This paper proposes Code-Modulated Synchronized (CMS) -OOK modulation scheme for normally-off wireless sensor networks, and demonstrates the operation of the transmitter for the CMS-OOK using 65nm SOTB (Silicon-On Thin Buried Oxide) CMOS technology. Based on investigating RF characteristics of SOTB CMOS, analog part of a CMS-OOK transmitter was designed, fabricated and evaluated in combination with based-FPGA digital part. With code modulation and controlling the carrier frequency by body bias of the SOTB devices, the spectrum of a CMS-OOK transmitter output is widen to achieve -62dBm/MHz peak power spectrum density at 15 MHz bandwidth. Chip of analog part on-board is supplied by 1V for power amplifier and 0.75V for the rest. It consumes average 83µW according to 83nJ/bit at 1kbps data transmission.

This paper describes a top-down design methodology to optimize resonant capacitance in a wireless power transfer system with 3-D stacked two receivers. A 1:2 selective wireless power transfer is realized by a frequency/time division multiplexing scheme. The power transfer function is analytically formulated and the optimum tuning capacitance is derived, which is validated by comparing with system simulation results. By using the optimized values, power transfer efficiencies at 6.78MHz and 13.56MHz are simulated to be 80% and 84%, respectively, which are <3% worse than a conventional wireless power transfer system.

To realize three-dimensional (3D) optical interconnection on large-scale integration (LSI) circuits, layer-to-layer couplers based on Si-photonics platform were reviewed. In terms of optical cross talk, more than 1 µm layer distance is required for 3D interconnection. To meet this requirement for the layer-to-layer optical coupler, we proposed two types of couplers: a pair of grating couplers with metal mirrors for multi-layer distance coupling and taper-type directional couplers for neighboring layer distance coupling. Both structures produced a high coupling efficiency with relatively compact (∼100 µm) device sizes with a complementary metal oxide semiconductor (CMOS) compatible fabrication process.

Development of narrowband thermal emitters whose emission wavelengths are dynamically tunable is highly desired for various applications including the sensing of gases and chemical compounds. In this paper, we review our recent demonstration of wavelength-switchable mid-infrared thermal emitters based on multiple quantum wells (MQWs) and photonic crystals (PCs). Through the control of absorptivity by using intersubband transitions in MQWs and optical resonances in PC slabs, we demonstrate novel control of thermal emission, including realization of high-Q (Q>100) thermal emission, dynamic control of thermal emission (∼MHz), and electrical wavelength switching of thermal emission from a single device.

This paper introduces a formation method for 3-dimensional 6 ch.×6 ch. shuffling structures with graded-index (GI) circular core in a multimode polymer optical waveguide for optical printed circuit boards (OPCBs) using a unique photomask-free fabrication technique named the Mosquito method. The interchannel pitch of the fabricated waveguides is 250µm, where all the channels consist of both horizontal and vertical bending structures and the last 6 channels in parallel cross over the first 6 channels. We also report 3-dimensional S-shaped polymer waveguides. In the S-shaped waveguides, the first and last 6 channels with both horizontal and vertical core bending composing the above 3-dimensional shuffling waveguide are separated, in order to evaluate the effect of over-crossing on the loss. It is experimentally confirmed that there is no excess insertion loss due to the shuffling structure in the 3-D shuffling waveguide. The evaluated crosstalk of the 3-D shuffling waveguide is lower than -30dB. The 3-D shuffling waveguide proposed in this paper will be a promising component to achieve high bandwidth density wiring for on-board optical interconnects.

Internal quantum efficiency (IQE) is usually estimated from temperature dependence of photoluminescence (PL) intensity by assuming that the IQE at cryogenic temperature is unity. III-nitride samples, however, usually have large defect density, and the assumption is not necessarily valid. In 2016, we proposed a new method to estimate accurate IQE values by simultaneous PL and photo-acoustic (PA) measurements, and demonstratively evaluated the IQE values for various GaN samples. In this study, we have applied the method to InGaN quantum-well active layers and have estimated the IQE values and their excitation carrier-density dependence in the layers.

We discuss our recent progress in photonic crystal nanocavity quantum dot lasers. We show how enhanced light matter interactions in the nanocavity lead to diverse and fascinating lasing phenomena that are in general inaccessible by conventional bulky semiconductor lasers. First, we demonstrate thresholdless lasing, in which any clear kink in the output laser curve does not appear. This is a result of near unity coupling of spontaneous emission into the lasing cavity mode, enabled by the strong Purcell effect supported in the nanocavity. Then, we discuss self-frequency conversion nanolasers, in which both near infrared lasing oscillation and nonlinear optical frequency conversion to visible light are simultaneously supported in the individual nanocavity. Owing to the tight optical confinement both in time and space, a high normalized conversion efficiency over a few hundred %/W is demonstrated. We also show that the intracavity nonlinear frequency conversion can be utilized to measure the statistics of the intracavity photons. These novel phenomena will be useful for developing various nano-optoelectronic devices with advanced functionalities.

Frequency chirp of a semiconductor laser is controlled by using hybrid modulation, which simultaneously modulates intra-cavity loss and injection current to the laser. The positive adiabatic chirp of injection-current modulation is compensated with the negative adiabatic chirp created by intra-cavity-loss modulation, which enhances the chromatic-dispersion tolerance of the laser. A proof-of-concept transmission experiment confirmed that the hybrid modulation laser has a larger dispersion tolerance than conventional directly modulated lasers due to the negative frequency chirp originating from intra-cavity-loss modulation.

Virtual Machine Placement (VMP) plays an important role in ensuring efficient resource provisioning of physical machines (PMs) and energy efficiency in Infrastructure as a Service (IaaS) data centers. Efficient server consolidation assisted by virtual machine (VM) migration can promote the utilization level of the servers and switch the idle PMs to sleep mode to save energy. The trade-off between energy and performance is difficult, because consolidation may cause performance degradation, even service level agreement (SLA) violations. A novel residual available capacity (RAC) resource model is proposed to resolve the VM selection and allocation problem from the cloud service provider (CSP) perspective. Furthermore, a novel heuristic VM selection policy for server consolidation, named Minimized Square Root available Resource (MISR) is proposed. Meanwhile, an efficient VM allocation policy, named Balanced Selection (BS) based on RAC is proposed. The effectiveness validation of the BS-MISR combination is conducted on CloudSim with real workloads from the CoMon project. Evaluation results of experiments show that the proposed combinationBS-MISR can significantly reduce the energy consumption, with an average of 36.35% compared to the Local Regression and Minimum Migration Time (LR-MMT) combination policy. Moreover, the BS-MISR ensures a reasonable level of SLAs compared to the benchmarks.

This paper focuses mainly on issues related to the pricing of American options under a fuzzy environment by taking into account the clustering of the underlying asset price volatility, leverage effect and stochastic jumps. By treating the volatility as a parabolic fuzzy number, we constructed a Levy-GJR-GARCH model based on an infinite pure jump process and combined the model with fuzzy simulation technology to perform numerical simulations based on the least squares Monte Carlo approach and the fuzzy binomial tree method. An empirical study was performed using American put option data from the Standard & Poor's 100 index. The findings are as follows: under a fuzzy environment, the result of the option valuation is more precise than the result under a clear environment, pricing simulations of short-term options have higher precision than those of medium- and long-term options, the least squares Monte Carlo approach yields more accurate valuation than the fuzzy binomial tree method, and the simulation effects of different Levy processes indicate that the NIG and CGMY models are superior to the VG model. Moreover, the option price increases as the time to expiration of options is extended and the exercise price increases, the membership function curve is asymmetric with an inclined left tendency, and the fuzzy interval narrows as the level set α and the exponent of membership function n increase. In addition, the results demonstrate that the quasi-random number and Brownian Bridge approaches can improve the convergence speed of the least squares Monte Carlo approach.

The accurate assessment of infants' pain is important for understanding their medical conditions and developing suitable treatment. Pediatric studies reported that the inadequate treatment of infants' pain might cause various neuroanatomical and psychological problems. The fact that infants can not communicate verbally motivates increasing interests to develop automatic pain assessment system that provides continuous and accurate pain assessment. In this paper, we propose a new set of pain facial activity features to describe the infants' facial expression of pain. Both dynamic facial texture feature and dynamic geometric feature are extracted from video sequences and utilized to classify facial expression of infants as pain or no pain. For the dynamic analysis of facial expression, we construct spatiotemporal domain representation for texture features and time series representation (i.e. time series of frame-level features) for geometric features. Multiple facial features are combined through both feature fusion and decision fusion schemes to evaluate their effectiveness in infants' pain assessment. Experiments are conducted on the video acquired from NICU infants, and the best accuracy of the proposed pain assessment approaches is 95.6%. Moreover, we find that although decision fusion does not perform better than that of feature fusion, the False Negative Rate of decision fusion (6.2%) is much lower than that of feature fusion (25%).

Certain open issues challenge the software engineering of autonomous robot software (ARS). One issue is to provide enabling software technologies to support autonomous and rational behaviours of robots operating in an open environment, and another issue is the development of an effective engineering approach to manage the complexity of ARS to simplify the development, deployment and evolution of ARS. We introduce the software framework AutoRobot to address these issues. This software provides abstraction and a model of accompanying behaviours to formulate the behaviour patterns of autonomous robots and enrich the coherence between task behaviours and observation behaviours, thereby improving the capabilities of obtaining and using the feedback regarding the changes. A dual-loop control model is presented to support flexible interactions among the control activities to support continuous adjustments of the robot's behaviours. A multi-agent software architecture is proposed to encapsulate the fundamental software components. Unlike most existing research, in AutoRobot, the ARS is designed as a multi-agent system in which the software agents interact and cooperate with each other to accomplish the robot's task. AutoRobot provides reusable software packages to support the development of ARS and infrastructure integrated with ROS to support the decentralized deployment and running of ARS. We develop an ARS sample to illustrate how to use the framework and validate its effectiveness.

In this letter, we explored the usage of spatio-temporal information in one unified framework to improve the performance of multichannel speech recognition. Generalized cross correlation (GCC) is served as spatial feature compensation, and an attention mechanism across time is embedded within long short-term memory (LSTM) neural networks. Experiments on the AMI meeting corpus show that the proposed method provides a 8.2% relative improvement in word error rate (WER) over the model trained directly on the concatenation of multiple microphone outputs.

This paper proposes a hardware configuration for uplink multi-user multiple-input multiple-output (MU-MIMO) transmissions in a distributed antenna system (DAS). The demand for high-speed transmission in the uplink has increased recently, because of which standardizations in LTE-advanced and IEEE 802.11ax networks is currently underway. User terminal (UT) scheduling on the downlink MU-MIMO transmission is easy even in unlicensed band such as those in wireless local area network (WLAN) systems. However, the detailed management of the UTs is difficult on the uplink MU-MIMO transmissions because of the decentralized wireless access control. The proposed configuration allows an antenna to be selected from an external device on the access point (AP). All AP antennas are divided into groups, and the received signal in each group is input to the amplitude detector via a directional coupler. Subsequently, the selected antenna is fed by a multiple-to-one switch instead of a matrix switch. To clarify the effectiveness of the proposed configuration, we conduct computer simulations based on the ray-tracing method for propagation channels in an indoor environment.

Real-time weather radar imaging technology is required for generating short-time weather forecasts. Moreover, such technology plays an important role in critical-weather warning systems that are based on vast Doppler weather radar data. In this study, we propose a weather radar imaging method that uses multi-layer contour detection and segmentation based on MAP-MRF estimation. The proposed method consists of three major steps. The first step involves generating reflectivity and velocity data using the Doppler radar in the form of raw data images of sweep unit in the polar coordinate system. Then, contour lines are detected on multi-layers using the adaptive median filter and modified Canny's detector based on curvature consistency. The second step interpolates contours on the Cartesian coordinate system using 3D scattered data interpolation and then segments the contours based on MAP-MRF prediction and the metropolis algorithm for each layer. The final step involves integrating the segmented contour layers and generating PPI images in sweep units. Experimental results show that the proposed method produces a visually improved PPI image in 45% of the time as compared to that for conventional methods.

MathML is a standard markup language for describing math expressions. MathML consists of two sets of elements: Presentation Markup and Content Markup. The former is widely used to display math expressions in Web pages, while the latter is more suited to the calculation of math expressions. In this letter, we focus on the former and consider classifying Presentation MathML expressions. Identifying the classes of given Presentation MathML expressions is helpful for several applications, e.g., Presentation to Content MathML conversion, text-to-speech, and so on. We propose a method for classifying Presentation MathML expressions by using multilayer perceptron. Experimental results show that our method classifies MathML expressions with high accuracy.

Rehabilitation training with pedaling wheelchair in combination with functional electrical stimulation (FES) can be effective for decreasing the risk of falling significantly. Automatic adjustment of cycling speed and making a turn without standstill has been desired for practical applications of the training with mobile FES cycling. This study aimed at developing closed-loop control system of cycling speed with the pedaling wheelchair. Considering clinical practical use with no requirement of extensive modifications of the wheelchair, measurement method of cycling speed with inertial motion measurement units (IMUs) was introduced, and fuzzy controller for adjusting stimulation intensity to regulate cycling speed was designed. The developed prototype of closed-loop FES control system achieved appropriately cycling speed for the different target speeds in most of control trials with neurologically intact subjects. In addition, all the control trials of low speed cycling including U-turn achieved maintaining the target speed without standstill. Cycling distance and cycling time increased with the closed-loop control of low cycling speed compensating decreasing of cycling speed caused by muscle fatigue. From these results, the developed closed-loop fuzzy FES control system was suggested to work reliably in mobile FES cycling.

An important step for improving software analyzability is applying refactorings during the maintenance phase to remove bad smells, especially the long method bad smell. Long method bad smell occurs most frequently and is a root cause of other bad smells. However, no research has proposed an approach to repeating refactoring identification, suggestion, and application until all long method bad smells have been removed completely without reducing software analyzability. This paper proposes an effective approach to identifying refactoring opportunities and suggesting an effective refactoring set for complete removal of long method bad smell without reducing code analyzability. This approach, called the long method remover or LMR, uses refactoring enabling conditions based on program analysis and code metrics to identify four refactoring techniques and uses a technique embedded in JDeodorant to identify extract method. For effective refactoring set suggestion, LMR uses two criteria: code analyzability level and the number of statements impacted by the refactorings. LMR also uses side effect analysis to ensure behavior preservation. To evaluate LMR, we apply it to the core package of a real world java application. Our evaluation criteria are 1) the preservation of code functionality, 2) the removal rate of long method characteristics, and 3) the improvement on analyzability. The result showed that the methods that apply suggested refactoring sets can completely remove long method bad smell, still have behavior preservation, and have not decreased analyzability. It is concluded that LMR meets the objectives in almost all classes. We also discussed the issues we found during evaluation as lesson learned.

This study proposes a mathematical model of a gesture-based pointing interface system for simulating pointing behaviors in various situations. We assume an interaction between a pointing interface and a user as a human-in-the-loop system and describe it using feedback control theory. The model is formulated as a hybrid of a target value follow-up component and a disturbance compensation one. These are induced from the same feedback loop but with different parameter sets to describe human pointing characteristics well. The two optimal parameter sets were determined individually to represent actual pointing behaviors accurately for step input signals and random walk disturbance sequences, respectively. The calibrated model is used to simulate pointing behaviors for arbitrary input signals expected in practical situations. Through experimental evaluations, we quantitatively analyzed the performance of the proposed hybrid model regarding how accurately it can simulate actual pointing behaviors and also discuss the advantage regarding the basic non-hybrid model. Model refinements for further accuracy are also suggested based on the evaluation results.

In this paper, we establish a novel bimodal emotion database from physiological signals and facial expression, which is named as PSFE. The physiological signals and facial expression of the PSFE database are respectively recorded by the equipment of the BIOPAC MP 150 and the Kinect for Windows in the meantime. The PSFE database altogether records 32 subjects which include 11 women and 21 man, and their age distribution is from 20 to 25. Moreover, the PSFE database records three basic emotion classes containing calmness, happiness and sadness, which respectively correspond to the neutral, positive and negative emotion state. The general sample number of the PSFE database is 288 and each emotion class contains 96 samples.