Small group detection is still a challenging problem in crowds. Traditional methods use the trajectory information to measure pairwise similarity which is sensitive to the variations of group density and interactive behaviors. In this paper, we propose two types of information by simultaneously incorporating trajectory and interaction information, to detect small groups in crowds. The trajectory information is used to describe the spatial proximity and motion information between trajectories. The interaction information is designed to capture the interactive behaviors from video sequence. To achieve this goal, two classifiers are exploited to discover interpersonal relations. The assumption is that interactive behaviors often occur in group members while there are no interactions between individuals in different groups. The pairwise similarity is enhanced by combining the two types of information. Finally, an efficient clustering approach is used to achieve small group detection. Experiments show that the significant improvement is gained by exploiting the interaction information and the proposed method outperforms the state-of-the-art methods.

Collaborative filtering with only implicit feedbacks has become a quite common scenario (e.g. purchase history, click-through log, and page visitation). This kind of feedback data only has a small portion of positive instances reflecting the user's interaction. Such characteristics pose great challenges to dealing with implicit recommendation problems. In this letter, we take full advantage of matrix factorization and relative preference to make the recommendation model more scalable and flexible. In addition, we propose to take into consideration the concept of covisitation which captures the underlying relationships between items or users. To this end, we propose the algorithm Integrated Collaborative Filtering for Implicit Feedback incorporating Covisitation (ICFIF-C) to integrate matrix factorization and collaborative ranking incorporating the covisitation of users and items simultaneously to model recommendation with implicit feedback. The experimental results show that the proposed model outperforms state-of-the-art algorithms on three standard datasets.

A comprehensive method applying a nonlinear frequency compression (FC) as complementary to multi-band loudness compensation is proposed, which is able to improve loudness compensation and simultaneously increase high-frequency speech intelligibility for digital hearing aids. The proposed nonlinear FC (NLFC) improves the conventional methods in the aspect that the compression ratio (CR) is adjusted based on the speech intelligibility percentage in different frequency ranges. Then, an adaptive wide dynamic range compression (AWDRC) with a time-varying CR is applied to achieve adaptive loudness compensation. The experimental test results show that the mean speech identification is improved in comparison with the state-of-art methods.

As autonomous underwater vehicles (AUVs) have been widely used to perform cooperative works with sensor nodes for data-gathering, the need for long-range AUVs has further grown to support the long-duration cooperation with sensor nodes. However, as existing data-gathering protocols for the cooperative works have not considered AUVs' energy consumption, AUVs can deplete their energy more quickly before fulfilling their missions. The objective of this work is to develop an AUV based data-gathering protocol that maximizes the duration for the cooperative works. Simulation results show that the proposed protocol outperforms existing protocols with respect to the long-range AUVs.

Based on the reconstruction of the augmented interference-plus-noise (IPN) covariance matrix (CM) and the estimation of the desired signal's extended steering vector (SV), we propose a novel robust widely linear (WL) beamforming algorithm. Firstly, an extension of the iterative adaptive approach (IAA) algorithm is employed to acquire the spatial spectrum. Secondly, the IAA spatial spectrum is adopted to reconstruct the augmented signal-plus-noise (SPN) CM and the augmented IPNCM. Thirdly, the extended SV of the desired signal is estimated by using the iterative robust Capon beamformer with adaptive uncertainty level (AU-IRCB). Compared with several representative robust WL beamforming algorithms, simulation results are provided to confirm that the proposed method can achieve a better performance and has a much lower complexity.

This letter proposes a heuristic algorithm to select check variables, which are points of comparison for error detection, for soft-error tolerant datapaths. Our soft-error tolerance scheme is based on check-and-retry computation and an efficient resource management named speculative resource sharing (SRS). Starting with the smallest set of check variables, the proposed algorithm repeats to add new check variable one by one incrementally and find the minimum latency solution among the series of generated solutions. During the process, each new check variable is selected so that the opportunity of SRS is enlarged. Experimental results show that improvements in latency are achieved compared with the choice of the smallest set of check variables.

Regression testing is essential for assuring the quality of a software product. Because rerunning all test cases in regression testing may be impractical under limited resources, test case prioritization is a feasible solution to optimize regression testing by reordering test cases for the current testing version. In this paper, we propose a novel test case prioritization approach that combines the clustering algorithm and the scheduling algorithm for improving the effectiveness of regression testing. By using the clustering algorithm, test cases with same or similar properties are merged into a cluster, and the scheduling algorithm helps allocate an execution priority for each test case by incorporating fault detection rates with the waiting time of test cases in candidate set. We have conducted several experiments on 12 C programs to validate the effectiveness of our proposed approach. Experimental results show that our approach is more effective than some well studied test case prioritization techniques in terms of average percentage of fault detected (APFD) values.

In general, semi-supervised clustering can outperform unsupervised clustering. Since 2001, pairwise constraints for semi-supervised clustering have been an important paradigm in this field. In this paper, we show that pairwise constraints (ECs) can affect the performance of clustering in certain situations and analyze the reasons for this in detail. To overcome these disadvantages, we first outline some exemplars constraints. Based on these constraints, we then describe a semi-supervised clustering framework, and design an exemplars constraints expectation-maximization algorithm. Finally, standard datasets are selected for experiments, and experimental results are presented, which show that the exemplars constraints outperform the corresponding unsupervised clustering and semi-supervised algorithms based on pairwise constraints.

We evaluated a technique for protecting the copyright of digital data for 3-D printing. To embed copyright information, the inside of a 3-D printed object is constructed from fine domains that have different physical characteristics from those of the object's main body surrounding them, and to read out the embedded information, these fine domains inside the objects are detected using nondestructive inspections such as X-ray photography or thermography. In the evaluation, copyright information embedded inside the 3-D printed object was expressed using the depth of fine cavities inside the object, and X-ray photography were used for reading them out from the object. The test sample was a cuboid 46mm wide, 42mm long, and 20mm deep. The cavities were 2mm wide and 2mm long. The difference in the depths of the cavities appeared as a difference in the luminance in the X-ray photographs, and 21 levels of depth could be detected on the basis of the difference in luminance. These results indicate that under the conditions of the experiment, each cavity expressed 4 to 5bits of information with its depth. We demonstrated that the proposed technique had the possibility of embedding a sufficient volume of information for expressing copyright information by using the depths of cavities.

A flexible orthogonal frequency-division multiplexing optical network enables the bandwidth to be flexibly changed by changing the number of sub-carriers. We assume that users request to dynamically change the number of sub-carriers. Dynamic bandwidth changes allow the network resources to be used more efficiently but each change takes a significant amount of time to complete. Service centric resource allocation must be considered in terms of the waiting time needed to change the number of sub-carriers. If the user demands drastically increase such as just after a disaster, the waiting time due to a chain-change of bandwidth becomes excessive because disaster priority telephone services are time-critical. This paper proposes a Grouped-elastic spectrum allocation scheme to satisfy the tolerable waiting time of the service in an optical fiber link. Spectra are grouped to restrict a waiting time in the proposed scheme. In addition, the proposed scheme determines a bandwidth margin between neighbor spectra to spectra to prevent frequent reallocation by estimating real traffic behavior in each group. Numerical results show that the bandwidth requirements can be minimized while satisfying the waiting time constraints. Additionally measurement granularity and channel alignment are discussed.

Narrow swipe sensor has been widely used in embedded systems such as smart-phone. However, the size of captured image is much smaller than that obtained by the traditional area sensor. Therefore, the limited template coverage is the performance bottleneck of such kind of systems. Aiming to increase the geometry coverage of templates, a novel fingerprint template feature synthesis scheme is proposed in the present study. This method could synthesis multiple input fingerprints into a wider template by clustering the minutiae descriptors. The proposed method consists of two modules. Firstly, a user behavior-based Registration Pattern Inspection (RPI) algorithm is proposed to select the qualified candidates. Secondly, an iterative clustering algorithm Modified Fuzzy C-Means (MFCM) is proposed to process the large amount of minutiae descriptors and then generate the final template. Experiments conducted over swipe fingerprint database validate that this innovative method gives rise to significant improvements in reducing FRR (False Reject Rate) and EER (Equal Error Rate).

We propose a microphone array speech enhancement method that integrates spatial-cue-based source power spectral density (PSD) estimation and statistical speech model-based PSD estimation. The goal of this research was to clearly pick up target speech even in noisy environments such as crowded places, factories, and cars running at high speed. Beamforming with post-Wiener filtering is commonly used in many conventional studies on microphone-array noise reduction. For calculating a Wiener filter, speech/noise PSDs are essential, and they are estimated using spatial cues obtained from microphone observations. Assuming that the sound sources are sparse in the temporal-spatial domain, speech/noise PSDs may be estimated accurately. However, PSD estimation errors increase under circumstances beyond this assumption. In this study, we integrated speech models and PSD-estimation-in-beamspace method to correct speech/noise PSD estimation errors. The roughly estimated noise PSD was obtained frame-by-frame by analyzing spatial cues from array observations. By combining noise PSD with the statistical model of clean-speech, the relationships between the PSD of the observed signal and that of the target speech, hereafter called the observation model, could be described without pre-training. By exploiting Bayes' theorem, a Wiener filter is statistically generated from observation models. Experiments conducted to evaluate the proposed method showed that the signal-to-noise ratio and naturalness of the output speech signal were significantly better than that with conventional methods.

Image steganography is a technique of embedding secret message into a digital image to securely send the information. In contrast, steganalysis focuses on detecting the presence of secret messages hidden by steganography. The modern approach in steganalysis is based on supervised learning where the training set must include the steganographic and natural image features. But if a new method of steganography is proposed, and the detector still trained on existing methods will generally lead to the serious detection accuracy drop due to the mismatch between training and detecting steganographic method. In this paper, we just attempt to process unsupervised learning problem and propose a detection model called self-learning ensemble discriminant clustering (SEDC), which aims at taking full advantage of the statistical property of the natural and testing images to estimate the optimal projection vector. This method can adaptively select the most discriminative subspace and then use K-means clustering to generate the ultimate class labels. Experimental results on J-UNIWARD and nsF5 steganographic methods with three feature extraction methods such as CC-JRM, DCTR, GFR show that the proposed scheme can effectively classification better than blind speculation.

Improvement of conventional networks with an incremental approach is an important design method for the development of the future internet. For this approach, we are developing a future aggregation network based on passive optical network (PON) technology to achieve both cost-effectiveness and high reliability. In this paper, we propose a timeslot (TS) synchronization method for sharing a TS from an optical burst mode transceiver between any route of arbitrary fiber length by changing both the route of the TS transmission and the TS control timing on the optical burst mode transceiver. We show the effectiveness of the proposed method for exchanging TSs in bidirectional bufferless wavelength division multiplexing (WDM) and time division multiplexing (TDM) multi-ring networks under the condition of the occurrence of a link failure through prototype systems. Also, we evaluate the reduction of the required number of optical interfaces in a multi-ring network by applying the proposed method.

In this paper, we have investigated the characteristics of PdYb-silicide layer formed by the silicidation of Pd/Yb/n-Si(100) stacked structures for the first time. Pd (12-20 nm)/Yb (0-8 nm) stacked layers were deposited on n-Si(100) substrates by the RF magnetron sputtering at room temperature. Then, 10 nm-thick HfN encapsulating layer was deposited at room temperature. Next, silicidation was carried out by the RTA at 500°C/1 min in N2 followed by the selective etching. From the J-V characteristics of fabricated Schottky diode, Schottky barrier height (SBH) for electron was reduced from 0.73 eV of Pd2Si to 0.4 eV of PdYb-silicide in case the Pd/Yb thicknesses were 14/6 nm, respectively.

Device-to-device (D2D) communication enables two local users to communicate with each other directly instead of relaying through a third party, e.g., base station. In this paper, we study a subchannel sharing strategy underlaying multichannel cellular network for D2D pairs and existing cellular users (CUs). In the investigated scenario, we try to improve the spectrum efficiency of D2D pairs, but inevitably brings cross interference between two user groups. To combat interference, we attempt to assign each D2D pair with appropriate subchannels, which may belong to different CUs, and manipulate transmission power of all users so as to maximize the sum rate of all D2D pairs, while assuring each CU with a minimum data rate on its subchannel set. The formulated problem is a nonconvex problem and thus, obtaining its optimal solution is a tough task. However, we can find optimal power and subchannel assignment for a special case by setting an independent data rate constraint on each subchannel. Then we find an efficient method to calculate a gradient for our original problem. Finally, we propose a gradient-based search method to address the problem with coupled minimum data rate constraint. The performance of our proposed subchannel sharing strategy is illustrated via extensive simulation results.

Image interpolation is one of the image upsampling technologies from a single input image. This technology obtains high resolution images by fitting functions or models. Although image interpolation methods are faster than other upsampling technologies, they tend to cause jaggies and blurs in edge and texture regions. Multi-surface Fitting is one of the image upsampling techniques from multiple input images. This algorithm utilizes multiple local functions and the weighted means of the estimations in each local function. Multi-surface Fitting obtains high quality upsampled images. However, its quality depends on the number of input images. Therefore, this method is used in only limited situations. In this paper, we propose an image interpolation method with both high quality and a low computational cost which can be used in many situations. We adapt the idea of Multi-surface Fitting for the image upsampling problems from a single input image. We also utilize local functions to reduce blurs. To improve the reliability of each local function, we introduce new weights in the estimation of the local functions. Besides, we improve the weights for weighted means to estimate a target pixel. Moreover, we utilize convolutions with small filters instead of the calculation of each local function in order to reduce the computational cost. Experimental results show our method obtains high quality output images without jaggies and blurs in short computational time.

This paper first reviews the state-of-the-art noise reduction methods and points out their vulnerability in noise reduction performance and speech quality, especially under the low signal-noise ratios (SNR) environments. Then this paper presents an improved perceptual multiband spectral subtraction (MBSS) noise reduction algorithm (NRA) and a novel robust voice activity detection (VAD) based on the amended sub-band SNR. The proposed SNR-based VAD can considerably increase the accuracy of discrimination between noise and speech frame. The simulation results show that the proposed NRA has better segmental SNR (segSNR) and perceptual evaluation of speech quality (PESQ) performance than other noise reduction algorithms especially under low SNR environments. In addition, a fully operational digital hearing aid chip is designed and fabricated in the 0.13 µm CMOS process based on the proposed NRA. The final chip implementation shows that the whole chip dissipates 1.3 mA at the 1.2 V operation. The acoustic test result shows that the maximum output sound pressure level (OSPL) is 114.6 dB SPL, the equivalent input noise is 5.9 dB SPL, and the total harmonic distortion is 2.5%. So the proposed digital hearing aid chip is a promising candidate for high performance hearing-aid systems.

In this paper, we study robust Principal Component Analysis (PCA)-based anomaly detection techniques in network traffic, which can detect traffic anomalies by projecting measured traffic data onto a normal subspace and an anomalous subspace. In a PCA-based anomaly detection, outliers, anomalies with excessively large traffic volume, may contaminate the subspaces and degrade the performance of the detector. To solve this problem, robust PCA methods have been studied. In a robust PCA-based anomaly detection scheme, outliers can be removed from the measured traffic data before constructing the subspaces. Although the robust PCA methods are promising, they incure high computational cost to obtain the optimal location vector and scatter matrix for the subspace. We propose a novel anomaly detection scheme by extending the minimum covariance determinant (MCD) estimator, a robust PCA method. The proposed scheme utilizes the daily periodicity in traffic volume and attempts to detect anomalies for every period of measured traffic. In each period, before constructing the subspace, outliers are removed from the measured traffic data by using a location vector and a scatter matrix obtained in the preceding period. We validate the proposed scheme by applying it to measured traffic data in the Abiline network. Numerical results show that the proposed scheme provides robust anomaly detection with less computational cost.

Attaining extremely low latency service in 5G cellular networks is an important challenge in the communication research field. A higher QoS in the next-generation network could enable several unprecedented services, such as Tactile Internet, Augmented Reality, and Virtual Reality. However, these services will all need support from powerful computational resources provided through cloud computing. Unfortunately, the geolocation of cloud data centers could be insufficient to satisfy the latency aimed for in 5G networks. The physical distance between servers and users will sometimes be too great to enable quick reaction within the service time boundary. The problem of long latency resulting from long communication distances can be solved by Mobile Edge Computing (MEC), though, which places many servers along the edges of networks. MEC can provide shorter communication latency, but total latency consists of both the transmission and the processing times. Always selecting the closest edge server will lead to a longer computing latency in many cases, especially when there is a mass of users around particular edge servers. Therefore, the research studies the effects of both latencies. The communication latency is represented by hop count, and the computation latency is modeled by processor sharing (PS). An optimization model and selection policies are also proposed. Quantitative evaluations using simulations show that selecting a server according to the lowest total latency leads to the best performance, and permitting an over-latency barrier would further improve results.