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With advances in the Internet of Things, patients in intensive care units are constantly monitored to expedite emergencies. Due to the COVID-19 pandemic, non-face-to-face monitoring has been required for the safety of patients and medical staff. A control center monitors the vital signs of patients in ICUs. However, some medical devices, such as ventilators and infusion pumps, operate in a standalone fashion without communication capabilities, requiring medical staff to check them manually. One promising solution is to use a robotic system with a camera. We propose a real-time optical digit recognition embedded system called ROMI. ROMI is a mobile robot that monitors patients by recognizing digits displayed on LCD screens of medical devices in real time. ROMI consists of three main functions for recognizing digits: digit localization, digit classification, and digit annotation. We developed ROMI by using Matlab Simulink, and the maximum digit recognition performance was 0.989 mAP on alexnet. The developed system was deployed on NVIDIA GPU embedded platforms: Jetson Nano, Jetson Xavier NX, and Jetson AGX Xavier. We also created a benchmark by evaluating the runtime performance by considering ten pre-trained CNN models and three NVIDIA GPU platforms. We expect that ROMI will support medical staff with non-face-to-face monitoring in ICUs, enabling more effective and prompt patient care. [ABSTRACT FROM AUTHOR]

This study investigates the synergy between centralized and decentralized (i.e., ad hoc) data scheduling in vehicular ad hoc networks (VANETs) for offloading and balancing the workloads of roadside units (RSU) in bidirectional road scenarios. In the centralized scheduling, an RSU schedules data dissemination using a hybrid of infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications. Specifically, RSUs cooperate with each other by transferring unserved requests, and each RSU schedules the data services based on its locally received requests and the transferred requests. In the decentralized scheduling, vehicles driving in opposite directions share the cached data items via V2V communication when out of the coverage of RSU. The ad hoc scheduling can be benefited from the cooperation among RSUs since the chance for V2V data sharing could be enhanced when considering transferred requests into scheduling at each RSU. We formulate a hybrid of centralized and ad hoc data scheduling (HCA) problem, aiming at best exploiting the synergistic effects of I2V and V2V communication based on centralized data broadcast and ad hoc data sharing. On this basis, we propose an RSU Cooperation-based Adaptive Scheduling (RCAS) algorithm that consists of three mechanisms, including a centralized scheduling mechanism at each RSU, an ad hoc scheduling mechanism for vehicles, and a cluster management mechanism. Finally, we build the simulation model and give a comprehensive performance evaluation, which demonstrates the superiority of the proposed solution under a variety of circumstances. [ABSTRACT FROM AUTHOR]

Processor thermal control in real-time systems is crucial because overheated processors may result in critical performance degradation or even system failure. The main challenges in processor thermal control in real-time systems are as follows: (i) the need to satisfy both real-time and thermal constraints; (ii) uncertain system dynamics; and (iii) thermal sensor noise. The first two issues have been resolved in substantial studies while the issue of sensor noise has not been thoroughly addressed. In this paper, we experimentally identify that even a small zero-mean sensor noise can induce a significant steady-state error in processor thermal control. This steady-state temperature error is contrary to the intuition that zero-mean sensor noise normally induces zero-mean fluctuations around the target temperature. We rigorously analyze the phenomenon based on the stochastic averaging theory and quantify the error in a closed form in terms of noise statistics and system parameters. We propose a thermal control architecture for effectively eliminating the thermal error and tightly controlling the processor temperature. Through extensive experiments, we validate the proposed architecture. [ABSTRACT FROM AUTHOR]

Temporal information services are critical in implementing emerging intelligent transportation systems. Nevertheless, it is challenging to realize timely temporal data update and dissemination due to an intermittent wireless connection and a limited communication bandwidth in dynamic vehicular networks. Some previous studies have considered the temporal data dissemination in vehicular networks, but they are limited to the service region, which is inside the coverage of roadside units. To enhance system scalability, it is imperative to exploit the synergic effect of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications for providing efficient temporal information services in such an environment. With the above motivations, we propose a novel system architecture to enable efficient data scheduling in hybrid V2I/V2V communications by having the global knowledge of network resources of the system. On this basis, we formulate a temporal data upload and dissemination (TDUD) problem, aiming at optimizing two conflict objectives simultaneously, which are enhancing the data quality and improving the delivery ratio. Furthermore, we propose an evolutionary multi-objective algorithm called MO-TDUD, which consists of a decomposition scheme for handling multiple objectives, a scalable chromosome representation for TDUD solution encoding, and an evolutionary operator designed for TDUD solution reproduction. The proposed MO-TDUD can be adaptive to different requirements on data quality and delivery ratio by selecting the best solution from the derived Pareto solutions. Last but not least, we build the simulation model and implement MO-TDUD for performance evaluation. The comprehensive simulation results demonstrate the superiority of the proposed solution. [ABSTRACT FROM AUTHOR]

This article presents an attack-resilient architecture for cyber-physical systems (CPSs). As a case study, the authors build a radio-based train control testbed and validate the proposed resilient design architecture. The target CPS application is a hierarchical control system. [ABSTRACT FROM AUTHOR]

This paper embarks the first study on exploiting the synergy between vehicular caching and network coding for enhancing the bandwidth efficiency of data broadcasting in heterogeneous vehicular networks by presenting a service architecture that exercises the software defined network concept. In particular, we consider the scenario where vehicles request a set of information and they could be served via heterogeneous wireless interfaces, such as roadside units and base stations (BSs). We formulate a novel problem of coding-assisted broadcast scheduling (CBS), aiming at maximizing the broadcast efficiency for the limited BS bandwidth by exploring the synergistic effect between vehicular caching and network coding. We prove the NP-hardness of the CBS problem by constructing a polynomial-time reduction from the simultaneous matrix completion problem. To efficiently solve the CBS problem, we employ memetic computing, which is a nature inspired computational paradigm for tackling complex problems. Specifically, we propose a memetic algorithm, which consists of a binary vector representation for encoding solutions, a fitness function for solution evaluation, a set of operators for offspring generation, a local search method for solution enhancement, and a repair operator for fixing infeasible solutions. Finally, we build the simulation model and give a comprehensive performance evaluation to demonstrate the superiority of the proposed solution. [ABSTRACT FROM AUTHOR]

Heterogeneous network resources are expected to cooperate with each other to support efficient data services in vehicular networks. However, current data scheduling methods cannot efficiently exploit the benefit of heterogeneous wireless communication interfaces in vehicular networks. In this paper, we propose a software-defined network based service architecture, which enables the scheduler to manage heterogeneous network resources in a centralized way. We analyze the heterogeneity of both data items and networks in terms of data sizes and network features (e.g., cost, transmission rate, coverage, etc.), respectively. On this basis, we formulate a data broadcast and network interface selection (DBNIS) problem, which aims to minimize both the service delay and the network access cost. To tackle the problem efficiently, we propose a coding-assisted multiobjective evolutionary algorithm (CMOEA), which consists of two components: packet encoding and network interface selection. Specifically, for packet encoding, we first develop a packet-size based random linear encoding (PRLE) technique to improve bandwidth efficiency. Then, we theoretically analyze the performance bound of PRLE. For network interface selection, we propose a multiobjective algorithm for network interface selection to adaptively satisfy dynamic requirements with respect to service delay and network access cost by deriving a set of pareto-solutions. Finally, we build the simulation model and implement CMOEA for performance evaluation. The comprehensive simulation results demonstrate the superiority of CMOEA under a wide range of scenarios. [ABSTRACT FROM AUTHOR]

Efficient data dissemination is critical for enabling emerging applications in vehicular ad hoc networks. As a typical traffic scenario, the bidirectional road scenario of highways bring unique challenges on well exploiting the benefit of vehicle-to-vehicle (V2V) communication for data sharing among vehicles driving in opposite directions. This paper is dedicated to investigating the characteristics of data services in such a scenario and exploring new opportunities for enhancing overall system performance. Specifically, we present a system architecture to enable the road-side unit assisted data scheduling via vehicle-to-infrastructure communication. Then, we give a theoretical analysis on the opportunity of successful data sharing among vehicles driving in opposite directions based on the analysis of signal-to-interference-noise-ratio of V2V communication. On this basis, we propose a clustering mechanism based on the design of a time division policy and the derivation of the optimal cluster length. In addition, a cluster association strategy is designed to enable vehicles to dynamically join or leave a cluster based on their real-time velocities. Furthermore, a two-phase backoff mechanism is designed for distributed data sharing based on V2V communication, and a cooperative scheduling algorithm is proposed for selecting sender vehicles as well as the corresponding data items for broadcasting. Finally, we build the simulation model and give a comprehensive simulation study, which demonstrates that the proposed solutions can effectively improve the overall system performance. [ABSTRACT FROM AUTHOR]

With the exponential improvement of software technology during the past decade, many efforts have been made to design remote and personalized healthcare applications. Many of these applications are built on mobile devices connected to the cloud. Although appealing, however, prototyping and validating the feasibility of an application-level idea is yet challenging without a solid understanding of the cloud, mobile, and the interconnectivity infrastructure. In this paper, we provide a solution to this by proposing a framework called HealthNode, which is a general-purpose framework for developing healthcare applications on cloud platforms using Node.js. To fully exploit the potential of Node.js when developing cloud applications, we focus on the fact that the implementation process should be eased. HealthNode presents an explicit guideline while supporting necessary features to achieve quick and expandable cloud-based healthcare applications. A case study applying HealthNode to various real-world health applications suggests that HealthNode can express architectural structure effectively within an implementation and that the proposed platform can support system understanding and software evolution. [ABSTRACT FROM AUTHOR]

The cumulative travel-time responsive (CTR) algorithm determines optimal green split for the next time interval by identifying the maximum cumulative travel time (CTT) estimated under the connected vehicle environment. This paper enhanced the CTR algorithm and evaluated its performance to verify a feasibility of field implementation in a near future. Standard Kalman filter (SKF) and adaptive Kalman filter (AKF) were applied to estimate CTT for each phase in the CTR algorithm. In addition, traffic demand, market penetration rate (MPR), and data availability were considered to evaluate the CTR algorithm's performance. An intersection in the Northern Virginia connected vehicle test bed is selected for a case study and evaluated within vissim and hardware in the loop simulations. As expected, the CTR algorithm's performance depends on MPR because the information collected from connected vehicle is a key enabling factor of the CTR algorithm. However, this paper found that the MPR requirement of the CTR algorithm could be addressed (i) when the data are collected from both connected vehicle and the infrastructure sensors and (ii) when the AKF is adopted. The minimum required MPRs to outperform the actuated traffic signal control were empirically found for each prediction technique (i.e., 30% for the SKF and 20% for the AKF) and data availability. Even without the infrastructure sensors, the CTR algorithm could be implemented at an intersection with high traffic demand and 50-60% MPR. The findings of this study are expected to contribute to the field implementation of the CTR algorithm to improve the traffic network performance. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Recent advances in autonomous vehicles and vehicular communications are envisioned to enable novel approaches to managing and controlling traffic intersections. In particular, with intersection controller units (ICUs), passing vehicles can be instructed to cross the intersection safely without traffic signals. Previous efforts on autonomous intersection control mainly focused on guaranteeing the safe passage of vehicles and improving intersection throughput, without considering the quality of the travel experience from the passengers' perspective. In this paper, we aim to design an enhanced autonomous intersection control mechanism, which not only ensures vehicle safety and enhances traffic efficiency but also cares about the travel experience of passengers. In particular, we design the metric of smoothness to quantitatively capture the quality of experience. In addition, we consider the travel time of individual vehicles when passing the intersection in scheduling to avoid a long delay of some vehicles, which not only helps with improving intersection throughput but also enhances the system's fairness. With the above considerations, we formulate the intersection control model and transform it into a convex optimization problem. On this basis, we propose a new algorithm to achieve an optimal solution with low overhead. Finally, we build the simulation model and implement the algorithm for performance evaluation. Comprehensive simulation results demonstrate the superiority of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Vehicle-to-vehicle/vehicle-to-infrastructure (referred to as V2X) communications have potential to revolutionize current road transportation systems with respect to vehicle safety, transportation efficiency, and travel experience. This paper puts the first effort on applying network coding in cooperative V2X communication environments to improve bandwidth efficiency and enhance data service performance. Specifically, we investigate new arising challenges on network-coding-assisted data dissemination by considering both communication constraints and application requirements in vehicular networks. We present the system model and give an insight into the characteristics of cooperative data dissemination with network coding. On this basis, we formulate the problem and propose a network-coding-assisted scheduling algorithm to enable the hybrid of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications and exploit their joint effects on providing efficient data services. We design a cache strategy that allows vehicles to retrieve their unrequested data items. This strategy not only increases the opportunity of data sharing among vehicles but also gives higher probability of packet decoding, which in turn enhances the data service performance. We give an intensive analysis on the scheduling overhead, which shows the scalability of the algorithm. Finally, we build the simulation model and conduct a comprehensive performance evaluation to demonstrate the superiority of the proposed solution. [ABSTRACT FROM PUBLISHER]

Efficient data dissemination is one of the fundamental requirements to enable emerging applications in vehicular cyber–physical systems. In this paper, we present the first study on real-time data services via roadside-to-vehicle communication by considering both the time constraint of data dissemination and the freshness of data items. Passing vehicles can submit their requests to the server, and the server disseminates data items accordingly to serve the vehicles within its coverage. Data items maintained in the database are periodically updated to keep the information up-to-date. We present the system model and analyze challenges on data dissemination by considering both application requirements and communication characteristics. On this basis, we formulate the temporal data dissemination (TDD) problem by introducing the snapshot consistency requirement on serving real-time requests for temporal data items. We prove that TDD is NP-hard by constructing a polynomial-time reduction from the Clique problem. Based on the analysis of the time bound on serving requests, we propose a heuristic scheduling algorithm, which considers the request characteristics of productivity, status, and urgency in scheduling. An extensive performance evaluation demonstrates that the proposed algorithm is able to effectively exploit the broadcast effect, improve the bandwidth efficiency, and enhance the request service chance. [ABSTRACT FROM PUBLISHER]

To achieve the important aims of identifying and marking disease progression, cell counting is crucial for various biological and medical procedures, especially in a Point-Of-Care (POC) setting. In contrast to the conventional manual method of counting cells, a software-based approach provides improved reliability, faster speeds, and greater ease of use. We present a novel software-based approach to count in-line holographic cell images using the calculation of a normalized 2D cross-correlation. This enables fast, computationally-efficient pattern matching between a set of cell library images and the test image. Our evaluation results show that the proposed system is capable of quickly counting cells whilst reliably and accurately following human counting capability. Our novel approach is 5760 times faster than manual counting and provides at least 68% improved accuracy compared to other image processing algorithms. [ABSTRACT FROM AUTHOR]

In the vehicular ad-hoc networks (VANETs), wireless access in vehicular environments (WAVE) as the core networking technology is suitable for supporting safety-critical applications, but it is difficult to guarantee its performance when transmitting non-safety data, especially high volumes of data, in a multi-hop manner. Therefore, to provide non-safety applications effectively and reliably for users, we propose a hybrid V2V communication system (HVCS) using hierarchical networking architecture: a centralized control model for the establishment of a fast connection and a local data propagation model for efficient and reliable transmissions. The centralized control model had the functionality of node discovery, local ad-hoc group (LAG) formation, a LAG owner (LAGO) determination, and LAG management. The local data propagation indicates that data are transmitted only within the LAG under the management of the LAGO. To support the end-to-end multi-hop transmission over V2V communication, vehicles outside the LAG employ the store and forward model. We designed three phases consisting of concise device discovery (CDD), concise provisioning (CP), and data transmission, so that the HVCS is highly efficient and robust on the hierarchical networking architecture. Under the centralized control, the phase of the CDD operates to improve connection establishment time, and the CP is to simplify operations required for security establishment. Our HVCS is implemented as a two-tier system using a traffic controller for centralized control using cellular networks and a smartphone for local data propagation over Wi-Fi Direct. The HVCS' performance was evaluated using Veins, and compared with WAVE in terms of throughput, connectivity, and quality of service (QoS). The effectiveness of the centralized control was demonstrated in comparative experiments with Wi-Fi Direct. The connection establishment time measured was only 0.95 s for the HVCS. In the case of video streaming services through the HVCS, about 98% of the events could be played over 16 frames per second. The throughput for the streaming data was between 74% to 81% when the vehicle density was over 50%. We demonstrated that the proposed system has high throughput and satisfies the QoS of streaming services even though the end-to-end delay is a bit longer when compared to that of WAVE. [ABSTRACT FROM AUTHOR]

Quality-aware realtime Embedded DataBase (QeDB) is a database for data-intensive real-time applications running on embedded devices. Currently, databases for embedded systems are best effort, providing no guarantees on their timeliness and data freshness. Existing real-time database (RTDB) technology cannot be applied to these embedded databases since it hypothesizes that the main memory of a system is large enough to hold the entire database. This, however, might not be true in data-intensive real-time applications. QeDB uses a novel feedback control scheme to support QoS in such embedded systems without requiring all data to reside in main memory. In particular, our approach is based on simultaneous control of both I/O and CPU resources to guarantee the desired timeliness. Unlike existing work on feedback control of RTDB performance, we implement and evaluate the proposed scheme on a modern embedded device. The experimental results show that our approach supports the desired timeliness of transactions while still maintaining high data freshness compared to baseline approaches. [ABSTRACT FROM AUTHOR]

Standard upper-limb motor function impairment assessments, such as the Fugl-Meyer Assessment (FMA), are a critical aspect of rehabilitation after neurological disorders. These assessments typically take a long time (about 30 min for the FMA) for a clinician to perform on a patient, which is a severe burden in a clinical environment. In this paper, we propose a framework for automating upper-limb motor assessments that uses low-cost sensors to collect movement data. The sensor data is then processed through a machine learning algorithm to determine a score for a patient's upper-limb functionality. To demonstrate the feasibility of the proposed approach, we implemented a system based on the proposed framework that can automate most of the FMA. Our experiment shows that the system provides similar FMA scores to clinician scores, and reduces the time spent evaluating each patient by 82%. Moreover, the proposed framework can be used to implement customized tests or tests specified in other existing standard assessment methods.

In smart environments, target tracking is an essential service used by numerous applications from activity recognition to personalized infotaintment. The target tracking relies on sensors with known locations to estimate and keep track of the path taken by the target, and hence, it is crucial to have an accurate map of such sensors. However, the need for manually entering their locations after deployment and expecting them to remain fixed, significantly limits the usability of target tracking. To remedy this drawback, we present a self-configuring and device-free localization protocol based on genetic algorithms that autonomously identifies the geographic topology of a network of ultrasonic range sensors as well as automatically detects any change in the established network structure in less than a minute and generates a new map within seconds. The proposed protocol significantly reduces hardware and deployment costs thanks to the use of low-cost off-the-shelf sensors with no manual configuration. Experiments on two real testbeds of different sizes show that the proposed protocol achieves an error of 7.16~17.53 cm in topology mapping, while also tracking a mobile target with an average error of 11.71~18.43 cm and detecting displacements of 1.41~3.16 m in approximately 30 s.

Research on smart environments saturated with ubiquitous computing devices is rapidly advancing while raising serious privacy issues. According to recent studies, privacy concerns significantly hinder widespread adoption of smart home technologies. Previous work has shown that it is possible to infer the activities of daily living within environments equipped with wireless sensors by monitoring radio fingerprints and traffic patterns. Since data encryption cannot prevent privacy invasions exploiting transmission pattern analysis and statistical inference, various methods based on fake data generation for concealing traffic patterns have been studied. In this paper, we describe an energy-efficient, light-weight, low-latency algorithm for creating dummy activities that are semantically similar to the observed phenomena. By using these cloaking activities, the amount of  fake data transmissions can be flexibly controlled to support a trade-off between energy efficiency and privacy protection. According to the experiments using real data collected from a smart home environment, our proposed method can extend the lifetime of the network by more than 2× compared to the previous methods in the literature. Furthermore, the activity cloaking method supports low latency transmission of real data while also significantly reducing the accuracy of the wireless snooping attacks.

In a point-of-care (POC) setting, it is critically important to reliably count the number of specific cells in a blood sample. Software-based cell counting, which is far faster than manual counting, while much cheaper than hardware-based counting, has emerged as an attractive solution potentially applicable to mobile POC testing. However, the existing software-based algorithm based on the normalized cross-correlation (NCC) method is too time- and, thus, energy-consuming to be deployed for battery-powered mobile POC testing platforms. In this paper, we identify inefficiencies in the NCC-based algorithm and propose two synergistic optimization techniques that can considerably reduce the runtime and, thus, energy consumption of the original algorithm with negligible impact on counting accuracy. We demonstrate that an AndroidTM smart phone running the optimized algorithm consumes 11.5× less runtime than the original algorithm.

Background: Episodes of bradycardia hypotension (BH) or vasovagal syncope have a reported incidence of 13-29% during arthroscopic shoulder surgery in the sitting position after an interscalene block (ISB). This study was designed to investigate whether intravenous fentanyl during shoulder arthroscopy in the sitting position after ISB would increase or worsen the incidence of BH episodes., Methods: In this prospective study, 20 minutes after being in a sitting position, 160 patients who underwent ISB were randomized to receive saline (S, n = 40), 50 µg of fentanyl (F-50, n = 40), 100 µg of fentanyl (F-100, n = 40) or 30 mg of ketorolac (K-30, n = 40) randomly. We assessed the incidence of BH episodes during the operation and the degree of maximal reduction (Rmax) of blood pressure (BP) and heart rate (HR)., Results: The incidence of BH episodes was 10%, 15%, 27.5% and 5% in the S, F-50, F-100 and K-30 groups, respectively. Mean Rmax of systolic BP in the F-100 group was significantly decreased as compared to the S group (-20.0 ± 4.5 versus -6.3 ± 1.6%, P = 0.004). Similarly, mean Rmax of diastolic BP in the F-100 group was also significantly decreased (P = 0.008) as compared to the S group., Conclusions: These results suggest that fentanyl can increase the incidence of BH episodes during shoulder arthroscopic surgery in the sitting position after ISB.