Individuals can be identified by features extracted from an electrocardiogram (ECG). However, irregular palpitations due to stress or exercise decrease the identification accuracy due to distortion of the ECG waveforms. In this letter, we propose a human identification scheme based on the frequency spectrums of an ECG, which can successfully extract features and thus identify individuals even while exercising. For the proposed scheme, we demonstrate an accuracy rate of 99.8% in a controlled experiment with exercising subjects. This level of accuracy is achieved by determining the significant features of individuals with a random forest classifier. In addition, the effectiveness of the proposed scheme is verified using a publicly available ECG database. We show that the proposed scheme also achieves a high accuracy with this public database.

We present a modular way of implementing adaptive decisions in performing scientific simulations. The proposed method employs modern software engineering mechanisms to allow for better software management in scientific computing, where software adaptation has often been implemented manually by the programmer or by using in-house tools, which complicates software management over time. By applying the aspect-oriented programming (AOP) paradigm, we consider software adaptation as a separate concern and, using popular AOP constructs, implement adaptive decision separately from the original code base, thereby improving software management. We demonstrate the effectiveness of our approach with applications to stochastic simulation software.

Cellular heterogeneous networks (HetNets) with densely deployed small cells can effectively boost network capacity. The co-channel interference and the prominent energy consumption are two crucial issues in HetNets which need to be addressed. Taking the traffic variations into account, this paper proposes a theoretical framework to analyze spectral efficiency (SE) and energy efficiency (EE) considering jointly further-enhanced inter-cell interference coordination (FeICIC) and spectrum allocation (SA) via a stochastic geometric approach for a two-tier downlink HetNet. SE and EE are respectively derived and validated by Monte Carlo simulations. To create spectrum and energy efficient HetNets that can adapt to traffic demands, a non-convex optimization problem with the power control factor, resource partitioning fraction and number of subchannels for the SE and EE tradeoff is formulated, based on which, an iterative algorithm with low complexity is proposed to achieve the sub-optimal solution. Numerical results confirm the effectiveness of the joint FeICIC and SA scheme in HetNets. Meanwhile, a system design insight on resource allocation for the SE and EE tradeoff is provided.

Video reranking is an effective way for improving the retrieval performance of text-based video search engines. This paper proposes a graph-based Web video search reranking method with local and global consistency analysis. Generally, the graph-based reranking approach constructs a graph whose nodes and edges respectively correspond to videos and their pairwise similarities. A lot of reranking methods are built based on a scheme which regularizes the smoothness of pairwise relevance scores between adjacent nodes with regard to a user's query. However, since the overall consistency is measured by aggregating only the local consistency over each pair, errors in score estimation increase when noisy samples are included within query-relevant videos' neighbors. To deal with the noisy samples, the proposed method leverages the global consistency of the graph structure, which is different from the conventional methods. Specifically, in order to detect this consistency, the propose method introduces a spectral clustering algorithm which can detect video groups, in which videos have strong semantic correlation, on the graph. Furthermore, a new regularization term, which smooths ranking scores within the same group, is introduced to the reranking framework. Since the score regularization is performed by both local and global aspects simultaneously, the accurate score estimation becomes feasible. Experimental results obtained by applying the proposed method to a real-world video collection show its effectiveness.

A novel Doppler spread estimation scheme is proposed for an orthogonal frequency division multiplexing (OFDM) system with a Rayleigh fading channel. The proposal develops a composite power spectral density (PSD) function by averaging the multiple PSD functions computed with multiple sets of the channel frequency response (CFR) coefficients. The Doppler spread is estimated by finding the maximum location of the composite PSD quantities larger than a threshold value given by a fixed fraction of the maximum composite PSD quantity. It is shown by simulation that the proposed scheme performs better than three conventional Doppler spread estimation schemes not only in isotropic scattering environments, but also in nonisotropic scattering environments. Moreover, the proposed scheme is shown to perform well in some Rician channel environments if the Rician K-factor is small.

We report room-temperature atomic layer deposition (ALD) of SnO2 using tetramethyltin (TMT) as a precursor and plasma-excited humidified argon as an oxidizing gas and investigate the saturation behaviors of these gases on SnO2-covered Si prisms by IR absorption spectroscopy to determine optimal precursor/oxidizer injection conditions. TMT is demonstrated to adsorb on the SnO2 surface by reacting with surface OH groups, which are regenerated by oxidizing the TMT-saturated surface by plasma-excited humidified argon. We provide a detailed discussion of the growth mechanism. We also report the RT ALD application to the RT TFT fabrication.

The elastic optical network (EON) is a promising new optical technology that uses spectrum resources much more efficiently than does traditional wavelength division multiplexing (WDM). This paper focuses on the routing, modulation level, spectrum and transceiver allocation (RMSTA) problems of the EON. In contrast to previous works that consider only the routing and spectrum allocation (RSA) or routing, modulation level and spectrum allocation (RMSA) problems, we additionally consider the transceiver allocation problem. Because transceivers can be used to regenerate signals (by connecting two transceivers back-to-back) along a transmission path, different regeneration sites on a transmission path result in different spectrum and transceiver usage. Thus, the RMSTA problem is both more complex and more challenging than are the RSA and RMSA problems. To address this problem, we first propose an integer linear programming (ILP) model whose objective is to optimize the balance between spectrum usage and transceiver usage by tuning a weighting coefficient to minimize the cost of network operations. Then, we propose a novel virtual network-based heuristic algorithm to solve the problem and present the results of experiments on representative network topologies. The results verify that, compared to previous works, the proposed algorithm can significantly reduce both resource consumption and time complexity.

Capital expenditure (CAPEX) reduction and efficient wavelength allocation are critical for the future access networks. Elastic lambda aggregation network (EλAN) based on WDM and OFDM technologies is expected to realize efficient wavelength allocation. In this paper, we propose adaptive bandwidth allocation (ABA) algorithm for EλAN under the conditions of crowded networks, in which modulation format, symbol rate and the number of sub-carriers are adaptively decided based on the distance of PON-section, QoS and bandwidth demand of each ONU. Network simulation results show that the proposed algorithm can effectively reduce the total bandwidth and achieve steady high spectrum efficiency and contribute to the further reduction of CAPEX of future optical access networks.

Cognitive radio (CR) is considered as the most promising solution to the so-called spectrum scarcity problem, in which channel sensing is an important problem. In this paper, the problem of determining the period of medium access control (MAC)-layer channel sensing in cognitive radio networks (CRNs) is studied. In our study, the channel state is statistically modeled as a continuous-time alternating renewal process (ARP) alternating between the OFF and ON states for the primary user (PU)'s communication activity. Based on the statistical ARP model, we analyze the CRNs with different SU MAC protocols, taking into consideration the effects of practical issues of imperfect channel sensing and non-negligible channel sensing time. Based on the analysis results, a constrained optimization problem to find the optimal sensing period is formulated and the feasibility of this problem is studied for systems with different OFF/ON channel state length distributions. Numerical results are presented to show the performance of the proposed sensing period optimization scheme. The effects of practical system parameters, including channel sensing errors and channel sensing time, on the performance and the computational complexity of the proposed sensing period optimization scheme are also investigated.

This paper presents a computationally efficient cyclostationarity detection based spectrum sensing technique in cognitive radio. Traditionally, several cyclostationarity detection based spectrum sensing techniques with a low computational complexity have been presented, e.g., peak detector (PD), maximum cyclic autocorrelation selection (MCAS), and so on. PD can be affected by noise uncertainty because it requires a noise floor estimation, whereas MCAS does not require the estimation. Furthermore, the computational complexity of MCAS is greater than that of PD because MCAS must compute some statistics for signal detection instead of the estimation unnecessary whereas PD must compute only one statistic. In the presented MCAS based techniques, only one statistic must be computed. The presented technique obtains other necessary statistics from the procedure that computes the statistic. Therefore, the computational complexity of the presented is almost the same as that of PD, and it does not require the noise floor estimation for threshold. Numerical examples are shown to validate the effectiveness of the presented technique.

A convenient formula for the estimation of the clutter rank of the diving platform radar is derived. Brennan's rule provides a general formula to estimate the clutter rank for the side looking radar with a linear array, which is normally called one-dimensional (1D) estimation problem. With the help of the clutter wavenumber spectrum, the traditional estimation of the clutter rank is extended to the diving scenario and the estimation problem is two-dimensional (2D). The proposed rule is verified by the numerical simulations.

A method for accurate estimation of personalized video preference using multiple users' viewing behavior is presented in this paper. The proposed method uses three kinds of features: a video, user's viewing behavior and evaluation scores for the video given by a target user. First, the proposed method applies Supervised Multiview Spectral Embedding (SMSE) to obtain lower-dimensional video features suitable for the following correlation analysis. Next, supervised Multi-View Canonical Correlation Analysis (sMVCCA) is applied to integrate the three kinds of features. Then we can get optimal projections to obtain new visual features, “canonical video features” reflecting the target user's individual preference for a video based on sMVCCA. Furthermore, in our method, we use not only the target user's viewing behavior but also other users' viewing behavior for obtaining the optimal canonical video features of the target user. This unique approach is the biggest contribution of this paper. Finally, by integrating these canonical video features, Support Vector Ordinal Regression with Implicit Constraints (SVORIM) is trained in our method. Consequently, the target user's preference for a video can be estimated by using the trained SVORIM. Experimental results show the effectiveness of our method.

Compressive spectral imaging (CSI) systems capture the 3D spatiospectral data by measuring the 2D compressed focal plane array (FPA) coded projection with the help of reconstruction algorithms exploiting the sparsity of signals. However, the contradiction between the multi-dimension of the scenes and the limited dimension of the sensors has limited improvement of recovery performance. In order to solve the problem, a novel CSI system based on a coded aperture snapshot spectral imager, RGB-CASSI, is proposed, which has two branches, one for CASSI, another for RGB images. In addition, considering that conventional reconstruction algorithms lead to oversmoothing, a RGB-guided low-rank (RGBLR) method for compressive hyperspectral image reconstruction based on compressed sensing and coded aperture spectral imaging system is presented, in which the available additional RGB information is used to guide the reconstruction and a low-rank regularization for compressive sensing and a non-convex surrogate of the rank is also used instead of nuclear norm for seeking a preferable solution. Experiments show that the proposed algorithm performs better in both PSNR and subjective effects compared with other state-of-art methods.

An energy efficient modulator for an ultra-low-power (ULP) 60-GHz IEEE transmitter is presented in this paper. The modulator consists of a differential duobinary coder and a semi-digital finite-impulse-response (FIR) pulse-shaping filter. By virtue of differential duobinary coding and pulse shaping, the transceiver successfully solves the adjacent-channel-power-ratio (ACPR) issue of conventional on-off-keying (OOK) transceivers. The proposed differential duobinary code adopts an over-sampling precoder, which relaxes timing requirement and reduces power consumption. The semi-digital FIR eliminates the power hungry digital multipliers and accumulators, and improves the power efficiency through optimization of filter parameters. Fabricated in a 65nm CMOS process, this modulator occupies a core area of 0.12mm2. With a throughput of 1.7Gbps/2.6Gbps, power consumption of modulator is 24.3mW/42.8mW respectively, while satisfying the IEEE 802.11ad spectrum mask.

In this paper, we propose a new structure for a compact matched filter bank (MFB) for an optical zero-correlation zone (ZCZ) sequence set with Zcz=2z. The proposed MFB can reduces operation elements such as 2-input adders and delay elements. The number of 2-input adders decrease from O(N2) to O(N log2 N), delay elements decrease from O(N2) to O(N). In addition, the proposed MFBs for the sequence of length 32, 64, 128 and 256 with Zcz=2,4 and 8 are implemented on a field programmable gate array (FPGA). As a result, the numbers of logic elements (LEs) of the proposed MFBs for the sequences with Zcz=2 of length 32, 64, 128 and 256 are suppressed to about 76.2%, 84.2%, 89.7% and 93.4% compared to that of the conventional MFBs, respectively.

In this paper, we theoretically analyse the influence of intersymbol interference (ISI) and continuous wave interference (CWI) on the bit error rate (BER) performance of the spread spectrum (SS) system using a real-valued Huffman sequence under the additive white Gaussian noise (AWGN) environment. The aperiodic correlation function of the Huffman sequence has zero sidelobes except the shift-end values at the left and right ends of shift. The system can give the unified communication and ranging system because the output of a matched filter (MF) is the ideal impulse by generating transmitted signal of the bit duration T=NTc, N=2n, n=1,2,… from the sequence of length M=2kN+1, k=0,1,…, where Tc is the chip duration and N is the spreading factor. As a result, the BER performance of the system is improved with decrease in the absolute value of the shift-end value, and is not influenced by ISI if the shift-end value is almost zero-value. In addition, the BER performance of the system of the bit duration T=NTc with CWI is improved with increase in the sequence length M=2kN+1, and the system can decrease the influence of CWI.

Regular expression matching is essential in network and big-data applications; however, it still has a serious performance bottleneck. The state-of-the-art schemes use a multi-pattern exact string-matching algorithm as a filtering module placed before a heavy regular expression engine. We design a new approximate string-matching filter using multiple q-grams; this filter not only achieves better space compactness, but it also has higher throughput than the existing filters.

This paper proposes an oscillation model for analyzing the dynamics of activity propagation across social media networks. In order to analyze such dynamics, we generally need to model asymmetric interactions between nodes. In matrix-based network models, asymmetric interaction is frequently modeled by a directed graph expressed as an asymmetric matrix. Unfortunately, the dynamics of an asymmetric matrix-based model is difficult to analyze. This paper, first of all, discusses a symmetric matrix-based model that can describe some types of link asymmetry, and then proposes an oscillation model on networks. Next, the proposed oscillation model is generalized to arbitrary link asymmetry. We describe the outlines of four important research topics derived from the proposed oscillation model. First, we show that the oscillation energy of each node gives a generalized notion of node centrality. Second, we introduce a framework that uses resonance to estimate the natural frequency of networks. Natural frequency is important information for recognizing network structure. Third, by generalizing the oscillation model on directed networks, we create a dynamical model that can describe flaming on social media networks. Finally, we show the fundamental equation of oscillation on networks, which provides an important breakthrough for generalizing the spectral graph theory applicable to directed graphs.

This paper presents a calibration method for RF switch channels of a near-range multistatic linear array radar. The method allows calibration of the channel transfer functions of the RF switches and antenna transfer functions in frequency domain data, without disconnecting the antennas from the radar system. In addition, the calibration of the channels is independent of the directivities of the transmitting and receiving antennas. We applied the calibration method to a 3D imaging step-frequency radar system at 10-20GHz suitable for the nondestructive inspection of the walls of wooden houses. The measurement range of the radar is limited to 0-240mm, shorter than the antenna array length 480mm. This radar system allows acquiring 3D imaging data with a single scan. Using synthetic aperture radar processing, the structural health of braces inside the walls of wooden houses can be evaluated from the obtained 3D volume images. Based on experiment results, we confirmed that the proposed calibration method significantly improves the subsurface 3D imaging quality. Low intensity ghost images behind the brace target were suppressed, deformations of the target in the volume image were rectified and errors the range distance were corrected.

Malaysia is one of the countries with the highest car crash fatality rates in Asia. The high implementation cost of in-vehicle driver behavior warning system and autonomous driving remains a significant challenge. Motivated by the large number of simple yet effective inventions that benefitted many developing countries, this study presents the findings of emotion recognition based on skin conductance response using a low-cost wearable sensor. Emotions were evoked by presenting the proposed display stimulus and driving stimulator. Meaningful power spectral density was extracted from the filtered signal. Experimental protocols and frameworks were established to reduce the complexity of the emotion elicitation process. The proof of concept in this work demonstrated the high accuracy of two-class and multiclass emotion classification results. Significant differences of features were identified using statistical analysis. This work is one of the most easy-to-use protocols and frameworks, but has high potential to be used as biomarker in intelligent automobile, which helps prevent accidents and saves lives through its simplicity.