This paper introduces a method that identifies human activity from the height and Doppler Radar Cross Section (RCS) information detected by Multiple-Input Multiple-Output (MIMO) radar. This method estimates the three-dimensional target location by applying the MUltiple SIgnal Classification (MUSIC) method to the observed MIMO channel; the Doppler RCS is calculated from the signal reflected from the target. A gesture recognition algorithm is applied to the trajectory of the temporal transition of the estimated human height and the Doppler RCS. In experiments, the proposed method achieves over 90% recognition rate (average).

This paper proposes a pulse-tail-feedback VCO, in which the tail transistor is driven using pulse-shaped voltage signals with rail-to-rail swing. The proposed pulse-tail-feedback (PTFB) VCO relies on reducing the current conduction period of the tail transistor and operating the tail transistors in triode region for reducing the flicker and thermal noise from the active elements. Mathematical analysis and circuit level simulations of the phase noise mechanism in the proposed PTFB-VCO is also presented in this paper for validating the effectiveness of the proposed technique. A prototype LC-VCO with the proposed PTFB technique is fabricated in a standard 180nm CMOS. Laboratory measurement shows a power consumption of 1.35mW from a 1.2V supply at 4.6GHz. The proposed PTFB-VCO achieves a flicker corner of 700Hz, which enables the VCO to maintain a fairly constant figure-of-merit (FoM) of -195dB within a wide offset frequency range of 1kHz-10MHz.

Redundant relations refer to explicit relations which can also be deducted implicitly. Although there exist several ontology redundancy elimination methods, they all do not take equivalent relations into consideration. Actually, real ontologies usually contain equivalent relations; their redundancies cannot be completely detected by existing algorithms. Aiming at solving this problem, this paper proposes a super-node based ontology redundancy elimination algorithm. The algorithm consists of super-node transformation and transitive redundancy elimination. During the super-node transformation process, nodes equivalent to each other are transferred into a super-node. Then by deleting the overlapped edges, redundancies relating to equivalent relations are eliminated. During the transitive redundancy elimination process, redundant relations are eliminated by comparing concept nodes' direct and indirect neighbors. Most notably, we proposed a theorem to validate real ontology's irredundancy. Our algorithm outperforms others on both real ontologies and synthetic dynamic ontologies.

Pre-trained CNNs on ImageNet have been widely used in object tracking for feature extraction. However, due to the domain mismatch between image classification and object tracking, the submergence of the target-specific features by noise largely decreases the expression ability of the convolutional features, resulting in an inefficient tracking. In this paper, we propose a robust tracking algorithm with low-dimensional target-specific feature extraction. First, a novel cascaded PCA module is proposed to have an explicit extraction of the low-dimensional target-specific features, which makes the new appearance model more effective and efficient. Next, a fast particle filter process is raised to further accelerate the whole tracking pipeline by sharing convolutional computation with a ROI-Align layer. Moreover, a classification-score guided scheme is used to update the appearance model for adapting to target variations while at the same time avoiding the model drift that caused by the object occlusion. Experimental results on OTB100 and Temple Color128 show that, the proposed algorithm has achieved a superior performance among real-time trackers. Besides, our algorithm is competitive with the state-of-the-art trackers in precision while runs at a real-time speed.

An integrated ambient light sensor (ALS) system in low-temperature polycrystalline silicon (LTPS) thin-film-transistor liquid-crystal-displays (TFT-LCDs) is proposed and prototyped in this study. It is designed as a 4-bit (16-step-grayscale) ALS and includes a noise subtraction circuit, a comparator as an analog-to-digital converter (ADC), 4-bit counters, and a parallel-to-serial converter. LTPS lateral p-i-n diodes with a long i-region are employed as photodetectors in the system. An LSI source driver is mounted on the LCD panel with a sensor control block which provides programmable clocks and reference voltages to the ALS circuit on the glass substrate for sensitivity tuning. The reliability tests were conducted for 300 hours with 30000 lux illumination at 70 °C and at -20 °C. The observed deviations of the ALS values for dark, 1000 lux, and 10000 lux were within ±1.

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 work presents the lowest power consumption sub-mm2 supply-modulated OOK transmitter for self-powering a continuous glucose monitoring (CGM) contact lens. By combining the transmitter with a glucose fuel cell that functions as both the power source and a sensing transducer, a self-powered CGM contact lens was developed. The 385×385μm2 test chip implemented in 65-nm standard CMOS technology operates at 270pW with a supply voltage of 0.165V. Self-powered operation of the transmitter using a 2×2mm2 solid-state glucose fuel cell was thus demonstrated.

A low voltage stochastic flash ADC (analog-to-digital converter) is presented, with an inverter-based comparative unit which is used to replace comparator for comparison. Aiming at the low voltage and low power consumption, a key of our design is in the simplicity of the structure. The inverter-based comparative unit replacing a comparator enables us to decrease the number of transistors for area saving and power reduction. We insert the inverter-chain in front of the comparative unit for the signal stability and discuss an appropriate circuit structure for the resolution by analyzing three different ones. Finally, we design the whole stochastic flash ADC for verifying our idea, where the supply voltage can go down to 0.6V on the 65nm CMOS process, and through post-layout simulation result, we can observe its advantage visually in voltage, area and power consumption.

Over the past several years, the research of micro-expression recognition (MER) has become an active topic in affective computing and computer vision because of its potential value in many application fields, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from different micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an effective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coefficient matrix based on the source samples and their provided label information and also enable this learned regression coefficient matrix to suit the target micro-expression database. We are thus able to use the learned regression coefficient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks.

A rapid single-flux-quantum (RSFQ) truncated multiplier based on bit-level processing is proposed. In the multiplier, two operands are transformed to two serialized patterns of bits (pulses), and the multiplication is carried out by processing those bits. The result is obtained by counting bits. By calculating in bit-level, the proposed multiplier can be implemented in small area. The gate level design of the multiplier is shown. The layout of the 4-bit multiplier was also designed.

In this paper, we propose a novel framework for structural magnetic resonance image (sMRI) classification of Alzheimer's disease (AD) with data combination, outlier removal, and entropy-based data selection using AlexNet. In order to overcome problems of conventional classical machine learning methods, the AlexNet classifier, with a deep learning architecture, was employed for training and classification. A data permutation scheme including slice integration, outlier removal, and entropy-based sMRI slice selection is proposed to utilize the benefits of AlexNet. Experimental results show that the proposed framework can effectively utilize the AlexNet with the proposed data permutation scheme by significantly improving overall classification accuracies for AD classification. The proposed method achieves 95.35% and 98.74% classification accuracies on the OASIS and ADNI datasets, respectively, for the binary classification of AD and Normal Control (NC), and also achieves 98.06% accuracy for the ternary classification of AD, NC, and Mild Cognitive Impairment (MCI) on the ADNI dataset. The proposed method can attain significantly improved accuracy of up to 18.15%, compared to previously developed methods.

In this letter, a novel frame aggregation scheduler is proposed to solve the head-of-line blocking problem for real-time user datagram protocol (UDP) traffic in error-prone and aggregation-enabled wireless local area networks (WLANs). The key to the proposed scheduler is to break the restriction of in-order delivery over the WLAN. The simulation results show that the proposed scheduler can achieve high UDP goodput and low delay compared to the conventional scheduler.

Machine learning algorithms are becoming more and more popular in current era. Data preprocessing especially feature selection is helpful for improving the performance of those algorithms. A new powerful feature selection algorithm is proposed. It combines the advantages of ant colony optimization and brain storm optimization which simulates the behavior of human beings. Six classical datasets and five state-of-art algorithms are used to make a comparison with our algorithm on binary classification problems. The results on accuracy, percent rate, recall rate, and F1 measures show that the developed algorithm is more excellent. Besides, it is no more complex than the compared approaches.

In recent years, intelligent transportation system (ITS) techniques have been widely exploited to enhance the quality of public services. As one of the worldwide leaders in recycling, Taiwan adopts the waste collection and disposal policy named “trash doesn't touch the ground”, which requires the public to deliver garbage directly to the collection points for awaiting garbage collection. This study develops a travel time prediction system based on data clustering for providing real-time information on the arrival time of waste collection vehicle (WCV). The developed system consists of mobile devices (MDs), on-board units (OBUs), a fleet management server (FMS), and a data analysis server (DAS). A travel time prediction model utilizing the adaptive-based clustering technique coupled with a data feature selection procedure is devised and embedded in the DAS. While receiving inquiries from users' MDs and relevant data from WCVs' OBUs through the FMS, the DAS performs the devised model to yield the predicted arrival time of WCV. Our experiment result demonstrates that the proposed prediction model achieves an accuracy rate of 75.0% and outperforms the reference linear regression method and neural network technique, the accuracy rates of which are 14.7% and 27.6%, respectively. The developed system is effective as well as efficient and has gone online.

360-degree video delivery in Virtual Reality is very challenging due to the fact that 360-degree videos require much higher bandwidth than conventional videos. To overcome this problem, viewport-adaptive streaming has been introduced. In this study, we propose a new adaptation method for tiling-based viewport-adaptive streaming of 360-degree videos. For content preparation, the Cubemap projection format is used, where faces or parts of a face are encoded as tiles. Also, the problem is formulated as an optimization problem, in which each visible tile is weighted based on how that tile overlaps with the viewport. To solve the problem, an approximation algorithm is proposed in this study. An evaluation of the proposed method and reference methods is carried out under different tiling schemes and bandwidths. Experiments show that the Cubemap format with tiling provides a lot of benefits in terms of storage, viewport quality across different viewing directions and bandwidths, and tolerance to prediction errors.

Adaptive voltage scaling is a promising approach to overcome manufacturing variability, dynamic environmental fluctuation, and aging. This paper focuses on error prediction based adaptive voltage scaling (EP-AVS) and proposes a mean time to failure (MTTF) aware design methodology for EP-AVS circuits. Main contributions of this work include (1) optimization of both voltage-scaled circuit and voltage control logic, and (2) quantitative evaluation of power saving for practically long MTTF. Experimental results show that the proposed EP-AVS design methodology achieves 38.0% power saving while satisfying given target MTTF.

Background subtraction algorithms generate a background model of the monitoring scene and compare the background model with the current video frame to detect foreground objects. In general, most of the background subtraction algorithms fail to detect foreground objects when the scene illumination changes. An entropy based background subtraction algorithm is proposed to address this problem. The proposed method adapts to illumination changes by updating the background model according to differences in entropy value between the current frame and the previous frame. This entropy based background modeling can efficiently handle both sudden and gradual illumination variations. The proposed algorithm is tested in six video sequences and compared with four algorithms to demonstrate its efficiency in terms of F-score, similarity and frame rate.

Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.

In this paper, to make asynchronous circuit design easy, we propose a conversion method from synchronous Register Transfer Level (RTL) models to asynchronous RTL models with bundled-data implementation. The proposed method consists of the generation of an intermediate representation from a given synchronous RTL model and the generation of an asynchronous RTL model from the intermediate representation. This allows us to deal with different representation styles of synchronous RTL models. We use the eXtensible Markup Language (XML) as the intermediate representation. In addition to the asynchronous RTL model, the proposed method generates a simulation model when the target implementation is a Field Programmable Gate Array and a set of non-optimization constraints for the control circuit used in logic synthesis and layout synthesis. In the experiment, we demonstrate that the proposed method can convert synchronous RTL models specified manually and obtained by a high-level synthesis tool to asynchronous ones.

VLSIs that perform signal processing near infrared sensors cooled to ultra-low temperature are demanded. Delay test of those chips must be executed at ultra-low temperature while functional test could be performed at room temperature as long as hold timing errors do not occur. In this letter, we focus on the hold timing violation and evaluate the feasibility of functional test of ultra-low temperature circuits at room temperature. Experimental evaluation with a case study shows that the functional test at room temperature is possible.