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With the development of medical artificial intelligence, automatic magnetic resonance image (MRI) segmentation method is quite desirable. Inspired by the power of deep neural networks, a novel deep adversarial network, dilated block adversarial network (DBAN), is proposed to perform left ventricle, right ventricle, and myocardium segmentation in short-axis cardiac MRI. DBAN contains a segmentor along with a discriminator. In the segmentor, the dilated block (DB) is proposed to capture, and aggregate multi-scale features. The segmentor can produce segmentation probability maps while the discriminator can differentiate the segmentation probability map, and the ground truth at the pixel level. In addition, confidence probability maps generated by the discriminator can guide the segmentor to modify segmentation probability maps. Extensive experiments demonstrate that DBAN has achieved the state-of-the-art performance on the ACDC dataset. Quantitative analyses indicate that cardiac function indices from DBAN are similar to those from clinical experts. Therefore, DBAN can be a potential candidate for short-axis cardiac MRI segmentation in clinical applications.

The ability to expertly control different fingers contributes to hand dexterity during object manipulation in daily life activities. The macroscopic spatial patterns of muscle activations during finger movements using global surface electromyography (sEMG) have been widely researched. However, the spatial activation patterns of microscopic motor units (MUs) under different finger movements have not been well investigated. The present work aims to quantify MU spatial activation patterns during movement of distinct fingers (index, middle, ring and little finger). Specifically, we focused on extensor muscles during extension contractions. Motor unit action potentials (MUAPs) during movement of each finger were obtained through decomposition of high-density sEMG (HD-sEMG). First, we quantified the spatial activation patterns of MUs for each finger based on 2-dimension (2-D) root-mean-square (RMS) maps of MUAP grids after spike-triggered averaging. We found that these activation patterns under different finger movements are distinct along the distal-proximal direction, but with partial overlap. Second, to further evaluate MU separability, we classified the spatial activation pattern of each individual MU under distinct finger movement and associated each MU with its corresponding finger with Regularized Uncorrelated Multilinear Discriminant Analysis (RUMLDA). A high accuracy of MU-finger classification tested on 12 subjects with a mean of 88.98% was achieved. The quantification of MU spatial activation patterns could be beneficial to studies of neural mechanisms of the hand. To the best of our knowledge, this is the first work which manages to quantify MU behaviors under different finger movements.

The focus of this study is development of an intelligent fault detection, diagnosis and health evaluation system for real industrial robots. The system uses principal component analysis based statistical process control with Nelson rules for online fault detection. Several suitable Nelson rules are chosen for sensitive detection. When a variation is detected, the system performs a diagnostic operation to acquire features of the time domain and the frequency domain from the motor encoder, motor current sensor and external accelerometer for fault diagnosis with a multi-class support vector machine. Additionally, a fuzzy logic based robot health index generator is proposed for evaluating the health of the robot, and the generator is an original design to reflect the health status of the robot. Finally, several real aging-related faults are implemented on a six-axis industrial robot, DRV90L7A6213N by Delta Electronics, and the proposed system is validated effectively by the experimental results.

Blood glucose level needs to be monitored regularly to manage the health condition of hyperglycemic patients. The current glucose measurement approaches still rely on invasive techniques which are uncomfortable and raise the risk of infection. To facilitate daily care at home, in this article, we propose an intelligent, noninvasive blood glucose monitoring system which can differentiate a user's blood glucose level into normal, borderline, and warning based on smartphone photoplethysmography (PPG) signals. The main implementation processes of the proposed system include 1) a novel algorithm for acquiring PPG signals using only smartphone camera videos; 2) a fitting-based sliding window algorithm to remove varying degrees of baseline drifts and segment the signal into single periods; 3) extracting characteristic features from the Gaussian functions by comparing PPG signals at different blood glucose levels; 4) categorizing the valid samples into three glucose levels by applying machine learning algorithms. Our proposed system was evaluated on a data set of 80 subjects. Experimental results demonstrate that the system can separate valid signals from invalid ones at an accuracy of 97.54 $\%$ and the overall accuracy of estimating the blood glucose levels reaches 81.49 $\%$ . The proposed system provides a reference for the introduction of noninvasive blood glucose technology into daily or clinical applications. This article also indicates that smartphone-based PPG signals have great potential to assess an individual's blood glucose level.

SUMMARYThe variable stiffness actuator (VSA) is helpful to realize the post-collision safety strategies for safe human–robot interaction.1 The stiffness of the robot will be reduced to protect the user from injury when the collision between the robot and human occurs. However, The VSA has a mechanism limit constraint that can cause harm to users even if the stiffness is minimized. Accordingly, in this article, a concept combining danger index and robust fault detection and isolation is presented and applied to active–passive variable stiffness elastic actuator (APVSEA). APVSEA can actively change joint stiffness with the change of danger index. Experimental results show that this concept can effectively confirm the fault mode and provide additional protection measures to ensure the safety of users when the joint stiffness has been adjusted to the minimum.

The combination of edge and cloud computing is going to make the Internet of Things (IoT) rapid, light, and more reliable. IoT and cloud-edge computing are distinct disciples that have evolved separately over time. However, they are increasingly becoming interdependent, and are what the future holds. A crucial aspect is how to design a compound of cloud and edge computing architectures, and implement IoT effectively. In this paper, we proposed a combination of Cloud and Edge Computing architecture and built a set of an intelligent air-quality monitoring system in Tunghai University as a case study. In this case, we implemented container-based virtualization which constructs Kubernetes Minion (Nodes) in the Docker container service independently for each service on the Edge side. Finally, to monitor the high-performance computing systems, clusters, and networks, we used Ganglia Monitoring System. Ganglia collects relevant information such as Central Processing Unit (CPU), memory, network and usage of Protocol Data Unit (PDU) to monitor the power consumption and makes a measurement and evaluation for Kubernetes Pods.

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

One of the prominent challenges for widespread adoption of silicon photonics (SiPh) technology is the availability of an integration platform that can simultaneously meet a wide range of power, performance, and cost criteria in different applications. As a result, there is a diversity of SiPh integrated solutions proposed or demonstrated, but none is considered as a common solution. In this paper, we will first survey industry proposed photonic engine structures in monolithic and heterogeneous integration on their strengths and weaknesses. We will then propose a compact and universal PE structure - COUPE (COmpact Universal Photonic Engine) that could consolidate different requirements onto the same integration platform. COUPE has the electrical IC – photonic IC integration with the electrical interface designed to minimize the EIC-PIC coupling loss. Compared with industry proposed PE technology, COUPE can provide low insertion loss for both grating coupler (GC) and edge coupler (EC). For either GC or EC, the COUPE is a solid structure without cavities or mechanically weak parts, thus enabling low insertion loss without contamination or mechanical concerns. COUPE also has the flexibility to be integrated easily with host ASIC to form a co-package structure. The COUPE integration scheme can meet the most demanding system requirements and pave the way for SiPh-based wafer level system integration (WLSI) for high performance computing applications.

Objective: To fight the “silent killer” hypertension, continuous blood pressure (BP) monitoring has been one of the most desired functions in wearable electronics. However, current BP measuring principles and protocols either involve a vessel occlusion process with a cuff or require multiple sensing nodes on the body, which makes it difficult to implement them in compact wearable electronics like smartwatches and wristbands with long-term wearability. Methods: In this work, we proposed a highly compact multi-wavelength photoplethysmography (MWPPG) module and a depth-resolved MWPPG approach for continuous monitoring of BP and systemic vascular resistance (SVR). By associating the wavelength-dependent light penetration depth in the skin with skin vasculatures, our method exploited the pulse transit time (PTT) on skin arterioles for tracking SVR ( n = 20). Then, we developed an arteriolar PTT-based method for beat-to-beat BP measurement. The BP estimation accuracy of the proposed arteriolar PTT method was validated against Finometer ( n = 20) and the arterial line ( n = 4). Results: The correlation between arteriolar PTT and SVR was theoretically deduced and experimentally validated on 20 human subjects performing various maneuvers. The proposed arteriolar PTT-based method outperformed the traditional arterial PTT-based method with better BP estimation accuracy and simpler measurement setup, i.e., with a single sensing node. Conclusion: The proposed depth-resolved MWPPG method can provide accurate measurements of SVR and BP, which are traditionally difficult to measure in a noninvasive or continuous fashion. Significance: This MWPPG work provides the wearable healthcare electronics of compact size with a low-cost and physiology-based solution for continuous measurement of BP and SVR.

A log management system is essential for the networks administrator. With a log management tool, we can collect, store, analyze, archive, and finally dispose of the log information. In this paper, we propose the architecture model of a log management system using ELK Stack with Ceph to provide a safe network, good Wi-Fi signal strength, and adequate backup data mechanism. In this case, we use our campus data of Wi-Fi log and NetFlow log. First, we collect and store data of our Wi-Fi log using Filebeats tool, and then, we use Elasticsearch, Logstash, and Kibana Stack to visualize the Wi-Fi log data. Second, we collect and store our NetFlow log using NFDUMP, and then, we also use ELK Stack to visualize the NetFlow log data. Third, we integrate the Wi-Fi log and NetFlow log data in one architecture using a distributed storage Ceph file system (CephFS). Moreover, we also compare the performance of RADOS Gateway and CephFS for better storage mechanism.

Synthetic Minority Over-sampling Technique (SMOTE) is an effective method for imbalanced data classification. Many variants of SMOTE have been proposed in the past decade. These methods mainly focused on how to select the crucial minority samples which implicitly assume the selection of key minority samples is binary. Thus, the cost of key sample selection is seldom considered. To this end, this paper proposes a three-way decision model (CTD) by considering the differences in the cost of selecting key samples. CTD first uses Constructive Covering Algorithm (CCA) to divide the minority samples into several covers. Then, a three-way decision model for key sample selection is constructed according to the density of the cover on minority samples. Finally, the corresponding threshold α and β of CTD are obtained based on the pattern of cover distribution on minority samples, after that key samples can be selected for SMOTE oversampling. Moreover, to overcome the shortage of CCA which may contain non-optimal by randomly selecting the cover center, an ensemble model based on CTD (CTDE) is further proposed to improve the performance of CTD. Numerical experiments on 10 imbalanced datasets show that our method is superior to the comparison methods. By constructing the ensemble of the three-way decision based key sample selection, performance of the model can be effectively improved compared with several state-of-the-art methods.

Cuffless technique holds great promise to measure blood pressure (BP) in an unobtrusive way, improving diagnostics and monitoring of hypertension and its related cardiovascular diseases, and maximizing the independence and participation of individual. Pulse transit time (PTT) has been the most commonly employed techniques for cuffless BP estimation. Many studies have been conducted to explore its feasibility and validate its performance in the clinical settings. However, there is still issues and challenges ahead before its wide application. This review will investigate the understanding and development of the PTT technique in depth, with a focus on the physiological regulation of arterial BP, the relationship between PTT and BP, and the summaries of the PTT-based models for BP estimation.

With the increasing popularity of intelligent devices, the mobile apps market has exploded. Due to a large number of candidate app services, it has become very difficult for users to choose the mobile apps that he/she wants to install. Therefore, it is crucial to improve users' experience and make personalized recommendations. In some cases, the traditional recommendation methods can be convenient, but they still have some shortcomings, resulting in inaccurate recommendations in general. To address this issue, this paper proposes a method for mobile app recommendations that are based on the Hyperlink-Induced Topic Search (HITS) algorithm combined with association rules. This method integrates the authority and hub scores into the candidate applications through the download and rating information, and it not only considers the importance of mobile apps in association rules but also takes the reliability factor of users into account. Experiments with the Huawei application market datasets show that the proposed method significantly improves the recommendation accuracies compared with the traditional methods.

The variable combined brake system (VCBS) is a mechanism for motorcycles to simultaneously activate the front and rear brake systems by using one brake lever or pedal. The purpose is to reduce the risk of rollover accidents due to misuse of the front brake when panic braking. Due to its ability in a wide variation range of braking force distribution (BFD) ratios between the front and rear wheels, the VCBS can simultaneously achieve high braking effort and driving comfort performances, provided that the BFD ratio is designed appropriately. This paper aimed to develop the design method for the VCBS. A mathematical model of the VCBS mechanism is derived, and a parameter matching design method that applies adaptive control theory is proposed. A prototype of VCBS is designed and built based on the proposed method. The straight-line braking test results show that the motorcycle equipped with the VCBS prototype effectively obtained a high braking performance in deceleration. The obtained maximum deceleration is an average of 6.37 m/s2 (0.65 g) under an average handbrake lever force of 154.29 N. For front brake failure, maximum deceleration is obtained at an average of 3.38 m/s2 (0.34 g), which is higher than the homologation requirement of 2.9 m/s2.

Extracting detailed 3D information of objects from video data is an important goal for holistic scene understanding. While recent methods have shown impressive results when reconstructing meshes of objects from a single image, results often remain ambiguous as part of the object is unobserved. Moreover, existing image-based datasets for mesh reconstruction don't permit to study models which integrate temporal information. To alleviate both concerns we present SAIL-VOS 3D: a synthetic video dataset with frame-by-frame mesh annotations which extends SAIL-VOS. We also develop first baselines for reconstruction of 3D meshes from video data via temporal models. We demonstrate efficacy of the proposed baseline on SAIL-VOS 3D and Pix3D, showing that temporal information improves reconstruction quality. Resources and additional information are available at http://sailvos.web.illinois.edu., Comment: CVPR 2021 Oral

Self-driving vehicle systems have become mainstream as major international research and development companies compete for road detection and mapping projects such as localization. The latter plays an important role in self-driving systems because an accurate determination of the vehicle's position improves trajectory and offset results. And with a good localization function, a high-precision map can be established. This paper proposes using pole maps to enhance vehicle localization while correcting previous vehicle localization trajectory offsets. We have established a pole feature map with pole-shaped characteristic point clouds along the road that are marked with spatial coordinate information. We use three-dimensional light detection and ranging (3D Lidar) as a sensor for environmental scanning, dynamic segment thresholds, and point cloud projections to the image to extract poles, which avoids identification errors typically caused by the scattering effect of 3D Lidar. The resulting pole map uses the occupancy grid map to filter out noise. In terms of localization, we continue to detect pole features while the vehicle is moving, and compare these with the map. When the results match, we update the current localized information. The localization point corrects the information of the trajectory of the undetected pole, which improves vehicle localization accuracy.

This paper presents the analysis and simulation of crowd in airport multiple transport modes. We analyze the different transportation modes in the airport and crowd distribution. A pedestrian behavior model in airport multiple transport modes based on the potential energy field is utilized to analyze the crowd flow. Based on the pedestrian behavior model of fine mesh and artificial potential field, a simulation system framework of crowd flow is proposed. Taking the real scene of an airport integrated hub as a scene, the process of crowd flow is simulated. The movement track and influencing factors of pedestrians are analyzed. On this basis, the flow time of different numbers of people is obtained. The results are basically consistent with the actual situation. In addition, a simulation-- optimization is presented for the crowd flow of airport integrated hub exit gates. The obtained results show that the optimized scenario shortens the crowd flow time within a certain number of people. This study would be helpful for architectural design, pedestrian organization, emergency plan formulation and so on.

Robots are widely used as unmanned vehicles in smart factories. In order to operate the robot in a known environment, the robot must be able to plan the path. The planned path must enable the robot to reach a destination from the starting point and the robot must avoid all obstacles during movement.This study uses a four-parameter logic curve path planning method that uses a closed formula solution and a curve with minimal design parameters to quickly generate an ideal path. The characteristics of the curve are used to derive the S and half-S curves as the solution path. The shortest path is used as the selection reference target to select parameters B and C for the four-parameter logic curve. For the S path, the best solution is chosen. The half -S curve is limited by the elastic end-point heading angle, which gives a unique set of parameter solutions. The generated path does not allow the robot to completely avoid obstacles so a hierarchical half-S curve path planning mechanism is used. A via point is generated at the collision point using the gradient vector for the edge of the obstacle. The planner continues to perform half-S path planning until the path planning ends.

Natural rubber latex is an important energy material. However, the harvest of latex is still manual, and there are few researches on automatic work. This paper proposed a method for robot to harvest from each rubber tree without stopping. The robot toggled the collection cup through the flexible actuator, the cup poured out the latex and rotated to the next collection position. In this way, it not only completed the harvest work, but also prepared for the next collection. Aiming at the problem of latex splashing during rapid toggle, the structural parameters of the collection cup and the flexible actuator were modeled and studied. This method made the rotation speed of the collection cup smoother and avoids splashing. To enable the robot to accurately complete the harvesting work, this paper used two-dimensional Light Detection and Ranging (LiDAR) and ranging sensor to locate the space position of the collection cup. The results show that when the toggle speed is 0.5 m/s and the latex volume is 300 ml, the average shaking height is 3.58 mm. In the field test, the lateral error of positioning is less than 8.86 mm, and the height error is less than 0.72 mm; the average harvest rate is 98.18%. The robot has high efficiency and good stability, and can be applied to the rubber plantation to harvest automatically.

Goal: The aim of the study herein reported was to review mobile health (mHealth) technologies and explore their use to monitor and mitigate the effects of the COVID-19 pandemic. Methods: A Task Force was assembled by recruiting individuals with expertise in electronic Patient-Reported Outcomes (ePRO), wearable sensors, and digital contact tracing technologies. Its members collected and discussed available information and summarized it in a series of reports. Results: The Task Force identified technologies that could be deployed in response to the COVID-19 pandemic and would likely be suitable for future pandemics. Criteria for their evaluation were agreed upon and applied to these systems. Conclusions: mHealth technologies are viable options to monitor COVID-19 patients and be used to predict symptom escalation for earlier intervention. These technologies could also be utilized to monitor individuals who are presumed non-infected and enable prediction of exposure to SARS-CoV-2, thus facilitating the prioritization of diagnostic testing., IEEE Open Journal of Engineering in Medicine and Biology, 1, ISSN:2644-1276

A logic-HBM2E power delivery system on a chip-on-wafer-on-substrate (CoWoS) platform with a deep trench capacitor (DTC) has been designed and analyzed for high performance computing (HPC) applications. The DTC integrated in the silicon interposer of the CoWoS provides the capacitance density of 300 nF/mm2 and low leakage current of

We demonstrate two heterogeneous solutions to improve power delivery to High-Performance Computing (HPC) processors. The scalable HPC vehicle integrates two 7nm CMOS processor chiplets, each with four ARM® Cortex®-A72 cores, that are mounted on a Chip-on-Wafer-on-Substrate (CoWoS®) silicon interposer [1]. In the first solution, Integrated Passive Device (IPD) capacitors are placed directly beneath the interposer to provide more accessible and effective supply noise decoupling. The result is 3.9% higher maximum clock frequency at a core supply of 1.135V. In the second solution, the processor is powered by a laterally mounted in-Package Voltage Regulator (PVR) built in 28nm CMOS augmented with high-per-meability on-die inductors. The processor performance provided by the buck converter-based PVR matches that by an off-package External Voltage Regulator (EVR). As processor power increases with higher core counts, PVRs with on-die inductors will be increasingly compelling for efficient system power delivery.

The detection of cherry tomatoes in greenhouse scene is of great significance for robotic harvesting. This paper states a method based on deep learning for cherry tomatoes detection to reduce the influence of illumination, growth difference, and occlusion. In view of such greenhouse operating environment and accuracy of deep learning, Single Shot multi-box Detector (SSD) was selected because of its excellent anti-interference ability and self-taught from datasets. The first step is to build datasets containing various conditions in greenhouse. According to the characteristics of cherry tomatoes, the image samples with illumination change, images rotation and noise enhancement were used to expand the datasets. Then training datasets were used to train and construct network model. To study the effect of base network and the input size of networks, one contrast experiment was designed on different base networks of VGG16, MobileNet, Inception V2 networks, and the other contrast experiment was conducted on changing the network input image size of 300 pixels by 300 pixels, 512 pixels by 512 pixels. Through the analysis of the experimental results, it is found that the Inception V2 network is the best base network with the average precision of 98.85% in greenhouse environment. Compared with other detection methods, this method shows substantial improvement in cherry tomatoes detection.

Video inpainting is an important technique for a wide variety of applications from video content editing to video restoration. Early approaches follow image inpainting paradigms, but are challenged by complex camera motion and non-rigid deformations. To address these challenges flow-guided propagation techniques have been proposed. However, computation of flow is non-trivial for unobserved regions and propagation across a whole video sequence is computationally demanding. In contrast, in this paper, we propose a video inpainting algorithm based on proposals: we use 3D convolutions to obtain an initial inpainting estimate which is subsequently refined by fusing a generated set of proposals. Different from existing approaches for video inpainting, and inspired by well-explored mechanisms for object detection, we argue that proposals provide a rich source of information that permits combining similarly looking patches that may be spatially and temporally far from the region to be inpainted. We validate the effectiveness of our method on the challenging YouTube VOS and DAVIS datasets using different settings and demonstrate results outperforming state-of-the-art on standard metrics.

The decentralization and consensus of blockchain provide the network trust and data certification of a low cost. In addition to financial technology, it has also attracted widespread attention in many applications. In this paper, we discuss on the design of the micro-course certification in university. The proposed architecture is suitable for the integration of school information systems and enhances the trust and micro-course elective. Unlike general blockchain applications, micro-course certification must be provided by a university-approved teacher or staff, thus it is important for their authentication. In this application, the password authentication is not secure enough, and the cost of public key certification is too high. Therefore, we use the authenticated public key technology of the RSA system technology, the authenticated data is the predetermined portion and embedded to RSA public key, such that the authenticated certifier and effective period can be directly provided by RSA public key. In comparison with traditional RSA, we only modified the key generation and the other part of RSA cryptosystem are all the same. This combination of blockchain and public key authentication can achieve a secure and easy-to-use micro-course certification system.

Nowadays, unmanned vehicles for navigation applications, such as factory unmanned vehicles and delivery dining systems, are becoming more and more extensive and gradually playing an indispensable role in our lives. Basically, navigation can be divided into three fields, the mapping, the localization and the path planning, in which the path planning is to use a pre-built map to plan a feasible path given the starting and the ending points. Hence, the path planning is the core part of the navigation operation, which is very important at the robot application level, such as the automatic driving and the driverless driving. For aircraft and space exploration, the path planning algorithms can be roughly divided into two types, the graph-based searching and the sampling-based searching. Among the two, the path planning based on random sampling provides with fast operation speed, high success rate on high-dimensional and complex problems, and disuse of extra considerations. The constraint of non-holonomic constraints, and the fast search for random trees are an algorithm based on random sampling. This paper mainly focuses on improving the path divergence in fixed iteration and on leading the direction from root to goal in fixed iteration condition. From the comparison of the experimental results, our approach is approximately 1.5 times better than the RRT on average path length.

Automated driving has been developed rapidly in the past five years. Many automakers have popularized SAE Level 2 on self-driving cars, some even on regular vehicles. To implement Level 3 or Level 4, it’s necessary to rely on a good localization system when GPS signal is not available. For this need, this paper proposes two effective methods to increase the accuracy of the real-time SLAM: The first method: When the original point cloud is input, the point cloud is divided into the short-range group, the medium-range group and the long-range group. An adaptive parameter adjustment method is then used to obtain the optimal parameters for each of these point cloud groups. However, Lidar’s physical characteristics can cause the point cloud to be insufficient, making an important part of points misjudged as outliers for the medium-range and long-range cases. Thanks to the help of the adaptive parameters, these point clouds, which were originally misjudged as outliers can be preserved in this paper. The second method: In point clouds, the same object in different ranges is represented with different point clouds. Hence, features, such as the roughness and density, can dramatically change with the variation of distance even when it is the same object. To solve this problem, we have designed three different range point cloud feature extraction methods to get more accurate point cloud features, such as the planes or edges. By combining these two steps, the LeGO-LOAM accuracy can be effectively increased by more than 30% while achieving the performance of the real-time SLAM, which is more accurate and faster than the NDT-Mapping used in Autoware.

Reliability issues are more severe in multi/many-core systems because of the integration of more devices in advanced technology nodes. To achieve robust computing in nanoscale designs, many circuit-level and architecture-level redundancy techniques had been proposed, which pose large fixed silicon area overhead and a lack of flexibility. In recent years, some methods have exploited the “inherent core redundancy” of many-core systems to implicitly implement N-modular redundant (NMR) subsystems to achieve area-efficient fault-tolerant computing. However, while facing the different levels of soft error rate, task vulnerability, and task significance in the many-core system, existing core-level redundancy methods become ineffective. To achieve robust computation in many-core systems with intercore variations and mixed workloads, we propose a variation-aware core-level redundancy scheme. Two novel approaches are presented in this scheme: 1) we construct NMR tables that store the degree of redundancy using mathematical models for systems affected by these variations and 2) we dynamically allocate each replicated task to a proper core with variation-aware mapping algorithms to achieve high reliability. Based on a modified multicore simulator, Sniper-Transient Error Process Variation (TEVR), the experimental results show that the proposed scheme can increase the reliability by 47.92% and achieve the energy saving of 39% compared with conventional core-level redundancy methods.

Cuffless technique enables continuous blood pressure (BP) measurement in an unobtrusive manner, and thus has the potential to revolutionize the conventional cuff-based approaches. This study extends the pulse transit time (PTT) based cuffless BP measurement method by introducing a new indicator – the photoplethysmogram (PPG) intensity ratio (PIR). The performance of the models with PTT and PIR was comprehensively evaluated in comparison with six models that are based on sole PTT. The validation conducted on 33 subjects with and without hypertension, at rest and under various maneuvers with induced BP changes, and over an extended calibration interval, respectively. The results showed that, comparing to the PTT models, the proposed methods achieved better accuracy on each subject group at rest state and over 24 hours calibration interval. Although the BP estimation errors under dynamic maneuvers and over extended calibration interval were significantly increased for all methods, the proposed methods still outperformed the compared methods in the latter situation. These findings suggest that additional BP-related indicator other than PTT has added value for improving the accuracy of cuffless BP measurement. This study also offers insights into future research in cuffless BP measurement for tracking dynamic BP changes and over extended periods of time.

SDN is a centrally managed network architecture that automatically and dynamically allocates network resources to different types of workloads according to their application characteristics as well as current network conditions. We note that state-of-the-art SDN measurement mechanisms generally are slow, require reserved network resources, or use protocol-specific information when collecting flow-level statistics and constructing global network views. The lack of a fast and accurate SDN measurement mechanism significantly limits the effectiveness of SDN. In this article, we focus on the design of a scalable, accurate, and fast SDN measurement mechanism. We exploit packet-level statistics and propose a low-latency load-aware two-tier measurement and control platform for SDN. The port counter of the SDN switch, which tracks the number of bytes and packets that are sent and received on each port, is used for estimating link utilizations. The SDN switch actively monitors link states and pushes the statistics back to the SDN controller periodically for the detection of network congestion. When necessary, the controller can retrieve the flow statistics of the congested link from the SDN switches for traffic engineering. We implement a small-scale testbed and conduct emulations for evaluation. The experiment results show that compared to OpenSample and Planck, our two-tier mechanism quickly and accurately estimates link utilization and controls flow routes for better bisection throughput.

We introduce SAIL-VOS (Semantic Amodal Instance Level Video Object Segmentation), a new dataset aiming to stimulate semantic amodal segmentation research. Humans can effortlessly recognize partially occluded objects and reliably estimate their spatial extent beyond the visible. However, few modern computer vision techniques are capable of reasoning about occluded parts of an object. This is partly due to the fact that very few image datasets and no video dataset exist which permit development of those methods. To address this issue, we present a synthetic dataset extracted from the photo-realistic game GTA-V. Each frame is accompanied with densely annotated, pixel-accurate visible and amodal segmentation masks with semantic labels. More than 1.8M objects are annotated resulting in 100 times more annotations than existing datasets. We demonstrate the challenges of the dataset by quantifying the performance of several baselines. Data and additional material is available at http://sailvos.web.illinois.edu.

Generative adversarial nets (GANs) and variational auto-encoders have significantly improved our distribution modeling capabilities, showing promise for dataset augmentation, image-to-image translation and feature learning. However, to model high-dimensional distributions, sequential training and stacked architectures are common, increasing the number of tunable hyper-parameters as well as the training time. Nonetheless, the sample complexity of the distance metrics remains one of the factors affecting GAN training. We first show that the recently proposed sliced Wasserstein distance has compelling sample complexity properties when compared to the Wasserstein distance. To further improve the sliced Wasserstein distance we then analyze its `projection complexity' and develop the max-sliced Wasserstein distance which enjoys compelling sample complexity while reducing projection complexity, albeit necessitating a max estimation. We finally illustrate that the proposed distance trains GANs on high-dimensional images up to a resolution of 256x256 easily., Accepted to CVPR 2019

Cloud computing technology has immediate impact on the development of software testing. However, the study about autonomic self-testing of regression and internationalization based on cloud computing is still in its early stage. Our study is based on a regression and i18n testing project about enterprise office collaboration suite. Firstly, automated function test script for baseline version (English) is developed by Java, and it is implemented reusability for i18n testing in other 9 languages. In addition, a comprehensive AST model of regression and i18n is proposed. The detailed design and implementation of this system based on the daily build is presented. Finally, the multi-server architecture and the basis process of testing are elaborated. The above mentioned testing script and platform have been applied to actual testing of web products, and achieved good results.

Log data is crucial for developers, decision maker, and end-users. With log data, we can inspect whether the system error or not, give warning events or whether our system processes are loFinally, the values in this tableading successfully or not. This paper aims to provide a virtualization platform for our campus Wi-Fi log data for further analysis. Our system used ELK Stack technology. It consists of three components: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search engine which used for searching all kinds of documents, Logstash used for managing centralize, transform and stash our data to Elasticsearch, and Kibana applied for visualizing Elasticsearch data and an excellent tool for a real-time data analytics and decision making. Our data is a Netflow traffic data. Approximately our data growth up 10 MB per day and our data have grown to 19 GB until now.

Each software always has log data. Log data is essential for developers and end-users. With log data, we can indicate the information of system error, warning events or whether our system processes are loading successfully. This paper aims to provide a virtualization platform for our campus NetFlow log data in order user can use for further analysis. Our system used Ceph storage system and ELK Stack technology. Ceph provides a vast amount of scalable storage and ELK consists of three components, namely: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search engine in which can be used for searching all kinds of documents. Logstash is used for managing centralize, transform and stash our data to Elasticsearch and Kibana is used for visualizing Elasticsearch data and an excellent tool for a real-time data analytics and decision making. Our data is continuous data. Approximately our data growth up 38 GB per day and our data have grown more than 8TB until now.

Container-based virtualization is one of the prominent technologies in the cloud computing. Containers virtualize at the operating system level which provides a lightweight operation than traditional virtualization on a hypervisor. The combination of the Internet of Things (IoT), edge computing and container-based virtualization is going to make system rapid, inexpensive, and more reliable. In this paper, we intend to implement a complete set of edge computing architectures based on containerization on an IoT environment. In this case, we implemented container-based virtualization which constructs Kubernetes Minion (Nodes) in the Docker container service independently for each service on the Edge side. We used humidity and temperature sensory data as our case study. We set up the Raspberry Pi on the Edge Gateway and Kubernetes minion on the Raspberry Pi to provide the service application, which contains Grafana, the open platform for analytics and monitoring. For short-term data storage, we use InfluxDB as a data store for large amounts of time-series data.

Public key certifications are formal and secure in user authentication. The infrastructure of certificate authorities (CA) is necessary in public key certifications. In many secure and non-restrictive applications, such as authentication in the secure wireless access points (AP), the password authentication is not secure enough, and the cost of public key certification is too high. In this article, we focus on the certificateless RSA public key authentication: the technology, limitation, content, and security of the predetermined portion in RSA public key. In order to authenticate RSA public key, we can predetermine portion in RSA key generation by using multiplication properties, multiplicative inverse with suitable authenticated content. In comparison with traditional RSA, the key generation must be modified. On the other side, the encryption, decryption, signature generation and verification are all the same in comparison with traditional RSA cryptosystem that is secure and convenient to many applications.

To compare multiple genome sequences, we transform each primary genome sequence into corresponding k-mer-based vectors. According to the principle of independent component analysis (ICA), the operation can be regarded as mixing multiple source genomic signals via several sensors, through which we can obtain the mixed vectors with equal-length from the corresponding genome sequences with different length. However, this mixing operation is performed by counting all the k-mer-based frequencies, instead of using real hardware of sensors. Thus, we name this preprocessing operation as virtual mixer (VM). Using ICA-based transformation, we projected all the vectors upon their independent components to capture the coefficients-based feature vector through the projection extractor (PE), which has been proved to have a property of distance preserving. Then, we used the proposed VMPE model upon three representative real datasets of genome sequence to test the efficiency for the model. The contrastive analysis results indicate that the proposed VMPE model performs well in similarity analysis.

The crowd evacuation simulation in airport terminal is an important method to improve the efficiency of the emergency evacuation decision. Different from traditional pedestrian simulation, the evacuation simulation in airport terminal should fully consider the layout of the terminal building and the characteristics of passenger behavior. This paper proposes a pedestrian behavior model based on potential energy field. On the one hand, the spatial region is modeled by fine grid, so as to model the pedestrian’s body type, pose and decision in airport terminal more realistically; on the other hand, the potential energy field mechanism fully considers following factors’ influence on the pedestrian behavior, including the shortest path to the destination, obstacles, other pedestrians and sudden events. Based on the model, this paper simulates the evacuation process on a certain airport terminal, and evaluates the evacuation ability of the building. Based on the simulation of evacuation time under different number of people and different utilization of building space, the quantitative emergency measures and building optimization recommendations are given. This research can provide advance scientific guidance for pedestrian organization, terminal building design, and emergency plan.

In the application of the Internet of Things (IoT), all data is stored in the cloud, that causes the long distance of the network logic between the cloud and the device side or client side, this might leads to network delay or slow response time. A challenging issue is how to increase the speed of response time in the cloud computing and the IoT environment for clients. In this paper, we propose a complete set of Edge Computing architecture. There are three layers, namely, Cloud side, Edge side, and Device side. Cloud side mainly deals with more complicated operations and data backup. For overall system infrastructure, we deployed Kubernetes cluster on an OpenStack platform. Edge side optimizes the service of cloud computing systems by performing data processing at the edge of the network. In this phase, we created an Edge Gateway to increase the capacity and performance and reduce the communications bandwidth needed between sensors and the central data.

Heartbeats based random binary sequences (RBSs) are the backbone for several security aspects in wireless body sensor networks (WBSNs). However, current heartbeats based methods require a lot of processing time (∼25–30 s) to generate 128-bit RBSs in real-time healthcare applications. In order to improve time efficiency, a biometric RBSs generation technique using interpulse intervals (IPIs) of heartbeats is developed in this study. The proposed technique incorporates a finite monotonic increasing sequences generation mechanism of IPIs and a cyclic block encoding procedure that extracts a high number of entropic bits from each IPI. To validate the proposed technique, 89 ECG recordings including 25 healthy individuals in a laboratory environment, 20 from MIT-BIH Arrhythmia Database, and 44 cardiac patients from the clinical environment are considered. By applying the proposed technique on the ECG signals, at most 16 random bits can be extracted from each heartbeat to generate 128-bit RBSs via concatenation of eight consecutive IPIs. And the randomness and distinctiveness of generated 128-bit RBSs are measured based on the National Institute of Standards and Technology statistical tests and hamming distance, respectively. From the experimental results, the generated 128-bit RBSs from both healthy subjects and patients can potentially be used as keys for encryption or entity identifiers to secure WBSNs. Moreover, the proposed approach is examined to be up to four times faster than the existing heartbeat-based RBSs generation schemes. Therefore, the developed technique necessitates less processing time (0–8 s) in real-time health monitoring scenarios to construct 128-bit RBSs in comparisons with current methods.

Video object segmentation is challenging yet important in a wide variety of applications for video analysis. Recent works formulate video object segmentation as a prediction task using deep nets to achieve appealing state-of-the-art performance. Due to the formulation as a prediction task, most of these methods require fine-tuning during test time, such that the deep nets memorize the appearance of the objects of interest in the given video. However, fine-tuning is time-consuming and computationally expensive, hence the algorithms are far from real time. To address this issue, we develop a novel matching based algorithm for video object segmentation. In contrast to memorization based classification techniques, the proposed approach learns to match extracted features to a provided template without memorizing the appearance of the objects. We validate the effectiveness and the robustness of the proposed method on the challenging DAVIS-16, DAVIS-17, Youtube-Objects and JumpCut datasets. Extensive results show that our method achieves comparable performance without fine-tuning and is much more favorable in terms of computational time.

Unsupervised video segmentation plays an important role in a wide variety of applications from object identification to compression. However, to date, fast motion, motion blur and occlusions pose significant challenges. To address these challenges for unsupervised video segmentation, we develop a novel saliency estimation technique as well as a novel neighborhood graph, based on optical flow and edge cues. Our approach leads to significantly better initial foreground-background estimates and their robust as well as accurate diffusion across time. We evaluate our proposed algorithm on the challenging DAVIS, SegTrack v2 and FBMS-59 datasets. Despite the usage of only a standard edge detector trained on 200 images, our method achieves state-of-the-art results outperforming deep learning based methods in the unsupervised setting. We even demonstrate competitive results comparable to deep learning based methods in the semi-supervised setting on the DAVIS dataset.

Pulse transit time (PTT) is promising for various clinical applications. This paper attempts to discuss different terminologies used for PTT and reveals the research discontinuity among different groups, with the aim to describe the definition of PTT and propose solutions to clarify the usage of PTT.

This work focuses on how to provide the best virtual desktop technology, virtualization performance and hardware in the computer classroom. There are using the three popular virtualization technologies in the same environment to establish the same conditions, pure performance comparison: VMware Horizon, Citrix XenDesktop, and Microsoft VDI. Test software used to the current popular software, system performance including a test CPU and RAM PassMark Performance Test,and observe the vCenter Server performance changes and record statistics, to determine the advantages and disadvantages of various virtualization technologies. Finally, hardware support exclusive GPU virtualization product - Nvidia GRID K2 through GPU virtualization technology sharing and vGPU to carry on VMware vSphere, via Cinebench, YouTube 1080p high-quality video for bandwidth flow test, and use 3DMark Fire Strike Extreme to enhance the performance of virtual desktop, and bring users to enjoy higher-quality cloud services. The results of this study show that virtual desktop technologies are good in different field. On the other hand, the hardware performance breakthrough Moore’s Law of this era, the single CPU has more number of cores, and the memory capacity of the larger single,and provided by the GPU for graphics acceleration processing capabilities, the performance of server has been perfect than the previous. Therefore, the quality of cloud services is also greatly improved. The study shows that the virtual test can be imported into the classroom through the stress test in the experiment. To make sure the its performance and cost are really suitable for classroom imported desktop virtualization system.

The progression of cheaper, faster and more reliable Internet technologies leads Internet of Things (IoT) to be applied in various fields of life nowadays. While tremendous data are generated and delivered from IOT devices, it is essential on the server side having scalable and effective data compression techniques to balance storage usage and data precision. This paper presents an adjustable content-sensitive data compression approach for IOT video streaming services and applications. Specifically, we adopt frame similarity on different aspects including illumination and structure as a matrix for streaming video frame reduction, and we are able to keep sufficient differences among video frames while reducing a significant amount of storage resources. We setup a general iot application platform in practice and show the effectiveness of the presented approach, keeping 96.5% precision with 40% storage reduction, on IOT video streaming applications.