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 we consider two non-parametric estimation methods for software reliability assessment without specifying the fault-detection time distribution, where the underlying stochastic process to describe software fault-counts in the system testing is given by a non-homogeneous Poisson process. The resulting data-driven methodologies can give the useful probabilistic information on the software reliability assessment under the incomplete knowledge on fault-detection time distribution. Throughout examples with real software fault data, it is shown that the proposed methods provide more accurate estimation results than the common parametric approach.

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.

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.

We present a method for recognition of acoustic events in conversation scenarios where speech usually overlaps with other acoustic events. While speech is usually considered the most informative acoustic event in a conversation scene, it does not always contain all the information. Non-speech events, such as a door knock, steps, or a keyboard typing can reveal aspects of the scene that speakers miss or avoid to mention. Moreover, being able to robustly detect these events could further support speech enhancement and recognition systems by providing useful information cues about the surrounding scenarios and noise. In acoustic event detection, state-of-the-art techniques are typically based on derived features (e.g. MFCC, or Mel-filter-banks) which have successfully parameterized the spectrogram of speech but reduce resolution and detail when we are targeting other kinds of events. In this paper, we propose a method that learns features in an unsupervised manner from high-resolution spectrogram patches (considering a patch as a certain number of consecutive frame features stacked together), and integrates within the deep neural network framework to detect and classify acoustic events. Superiority over both previous works in the field, and similar approaches based on derived features, has been assessed by statical measures and evaluation with CHIL2007 corpus, an annotated database of seminar recordings.

By investigating the properties that the offsets should satisfy, this letter presents a brief proof of general QAM Golay complementary sequences (GCSs) in Cases I-III constructions. Our aim is to provide a brief, clear, and intelligible derivation so that it is easy for the reader to understand the known Cases I-III constructions of general QAM GCSs.

In this paper, we first propose ten new discrimination features of SAR images in the moving and stationary target acquisition and recognition (MSTAR) database. The Ada_MCBoost algorithm is then proposed to classify multiclass SAR targets. In the new algorithm, we introduce a novel large-margin loss function to design a multiclass classifier directly instead of decomposing the multiclass problem into a set of binary ones through the error-correcting output codes (ECOC) method. Finally, experiments show that the new features are helpful for SAR targets discrimination; the new algorithm had better recognition performance than three other contrast methods.

Multi-agent based simulation has been widely used in behavior finance, and several single-processed simulation platforms with Agent-Based Modeling (ABM) have been proposed. However, traditional simulations of stock markets on single processed computers are limited by the computing capability since financial researchers need larger and larger number of agents and more and more rounds to evolve agents' intelligence and get more efficient data. This paper introduces a distributed multi-agent simulation platform, named PSSPAM, for stock market simulation focusing on large scale of parallel agents, communication system and simulation scheduling. A logical architecture for distributed artificial stock market simulation is proposed, containing four loosely coupled modules: agent module, market module, communication system and user interface. With the customizable trading strategies inside, agents are deployed to multiple computing nodes. Agents exchange messages with each other and with the market based on a customizable network topology through a uniform communication system. With a large number of agent threads, the round scheduling strategy is used during the simulation, and a worker pool is applied in the market module. Financial researchers can design their own financial models and run the simulation through the user interface, without caring about the complexity of parallelization and related problems. Two groups of experiments are conducted, one with internal communication between agents and the other without communication between agents, to verify PSSPAM to be compatible with the data from Euronext-NYSE. And the platform shows fair scalability and performance under different parallelism configurations.

Two-track squeeze and adjacent track interference (ATI) are major barriers to increasing track density in hard disk drives (HDD). These depend on skew angles made by a magnetic head and circumferential direction on a magnetic disk. This paper describes relationships between the skew angle and the magnetic core width (MCW) which affects two-track squeeze and ATI performance. We propose a design concept of a track pitch profile at different skew angles considering MCW. Equivalent robustness of ATI performance on different skew angle conditions is obtained with the optimized track pitch.

Due to ease of implementation for various user interactive applications, much research on motion recognition has been completed using Kinect. However, one drawback of Kinect is that the skeletal information obtained is provided under the assumption that the user faces Kinect. Thus, the skeletal information is likely incorrect when the user turns his back to Kinect, which may lead to difficulty in motion recognition from the application. In this paper, we implement a highly accurate human motion capture system by installing six Kinect sensors over 360 degrees. The proposed method enables skeleton to be obtained more accurately by assigning higher weights to skeletons captured by Kinect in which the user faces forward. Toward this goal, the front vector of the user is temporally traced to determine whether the user is facing Kinect. Then, more reliable joint information is utilized to construct a skeletal representation of each user.

User context and user location in particular play an important role in location-based services (LBS). The location can be determined by various positioning methods. These are typically evaluated with average positioning error or percentile values, which are not the most suitable metrics for evaluation of how a positioning method functions in the semantic space. Therefore, we propose a new method for evaluation of positioning accuracy in the semantic space. We focus on two types of semantic user locations that are widely available in urban areas: the street address and the categories of the surrounding points of interest (POIs). We demonstrate its use on ten different positioning methods: a standalone satellite navigation device, GPS module on a smartphone, two versions of Foursquare positioning service, Google positioning service, a positioning service of the local mobile operator, and four other possible variants of mobile operator-based positioning methods. The evaluation suggests that approach with the street addresses is more promising approach due to either sparse or unevenly distributed POIs. Furthermore, some of the positioning methods that are less accurate in Euclidean space, such as a combination of the GPS data with the mobile operator-based method that relies on the propagation models, performed comparably well in the semantic space as the methods that are using more accurate technologies, such as Google and Foursquare.

Even though cloud users want to keep their data on clouds secure, it is not easy to protect the data because cloud administrators could be malicious and hypervisor could be compromised. To solve this problem, hardware-based memory isolation schemes have been proposed. However, the data in virtual storage are not protected by the memory isolation schemes, and thus, a guest OS should encrypt the data. In this paper, we address the problems of the previous schemes and propose a hardware-based storage isolation scheme. The proposed scheme enables to protect user data securely and to achieve performance improvement.

Segmenting foreground objects in unconstrained dynamic scenes still remains a difficult problem. We present a novel unsupervised segmentation approach that allows robust object segmentation of dynamic scenes with large displacements. To make this possible, we project motion based foreground region hypotheses generated via standard optical flow onto visual saliency regions. The motion hypotheses correspond to inside seeds mapping of the motion boundary. For visual saliency, we generalize the image signature method from images to videos to delineate saliency mapping of object proposals. The mapping of image signatures estimated in Discrete Cosine Transform (DCT) domain favor stand-out regions in the human visual system. We leverage a Markov random field built on superpixels to impose both spatial and temporal consistence constraints on the motion-saliency combined segments. Projecting salient regions via an image signature with inside mapping seeds facilitates segmenting ambiguous objects from unconstrained dynamic scenes in presence of large displacements. We demonstrate the performance on fourteen challenging unconstrained dynamic scenes, compare our method with two state-of-the-art unsupervised video segmentation algorithms, and provide quantitative and qualitative performance comparisons.

In cognitive radar systems (CRSs), target scattering coefficients (TSC) can be utilized to improve the performance of target identification and classification. This work considers the problem of TSC estimation for temporally correlated target. Multiple receive antennas are adopted to receive the echo waveforms, which are interfered by the signal-dependent clutter. Unlike existing estimation methods in time domain, a novel estimation method based on Kalman filtering (KF) is proposed in frequency domain to exploit the temporal TSC correlation, and reduce the complexity of subsequent waveform optimization. Additionally, to minimize the mean square error of estimated TSC at each KF iteration, in contrary to existing works, we directly model the design process as an optimization problem, which is non-convex and cannot be solved efficiently. Therefore, we propose a novel method, similar in some way to semi-definite programming (SDP), to convert the non-convex problem into a convex one. Simulation results demonstrate that the estimation performance can be significantly improved by the KF estimation with optimized waveform.

An integral attack is one of the most powerful attacks against block ciphers. We propose a new technique for the integral attack called the Fast Fourier Transform (FFT) key recovery. When N chosen plaintexts are required for the integral characteristic and the guessed key is k bits, a straightforward key recovery requires the time complexity of O(N2k). However, the FFT key recovery only requires the time complexity of O(N+k2k). As a previous result using FFT, at ICISC 2007, Collard etal proposed that FFT can reduce the time complexity of a linear attack. We show that FFT can also reduce the complexity of the integral attack. Moreover, the estimation of the complexity is very simple. We first show the complexity of the FFT key recovery against three structures, the Even-Mansour scheme, a key-alternating cipher, and the Feistel structure. As examples of these structures, we show integral attacks against Prøst, AES, PRESENT, and CLEFIA. As a result, an 8-round Prøst P128,K can be attacked with about an approximate time complexity of 279.6. For the key-alternating cipher, a 6-round AES and a 10-round PRESENT can be attacked with approximate time complexities of 251.7 and 297.4, respectively. For the Feistel structure, a 12-round CLEFIA can be attacked with approximate time complexities of 287.5.

To overcome the target-aspect sensitivity in radar high resolution range profile (HRRP) recognition, a novel method called Improved Kernel Distance Fuzzy C-means Clustering Method (IKDFCM) is proposed in this paper, which introduces kernel function into fuzzy c-means clustering and relaxes the constraint in the membership matrix. The new method finds the underlying geometric structure information hiding in HRRP target and uses it to overcome the HRRP target-aspect sensitivity. The relaxing of constraint in the membership matrix improves anti-noise performance and robustness of the algorithm. Finally, experiments on three kinds of ground HRRP target under different SNRs and four UCI datasets demonstrate the proposed method not only has better recognition accuracy but also more robust than the other three comparison methods.

We propose a novel sparse representation-based direction-of-arrival (DOA) estimation method. In contrast to those that approximate l0-norm minimization by l1-norm minimization, our method designs a reweighted l1 norm to substitute the l0 norm. The capability of the reweighted l1 norm to bridge the gap between the l0- and l1-norm minimization is then justified. In addition, an array covariance vector without redundancy is utilized to extend the aperture. It is proved that the degree of freedom is increased as such. The simulation results show that the proposed method performs much better than l1-type methods when the signal-to-noise ratio (SNR) is low and when the number of snapshots is small.

This letter deals with the consensus problem of multi-agent systems, which are composed of feedforward nonlinear systems under a directed network with a communication time delay. In order to solve this problem, a new consensus protocol with a low gain parameter is proposed. Moreover, it is shown that under some sufficient conditions, the proposed protocol can solve the consensus problem of nonlinear multi-agent systems even in the presence of an arbitrarily large communication delay. An illustrative example is presented to verify the validity of the proposed approach.

In this letter, a robust algorithm for jointly finding an estimate of the start of the frame and transmission mode is proposed in a digital audio broadcasting (DAB) system. In doing so, the use of differential-correlation based joint detection is proposed, which considers not only the height of correlation peak but also its plateau. We show via simulations that the proposed detection algorithm is capable of robustly detecting the start of a frame and its mode against the variation of signal-to-noise ratio, providing a performance advantage over the conventional algorithm.