We present a novel burst-mode transimpedance amplifier (TIA) with a gain-mode switching. The proposed TIA utilizes a regulated-cascode (RGC) input stage for broadband characteristics. To expand a dynamic range, the RGC controls a linear operating range depending on transimpedance gains by adjusting bias conditions. This TIA is implemented using the 0.18μm-CMOS technology. The experimental results show that the proposed TIA IC has a good eye-opening and can respond quickly to the burst data.

Cell phones with GPS function as well as GPS loggers are widely used and we can easily obtain users' geographic information. Now classifying the measured GPS positions into indoor/outdoor positions is one of the major challenges. In this letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions utilizing machine learning. Given a set of clusters of measured positions whose center position shows the user's estimated stayed position, we calculate the feature values composed of: positioning accuracy, spatial features, and temporal feature of measured positions included in every cluster. Then a random forest classifier learns these feature values of the known data set. Finally, we classify the unknown clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.

Nowadays, many household computers are used to mine ASIC-resistant cryptocurrency, which brings serious safety risks. In this letter, a light weight system is put forward to achieve proactive risk management for the kind of mining. Based on the system requirement analysis, a brief system design is presented and furthermore, key techniques to implement it with open source hardware and software are given to show its feasibility.

Android malwares are rapidly becoming a potential threat to users. Among several Android malware detection schemes, the scheme using Inter-Component Communication (ICC) is gathering attention. That scheme extracts numerous ICC-related features to detect malwares by machine learning. In order to mitigate the degradation of detection performance caused by redundant features, Correlation-based Feature Selection (CFS) is applied to feature before machine learning. CFS selects useful features for detection in accordance with the theory that a good feature subset has little correlation with mutual features. However, CFS may remove useful ICC-related features because of strong correlation between them. In this paper, we propose an effective feature selection scheme for Android ICC-based malware detection using the gap of the appearance ratio. We argue that the features frequently appearing in either benign apps or malwares are useful for malware detection, even if they are strongly correlated with each other. To select useful features based on our argument, we introduce the proportion of the appearance ratio of a feature between benign apps and malwares. Since the proportion can represent whether a feature frequently appears in either benign apps or malwares, this metric is useful for feature selection based on our argument. Unfortunately, the proportion is ineffective when a feature appears only once in all apps. Thus, we also introduce the difference of the appearance ratio of a feature between benign apps and malwares. Since the difference simply represents the gap of the appearance ratio, we can select useful features by using this metric when such a situation occurs. By computer simulation with real dataset, we demonstrate our scheme improves detection accuracy by selecting the useful features discarded in the previous scheme.

We have proposed a novel concept of a digital watermarking technique for music data that focuses on the use of sound synthesis and sound effect techniques. This paper describes the details of our proposed technique that employs the distortion effect, one of the most common sound effects frequently utilized especially for guitar and bass instruments. This paper describes the experimental results of evaluating the resistance of the proposed technique against some basic malicious attacks utilizing MP3 coding, tempo alteration, pitch alteration, and high-pass filtering. It is demonstrated that the proposed technique potentially has appropriate resistance against such attacks except for the high-pass filtering attack. A technique for increasing the resistance against the high-pass filtering attack is also supplementarily discussed.

Efficient road-lane detection is expected to be achievable by application of the Hough transform (HT) which realizes high-accuracy straight-line extraction from images. The main challenge for HT-hardware implementation in actual applications is the trade-off optimization between accuracy maximization, power-dissipation reduction and real-time requirements. We report a HT-hardware architecture for road-lane detection with parallelized voting procedure, local maximum algorithm and FPGA-prototype implementation. Parallelization of the global design is realized on the basis of θ-value discretization in the Hough space. Four major hardware modules are developed for edge detection in the original video frames, computation of the characteristic edge-pixel values (ρ,θ) in Hough-space, voting procedure for each (ρ,θ) pair with parallel local-maximum-based peak voting-point extraction in Hough space to determine the detected straight lines. Implementation of a prototype system for real-time road-lane detection on a low-cost DE1 platform with a Cyclone II FPGA device was verified to be possible. An average detection speed of 135 frames/s for VGA (640x480)-frames was achieved at 50 MHz working frequency.

This article proposes a prosody correction method based on partial model adaptation for Chinese-accented Japanese hidden Markov model (HMM)-based text-to-speech synthesis. Although text-to-speech synthesis built from non-native speech accurately reproduces the speaker's individuality in synthetic speech, the naturalness of the synthetic speech is strongly degraded. In the proposed model, to improve the naturalness while preserving the speaker individuality of Chinese-accented Japanese text-to-speech synthesis, we partially utilize HMM parameters of native Japanese speech to synthesize prosody-corrected synthetic speech. Results of an experimental evaluation demonstrate that duration and F0 correction are significantly effective for improving naturalness.

Estimation of the gradient of the logarithm of a probability density function is a versatile tool in statistical data analysis. A recent method for model-seeking clustering called the least-squares log-density gradient clustering (LSLDGC) [Sasaki et al., 2014] employs a sophisticated gradient estimator, which directly estimates the log-density gradients without going through density estimation. However, the typical implementation of LSLDGC is based on a spherical Gaussian function, which may not work well when the probability density function for data has highly correlated local structures. To cope with this problem, we propose a new gradient estimator for log-density gradients with Gaussian mixture models (GMMs). Covariance matrices in GMMs enable the new estimator to capture the highly correlated structures. Through the application of the new gradient estimator to mode-seeking clustering and hierarchical clustering, we experimentally demonstrate the usefulness of our clustering methods over existing methods.

For the further scaling and lower voltage applications of nonvolatile ferroelectric memory, the effect of Kr/O2 sputtering for SrBi2Ta2O9 (SBT) thin film formation was investigated utilizing a SrBi2Ta2O9 target. The 80-nm-thick SBT films were deposited by radio-frequency (RF) magnetron sputtering on Pt/Ti/SiO2/Si(100). Compared with Ar/O2 sputtering, the ferroelectric properties such as larger remnant polarization (Pr) of 3.2 μC/cm2 were observed with decrease of leakage current in case of Kr/O2 sputtering. X-ray diffraction (XRD) patterns indicated that improvement of the crystallinity with suppressing pyrochlore phases and enhancing ferroelectric phases was realized by Kr/O2 sputtering.

This paper presents a rigorous analysis of the electromagnetic scattering and transmission of misaligned dual metallic grating screens. The Fourier transform and the mode-matching technique are employed to obtain an analytical solution. Numerical results show that misaligned dual metal grating screens exhibit asymmetric scattering and transmission properties with respect to the scattering and transmission angles. Parametric studies are conducted in terms of the lateral displacement and vertical distance between the dual metallic grating screens. For validation, the proposed method is compared with a numerical simulation and good agreement has been achieved.

This paper describes a non-contact and noise-tolerant heart rate monitoring system using a 24-GHz microwave Doppler sensor. The microwave Doppler sensor placed at some distance from the user's chest detects the small vibrations of the body surface due to the heartbeats. The objective of this work is to detect the instantaneous heart rate (IHR) using this non-contact system in a car, because the possible application of the proposed system is a driver health monitoring based on heart rate variability analysis. IHR can contribute to preventing heart-triggered disasters and to detect mental stress state. However, the Doppler sensor system is very sensitive and it can be easily contaminated by motion artifacts and road noise especially while driving. To address this problem, time-frequency analysis using the parametric method and template matching method are employed. Measurement results show that the Doppler sensor, which is pasted on the clothing surface, can successfully extract the heart rate through clothes. The proposed method achieves 13.1-ms RMS error in IHR measurements conducted on 11 subjects in a car on an ordinary road.

Sharing perceptual data (e.g., camera and LiDAR data) with other vehicles enhances the traffic safety of autonomous vehicles because it helps vehicles locate other vehicles and pedestrians in their blind spots. Such safety applications require high throughput and short delay, which cannot be achieved by conventional microwave vehicular communication systems. Therefore, millimeter-wave (mmWave) communications are considered to be a key technology for sharing perceptual data because of their wide bandwidth. One of the challenges of data sharing in mmWave communications is broadcasting because narrow-beam directional antennas are used to obtain high gain. Because many vehicles should share their perceptual data to others within a short time frame in order to enlarge the areas that can be perceived based on shared perceptual data, an efficient scheduling for concurrent transmission that improves spatial reuse is required for perceptual data sharing. This paper proposes a data sharing algorithm that employs a graph-based concurrent transmission scheduling. The proposed algorithm realizes concurrent transmission to improve spatial reuse by designing a rule that is utilized to determine if the two pairs of transmitters and receivers interfere with each other by considering the radio propagation characteristics of narrow-beam antennas. A prioritization method that considers the geographical information in perceptual data is also designed to enlarge perceivable areas in situations where data sharing time is limited and not all data can be shared. Simulation results demonstrate that the proposed algorithm doubles the area of the cooperatively perceivable region compared with a conventional algorithm that does not consider mmWave communications because the proposed algorithm achieves high-throughput transmission by improving spatial reuse. The prioritization also enlarges the perceivable region by a maximum of 20%.

This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.

Facial micro-expression is momentary and subtle facial reactions, and it is still challenging to automatically recognize facial micro-expression with high accuracy in practical applications. Extracting spatiotemporal features from facial image sequences is essential for facial micro-expression recognition. In this paper, we employed 3D Convolutional Neural Networks (3D-CNNs) for self-learning feature extraction to represent facial micro-expression effectively, since the 3D-CNNs could well extract the spatiotemporal features from facial image sequences. Moreover, transfer learning was utilized to deal with the problem of insufficient samples in the facial micro-expression database. We primarily pre-trained the 3D-CNNs on normal facial expression database Oulu-CASIA by supervised learning, then the pre-trained model was effectively transferred to the target domain, which was the facial micro-expression recognition task. The proposed method was evaluated on two available facial micro-expression datasets, i.e. CASME II and SMIC-HS. We obtained the overall accuracy of 97.6% on CASME II, and 97.4% on SMIC, which were 3.4% and 1.6% higher than the 3D-CNNs model without transfer learning, respectively. And the experimental results demonstrated that our method achieved superior performance compared to state-of-the-art methods.

In this letter, as a generalization of Luo et al.'s constructions, a construction of codebook, which meets the Welch bound asymptotically, is proposed. The parameters of codebook presented in this paper are new in some cases.

In this paper, we consider a group testing (GT) problem. We derive a lower bound on the probability of error for successful decoding of defected binary signals. To this end, we exploit Fano's inequality theorem in the information theory. We show that the probability of error is bounded as an entropy function, a density of a pooling matrix and a sparsity of a binary signal. We evaluate that for decoding of highly sparse signals, the pooling matrix is required to be dense. Conversely, if dense signals are needed to decode, the sparse pooling matrix should be designed to achieve the small probability of error.

In content oriented networks (CON), routers in a network are generally equipped with local cache storages and store incoming contents temporarily. Efficient utilization of total cache storage in networks is one of the most important technical issues in CON, as it can reduce content server load, content download latency and network traffic. Performance of networked cache is reported to strongly depend on both cache decision and content request routing. In this paper, we evaluate several combinations of these two strategies. Especially for routing, we take up off-path cache routing, Breadcrumbs, as one of the content request routing proposals. Our performance evaluation results show that off-path cache routing, Breadcrumbs, suffers low performance with cache decisions which generally has high performance with shortest path routing (SPR), and obtains excellent performance with TERC (Transparent En-Route Cache) which is well-known to have low performance with widely used SPR. Our detailed evaluation results in two network environments, emerging CONs and conventional IP, show these insights hold in both of these two network environments.

This paper presents the mode matching (MM)/finite element method (FEM) hybrid analysis for a short-slot 2-plane coupler, and an optimization process for a wideband design based on a genetic algorithm (GA). The method of the analysis combines a fast modal analysis of the MM which reduces the computation time, with the flexibility of an FEM which can be used with an arbitrary cross-section. In the analysis, the model is reduced into the one-eighth model by using the three-dimensional structural symmetry. The computed results agree well with those by the simulation and the computation time is reduced. The bandwidth is improved by the optimization based on the GA from 2.4% to 6.9% for the 2-plane hybrid coupler and from 5.4% to 7.5% for the 2-plane cross coupler. The measured results confirm the wideband design.

In this paper, we investigate different power allocation optimization problems with interferences for distributed antenna systems (DAS) with and without D2D communication, respectively. The first objective problem is maximizing spectral efficiency (SE) of the DAS with D2D communication under the constraints of the minimum SE requirements of user equipment (UE) and D2D pair, maximum transmit power of each remote access unit (RAU) and maximum transmit power of D2D transmitter. We transform this non-convex objective function into a difference of convex functions (D.C.) then using the concave-convex procedure (CCCP) algorithm to solve the optimization problem. The second objective is maximizing energy efficiency (EE) of the DAS with D2D communication under the same constraints. We first exploit fractional programming theory to obtain the equivalent objective function of the second problem with subtract form, and then transform it into a D.C. problem and use CCCP algorithm to obtain the optimal power allocation. In each part, we summarize the corresponding optimal power allocation algorithms and also use similar method to obtain optimal solutions of the same optimization problems in DAS. Simulation results are provided to demonstrate the effectiveness of the designed power allocation algorithms and illustrate the SE and EE of the DAS by using D2D communication are much better than DAS without D2D communication.

Vehicle detection is challenging in natural traffic scenes because there exist a lot of occlusion. Because of occlusion, detector's training strategy may lead to mismatch between features and labels. As a result, some predicted bounding boxes may shift to surrounding vehicles and lead to lower confidences. These bounding boxes will lead to lower AP value. In this letter, we propose a new approach to address this problem. We calculate the center of visible part of current vehicle based on road information. Then a variable-radius Gaussian weight based method is applied to reweight each anchor box in loss function based on the center of visible part in training time of SSD. The reweighted method has ability to predict higher confidences and more accurate bounding boxes. Besides, the model also has high speed and can be trained end-to-end. Experimental results show that our proposed method outperforms some competitive methods in terms of speed and accuracy.