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Low-speed control of direct-drive permanent magnet synchronous motor (DD-PMSM) is a troublesome problem, partly since the motor is more sensitive to disturbance under low/ultra-low speed situations. To attenuate the low-frequency disturbances of the mechanical system and realize smooth low-speed control, this paper presents a control strategy using a two-degree-of-freedom (2-DOF) structure. The proposed method directly shapes the two closed-loop transfer functions of reference tracking and disturbance rejection with a 2-DOF scheme, which improves the disturbance attenuation while guarantees satisfactory tracking performance simultaneously. In addition, the system stability and robustness are also analyzed. The effectiveness of the proposed method and the advantages compared to other common-used control strategies are verified through simulations and experimental tests.

The novel coronavirus 2019 (COVID-19) becomes recently a global pandemic as declared by the World Health Organization (WHO) in March 2020. COVID-19 rapidly spread and attacked people in more than 200 countries worldwide. The use of artificial intelligence (AI) techniques has become urgent to prevent the exacerbation of the astounding spread of this pernicious disease. This paper presents a novel rapid deep learning computer-aided Diagnosis (CAD) framework for simultaneously detecting and diagnosing the COVID-19 against different respiratory diseases such as pneumonia, atelectasis, cardiomegaly, infiltration, mass, pneumothorax, nodule, and effusion. To develop a useful patient triage system for detecting COVID-19 in early-stage, rapidly extract the visual diagnosis knowledge from the input chest X-ray (CXR) images is extremely required. The proposed CAD framework shows its capability to automatically detect and diagnose COVID-19 with a detection accuracy of 96.31% and a classification accuracy of 97.40%. Meanwhile, the real-time prediction speed of 0.0093 seconds is achieved for a single testing CXR image. To achieve a high accuracy of the knowledge extraction from the entire CXR images with a high prediction speed would represent a key for developing a comprehensive and useful smart patient triage system in hospitals and healthcare systems.

Deep learning (DL) nowadays activates the advancement of multiple research fields, from signal processing to computer vision and communication. Compared with traditional machine learning (ML) algorithms, some DL architectures such as convolutional neural network (CNN) allow learning feature automatically without expert knowledge in a specific domain. Inspired by the notable achievement of DL for general image classification, this work studies an efficient CNN-based approach for automatic modulation classification which is recognized as the fundamental task in several modern communication systems. Besides the architecture organized by several blocks of parallel asymmetric convolutional layers, the proposed network is specialized by two mechanisms: skip-connection for the prevention of vanishing gradient and reusable-feature depth-concatenation for an optimal feature utilization via accumulating the deep feature at multi-scale representation. According performance evaluation, our method gains the 24-modulation classification rate of 85.10% at +10 dB SNR on the most challenging and large-scale DeepSig: RadioML (version 2018.01A) dataset.

Modulation classification, recognized as the intermediate step between signal detection and demodulation, is widely deployed in several modern wireless communication systems. Although many approaches have been studied in the last decades for identifying the modulation format of an incoming signal, they often reveal the obstacle of learning radio characteristics for most traditional machine learning algorithms. To overcome this drawback, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. Particularly, a convolutional neural network is developed for proficiently learning the most relevant radio characteristics of gray-scale constellation image. The deep network is specified by multiple processing blocks, where several grouped and asymmetric convolutional layers in each block are organized by a flow-in-flow structure for feature enrichment. These blocks are connected via skip-connection to prevent the vanishing gradient problem while effectively preserving the information identify throughout the network. Regarding several intensive simulations on the constellation image dataset of eight digital modulations, the proposed deep network achieves the remarkable classification accuracy of approximately 87% at 0 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further outperforms some state-of-the-art deep models of constellation-based modulation classification., Comment: Accepted for presentation at IEEE GLOBECOM 2020

Pseudo-random sequence is widely used in the field of information security. This design uses Verilog HDL to complete the construction of Logistic chaotic pseudo-random sequence generator with modular programming ideas. The test results show that the correlation performance and test statistics of the output sequence can meet the requirements of chaotic pseudo-sequence. The effect of applying it to image encryption also verifies that the sequence has good randomness.

In this decade, although numerous conventional methods have been introduced for three-dimensional (3D) skeleton-based human action recognition, they have posed a primary limitation of learning a vulnerable recognition model from low-level handcrafted features. This paper proposes an effective deep convolutional neural network (CNN) with a dual-stream architecture to simultaneously learn the geometric-based static pose and dynamic motion features for high-performance action recognition. Each stream consists of several advanced blocks of regular and grouped convolutional layers, wherein various kernel sizes are configured to enrich representational features. Remarkably, the blocks in each stream are associated via skip-connection scheme to overcome the vanishing gradient problem, meanwhile, the blocks of two stream are jointly connected via a customized layer to partly share high-relevant knowledge gained during the model training process. In the experiments, the action recognition method is intensively evaluated on the NTU RGB+D dataset and its upgraded version with up to 120 action classes, where the proposed CNN achieves a competitive performance in terms of accuracy and complexity compared to several other deep models.

Modulation classification, an intermediate step between signal detection and demodulation, is commonly deployed in many modern wireless communication systems. Although many approaches have been introduced in the last decades for identifying the modulation format of the incoming signal, they have the obstacle of mining radio characteristics for most traditional machine learning algorithms. To effectively handle this limitation, we propose an accurate modulation classification method by exploiting deep learning for being compatible with constellation diagram. A convolutional neural network (CNN), namely CRNet, is developed to proficiently learn the most relevant radio characteristics from transformed gray-scale constellation image by cross-residual connection, a novel structure for associating the intrinsic information between two processing flows specified by regular and grouped convolutional layers. Based on the experimental evaluation, CRNet achieves the classification rate of approximately 90% at +10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel and further performs more accurately than some existing deep models for constellation-based modulation classification.

Diabetic Retinopathy (DR), the complication leading to vision loss, is generally graded according to the amalgamation of various structural factors in fundus photography such as number of microaneurysms, hemorrhages, vascular abnormalities, etc. To this end, Convolution Neural Network (CNN) with impressively representational power has been exhaustively utilized to address this problem. However, while existing multi-stream networks are costly, the conventional CNNs do not consider multiple levels of semantic context, which suffers from the loss of spatial correlations between the aforementioned DR-related signs. Therefore, this paper proposes a Densely Reversed Attention based CNN (DRAN) to leverage the learnable integration of channel-wise attention at multi-level features in a pretrained network for unambiguously involving spatial representations of important DR-oriented factors. Consequently, the proposed approach gains a quadratic weighted kappa of 85.6% on Kaggle DR detection dataset, which is competitive with the state-of-the-arts.

Being able to precisely control the carrier polarity and conductivity plays a vital role while developing future two-dimensional (2D) transition metal dichalcogenides (TMDs)-based devices. Achieving such controllability in TMD material, however, remains challenging as a result of the strong Fermi-level pinning with contact metals [1] . MoTe 2 , one of the group-VI TMDs, has high mobility, a moderate bandgap, and the lower energy difference between polymorphic semiconducting 2H and metallic 1T’ phases, allowing versatile electrical properties. It’s known that controlling the number of chalcogen defects in TMD considerably alters its electrical characteristics [2] , [3] . In this study, we report the results of engineering Te defects in MoTe 2 by plasma treatment where (1) 2H phase is stable at a Te/Mo ratio between 1.88 and 2.13, and (2) MoTe 2 transistors can be converted from p- to n-type conduction by the defect-induced conduction band edge (CBE) lowering.

Recently, deep learning (DL) is an innovative machine learning (ML) technique that has gained the outstanding achievements in computer vision and natural language processing. This work takes advantage of DL for effectively handling automatic modulation classification (AMC), which is the fundamental function of numerous cognitive radio-based and spectrum sensing-based applications in many modern communication systems. Concretely, a novel deep convolutional neural network (DCNN) is proposed for learning a classification model from a massive amount of modulated signals, in which the network architecture has several convolutional blocks specialized to simultaneously capture the temporal intra-signal correlations and the spatial inter-signal relations. To this end, each block comprises various convolutional layers of asymmetric convolution kernels, whose outputs are gathered via a concatenation layer. For the enrichment of multi-scale deep feature and the prevention of gradient vanishing problem, these blocks are associated by skip connections to take into account the useful residual information. In experiments, the proposed CNN-based AMC method achieves the overall 24-modulation classification rate of 88.22% at 10dB SNR on the well-known DeepSig dataset.

Recently, regarding several beneficial properties of depth camera, numerous 3D action recognition frameworks have studied high-level features by exploiting deep learning techniques, but nevertheless they cannot seize the meaningful characteristics of static human pose and dynamic action motion of a whole sequence. This paper introduces a deep network configured by two parallel streams of convolutional stacks for fully learning the deep intra-frame joint associations and inter-frame joint correlations, wherein the structure of each stream is learned from Inception-v3. In experiments, besides the compatibility verification with various backbone networks, the proposed approach achieves the state-of-theart performance in battle with several deep learning-based methods on the updated NTU RGB+D 120 dataset.

Numerous existing handcrafted feature-based and conventional machine learning-based approaches cannot seize the intensive correlations of skeleton structure in the spatiotemporal dimension. On another hand, some modern methods exploiting Long Short Term Memory (LSTM) to learn temporal action attribute, which lack an efficient scheme of revealing high-level informative features. To handle the aforementioned issues, this research introduces a novel hierarchical deep feature fusion model for 3D skeleton-based human action recognition, in which the deep information for modeling human appearance and action dynamic is gained by Convolutional Neural Networks (CNNs). The deep features of geometrical joint distance and orientation are extracted via a multi-stream CNN architecture to uncovering the hidden correlations in both the spatial and temporal dimensions. The experimental results on the NTU RGB+D dataset demonstrates the superiority of the proposed fusion model against several recently deep learning (DL)-based action recognition approaches.

The emergence of convolutional neural network (CNN) has enabled facial expression recognition to accomplish significant outcomes nowadays. However, while existing multistream networks are subject to costly computation, the attention- embedded approaches do not involve multiple levels of semantic context in the predefined CNN. Based on the observation that emotions via a person's face are fusion of various muscular modalities, relying upon the outputs and corresponding attentional features of the deepest layer in the CNN is insufficient due to loss of informative details through multiple sub-sampling stages. Therefore, this paper introduces a CNN with densely backward attention to leverage the aggregation of channel-wise attention at multi-level features in a backbone network for reaching high recognition performance with cost-effective resource consumption. Particularly, cross-channel semantic information in high-level features are exploited densely to recalibrate finegrained details in low-level versions. Then, a step of multi-level aggregation is further executed for thorougly involving spatial representations of important facial modalities. As a consequence, the proposed approach gains highest mean class accuracy of 79.37% on RAF-DB, which is competitive with the state-of-the- arts.

The spread of illegal replication of digital image has seriously affected the protection of intellectual property rights. Nowadays, although watermarking is the most popular technology for copyright protection and ownership authentication, most of the current works cannot encounter numerous intentional attacks of watermark destruction. In this research, we introduce a novel framework of blind image watermarking that is able to learn attacking patterns effectively based on a deep convolutional encoder-decoder network. For details, a binary watermark image is hidden into selective wavelet blocks by the mean of an optimal encoding rule, wherein the quality image degradation is minimized over the Mean Square Error metric for a significant image imperceptibility enhancement. Then, the embedding maps, defined as the wavelet coefficient difference values, of various attacks simulated as digital image transformations of the watermarked image are revealed for training the deep learning-based watermark extraction model. Accordingly, the watermark information hidden in an attacked image can be precisely recovered from its embedding map by the trained model. From experimental results, the proposed watermark framework achieves a good performance trade-off between image imperceptibility and watermark robustness through an adjustable embedding strength. In addition, our approach is strongly defeats several state-of-the-art methods in terms of watermark robustness against many critical attacks.

With the recent advent of deep learning in medical image processing, retinal blood vessel segmentation topic has been comprehensively handled by numerous research works. However, since the ratio between the number of vessel and background pixels is heavily imbalanced, many attempts utilized patches augmented from original fundus images along with fully convolutional networks for addressing such pixel-wise labeling problem, which significantly costs computational resources. In this paper, a method using Round-wise Features Aggregation on Bracket-shaped convolutional neural networks (RFA-BNet) is proposed to exclude the necessity of patches augmentation while efficiently handling the irregular and diverse representation of retinal vessels. Particularly, given raw fundus images, typical feature maps extracted from a pretrained backbone network are employed for a bracket-shaped decoder, wherein middle-scale features are continuously exploited round-by-round. Then, the decoded maps having highest resolution of each round are aggregated to enable the built model to flexibly learn various degrees of embedded semantic details while retaining proper annotations of thin and small vessels. Finally, the proposed approach showed its effectiveness in terms of sensitivity (0.7932), specificity (0.9741), accuracy (0.9511), and AUROC (0.9732) on DRIVE dataset.

Nowadays human activity recognition (HAR) plays an crucial role in the healthcare and wellness domains, for example, HAR contributes to context-aware systems like elder home assistance and care as a core technology. Despite promising performance in terms of recognition accuracy achieved by the advancement of machine learning for classification tasks, most of the existing HAR approaches, which adopt low-level handcrafted features, cannot completely deal with practical activities. Therefore, in this paper, we present an efficient wearable sensor based activity recognition method that allows encoding inertial data into color image data for learning highly discriminative features by convolutional neural networks (CNNs). The proposed data encoding technique converts tri-axial samples to color pixels and then arranges them for image-formed representation. Our method reaches the recognition accuracy of over 95% on two challenging activities datasets and further outperforms other deep learning-based HAR approaches.

Recently, 3D action recognition has received more attention of research and industrial communities thanks to the popularity of depth sensors and the efficiency of skeleton estimation algorithms. Accordingly, a large number of methods have been studied by using either handcrafted features with traditional classifiers or recurrent neural networks. However, they cannot learn high-level spatial and temporal features of a whole skeleton sequence exhaustively. In this paper, we proposed a novel encoding technique to transform the pose features of joint-joint distance and joint-joint orientation to color pixels. By concatenating the features of all frames in a sequence, the spatial joint correlations and temporal pose dynamics of action appearance are depicted by a color image. For learning action models, we adopt the strategy of end-to-end fine-tuning a pre-trained deep convolutional neural networks to completely capture multiple high-level features at multi-scale action representation. The proposed method achieves the state-of-the-art performance on NTU RGB+D, the largest and most challenging 3D action recognition dataset, for both the cross-subject and cross-view evaluation protocols.

To build up a state-of-the-art semantic scene segmentation model, a balanced combination between coarsely and finely contextual details is required for eliminating class-wise ambiguities and reaching high accuracy of pixel-wise labeling, respectively. Accordingly, with deep learning integration, prior works have achieved impressive performance in general, but found difficulties in correctly labeling medium to small objects. For the purpose of overcoming such issue, this paper proposes a deep convolutional network with bracket-style decoder, namely B-Net, to leverage the utilization of features learned at middle layers in the backbone networks (encoder) for constructing a final prediction map of densely enhanced semantic information. In particular, every feature map of interest combines with its adjacent version of higher spatial resolution through lateral connection modules to produce finer outputs that repeat such routine round-by-round until retrieving the finest-resolution map for dense prediction. Consequently, benchmarking results on CamVid dataset showed the effectiveness of the proposed method with mean class-wise accuracy, pixel-wise accuracy, and mean union intersection of 76.2%, 87.1%, and 66.4%, respectively.

Among extensively researched 2D-TMDs, MoTe 2 is an emerging but understudied material because of the lack of reliable and reproducible synthesis methods. The difficulties of its synthesis originate from the weak Mo-Te bonding energy, which results in the low chemical reactivity of MoTe 2 . Additionally, compared with other TMDs, MoTe 2 is relatively unstable. It easily oxidizes and decomposes at high temperatures, which complicate the synthesis using conventional CVD. We have previously developed a PVD method capable of depositing large-area, high-crystallinity, few-layer 2D MoTe 2 without complex designs of gas-phase transport or Mo-Te chemical reaction. MoTe 2 is directly sputtered and post-annealed in a 2D encapsulation structure to improve its crystallinity. MoTe 2 can be synthesized in either homogeneous semiconducting 2H or metallic 1T’ phase by controlling the adequate thermal budget during annealing. This talk will mainly discuss the process optimization and electrical properties of 1T'-MoTe 2 , which is potentially important in several emerging research fields, including 2D interconnect, topological superconductor, and 2H-lT’ in-plane homojunction transistor.

Variable flux memory (VFM) permanent magnet (PM) machines exhibit an additional degree-of-freedom for control, i.e. PM magnetization state, and thus excellent flux controllability. Moreover, the hybrid PM topologies having variable PM (VPM) with low coercive force and constant PM (CPM) with high coercive force at the same time, are employed to improve the torque density and the flux controllability. The parallel and series connections between the two different kinds of PMs are both feasible. Based on two-dimensional (2-D) finite element (FE) analysis, the VFM machines with these two connection types are investigated and compared in this paper. The results reveal that the VPM with series connected CPM is beneficial for more stable working point and better torque density. A pair of VFM prototypes with parallel and series hybrid PMs respectively are manufactured and tested to validate the analyses.

Hybrid permanent magnet (PM) variable flux memory (VFM) machines employ both the high coercive force PM, i.e. the constant PM (CPM), and the low coercive force PM, i.e. the variable PM (VPM), with which the PM magnetization state can be flexibly regulated to match various operation requirements. These two kinds of PMs can be magnetically connected with either parallel or series configuration so that different characteristics can be obtained. Based on two-dimensional (2-D) and three-dimensional (3-D) finite element (FE) analyses, the end effects of a pair of VFM machines with parallel and series connections, respectively, are investigated to identify their different features. The prototype machines are manufactured and tested to validate the predictions.

This paper presents a novel background subtraction method that is flexible for various background scenarios. The method includes automated-directional masking (ADM) algorithm for adaptive background modeling and historical intensity pattern reference (HIPaR) algorithm for foreground segmentation. By selecting an appropriate mask in a set based on directional feature, ADM updates background smoothly and precisely following a boundary-based strategy with an intensity correction rule. In order to segment foreground, HIPaR refers intensity patterns of previous backgrounds and input frames and then compares their mean difference with a checking threshold to make foreground decision. Experimental results prove that our proposed ADM-HIPaR outperforms other state-of-the-art methods in terms of foreground detection accuracy.

A series-hybrid variable flux memory (VFM) machine employing two different kinds of permanent magnets (PMs) on alternate rotor poles is investigated. This machine features controllable PM flux and good flux-weakening capability, as well as improved torque density. However, the unipolar end leakage flux occurs when the fluxes produced by two different kinds of PMs are not balanced. Therefore, the series connection of two kinds of PMs is analyzed, based on which the mechanism of unbalanced rotor poles and the unipolar end leakage flux are revealed. The rule of obtaining balanced poles is identified. A series-hybrid VFM machine prototype is fabricated and tested.

This paper proposes a novel variable flux memory (VFM) machine, in which the “constant” permanent magnet (PM) (CPM) with high coercive force and the “variable” PM (VPM) with low coercive force are alternatively located in the interior-PM (IPM) rotor. Thus, the VPMs and CPMs are magnetically connected in series, with which the CPMs can assist the VPMs to withstand the unintentional demagnetization caused by armature reaction. Therefore, a high armature current can be applied to the machine. Meanwhile, the reluctance torque is retrieved. Thus, a high torque density can be obtained. Based on two-dimensional (2-D) finite element (FE) analysis, the electromagnetic performance of the proposed VFM machine in two extreme magnetization states is firstly evaluated in detail. Then, the demagnetization and re-magnetization characteristics are investigated, in which the working points of VPMs are illustrated. Furthermore, the advantages of improved efficiency of the proposed VFM machine are demonstrated. A prototype machine is manufactured and tested to validate the predictions.

Switched reluctance machines (SRMs) are becoming increasingly attractive due to their inherent advantages, such as robust and rare-earth-permanent magnet-free topology, as well as excellent fault-tolerance and temperature management. However, the accurate and efficient analyses of SRMs are always challenging since they operate with variable reluctance and pulsating excitation, suffering severe nonlinear characteristics. This paper first introduces a general power equation of SRMs, which can be a powerful tool in the investigation of SRMs. Then, the proposed power equation is validated by finite element (FE) results and experimental results with different excitations. Afterwards, the calculated results with various conditions including different stator inner diameters and different stator outer diameters are compared with the FE-predicted results. Finally, the comparison between SRMs and several conventional machines is carried out to evaluate the power density of SRMs.

Partitioned stator (PS) machines employ two separate stators to accommodate armature windings and permanent magnets (PMs) respectively, alleviating the stator space conflict in stator-PM machines and hence significantly boosting the torque density. However, the PM usage volume of PS machines is relatively large and the cost is high, which is undesirable for cost sensitive applications. In this paper, a PS machine employing ferrite PMs is presented, which can take the advantage of large space for PMs in PS machines as well as balance the performance and cost. The electromagnetic characteristics of an optimized ferrite PS machine and an optimized NdFeB PS machine are compared, together with a classic Prius2010 interior-PM (IPM) machine as a baseline for comparison. In addition, the economic issues are considered as well. The predicted results reveal that the NdFeB PS machine can produce much higher torque density but is more expensive than the Prius2010 IPM machine, whilst the ferrite PS machine can significantly reduce the cost and still exhibit comparable performance as the Prius2010 IPM machine and the highest torque per cost. The demagnetization behavior of ferrite PS machine is evaluated and a method to improve the demagnetization withstand capability is proposed. A pair of scaled prototypes are manufactured to validate the finite element predictions.

The partitioned stator (PS) machine adopts two stators to allocate coils and permanent magnets (PMs) separately, which can increase the space for PMs and coils to boost the electromagnetic torque and to improve the thermal condition of PMs which are on the stator. In this paper, the PS switched flux PM machine and PS flux reversal PM machine are proved to inherently share the same operating principle and the same machine topology but with interior PM (IPM) and surface-mounted PM (SPM) stators, respectively. Based on this concept, four PS machines with different PM excitation stator topologies are introduced and compared in terms of back-EMF, cogging torque, electromagnetic torque, torque per PM volume and flux-weakening capability. The results show that the spoke-IPM inner stator exhibits the maximum back-EMF and hence the largest average torque, while the IPM inner stator has the best flux-weakening capability and the V-shaped IPM (VIPM) produces the highest torque per PM volume.

This paper proposes two new switched flux memory machines (SFMMs) based on the concept of E-core and C-core conventional SF machines. With the proposed topologies, the torque density can be enhanced while the magnet usage can be reduced compared with their counterpart conventional SF machines. Meanwhile, since both normal PM and low coercive force PM materials are adopted, the proposed machines also obtain obvious flux adjustable capability, which is particular suitable for electric vehicular applications. In this paper, the stator/rotor pole (N s /N r ) combination, magnet dimensions and design parameters will be optimized for the proposed topologies. Then, the electromagnetic performance of the proposed machines will be investigated and compared with the conventional SFMMs to validate their improvements.

Given the strong link between energy and behavior, sensing and metering per-user energy consumption is critical for understanding individual energy behavior and for customizing personalized feedback to promote energy-saving behavior. This paper explores the feasibility of per-user energy metering by proposing a per-user energy metering system that uses thermal-imaging and thermal-identification to track and associate energy usage among individual occupants in a shared working/living space. Each occupant wears a thermal tag that emits a unique temperature signature for user identification. The system introduces location-based per-user energy disaggregation that accounts per-appliance energy usage to individual energy consumer (s), i.e., Occupant (s) nearby activated appliances. We have designed, prototyped, and tested the Thermal Probe system. Results show that the system meters per-user energy consumption with an average error of 12.66%.

For the two graphics whose internal parameter matrix is known, their epipolar geometry can be determined through the image coordinate matching points which have been observed. However, matching noise will have some impact on the estimation of motion parameter. In this paper, the essential matrix is decomposed by factorization algorithm to get external parameters, which is based on the Singular Value Decomposition (SVD) theory. The error propagation of motion estimation is analyzed based on matrix perturbation and covariance propagation theory of linear model. Meanwhile, the expression of motion estimation error which changes with the essential matrix error is deduced. Finally, the correctness of the deduced results is proved by matlab simulation experiment.

Aging population is growing, and the young people are more concerned about the real-time condition of the elderly. This paper aims to propose a real-time monitoring system, obtaining and analyzing the outdoors dynamic information of the elderly, which is composed of three cooperative modules (mobile client, server and sensor). The sensor is equipped with a Bluetooth module to the data transmission, and the server is mainly responsible for distinguishing between the falling attitude and normal activity and record the outdoors motion path of the elderly. The result is displayed on the client, including the mobile phone and browser. If the abnormal condition emerges, the phone of the elderly immediately alarms and the server sends message to ask their children and relevant departments for help at once. The system shows a good real-time performance and an accurate result.

In this demonstration, we show a software framework for generic game development, including game record editing and job-level (JL) computing. For the former, the framework supports the display and editing of game positions and the browsing of the game position tree. Currently, we have developed game record editors for Connect6, Go, Chinese Chess, Mahjong, etc. In this demonstration, we show how easily an editor for the game Tic-Tac-Toe is built. For the latter, the framework supports job submission to a volunteer computing system, named Computer Game Desktop Grid developed by our team, to help solve or analyze game positions. Currently, we support JL proof number search, JL alpha-beta search, and JL Monte-Carlo tree search. In this demonstration, we show how easily JL-PNS is designed.

In this demonstration, we port our current Connect6 program, NCTU6, to mobile devices, such as iOS devices, Android devices. However, computing powers of mobile devices are also relatively weaker when compared with high-end machines. For example, the CPU speeds of mobile devices are about 1/5 to 1/8 of those of desktops, and the memory of mobile devices is usually limited to several megabytes. In this demonstration, we tune the program NCTU6 to fit in mobile devices, while maintaining reasonable strength. The program also won the silver medal in TCGA 2012. In addition, we also support Connect6 puzzles in the program.

It is generally considered a trivial task to synchronize time in a distributed sensor network having a GPS module on board for each sensor node. This is not quite true depending on the application at hand. Commissioned to implement YuShanNet, a delay-tolerant sensor network (DTSN) enabling encounter information collection for hiker search and rescue, we find that the naive use of standard GPSs time acquisition functionalities gives a synchronization error at the scale of 10–100s milliseconds, which is not sufficient given two application-specific considerations: (1) data goodput and (2) energy efficiency. The two requirements are crucial in that the matter is life or death and the hiking trips usually take days. Towards efficient use of the intermittent connectivity in YuShanNet, we design, implement, and evaluate a time synchronization mechanism that improves the synchronization accuracy by approximately two orders of magnitude. This enables TDMA-fashioned MAC for high-goodput data transmission while the GPS is duty-cycled for energy efficiency.

This paper proposes a blimp-based vehicular sensing sys-tem, called BlimpProbe, which travels above ground to perform environmental monitoring. We describe an application in atmosphere science that is suitable for this blimp-based sensing system.

Growth of the elder population gives rise to the increasing demand of long-term elder-care facilities. Most elders living in such facilities suffer from multiple chronic illnesses. Accompanying these illnesses are slow declines of the elders' physical and mental health. Continuous tracking of the elders' daily activities is required to detect the declines early. To facilitate long-term mobility tracking of the elders, we investigate, in this work, the potential of a WSN-based indoor location system as a support for the already-overloaded nursing staff. Through the deployment of a WSN-based Real-Time Indoor Location System (WSN-RTLS) in the university hospital's elder care center for 8 months, we find that: (1) each elder's daily mobility shows a reoccurring pattern. The pattern, however, differs from individual to individual. (2) The mobility level shows a significant variability, i.e., not all elders show reoccurring patterns in mobility levels. These suggest that mere quantity of how much the elders move around the facility will not be a suitable target for behavioral modeling. Exact location of the elders' presence, rather, is more relevant. Insitu WSN-RTLSs would be an enabler towards early detection of the elders' physical or mental decline.

Due to the increasing competition of globalization and fast technological improvements, manufacturing enterprise pay more attention to its purchasing risk. In this paper, we proposed a supply chain purchasing risk evaluation system of manufacturing enterprise based on Fuzzy Analytic Hierarchy Process(FAHP). The FAHP with probability is introduced to provide more information and meet practical things. At last an example was given to help manager of enterprise to make better decisions under uncertain supply chain purchasing circumstances.