In recent years, a rise in healthy eating has led to various food management applications which have image recognition function to record everyday meals automatically. However, most of the image recognition functions in the existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network (CNN). CNN has improved accuracies of various kinds of image recognition tasks such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates dish locations and food calories simultaneously by multi-task learning of food dish detection and food calorie estimation with a single CNN. It is expected to achieve high speed and small network size by simultaneous estimation in a single network. Because currently there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, in this work we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos annotated with bounding boxes and single-dish food photos with food calories. Our results showed that our multi-task method achieved higher accuracy, higher speed and smaller network size than a sequential model of food detection and food calorie estimation.

This work presents an FSK inductive-coupling transceiver using a load-modulated transmitter and LC-oscillator-based receiver for energy-budget-unbalanced applications. By introducing the time-domain load modulated transmitter for FSK instead of the conventional current-driven scheme, energy reduction of the transmitter side is possible. For verifying the proposed scheme, a test chip was fabricated in 65nm CMOS, and two chips were stacked for verifying the inter-chip communication. The measurement results show 0.64fJ/bit transmitter power consumption while its input voltage is 60mV, and the communication distance is 150μm. The footprint of the transmitter is 0.0016mm2.

This paper discusses non-ideal issues in a fully passive noise shaping successive approximation register analog-to-digital converter. The fully passive noise shaping techniques are realized by switches and capacitors without operational amplifiers to be scalable and power efficient. However, some non-ideal issues, such as parasitic capacitance, comparator noise, thermal noise, will affect the performance of the noise shaping and then degrade the final achievable resolution. This paper analyzes the effects of the main non-ideal issues and provides the design reference for fully passive noise shaping techniques. The analysis is based on 2nd order fully passive noise shaping SAR ADC with an 8-bit architecture and an OSR of 4.

In this research, we propose a novel method to determine fingerprint liveness to improve the discriminative behavior and classification accuracy of the combined features. This approach detects if a fingerprint is from a live or fake source. In this approach, fingerprint images are analyzed in the differential excitation (DE) component and the centralized binary pattern (CBP) component, which yield the DE image and CBP image, respectively. The images obtained are used to generate a two-dimensional histogram that is subsequently used as a feature vector. To decide if a fingerprint image is from a live or fake source, the feature vector is processed using support vector machine (SVM) classifiers. To evaluate the performance of the proposed method and compare it to existing approaches, we conducted experiments using the datasets from the 2011 and 2015 Liveness Detection Competition (LivDet), collected from four sensors. The results show that the proposed method gave comparable or even better results and further prove that methods derived from combination of features provide a better performance than existing methods.

Salient region detection is a fundamental problem in computer vision and image processing. Deep learning models perform better than traditional approaches but suffer from their huge parameters and slow speeds. To handle these problems, in this paper we propose the multi-feature fusion network (MFFN) - a efficient salient region detection architecture based on Convolution Neural Network (CNN). A novel feature extraction structure is designed to obtain feature maps from CNN. A fusion dense block is used to fuse all low-level and high-level feature maps to derive salient region results. MFFN is an end-to-end architecture which does not need any post-processing procedures. Experiments on the benchmark datasets demonstrate that MFFN achieves the state-of-the-art performance on salient region detection and requires much less parameters and computation time. Ablation experiments demonstrate the effectiveness of each module in MFFN.

In this paper, we have investigated the etching selectivity of HfN encapsulating layer for high quality PtHf-alloy silicide (PtHfSi) formation with low contact resistivity on Si(100). The HfN(10 nm)/PtHf(20 nm)/p-Si(100) stacked layer was in-situ deposited by RF-magnetron sputtering at room temperature. Then, silicidation was carried out at 500°C/20 min in N2/4.9%H2 ambient. Next, the HfN encapsulating layer was etched for 1-10 min by buffered-HF (BHF) followed by the unreacted PtHf metal etching. We have found that the etching duration of the 10-nm-thick HfN encapsulating layer should be shorter than 6 min to maintain the PtHfSi crystallinity. This is probably because the PtHf-alloy silicide was gradually etched by BHF especially for the Hf atoms after the HfN was completely removed. The optimized etching process realized the ultra-low contact resistivity of PtHfSi to p+/n-Si(100) and n+/p-Si(100) such as 9.4×10-9Ωcm2 and 4.8×10-9Ωcm2, respectively, utilizing the dopant segregation process. The control of etching duration of HfN encapsulating layer is important to realize the high quality PtHfSi formation with low contact resistivity.

The blind multichannel identification is useful in many applications. Although many approaches have been proposed to address this challenging problem, the adaptive filtering-based methods are attractive due to their computational efficiency and good convergence property. The multichannel normalized least mean-square (MCNLMS) algorithm is easy to implement, but it converges very slowly for a correlated input. The multichannel affine projection algorithm (MCAPA) is thus proposed to speed up the convergence. However, the convergence of the MCNLMS and MCAPA is still unsatisfactory in practice. In this paper, we propose a time-domain Kalman filtering approach to the blind multichannel identification problem. Specifically, the proposed adaptive Kalman filter is based on the cross relation method and also uses more past input vectors to explore the decorrelation property. Simulation results indicate that the proposed method outperforms the MCNLMS and MCAPA significantly in terms of the initial convergence and tracking capability.

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.

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.

Wireless communication security has been increasingly important nowadays. Directional modulation (DM) is seen as a promising wireless physical layer security technology. Traditional DM is a transmit-side technology that projects digitally modulated information signals in the desired directions (or at the desired locations) while simultaneously distorting the constellation formats of the same signals in other directions (or at all other locations). However, these directly exposed digitally modulated information signals are easily intercepted by eavesdroppers along the desired directions (or around the desired locations). A new DM scheme for secure point-to-multipoint communication based on the spread spectrum assisted orthogonal frequency diverse array (short for SS-OFDA-M-DM) is proposed in this paper. It can achieve point-to-multipoint secure communication for multiple cooperative receivers at different locations. In the proposed SS-OFDA-M-DM scheme, only cooperative users that use specific DM receivers with right spread spectrum parameters can retrieve right symbols. Eavesdroppers without knowledge of spread spectrum parameters cannot intercept useful signals directly at the desired locations. Moreover, they cannot receive normal symbols at other locations either even if the right spread spectrum parameters are known. Numerical simulation results verify the validity of our proposed scheme.

In this paper, a reduction of the number of components included in direct simulation type active complex filter is proposed. The proposed method is achieved by sharing NIC's (Negative Impedance Converters) which satisfy some conditions. Compared with the conventional method, the proposed one has wide generality. As an example, a third-order complex elliptic filter is designed. The validity of the proposed method is confirmed through experiment.

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.

This study tries to construct an accurate ranking method for five team ball games at the Olympic Games. First, the study uses a statistical rating method for team ball games. A single parameter, called a rating, shows the strength and skill of each team. We assume that the difference between the rating values explains the scoring ratio in a match based on a logistic regression model. The rating values are estimated from the scores of major international competitions that are held before the Rio Olympic Games. The predictions at the Rio Olympic Games demonstrate that the proposed method can more accurately predict the match results than the official world rankings or world ranking points. The proposed method enabled 262 correct predictions out of 370 matches, whereas using the official world rankings resulted in only 238 correct predictions. This result shows a significant difference between the two criteria.

Signed networks are ubiquitous in the real world. It is of great significance to study the problem of community detection in signed networks. In general, the behaviors of nodes in a signed network are rational, which coincide with the players in the theory of game that can be used to model the process of the community formation. Unlike unsigned networks, signed networks include both positive and negative edges, representing the relationship of friends and foes respectively. In the process of community formation, nodes usually choose to be in the same community with friends and between different communities with enemies. Based on this idea, we proposed a game theory model to address the problem of community detection in signed networks. Taking nodes as players, we build a gain function based on the numbers of positive edges and negative edges inside and outside a community, and prove the existence of Nash equilibrium point. In this way, when the game reaches the Nash equilibrium state, the optimal strategy space for all nodes is the result of the final community division. To systematically investigate the performance of our method, elaborated experiments on both synthetic networks and real-world networks are conducted. Experimental results demonstrate that our method is not only more accurate than other existing algorithms, but also more robust to noise.

Rotation symmetric Boolean functions which are invariant under the action of cyclic group have been used in many different cryptosystems. This paper presents a new construction of balanced odd-variable rotation symmetric Boolean functions with optimum algebraic immunity. It is checked that, at least for some small variables, such functions have very good behavior against fast algebraic attacks. Compared with some known rotation symmetric Boolean functions with optimum algebraic immunity, the new construction has really better nonlinearity. Further, the algebraic degree of the constructed functions is also high enough.

The electric properties of a field-effect transistor not only depend on gate surface sidewall but also on channel orientation when applying channel stain engineering. The change of the gate surface and channel orientation through the rotated FinFETs provides the capability to compare the orientation dependence of performance and reliability. This study characterized the <100> and <110> channels of FinFETs on the same wafer under tensile and compressive stresses by cutting the wafer into rectangular silicon pieces and evaluated their piezoresistance coefficients. The piezoresistance coefficients of the <100> and <110> silicon under tensile and compressive stresses were first evaluated based on the current setup. Tensile stresses enhance the mobilities of both <100> and <110> channels, whereas compressive stresses degrade them. Electrical characterization revealed that the threshold voltage variation and drive current degradation of the {100} surface were significantly higher than those of {110} for positive bias temperature instability and hot carrier injection with equal gate and drain voltage (VG=VD). By contrast, insignificant difference is noted for the subthreshold slope degradation. These findings imply that a higher ratio of bulk defect trapping is generated by gate voltage on the <100> surface than that on the <110> surface.

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.

This paper proposes a novel low complexity lattice reduction-aided iterative receiver for overloaded MIMO. Novel noise cancellation is proposed that increases an equivalent channel gain with a scalar gain introduced in this paper, which results in the improvement of the signal to noise power ratio (SNR). We theoretically analyze the performance of the proposed receiver that the lattice reduction raises the SNR of the detector output signals as the scalar gain increases, when the Lenstra-Lenstra-Lova's (LLL) algorithm is applied to implement the lattice reduction. Because the SNR improvement causes the scalar gain to increase, the performance is improved by iterating the reception process. Computer simulations confirm the performance. The proposed receiver attains a gain of about 5dB at the BER of 10-4 in a 6×2 overloaded MIMO channel. Computational complexity of the proposed receiver is about 1/50 as much as that of the maximum likelihood detection (MLD).

In sparsity-based optimization problems for two dimensional (2-D) direction-of-arrival (DOA) estimation using L-shaped nested arrays, one of the major issues is computational complexity. A 2-D DOA estimation algorithm is proposed based on reconsitution sparse Bayesian learning (RSBL) and cross covariance matrix decomposition. A single measurement vector (SMV) model is obtained by the difference coarray corresponding to one-dimensional nested array. Through spatial smoothing, the signal measurement vector is transformed into a multiple measurement vector (MMV) matrix. The signal matrix is separated by singular values decomposition (SVD) of the matrix. Using this method, the dimensionality of the sensing matrix and data size can be reduced. The sparse Bayesian learning algorithm is used to estimate one-dimensional angles. By using the one-dimensional angle estimations, the steering vector matrix is reconstructed. The cross covariance matrix of two dimensions is decomposed and transformed. Then the closed expression of the steering vector matrix of another dimension is derived, and the angles are estimated. Automatic pairing can be achieved in two dimensions. Through the proposed algorithm, the 2-D search problem is transformed into a one-dimensional search problem and a matrix transformation problem. Simulations show that the proposed algorithm has better angle estimation accuracy than the traditional two-dimensional direction finding algorithm at low signal-to-noise ratio and few samples.

The aim of this research is to support real-time drawingin talking by using multimodal user interface technologies. In this situation, if talking and drawing are considered as commands by mistake during presentation, it will disturb users' natural talking and drawing. To prevent this problem, we introduce two modes of a command mode and a free mode, and explore smooth mode switching techniques that does not interfere with users' natural talking and drawing. We evaluate four techniques. Among them, a technique that specifies the command mode after actions using a pen gesture was the most effective. In this technique, users could quickly draw diagrams, and specifying mode switching didn't interfere with users' natural talk.