Virtualization technologies are widely deployed in data centers to improve system utilization. However, they increase the workload for operators, who have to manage the structure of virtual networks in data centers. A virtual-network management system which automates the integration of the configurations of the virtual networks is provided. The proposed system collects the configurations from server virtualization platforms and VLAN-supported switches, and integrates these configurations according to a newly developed XML-based management information model for virtual-network configurations. Preliminary evaluations show that the proposed system helps operators by reducing the time to acquire the configurations from devices and correct the inconsistency of operators' configuration management database by about 40 percent. Further, they also show that the proposed system has excellent scalability; the system takes less than 20 minutes to acquire the virtual-network configurations from a large scale network that includes 300 virtual machines. These results imply that the proposed system is effective for improving the configuration management process for virtual networks in data centers.

Classical jamming attack models in the time domain have been proposed, such as constant jammer, random jammer, and reactive jammer. In this letter, we consider a new problem: given k jammers, how does the attacker minimize the pair-wise connectivity among the nodes in a Wireless Sensor Network (WSN)? We call this problem k-Jammer Deployment Problem (k-JDP). To the best of our knowledge, this is the first attempt at considering the position-critical jamming attack against wireless sensor network. We mainly make three contributions. First, we prove that the decision version of k-JDP is NP-complete even in the ideal situation where the attacker has full knowledge of the topology information of sensor network. Second, we propose a mathematical formulation based on Integer Programming (IP) model which yields an optimal solution. Third, we present a heuristic algorithm HAJDP, and compare it with the IP model. Numerical results show that our heuristic algorithm is computationally efficient.

In this letter, we analyze the influence of motion and out-of-focus blur on both frequency spectrum and cepstrum of an iris image. Based on their characteristics, we define two new discriminative blur features represented by Energy Spectral Density Distribution (ESDD) and Singular Cepstrum Histogram (SCH). To merge the two features for blur detection, a merging kernel which is a linear combination of two kernels is proposed when employing Support Vector Machine. Extensive experiments demonstrate the validity of our method by showing the improved blur detection performance on both synthetic and real datasets.

We have developed a long-range asynchronous on-chip data-transmission link based on multiple-valued single-track signaling for a highly reliable asynchronous Network-on-Chip. In the proposed signaling, 1-bit data with control information is represented by using a one-digit multi-level signal, so serial data can be transmitted asynchronously using only a single wire. The small number of wires alleviates the routing complexity of wiring long-range interconnects. The use of current-mode signaling makes it possible to transmit data at high speed without buffers or repeaters over a long interconnect wire because of the low-voltage swing of signaling, and it leads to low-latency data transmission. We achieve a latency of 0.45 ns, a throughput of 1.25 Gbps, and energy dissipation of 0.58 pJ/bit with a 10-mm interconnect wire under a 0.13 µm CMOS technology. This represents an 85% decrease in latency, a 150% increase in throughput, and a 90% decrease in energy dissipation compared to a conventional serial asynchronous data-transmission link.

This paper investigates the non-data-aided (NDA) carrier frequency estimation of amplitude and phase shift keying (APSK) signals. The true Cramer-Rao bound (CRB) for NDA frequency estimation of APSK signals are derived and evaluated numerically. Characteristic and jitter variance of NDA Luise and Reggiannini (L&R) frequency estimator are analyzed. Verified by Monte Carlo simulations, the analytical results are shown to be accurate for medium-to-high signal-to-noise ratio (SNR) values. Using the proposed closed-form expression, parameters of the algorithm are optimized efficiently to minimize the jitter variance.

In this paper, we consider the problem of waveform optimization for multi-input multi-output (MIMO) radar in the presence of signal-dependent noise. A novel diagonal loading (DL) based method is proposed to optimize the waveform covariance matrix (WCM) for minimizing the Cramer-Rao bound (CRB) which improves the performance of parameter estimation. The resulting nonlinear optimization problem is solved by resorting to a convex relaxation that belongs to the semidefinite programming (SDP) class. An optimal solution to the initial problem is then constructed through a suitable approximation to an optimal solution of the relaxed one (in a least squares (LS) sense). Numerical results show that the performance of parameter estimation can be improved considerably by the proposed method compared to uncorrelated waveforms.

A new high-throughput turbo decoding scheme adopting double flow, sliding window and shuffled decoding is proposed. Analytical and numerical results show that the proposed scheme requires low number of clock cycles and small memory size to achieve a BER performance equivalent to those of existing schemes.

An amplitude-redistribution technique – which improves phase-noise performance of millimeter (mm)-wave and quasi mm-wave cross-coupled VCOs by controlling the distribution of voltage swings on the oscillator nodes – is proposed. A 28-GHz VCO, fabricated in 0.13-µm CMOS technology, uses this technique and demonstrates low phase-noise performance of -112.9-dBc/Hz at 1-MHz offset and FOMT of -187.4-dBc/Hz, which is the highest FOMT so far reported in regard to CMOS VCOs operating above 25 GHz.

A novel hybrid latch based flip-flop scheme is introduced in this paper. A pulse generator is eliminated to simplify clock distribution and save power. It also achieves high speed by shortening the critical data path. In addition, it avoids output node glitches which exist in conventional hybrid latch based flip-flops. HSPICE simulation results revealed that the proposed PHLFF performs best among referenced schemes. It can reduce 47.5% power dissipation, 16.5% clock-to-output latency and 56.4% PDP, as compared to conventional HLFF.

A smart community can be considered an essential component to realize a sustainable, low-carbon, and disaster-tolerant society, thereby providing a base for community inhabitants to lead a simple, healthy, and energy-saving way of life as well as ensuring safety, security, and a high quality-of-life in the community. In particular, a smart community can be essential for senior citizens in an aging society. Smart community enablers such as information and communication technology (ICT) and electric vehicles (EVs) can perform essential roles to realize a smart community. With regard to ICT, the necessity of a dedicated wireless sensor backbone has been identified. With regard to EV, a small-sized EV with one or two seats (Mini-EV) has been identified as an emerging player to support personal daily mobility in an aged society. The Mini-EV may be powered by a solar battery, thereby mitigating vehicular maintenance burden for the elderly. It is essential to realize a dependable ICT network and communication service for a smart community. In the study, we present the concept of trans-locatable design to achieve this goal. The two possible roles of EVs in contributing to a dependable ICT network are highlighted; these include EV charging of the batteries of the base stations in the network, and the creation of a Mini-EV based ad-hoc network that can enable applications such as safe driving assistance and secure neighborhoods.

The RFID tag system has received a lot of attention for ubiquitous computing. An RFID tag is attached to an object. With the unique ID of the RFID tag, a user identifies the object provided with the RFID tag and derives appropriate information about the object. One important application in the RFID technology is localizing RFID tags, which can be very useful in acquiring the position information concerning the RFID tags. It can be applied to navigation systems and positional detection systems for mobile robots. This paper proposes a new adaptive multi-range-sensing method for 3D localization of passive RFID tags by using a probabilistic approach. In this method, a mobile object (human, robot, etc.) with an RFID reader estimates the positions of RFID tags with multiple communication ranges dynamically. The effectiveness of the proposed method was demonstrated in experiments.

The current version of IEEE 802.15.4 MAC protocol does not support energy-efficient mobility for the low-power device. In this paper, we propose an energy-efficient sleep mode as part of the IEEE 802.15.4 that can conserve energy by considering mobility of mobile sensor devices. The proposed energy-efficient sleep mode dynamically extends the sleep interval if there is no data to transmit from the device or receive from corresponding nodes.

A simple DFT-based noise variance estimator for orthogonal frequency division multiplexing access (OFDMA) systems is proposed. The conventional DFT-based estimator differentiates the channel impulse response and noise in the time domain. However, for partial frequency response, its time domain signal will leak to all taps due to the windowing effect. The noise and channel leakage power become mixed. In order to accurately derive the noise power, we propose a novel symmetric extension method to reduce the channel leakage power. This method is based on the improved signal continuity at the boundaries introduced by symmetric extension. Numerical results show that the normalized mean square error (NMSE) of our proposed method is significantly lower than that of the conventional DFT method.

We propose a method for halftoning grayscale images by drawing weighted centroidal Voronoi tessellations (WCVTs) with black lines on white image planes. Based on the fact that CVT approaches a uniform hexagonal lattice asymptotically, we derive a relationship of darkness between input grayscale images and the corresponding halftone images. Then the derived relationship is used for adjusting the contrast of the halftone images. Experimental results show that the generated halftone images can reproduce the original tone in the input images faithfully.

In this paper, we present a new biometric verification system. The proposed system employs a novel biometric hashing scheme that uses our proposed quantization method. The proposed quantization method is based on error-correcting output codes which are used for classification problems in the literature. We improve the performance of the random projection based biometric hashing scheme proposed by Ngo et al. in the literature [5]. We evaluate the performance of the novel biometric hashing scheme with two use case scenarios including the case where an attacker steals the secret key of a legitimate user. Simulation results demonstrate the superior performance of the proposed scheme.

We present a fast voting scheme for localizing circular objects among clutter and occlusion. Typical solutions for the problem are based on Hough transform that evaluates an instance of circle by counting the number of edge points along its boundary. The evaluated value is proportional to radius, making the normalization with respect to the factor necessary for detecting circles with different radii. By representing circle with a number of sampled points, we get rid of the step. To evaluate an instance then involves obtaining the same number of edge points, each close to a sampled point in both spatial position and orientation. The closeness is measured by compatibility function, where a truncating operation is used to suppress noise and deal with occlusion. To evaluate all instances of circle is fulfilled by letting edge point vote in a maximizing way such that any instance possesses a set of maximally compatible edge points. The voting process is further separated into the radius-independent and -dependent parts. The time-consuming independent part can be shared by different radii and outputs the sparse matrices. The radius-dependent part shifts these sparse matrices according to the radius. We present precision-recall curves showing that the proposed approach outperforms the solutions based on Hough transform, at the same time, achieves the comparable time complexity as algorithm of Hough transform using 2D accumulator array.

This paper presents a tutorial overview of Continuous-Time Delta-Sigma Modulators (CTDSM); their operating principles to understand what is important intuitively and architectures to achieve higher conversion efficiency and to operate low supply voltage, design methods against loop stability problem, tuning methods of the bandwidth and so on. A survey of cutting-edge CMOS implementations is described.

This paper proposes a method for recognizing hand-shapes by using multi-viewpoint image sets. The recognition of a hand-shape is a difficult problem, as appearance of the hand changes largely depending on viewpoint, illumination conditions and individual characteristics. To overcome this problem, we apply the Kernel Orthogonal Mutual Subspace Method (KOMSM) to shift-invariance features obtained from multi-viewpoint images of a hand. When applying KOMSM to hand recognition with a lot of learning images from each class, it is necessary to consider how to run the KOMSM with heavy computational cost due to the kernel trick technique. We propose a new method that can drastically reduce the computational cost of KOMSM by adopting centroids and the number of images belonging to the centroids, which are obtained by using k-means clustering. The validity of the proposed method is demonstrated through evaluation experiments using multi-viewpoint image sets of 30 classes of hand-shapes.

An active learning method, called Two-stage Active learning algorithm (TAL), is developed for software defect prediction. Combining the clustering and support vector machine techniques, this method improves the performance of the predictor with less labeling effort. Experiments validate its effectiveness.

Lazy-task creation is an efficient method of overcoming the overhead of the grain-size problem in parallel computing. Work stealing is an effective load balancing strategy for parallel computing. In this paper, we present dynamic work stealing strategies in a lazy-task creation technique for efficient fine-grain task scheduling. The basic idea is to control load balancing granularity depending on the number of task parents in a stack. The dynamic-length strategy of work stealing uses run-time information, which is information on the load of the victim, to determine the number of tasks that a thief is allowed to steal. We compare it with the bottommost first work stealing strategy used in StackThread/MP, and the fixed-length strategy of work stealing, where a thief requests to steal a fixed number of tasks, as well as other multithreaded frameworks such as Cilk and OpenMP task implementations. The experiments show that the dynamic-length strategy of work stealing performs well in irregular workloads such as in UTS benchmarks, as well as in regular workloads such as Fibonacci, Strassen's matrix multiplication, FFT, and Sparse-LU factorization. The dynamic-length strategy works better than the fixed-length strategy because it is more flexible than the latter; this strategy can avoid load imbalance due to overstealing.