Data integrity is a key metric of security for Internet of Things (IoT) which refers to accuracy and reliability of data during transmission, storage and retrieval. Cryptographic hash functions are common means used for data integrity verification. Newly announced SHA-3 is the next generation hash function standard to replace existing SHA-1 and SHA-2 standards for better security. However, its underlying Keccak algorithm is computation intensive and thus limits its deployment on IoT systems which are normally equipped with 32-bit resource constrained embedded processors. This paper proposes two efficient SHA-3 ASIPs based on an open 32-bit RISC-V embedded processor named Z-scale. The first operation-oriented ASIP (OASIP) focuses on accelerating time-consuming operations with instruction set extensions to improve resource efficiency. And next datapath-oriented ASIP (DASIP) targets exploiting advance data and instruction level parallelism with extended auxiliary registers and customized datapath to achieve high performance. Implementation results show that both proposed ASIPs can effectively accelerate SHA-3 algorithm with 14.6% and 26.9% code size reductions, 30% and 87% resource efficiency improvements, 71% and 262% better maximum throughputs as well as 40% and 288% better power efficiencies than reference design. This work makes SHA-3 algorithm integration practical for both low-cost and high-performance IoT systems.

In this paper, we propose a novel primary user detection scheme for spectrum sensing in cognitive radio. Inspired by the conventional signal classification approach, the spectrum sensing is translated into a classification problem. On the basis of feature-based classification, the spectral correlation of a second-order cyclostationary analysis is applied as the feature extraction method, whereas a stacked denoising autoencoders network is applied as the classifier. Two training methods for signal detection, interception-based detection and simulation-based detection, are considered, for different prior information and implementation conditions. In an interception-based detection method, inspired by the two-step sensing, we obtain training data from the interception of actual signals after a sophisticated sensing procedure, to achieve detection without priori information. In addition, benefiting from practical training data, this interception-based detection is superior under actual transmission environment conditions. The alternative, a simulation-based detection method utilizes some undisguised parameters of the primary user in the spectrum of interest. Owing to the diversified predetermined training data, simulation-based detection exhibits transcendental robustness against harsh noise environments, although it demands a more complicated classifier network structure. Additionally, for the above-described training methods, we discuss the classifier complexity over implementation conditions and the trade-off between robustness and detection performance. The simulation results show the advantages of the proposed method over conventional spectrum-sensing schemes.

We have developed the high speed bandpass liquid crystal filter with narrow full width at half maximum (FWHM) of 5nm for real-time multi spectral imaging systems. We have successfully achieved short wavelength-switching time of 30ms by the optimization of phase retardation of thin liquid crystal cells.

A wind-noise suppressor with SNR based wind-noise detection and speech-wind discrimination is proposed. Wind-noise detection is performed in each frame and frequency based on the power ratio of the noisy speech and an estimated stationary noise. The detection result is modified by speech presence likelihood representing spectral smoothness to eliminate speech components. To suppress wind noise with little speech distortion, spectral gains are made smaller in the frame and the frequency where wind-noise is detected. Subjective evaluation results show that the 5-grade MOS for the proposed wind-noise suppressor reaches 3.4 and is 0.56 higher than that by a conventional noise suppressor with a statistically significant difference.

Spectrum databases are required to assist the process of radio propagation estimation for spectrum sharing. Especially, a measurement-based spectrum database achieves highly efficient spectrum sharing by storing the observed radio environment information such as the signal power transmitted from a primary user. However, when the average received signal power is calculated in a given square mesh, the bias of the observation locations within the mesh strongly degrades the accuracy of the statistics because of the influence of terrain and buildings. This paper proposes a method for determining the statistics by using mesh clustering. The proposed method clusters the feature vectors of the measured data by using the k-means and Gaussian mixture model methods. Simulation results show that the proposed method can decrease the error between the measured value and the statistically processed value even if only a small amount of data is available in the spectrum database.

We are developing an Ultra-Low Field (ULF) Magnetic Resonance Imaging (MRI) system with a tuned high-Tc (HTS)-rf-SQUID for food inspection. We previously reported that a small hole in a piece of cucumber can be detected. The acquired image was based on filtered back-projection reconstruction using a polarizing permanent magnet. However the resolution of the image was insufficient for food inspection and took a long time to process. The purpose of this study is to improve image quality and shorten processing time. We constructed a specially designed cryostat, which consists of a liquid nitrogen tank for cooling an electromagnetic polarizing coil (135mT) at 77K and a room temperature bore. A Cu pickup coil was installed at the room temperature bore and detected an NMR signal from a sample. The signal was then transferred to an HTS SQUID via an input coil. Following a proper MRI sequence, spatial frequency data at 64×32 points in k-space were obtained. Then, a 2D-FFT (Fast Fourier Transformation) method was applied to reconstruct the 2D-MR images. As a result, we successfully obtained a clear water image of the characters “TUT”, which contains a narrowest width of 0.5mm. The imaging time was also shortened by a factor of 10 when compared to the previous system.

With the expansion of the Internet, the number of available Web services has increased. Web service clustering to identify functionally similar clusters has become a major approach to the efficient discovery of suitable Web services. In this study, we propose a Web service clustering approach that uses novel ontology learning and a similarity calculation method based on the specificity of an ontology in a domain with respect to information theory. Instead of using traditional methods, we generate the ontology using a novel method that considers the specificity and similarity of terms. The specificity of a term describes the amount of domain-specific information contained in that term. Although general terms contain little domain-specific information, specific terms may contain much more domain-related information. The generated ontology is used in the similarity calculations. New logic-based filters are introduced for the similarity-calculation procedure. If similarity calculations using the specified filters fail, then information-retrieval-based methods are applied to the similarity calculations. Finally, an agglomerative clustering algorithm, based on the calculated similarity values, is used for the clustering. We achieved highly efficient and accurate results with this clustering approach, as measured by improved average precision, recall, F-measure, purity and entropy values. According to the results, specificity of terms plays a major role when classifying domain information. Our novel ontology-based clustering approach outperforms comparable existing approaches that do not consider the specificity of terms.

In this paper we extend hyperparameter-free sparse signal reconstruction approaches to permit the high-resolution time delay estimation of spread spectrum signals and demonstrate their feasibility in terms of both performance and computation complexity by applying them to the ISO/IEC 24730-2.1 real-time locating system (RTLS). Numerical examples show that the sparse asymptotic minimum variance (SAMV) approach outperforms other sparse algorithms and multiple signal classification (MUSIC) regardless of the signal correlation, especially in the case where the incoming signals are closely spaced within a Rayleigh resolution limit. The performance difference among the hyperparameter-free approaches decreases significantly as the signals become more widely separated. SAMV is sometimes strongly influenced by the noise correlation, but the degrading effect of the correlated noise can be mitigated through the noise-whitening process. The computation complexity of SAMV can be feasible for practical system use by setting the power update threshold and the grid size properly, and/or via parallel implementations.

We propose a cooperative cognitive radio network (CCRN) with secondary users (SUs) employing two simultaneous transmit and receive (STAR) antennas. In the proposed framework of full-duplex (FD) multiple-input-multiple-output (MIMO) CCRN, the region of achievable rate is expanded via FD communication among SUs enabled by the STAR antennas adopted for the SUs. The link capacity of the proposed framework is analyzed theoretically. It is shown through numerical analysis that the proposed FD MIMO-CCRN framework can provide a considerable performance gain over the conventional frameworks of CCRN and MIMO-CCRN.

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.

Code smells are indicators of design flaws or problems in the source code. Various tools and techniques have been proposed for detecting code smells. These tools generally detect a large number of code smells, so approaches have also been developed for prioritizing and filtering code smells. However, lack of empirical data detailing how developers filter and prioritize code smells hinders improvements to these approaches. In this study, we investigated ten professional developers to determine the factors they use for filtering and prioritizing code smells in an open source project under the condition that they complete a list of five tasks. In total, we obtained 69 responses for code smell filtration and 50 responses for code smell prioritization from the ten professional developers. We found that Task relevance and Smell severity were most commonly considered during code smell filtration, while Module importance and Task relevance were employed most often for code smell prioritization. These results may facilitate further research into code smell detection, prioritization, and filtration to better focus on the actual needs of developers.

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.

A multi-channel cognitive radio is a powerful solution for recovering the exhaustion of frequency spectrum resources. In a cognitive radio, although master and slave terminals (which construct a communication link) have the freedom to access arbitrary channels, access channel mismatch is caused. A rendezvous scheme based on frequency hopping can compensate for this mismatch by exchanging control signals through a selected channel in accordance with a certain rule. However, conventional frequency hopping schemes do not consider an access protocol of both control signals in the rendezvous scheme and the signal caused by channel access from other systems. Further, they do not consider an information sharing method to reach a consensus between the master and slave terminals. This paper proposes a modified rendezvous scheme based on learning-based channel occupancy rate (COR) estimation and describes a specific channel-access rule in the slave terminal. On the basis of this rule, the master estimates a channel selected by the slave by considering the average COR of the other systems. Since the master can narrow down the number of channels, a fast rendezvous scheme with a few control signals is established.

We introduce a Welch FFT segment size selection method for FFT-based wide band spectrum measurement in the context of smart spectrum access (SSA), in which statistical spectrum usage information of primary users (PUs), such as duty cycle (DC), will be exploited by secondary users (SUs). Energy detectors (EDs) based on Welch FFT can detect the presence of PU signals in a broadband environment efficiently, and DC can be estimated properly if a Welch FFT segment size is set suitably. There is a trade-off between detection performance and frequency resolution in terms of the Welch FFT segment size. The optimum segment size depends on signal-to-noise ratio (SNR) which makes practical and optimum segment size setting difficult. For this issue, we previously proposed a segment size selection method employing a relationship between noise floor (NF) estimation output and the segment size without SNR information. It can achieve accurate spectrum awareness at the expense of relatively high computational complexity since it employs exhaustive search to select a proper segment size. In this paper, we propose a segment size selection method that offers reasonable spectrum awareness performance with low computational complexity since limited search is used. Numerical evaluations show that the proposed method can match the spectrum awareness performance of the conventional method with 70% lower complexity or less.

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.

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.

Motivated by the observation that certain convolutional channels of a Convolutional Neural Network (CNN) exhibit object specific responses, we seek to discover and exploit the convolutional channels of a CNN in which neurons are activated by the presence of specific objects in the input image. A method for explicitly fine-tuning a pre-trained CNN to induce object specific channel (OSC) and systematically identifying it for the human faces has been developed. In this paper, we introduce a multi-scale approach to constructing robust face heatmaps based on OSC features for rapidly filtering out non-face regions thus significantly improving search efficiency for face detection. We show that multi-scale OSC can be used to develop simple and compact face detectors in unconstrained settings with state of the art performance.

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.

An increasing number of psychological studies have demonstrated that the six basic expressions of emotions are not culturally universal. However, automatic facial expression recognition (FER) systems disregard these findings and assume that facial expressions are universally expressed and recognized across different cultures. Therefore, this paper presents an analysis of Western-Caucasian and East-Asian facial expressions of emotions based on visual representations and cross-cultural FER. The visual analysis builds on the Eigenfaces method, and the cross-cultural FER combines appearance and geometric features by extracting Local Fourier Coefficients (LFC) and Facial Fourier Descriptors (FFD) respectively. Furthermore, two possible solutions for FER under multicultural environments are proposed. These are based on an early race detection, and independent models for culture-specific facial expressions found by the analysis evaluation. HSV color quantization combined with LFC and FFD compose the feature extraction for race detection, whereas culture-independent models of anger, disgust and fear are analyzed for the second solution. All tests were performed using Support Vector Machines (SVM) for classification and evaluated using five standard databases. Experimental results show that both solutions overcome the accuracy of FER systems under multicultural environments. However, the approach which individually considers the culture-specific facial expressions achieved the highest recognition rate.

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.