This work extends the optimum Neymann-Pearson methodology to detection of a subspace signal in the correlated additive Gaussian noise when the noise power may be different under the null (H0) and alternative (H1) hypotheses. Moreover, it is assumed that the noise covariance structure and power under the null hypothesis are known but under the alternative hypothesis the noise power can be unknown. This situation occurs when the presence of a small point (subpixel) target decreases the noise power. The conventional matched subspace detector (MSD) neglects this phenomenon and causes a consistent loss in the detection performance. We derive the generalized likelihood ratio test (GLRT) for such a detection problem comparing it against the conventional MSD. The designed detector is theoretically justified and numerically evaluated. Both the theoretical and computer simulation results have shown that the proposed detector outperforms the conventional MSD. As to the detection performance, it has been shown that the detectivity of the proposed detector depends on the additional adaptive corrective term in the threshold. This corrective term decreases the value of presumed threshold automatically and, therefore, increases the probability of detection. The influence of this corrective term on the detector performance has been evaluated for an example scenario.

Silver electrical contacts are separated to generate break arcs in a DC48 V/6-24 A resistive circuit. The transverse magnetic field formed by a permanent magnet is applied to the break arcs. A series of experiments are carried out for two different experimental conditions. One condition is a constant contact separating speed while the magnetic flux density is changed to investigate the shortening effect of the arc duration. Another condition is a constant magnetic flux density while the contact separating speed is changed to investigate the changes in the arc duration and the contact gap when the break arc is extinguished. As a result, with constant separating speed, it is confirmed that the duration of break arcs is shortened by the transverse magnetic field and the break arcs are extinguished when the arc length reaches a certain value L. Under the condition of constant transverse magnetic field, (i) the arc duration is shortened by increasing the separation speed; (ii) the contact gap when the break arc is extinguished is almost constant when the separating speed v is sufficiently faster than 5 mm/s.

An efficient pruning method is proposed for the infinity-norm sphere decoding based on Schnorr-Euchner enumeration in multiple-input multiple-output spatial multiplexing systems. The proposed method is based on the characteristics of the infinity norm, and utilizes the information of the layer at which the infinity-norm value is selected in order to decide unnecessary sub-trees that can be pruned without affecting error-rate performance. Compared to conventional pruning, the proposed pruning decreases the average number of tree-visits by up to 37.16% in 44 16-QAM systems and 33.75% in 66 64-QAM systems.

This paper presents a high-speed SHA-1 implementation. Unlike the conventional unfolding transformation, the proposed unfolding transformation technique makes the combined hash operation blocks to have almost the same delay overhead regardless of the unfolding factor. It can achieve high throughput of SHA-1 implementation by avoiding the performance degradation caused by the first hash computation. We demonstrate the proposed SHA-1 architecture on a FPGA chip. From the experimental results, the SHA-1 architecture with unfolding factor 5 shows 1.17 Gbps. The proposed SHA-1 architecture can achieve about 31% performance improvements compared to its counterparts. Thus, the proposed SHA-1 can be applicable for the security of the high-speed but compact mobile appliances.

In this paper, we propose an Estimation of Signal Parameter via Rotational Invariance Techniques (ESPRIT) based algorithm for estimating the two-dimensional-direction-of-arrivals (2D-DOA) of signals impinging on a uniform rectangular array (URA). The basic idea of the proposed algorithm is to successively apply two rounds of one-dimensional ESPRIT (1D-ESPRIT) algorithm for 2D-DOA estimation. The first round 1D-ESPRIT is applied on columns of the URA whereas the other round 1D-ESPRIT is on the rows of the URA. In between, a grouping technique is developed to produces signal groups each containing signals with distinguishable spatial signatures. The grouping technique is performed by using the subspace projection method where the needed spatial information is provided by the first round 1D-ESPRIT algorithm. Computer simulations show that, in addition to having significantly reduced computational complexity, the proposed algorithm possesses better estimation accuracy as compared to the conventional 2D-ESPRIT algorithm.

Proving the security of cancelable biometrics and other template protection techniques is a key prerequisite for the widespread deployment of biometric technologies. BioEncoding is a cancelable biometrics scheme that has been proposed recently to protect biometric templates represented as binary strings like iris codes. Unlike other template protection schemes, BioEncoding does not require user-specific keys or tokens. Moreover, it satisfies the requirements of untraceable biometrics without sacrificing the matching accuracy. However, the security of BioEncoding against smart attacks, such as correlation and optimization-based attacks, has to be proved before recommending it for practical deployment. In this paper, the security of BioEncopding, in terms of both non-invertibility and privacy protection, is analyzed. First, resistance of protected templates generated using BioEncoding against brute-force search attacks is revisited rigorously. Then, vulnerabilities of BioEncoding with respect to correlation attacks and optimization based attacks are identified and explained. Furthermore, an important modification to the BioEncoding algorithm is proposed to enhance its security against correlation attacks. The effect of integrating this modification into BioEncoding is validated and its impact on the matching accuracy is investigated empirically using CASIA-IrisV3-Interval dataset. Experimental results confirm the efficacy of the proposed modification and show that it has no negative impact on the matching accuracy.

With energy shortages and global climate change leading our concerns these days, the energy consumption of datacenters has become a key issue. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This paper aims at designing, implementing and evaluating a Green Scheduler for reducing energy consumption of datacenters in Cloud computing platforms. It is composed of four algorithms: prediction, ON/OFF, task scheduling, and evaluation algorithms. The prediction algorithm employs a neural predictor to predict future load demand based on historical demand. According to the prediction, the ON/OFF algorithm dynamically adjusts server allocations to minimize the number of servers running, thus minimizing the energy use at the points of consumption to benefit all other levels. The task scheduling algorithm is responsible for directing request traffic away from powered-down servers and toward active servers. The performance is monitored by the evaluation algorithm to balance the system's adaptability against stability. For evaluation, we perform simulations with two load traces. The results show that the prediction mode, with a combination of dynamic training and dynamic provisioning of 20% additional servers, can reduce energy consumption by 49.8% with a drop rate of 0.02% on one load trace, and a drop rate of 0.16% with an energy consumption reduction of 55.4% on the other. Our method is also proven to have a distinct advantage over its counterparts.

This paper presents the design and implementation of a supply and process-insensitive 12-bit Digital Pulse Width Modulator (DPWM) for digital DC-DC converters. The DPWM is realized by a ring oscillator-based segmented tapped delay line and a counter-comparator. The number of delay cells required is reduced by employing a proposed delay cell reuse technique. The ring oscillator of the tapped delay line is made insensitive to supply and process variation by biasing the differential delay cells with a supply-insensitive replica bias circuit. Simulation results show that the variation of the switching frequency of the DPWM at 1.02 MHz is 0.4% for supply voltage variation between 1.5 V and 2.5 V and 0.95% over the temperature range from -40 to 90. Monte-Carlo simulation was also performed to account for the effect of mismatch between the transistors of the ring oscillator. The worst case delay of the delay cells is 0.87% for 5% (3-σ) mismatch. The design was fabricated in CMOS 0.18 µm process and the fabricated DPWM achieved a supply sensitivity of 0.82% and a current consumption of 14 µA.

In this paper, a novel algorithm, Cross Low-dimension Pursuit, based on a new structured sparse matrix, Permuted Block Diagonal (PBD) matrix, is proposed in order to recover sparse signals from incomplete linear measurements. The main idea of the proposed method is using the PBD matrix to convert a high-dimension sparse recovery problem into two (or more) groups of highly low-dimension problems and crossly recover the entries of the original signal from them in an iterative way. By sampling a sufficiently sparse signal with a PBD matrix, the proposed algorithm can recover it efficiently. It has the following advantages over conventional algorithms: (1) low complexity, i.e., the algorithm has linear complexity, which is much lower than that of existing algorithms including greedy algorithms such as Orthogonal Matching Pursuit and (2) high recovery ability, i.e., the proposed algorithm can recover much less sparse signals than even 1-norm minimization algorithms. Moreover, we demonstrate both theoretically and empirically that the proposed algorithm can reliably recover a sparse signal from highly incomplete measurements.

In this letter, coordinated power allocation (PA) is investigated for the downlink of a generalized multi-cluster distributed antenna system (DAS). Motivated by practical applications, we assume only the global large-scale channel state information is known at the transmitter. First, an upper bound (UB) for the ergodic sum capacity of the system is derived and used as a simplified optimization target. Then, a coordinated PA scheme is proposed based on Geometric Programming (GP), which is demonstrated to be nearly optimal by Monte Carlo simulations.

The induced voltage at the terminals of an implantable cardiac pacemaker of unipolar type was investigated by numerical calculations. Operating frequency was assumed 5 MHz according to a recent product. The dependencies of the induced voltage on various conditions were investigated including those on the locations of the transmitter and the pacemaker, and on the electric properties and the size of the phantom. The results showed that they were reasonably explained by considerations of quasi-static coupling of the electric field between the device and the pacemaker. Regarding the effect of electrical properties of the phantom a conservative result was obtained by using a phantom of homogeneous material with electric constants of fat. With regard to the phantom size the phantom used in previous studies provided more conservative results than that of larger size. The results suggested that the electric near-field intra-body communication devices are not likely to interfere with implantable cardiac pacemakers as far as the situation assumed in this study.

Scene-context plays an important role in scene analysis and object recognition. Among various sources of scene-context, we focus on scene-context scale, which means the effective scale of local context to classify an image pixel in a scene. This paper presents random forests based image categorization using the scene-context scale. The proposed method uses random forests, which are ensembles of randomized decision trees. Since the random forests are extremely fast in both training and testing, it is possible to perform classification, clustering and regression in real time. We train multi-scale texton forests which efficiently provide both a hierarchical clustering into semantic textons and local classification in various scale levels. The scene-context scale can be estimated by the entropy of the leaf node in the multi-scale texton forests. For image categorization, we combine the classified category distributions in each scale and the estimated scene-context scale. We evaluate on the MSRC21 segmentation dataset and find that the use of the scene-context scale improves image categorization performance. Our results have outperformed the state-of-the-art in image categorization accuracy.

Using a self-developed ASTM test system of contact material electrical properties under low voltage (LV), small-capacity, the current-frequency variable and a photoelectric analytical balance, the electric performance comparison experiments and material weighing of silver-based electrical contact materials, such as silver/tungsten and silver/cadmium oxide contact materials, are completed under LV, pure resistive load and small current at 400 Hz/50 Hz. The surface profiles and constituents of silver/tungsten contact material were observed and analyzed by SEM and EDAX. Researches indicate that the form of the contact material arc burnout at 400 Hz is stasis, not an eddy flow style at 50 Hz; meanwhile, the area of the contact burnout at 400 Hz is less than that of 50 Hz, and the local ablation on the surface layer at 400 Hz is more serious. Comparing the capacities of the silver-based contact materials with different second element such as CAgW50, CAgNi10, CAgC4 and CAgCdO15 at 400 Hz, no matter what the performances of arc erosion resistance or welding resistance, it can be found that the capacities of the silver/tungsten material is the best.

This paper presents novel structures of band-pass filters using two configurations of open loop ring resonators (OPLRR): a resonator with embedded quadruply-stepped impedance transmission lines (QSITL) in coupled lines, and a stepped impedance resonator (SIR). Both types of OPLRR have the capability of suppressing the second spurious response and shifting the third spurious response to a higher frequency as well. To demonstrate the performances of both proposed resonators, two sections of each structure with cascaded and crossed configurations at an operating frequency of 0.9 GHz are presented. Both methodologies are easy to design and implement. The methodology with a SIR has a better performance than the SITL. The measurement results of the proposed circuits are in full agreement with the simulated prediction results.

A spectrum sensing scheme for cognitive radio that includes coarse and fine sensing stages based on cyclostationarity is proposed in this paper. The cyclostationary feature detection (CFD) based on a single cyclic frequency (SCF) is used in the coarse sensing stage and that based on multiple cyclic frequencies (MCF) is employed in the fine sensing stage. Whether the fine sensing stage is performed or not is decided by comparing the statistic constructed in the coarse sensing stage with two thresholds. Theoretical analyses and simulation results show that the proposed sensing scheme has superior sensing performance and needs shorter sensing time.

In this letter, we analyze the amplify-and-forward (AF) two-way cooperative relaying scheme with regard to the average data transmission rate and the symbol error probability. By investigating the Moment-Generating function (MGF) and the k-th moment of “extra-harmonic” mean of two variables, we derive an exact closed-form expression for the symbol error probability (SEP) and the approximate average sum rate. Analysis results show that the proposed scheme achieves higher SEP performance as well as a lower data rate than the conventional AF two-way scheme. Additionally, it also matches the SEP performance of the one-way AF cooperative scheme but attains higher sum rate. Finally, Monte Carlo simulation results will be shown to confirm our analytical results.

The brain-machine interface (BMI) is a new method for man-machine interface, which enables us to control machines and to communicate with others, without input devices but directly using brain signals. Previously, we successfully developed a real time control system for operating a robot arm using brain-machine interfaces based on the brain surface electrodes, with the purpose of restoring motor and communication functions in severely disabled people such as amyotrophic lateral sclerosis patients. A fully-implantable wireless system is indispensable for the clinical application of invasive BMI in order to reduce the risk of infection. This system includes many new technologies such as two 64-channel integrated analog amplifier chips, a Bluetooth wireless data transfer circuit, a wirelessly rechargeable battery, 3 dimensional tissue-fitting high density electrodes, a titanium head casing, and a fluorine polymer body casing. This paper describes key features of the first prototype of the BMI system for clinical application.

A novel algorithm is presented for near-field source localization with a symmetric uniform linear array (ULA) consisting of an even number of sensors. Based on element reordering of a symmetric ULA, the steering vector is factorised with respect to the range-independent bearing parameters and range-relevant 2-D location parameters, which allows the range-independent bearing estimation with rank-reduction idea. With the estimated bearing, the range estimation for each source is then obtained by defining the 1-D MUSIC spectrum. Simulation results are presented to validate the performance of the proposed algorithm.

In this paper, we propose a new method to estimate and effectively reduce the effect of thermal asperity (TA) in the perpendicular magnetic recording (PMR) channels with the state trellis. The TA is estimated from the state trellis, then its average is used to modify the equalized signal entering the Viterbi detector. For the partial response (PR) targets with DC component, the proposed method with a maximum-likelihood detector can improve the bit error rate performance by more than an order of magnitude when TA occurs and degrades when the giant magneto-resistive (GMR) nonlinearity and base line wander (BLW) effects are present. Unlike the previous studies, this method allows the use of PR targets with DC component under the presence of TA.

The advantages of the orthogonal frequency division multiplexing (OFDM) are high spectral efficiency, resiliency to RF interference, lower multi-path distortion and others. To further utilize the vast channel capacity of the multiuser OFDM, one has to find the efficient adaptive subcarrier and bit allocation among users. In this paper, we propose a 0-1 integer programming model formulating the optimal subcarrier and bit allocation problem of the multiuser OFDM. We proved that the continuous relaxation of our formulation is tighter than the previous convex optimization formulation based on perspective function and the Lagrangian dual bound of our formulation is equivalent to the linear programming relaxation bound. The proposed Lagrangian dual is seperable with respect to subcarriers and allows an efficient dual maximization algorithm. We compared the performance of the integer programming formulation and the Lagrangian dual of our formulation and the continuous relaxation and the primal heuristic proposed in [3]. Computer simulation on a system employing M-ary quadrature amplitude modulation (MQAM) assuming a frequency-selective channel consisting of three independent Rayleigh multipaths is carried out with the optimal subcarrier and bit allocation solution generated by the 0-1 integer programming model.