource of the unlicensed band is expected be more depleted in 2020s. In such a wireless environment, the throughput could be extremely low and unstable due to the hidden terminal problem and exposed terminal problem despite of the large resources of the allocated frequency band and high peak PHY rate. In this paper, we propose user equipment (UE) centric access in the unlicensed band, with support by licensed band access in the mobile network. The proposed access enables robust downlink transmission from the access point (AP) to the UEs by mitigating the hidden terminal problem. The licensed spectrum access passes information on the user data waiting at the AP to the UEs and triggers UE reception opportunity (RXOP) acquisition. Furthermore, the adaptive use of UE centric downlink access is presented by using the channel utilization measured at the AP. Computer simulations confirm that licensed access assistance enhances the robustness of the unlicensed band access against the hidden terminal problem.

Although the use of software-defined networking (SDN) enables routes of packets to be controlled with finer granularity (down to the individual flow level) by using traffic engineering (TE) and thereby enables better balancing of the link loads, the corresponding increase in the number of states that need to be managed at routers and controller is problematic in large-scale networks. Aggregating flows into macro flows and assigning routes by macro flow should be an effective approach to solving this problem. However, when macro flows are constructed as TE targets, variations of traffic rates in each macro flow should be minimized to improve route stability. We propose two methods for generating macro flows: one is based on a greedy algorithm that minimizes the variation in rates, and the other clusters micro flows with similar traffic variation patterns into groups and optimizes the traffic ratio of extracted from each cluster to aggregate into each macro flow. Evaluation using traffic demand matrixes for 48 hours of Internet2 traffic demonstrated that the proposed methods can reduce the number of TE targets to about 1/50 ∼ 1/400 without degrading the link-load balancing effect of TE.

In this paper, we propose a transmit multi-block frequency-domain equalization (MB-FDE) for frequency-domain space-time block coded joint transmit/receive diversity (FD-STBC-JTRD). Noting that a STBC codeword consists of multiple coded blocks, the transmit MB-FDE uses the multiple transmit FDE weight matrices, each associated with each coded block. Both single-carrier (SC) transmission and orthogonal frequency-division multiplexing (OFDM) transmission are considered. For SC transmission, the transmit MB-FDE weight matrices are jointly optimized so as to minimize the mean square error (MSE) between the transmit signal before STBC encoding and the received signal after STBC decoding. For OFDM transmission, they are jointly optimized so as to maximize the received signal-to-noise power ratio (SNR) after STBC decoding. We show by theoretical analysis that the proposed transmit MB-FDE can achieve 1/RSTBC times higher received SNR than the conventional transmit single-block FDE (SB-FDE), where RSTBC represents the code rate of STBC. It is confirmed by computer simulation that, when more than 2 receive antennas are used, MB-FDE can always achieve better BER performance than SB-FDE irrespective of the number of transmit antennas, and the channel frequency-selectivity.

Methods of window matching to estimate 3D points are the most serious factors affecting the accuracy, robustness, and computational cost of Multi-View Stereo (MVS) algorithms. Most existing MVS algorithms employ window matching based on Normalized Cross-Correlation (NCC) to estimate the depth of a 3D point. NCC-based window matching estimates the displacement between matching windows with sub-pixel accuracy by linear/cubic interpolation, which does not represent accurate sub-pixel values of matching windows. This paper proposes a technique of window matching that is very accurate using Phase-Only Correlation (POC) with geometric correction for MVS. The accurate sub-pixel displacement between two matching windows can be estimated by fitting the analytical correlation peak model of the POC function. The proposed method also corrects the geometric transformations of matching windows by taking into consideration the 3D shape of a target object. The use of the proposed geometric correction approach makes it possible to achieve accurate 3D reconstruction from multi-view images even for images with large transformations. The proposed method demonstrates more accurate 3D reconstruction from multi-view images than the conventional methods in a set of experiments.

Real-time high-quality reverberation simulation plays an important role in many modern applications, such as computer games and virtual reality. Traditional physically and perceptually-based reverberation simulation techniques, however, suffer from the high computational cost and lack of the specific characteristics of the enclosure. In this paper, a hybrid reverberation simulation approach is proposed in which early reflections are reproduced by convolving with the impulse response modeled by the image-source method and late reverberation is reproduced by the feedback delay network. A parametric predictor of energy decay relief is presented for the modeled early reflections and then exploited to compute the parameters of the feedback delay network. This ensures the smooth transition in the time-frequency domain from early to late reflections. Numerical simulation and listening tests validate the effectiveness of this proposed hybrid reverberation simulation approach.

In this paper we discuss the system failure probability of a k-out-of-n system considering common-cause failures. The conventional implicit technique is first introduced. Then the failure probabilities are formulated when the independence between common-cause failure events is assumed. We also provide algorithms to enumerate all the cut sets and the minimal cut sets, and to calculate the system failure probability. These methods are extendable to the case of systems with non-identical components. We verify the effectiveness of our method by comparison with the exact solution obtained by numerical calculation.

This paper proposes a construction method of binary Z-periodic complementary sequence set (Z-PCSs) based on binary aperiodic complementary sequence pair (Golay pair) and interleaved technique. The constructed set is optimal or almost optimal with respect to the theoretical bound in different conditons. The set can be used in multi-carrier code division multiple access communication systems. The designed sequence has periodic complementary characteristics, which lead to a strong ability to resist multi-path interference and multiple access interference.

We consider the exploration problem with a single agent in an undirected graph. The problem requires the agent starting from an arbitrary node to explore all the nodes and edges in the graph and return to the starting node. Our goal is to minimize both the number of agent moves and the memory size of the agent, which dominate the amount of communication during the exploration. We focus on the local memory called the whiteboard of each node. There are several exploration algorithms which are very fast (i.e. the exploration is completed within a small number of agent moves such as 2m and m+3n) and do not use whiteboards. These algorithms, however, require large agent memory because the agent must keep the entire information in its memory to explore a graph. We achieve the above goal by reducing the agent memory size of such algorithms with using whiteboards. Specifically, we present two algorithms with no agent memory based on the traditional depth-first traversal and two algorithms with O(n) and O(nlog n) space of agent memory respectively based on the fastest algorithms in the literature by Panaite and Pelc [J. Alg., Vol.33 No.2, 1999].

We present a power-saving method for large-scale storage systems of cloud data sharing services, particularly those providing media (video and photograph) sharing services. The idea behind our method is to periodically rearrange stored data in a disk array, so that the workload is skewed toward a small subset of disks, while other disks can be sent to standby mode. This idea is borrowed from the Popular Data Concentration (PDC) technique, but to avoid an increase in response time caused by the accesses to disks in standby mode, we introduce a function that predicts future access frequencies of the uploaded files. This function uses the correlation of potential future accesses with the combination of elapsed time after upload and the total number of accesses in the past. We obtain this function in statistical analysis of the real access patterns of 50,000 randomly selected publicly available photographs on Flickr over 7,000 hours (around 10 months). Moreover, to adapt to a constant massive influx of data, we propose a mechanism that effectively packs the continuously uploaded data into the disk array in a storage system based on the PDC. To evaluate the effectiveness of our method, we measured the performance in simulations and a prototype implementation. We observed that our method consumed 12.2% less energy than the static configuration (in which all disks are in active mode). At the same time, our method maintained a preferred response time, with 0.23% of the total accesses involving disks in standby mode.