The rapid variation of wireless channels and feedback delay make the available channel state information (CSI) outdated in dynamic wireless multi-hop networks, which significantly degrades the accuracy of cross-layer resource allocation. To deal with this problem, a cross-layer resource allocation scheme is proposed for wireless multi-hop networks by taking the outdated CSI into account and basing compensation on the results of channel prediction. The cross-layer resource allocation is formulated as a network utility maximization problem, which jointly considers congestion control, channel allocation, power control, scheduling and routing with the compensated CSI. Based on a dual decomposition approach, the problem is solved in a distributed manner. Simulation results show that the proposed algorithm can reasonably allocate the resources, and significantly improve the throughput and energy efficiency in the network.

Traditionally, in computer systems, file I/O has been a big performance bottleneck for I/O intensive applications. The recent advent of non-volatile byte-addressable memory (NVM) technologies such as STT-MRAM and PCM, provides a chance to store persistent data with a high performance close to DRAM's. However, as the location of the persistent storage device gets closer to the CPU, the system software layers overheads for accessing the data such as file system layer including virtual file system layer and device driver are no longer negligible. In this paper, we propose a light-weight user-level persistent storage, called UStore, which is physically allocated on the NVM and is mapped directly into the virtual address space of an application. UStore makes it possible for the application to fast access the persistent data without the system software overheads and extra data copy between the user space and kernel space. We show how UStore is easily applied to existing applications with little elaboration and evaluate its performance enhancement through several benchmark tests.

Gait-based owner authentication using accelerometers has recently been extensively studied owing to the development of wearable electronic devices. An actual gait signal is always subject to change due to many factors including variation of sensor attachment. In this research, we tackle to the practical sensor-orientation inconsistency, for which signal sequences are captured at different sensor orientations. We present an iterative signal matching algorithm based on phase-registration technique to simultaneously estimate relative sensor-orientation and register the 3D acceleration signals. The iterative framework is initialized by using 1D orientation-invariant resultant signals which are computed from 3D signals. As a result, the matching algorithm is robust to any initial sensor-orientation. This matching algorithm is used to match a probe and a gallery signals in the proposed owner authentication method. Experiments using actual gait signals under various conditions such as different days, sensors, weights being carried, and sensor orientations show that our authentication method achieves positive results.

Hardware Trojan (HT) has emerged as an impending security threat to hardware systems. However, conventional functional tests fail to detect HT since Trojans are triggered by rare events. Most of the existing side-channel based HT detection techniques just simply compare and analyze circuit's parameters and offer no signal calibration or error correction properties, so they suffer from the challenge and interference of large process variations (PV) and noises in modern nanotechnology which can completely mask Trojan's contribution to the circuit. This paper presents a novel HT detection method based on subspace technique which can detect tiny HT characteristics under large PV and noises. First, we formulate the HT detection problem as a weak signal detection problem, and then we model it as a feature extraction model. After that, we propose a novel subspace HT detection technique based on time domain constrained estimator. It is proved that we can distinguish the weak HT from variations and noises through particular subspace projections and reconstructed clean signal analysis. The reconstructed clean signal of the proposed algorithm can also be used for accurate parameter estimation of circuits, e.g. power estimation. The proposed technique is a general method for related HT detection schemes to eliminate noises and PV. Both simulations on benchmarks and hardware implementation validations on FPGA boards show the effectiveness and high sensitivity of the new HT detection technique.

This letter presents three methods for producing 8-QAM+ sequences. The first method transforms a ternary complementary sequence set (CSS) with even number of sub-sequences into an 8-QAM+ periodic CSS with both of the period and the number of sub-sequences unaltered. The second method results in an 8-QAM+ aperiodic CSS with confining neither the period nor the number of sub-sequences. The third method produces 8-QAM+ periodic sequences having ideal autocorrelation property on the real part of the autocorrelation function. The proposed sequences can be potentially applied to suppression of multiple access interference or synchronization in a communication system.

A digital background calibration technique using signal-dependent dithering is proposed, to correct the nonlinear errors which results from capacitor mismatches and finite opamp gain in pipelined analog-to-digital converter (ADC). Large magnitude dithers are used to measure and correct both errors simultaneously in background. In the proposed calibration system, the 2.5-bit capacitor-flip-over multiplying digital-to-analog converter (MDAC) stage is modified for the injection of large magnitude dithering by adding six additional comparators, and thus only three correction parameters in every stage subjected to correction were measured and extracted by a simple calibration algorithm with multibit first stage. Behavioral simulation results show that, using the proposed calibration technique, the signal-to-noise-and-distortion ratio improves from 63.3 to 79.3dB and the spurious-free dynamic range is increased from 63.9 to 96.4dB after calibrating the first two stages, in a 14-bit 100-MS/s pipelined ADC with σ=0.2% capacitor mismatches and 60dB nonideal opamp gain. The time of calibrating the first two stages is around 1.34 seconds for the modeled ADC.

We introduce the notion of 'Composition', 'Union' and 'Division' of cellular automata on groups. A kind of notions of compositions was investigated by Sato [10] and Manzini [6] for linear cellular automata, we extend the notion to general cellular automata on groups and investigated their properties. We observe the all unions and compositions generated by one-dimensional 2-neighborhood cellular automata over Z2 including non-linear cellular automata. Next we prove that the composition is right-distributive over union, but is not left-distributive. Finally, we conclude by showing reformulation of our definition of cellular automata on group which admit more than three states. We also show our formulation contains the representation using formal power series for linear cellular automata in Manzini [6].

Providing diverse Quality of Service (QoS) with ultra-low power consumption and support of mobility is the most important and challenging issue in wireless body area networks (WBANs). The IEEE 802.15.4 standard exhibits a desirable feature for WBAN, but its inability of mobility support makes it insufficient. In this paper, we show what is required for node mobility support and propose two strategies for the support. We observed that the amount of time required for the association process is the key reason IEEE 802.15.4 is unable to handle mobility. In this paper, we present a new fast association technique, which prevents nodes from scanning multiple channels. In the proposed scheme, by scanning just a single channel, a node can learn about all the coordinators working in different channels. The single channel scanning scheme is able to decrease the association time of IEEE 802.15.4 operating in 2.4GHz by 32 times. Furthermore, in this paper, a method to increase the node connectivity time with its coordinator in IEEE 802.15.4 beacon-enabled mode is introduced. The method tries to anticipate whether the node is moving towards or away from the coordinator by analyzing the signal strength of multiple beacons received from the same coordinator. Thus, the connectivity time is increased by choosing the coordinator with good signal strength, but located both furthest from the node and toward the direction which mobile node is moving. Our approach results in significant improvement by reducing the number of times the moving node switches coordinators. Experimental results have verified that our schemes work well in the mobile sensor network environment.

AC conversion has a huge variety of applications and so there are many ongoing research topics as in every type of power electronic conversion. New semiconductors allow the increase of the switching frequency fact that brings a whole new prospective improvement in converter's operation. Many other possible nonlinear operation regimes, including period doubling and chaotic oscillations, appear besides the conventional steady state operation. In this work, a nonlinear discrete-time model of an AC/AC buck type converter is proposed. A discrete time iterative map is derived to highlight the sensitive switching dynamics. The model is able to observe fast scale phenomena and short transient effects. It offers more information compared to other methods such as the averaging ones. According to Electro-Magnetic Compatibility (EMC) regulations, low wide-band noise is more acceptable than the high narrow-band, therefore the goal of this work is to spread the harmonic noise into a wide frequency spectrum which has lower amplitudes compared to the conventional comb-like spectrum with distinctive amplitudes at switching frequency multiples. Through the numerical and experimental consideration the converter can operate in a chaotic motion and the advantages of the performance improvement are also discussed.

This paper presents an analysis of radiation-induced clock-perturbation in phase-locked loop (PLL). Due to a trade-off between cost, performance, and reliability, radiation hardened PLL design need robust strategy. Thus, evaluation of radiation vulnerability is important to choose the robust strategy. The conventional evaluation-method is however based on brute-force analysis — SPICE simulation and experiment. The presented analysis result eliminates the brute-force analysis in evaluation of the radiation vulnerability. A set of equations enables to predict the radiation-induced clock-perturbation at the every sub-circuits. From a demonstration, the most vulnerable nodes have been found, which are validated using a PLL fabricated with 0.18µm CMOS process.

The estimation of the power spectral density (PSD) of noise is crucial for retrieving speech in noisy environments. In this study, we propose a novel method for estimating the non-white noise PSD from noisy speech on the basis of a generalized gamma distribution and the minimum mean square error (MMSE) approach. Because of the highly non-stationary nature of speech, deriving its actual spectral probability density function (PDF) using conventional modeling techniques is difficult. On the other hand, spectral components of noise are more stationary than those of speech and can be represented more accurately by a generalized gamma PDF. The generalized gamma PDF can be adapted to optimally match the actual distribution of the noise spectral amplitudes observed at each frequency bin utilizing two real-time updated parameters, which are calculated in each frame based on the moment matching method. The MMSE noise PSD estimator is derived on the basis of the generalized gamma PDF and Gaussian PDF models for noise and speech spectral amplitudes, respectively. Combined with an improved Weiner filter, the proposed noise PSD estimate method exhibits the best performance compared with the minimum statistics, weighted noise estimation, and MMSE-based noise PSD estimation methods in terms of both subjective and objective measures.

Detection of human respiration and heartbeat is an essential demand in medical monitoring, healthcare vigilance, as well as in rescue activities after earthquakes. Radar is an important tool to detect human respiration and heartbeat. Compared to body-attached sensors, radar has the advantage of conducting detection without contacting the subject, which is favorable in practical usage. In this paper, we conduct fundamental studies on ultra-wideband (UWB) radar for detection of the respiration and heartbeat by computer simulations. The main achievement of our work is the development of a UWB radar simulation system. Using the developed simulation system, three UWB frequency bands, i.e., 3.4-4.8GHz, 7.25-10.25GHz, as well as 3.1-10.6GHz, are compared in terms of their respiration and heartbeat detection performance. Our results show that the first two bands present identical performance, while the third one presents much better performance. The effects of using multiple antennas are also evaluated. Our results show that increasing the number of antennas can steadily increase the detection ability.

In this letter, we research the method of using face and gesture image sequences to deal with the video-based bimodal emotion recognition problem, in which both Harris plus cuboids spatio-temporal feature (HST) and sparse canonical correlation analysis (SCCA) fusion method are applied to this end. To efficaciously pick up the spatio-temporal features, we adopt the Harris 3D feature detector proposed by Laptev and Lindeberg to find the points from both face and gesture videos, and then apply the cuboids feature descriptor to extract the facial expression and gesture emotion features [1],[2]. To further extract the common emotion features from both facial expression feature set and gesture feature set, the SCCA method is applied and the extracted emotion features are used for the biomodal emotion classification, where the K-nearest neighbor classifier and the SVM classifier are respectively used for this purpose. We test this method on the biomodal face and body gesture (FABO) database and the experimental results demonstrate the better recognition accuracy compared with other methods.

In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.

Recently, we proposed a new data-path architecture, named a large-scale reconfigurable data-path (LSRDP), based on single-flux-quantum (SFQ) circuits, to establish a fundamental technology for future high-end computers. In this architecture, a large number of SFQ floating-point units (FPUs) are used as core components, and their high performance and low power consumption are essential. In this research, we implemented an SFQ half-precision bit-serial floating-point multiplier (FPM) with a target clock frequency of 50GHz, using the AIST 10kA/cm2 Nb process. The FPM was designed, based on a systolic-array architecture. It contains 11,066 Josephson junctions, including on-chip high-speed test circuits. The size and power consumption of the FPM are 6.66mm × 1.92mm and 2.83mW, respectively. Its correct operation was confirmed at a maximum frequency of 93.4GHz for the exponent part and of 72.0GHz for the significand part by on-chip high-speed tests.

A self-powered urinary-incontinence sensor with a flexible wire-type urine-activated battery has been developed as an application for wireless biosensor networks. It is disposable and can be embedded in a diaper. The battery consists of two long film-type line electrodes printed on a flexible plastic sheet that abuts the absorbent material of the diaper. It conforms to the shape of the diaper when the diaper is worn. The stress produced by the curvature of the diaper presses the electrodes firmly against the diaper material, providing greater contact with any urine present. Thus, the battery generates more power than when it is flat, as in an unworn diaper. To verify the effectiveness of the battery, we fabricated a battery and a prototype sensor, which consists of an intermittent-power-supply circuit and a wireless transmitter, and embedded the battery in a diaper. The anode of the battery also acts as a wide ground plane for the antenna of the wireless transmitter, which radiates a large amount of power. When 80cc of urine is poured onto the diaper, the battery outputs a voltage of around 1V, which allows the sensor to transmit an ID signal over a distance of 5m every 40 seconds or so.

Biometric identification has recently attracted attention because of its convenience: it does not require a user ID nor a smart card. However, both the identification error rate and response time increase as the number of enrollees increases. In this paper, we combine a score level fusion scheme and a metric space indexing scheme to improve the accuracy and response time in biometric identification, using only scores as information sources. We firstly propose a score level indexing and fusion framework which can be constructed from the following three schemes: (I) a pseudo-score based indexing scheme, (II) a multi-biometric search scheme, and (III) a score level fusion scheme which handles missing scores. A multi-biometric search scheme can be newly obtained by applying a pseudo-score based indexing scheme to multi-biometric identification. We secondly propose the NBS (Naive Bayes search) scheme as a multi-biometric search scheme and discuss its optimality with respect to the retrieval error rate. We evaluated our proposal using the datasets of multiple fingerprints and face scores from multiple matchers. The results showed that our proposal significantly improved the accuracy of the unimodal biometrics while reducing the average number of score computations in both the datasets.

In this paper, we consider the problem of partitioning a given collection of node sets into k collections such that the average size of collections is the largest, where the size of a collection is defined as the cardinarity of the union of the subsets contained in the collection. More concretely, we give an upper bound on the performance ratio of an approximation algorithm proposed by Abrams et al., which is known to have a performance ratio of at least 1-1/e≅0.6321 where e is Napier's constant. The proposed upper bound is 1-(2-d+1√2)d+1/2 for any d≥1 provided that k=o(n) which approaches to 0.75 as d increases.

Tracking capsule endoscope location is one of the promising applications offered by implant body area networks (BANs). When tracking the capsule endoscope location, i.e., continuously localize it, it is effective to take the weighted sum of its past locations to its present location, in other words, to low-pass filter its past locations. Furthermore, creating an exact mathematical model of location transition will improve tracking performance. Therefore, in this paper, we investigate two tracking methods with received signal strength indicator (RSSI)-based localization in order to solve the capsule endoscope location tracking problem. One of the two tracking methods is finite impulse response (FIR) filter-based tracking, which tracks the capsule endoscope location by averaging its past locations. The other one is particle filter-based tracking in order to deal with a nonlinear transition model on the capsule endoscope. However, the particle filter requires that the particle weight is calculated according to its condition (namely, its likelihood value), while the transition model on capsule endoscope location has some model parameters which cannot be estimated from the received wireless signal. Therefore, for the purpose of applying the particle filter to capsule endoscope tracking, this paper makes some modifications in the resampling step of the particle filter algorithm. Our computer simulation results demonstrate that the two tracking methods can improve the performance as compared with the conventional maximum likelihood (ML) localization. Furthermore, we confirm that the particle filter-based tracking outperforms the conventional FIR filter-based tracking by taking the realistic capsule endoscope transition model into consideration.

Attenuated and delayed versions of the pulse signal overlap in multipath propagation. Previous algorithms can resolve them only if signal sampling is ideal, but fail to resolve two counterparts with non-ideal sampling. In this paper, we propose a novel method which can resolve the general types of non-ideally sampled pulse signals in the time domain via Taylor Series Expansion (TSE) and estimate multipath signals' precise time delays and amplitudes. In combination with the CLEAN algorithm, the overlapped pulse signal parameters are estimated one by one through an iteration method. Simulation results verify the effectiveness of the proposed method.