Nattha Jindapetch

Vehicle-to-vehicle (V2V) communications are important for intelligent transportation system (ITS) development for driving safety, traffic efficiency, and the development of autonomous vehicles. V2V communication channels, environments, mobility patterns, and mobility speed significantly affect the accuracy of autonomous vehicle control. In this paper, we propose a versatile system-level framework that can be used for investigation, experimentation, and verification to expedite the development of autonomous vehicles. Once vehicle functionality, communication channels, and driving scenarios were modelled, experiments with different mobility speeds and communication channels were set up to measure the communication quality and the effects on the vehicle’s following control. In our experiment, the leader vehicle was set to travel through a high-building environment with a constant speed of 36 km/h and suddenly changed lanes in front of the follower vehicle. The speed of the follower vehicle ranged from 40 km/h to 80 km/h. The experimental results show that the quality of single-input and single-output (SISO) communication is less efficient than multiple-input and multiple-output (MIMO) communication. The quality of SISO communication between vehicles with a speed difference of 4 km/h (leader 36 km/h and follower 40 km/h) had a link quality worse than 0.85, which caused unstable control in the follower vehicle speed. However, it was also found that if the speed of the follower vehicle increased to 80 km/h, the link quality of SISO communication was better, close to 0.95, due to the decreased distance between the vehicles, resulting in better control. Moreover, it was found that the impact of SISO communication can be overcome by using the MIMO communication technique and selecting the best input signal at each time. MIMO communication has less signal loss, allowing the follower vehicle to make correct decisions throughout the movement.

In this work, we present a knee rehabilitation system focusing on the processing of surface electromyography (sEMG) signals measured from the vastus lateralis (VL) and vastus medialis (VM) muscles of rehabilitation participants. A two-channel electromyography (EMG) device and the NI-myRIO embedded device are used to collect real-time sEMG signals in accordance with pre-designed rehabilitation programs. The novelty and contribution of this work is that we develop an sEMG processing function where real-time sEMG data are automatically processed and sEMG results of both VL and VM in terms of root mean square value (RMS), different RMS levels of VL and VM, and maximum RMS for each round of knee movements are provided. The results here indicate how well the rehabilitation users can move their knees during rehabilitation, referring to knee and muscle performances. Experimental results from healthy participants show that we can automatically and efficiently collect and monitor rehabilitation results, allowing rehabilitation participants to know how their knees performed during testing and medical experts to evaluate and design treatment.

In this paper, we demonstrate the realistic test of a 2.4 GHz multi-hop wireless network for mountainous forest and watercourse environments. A multi-hop network using IEEE 802.15.4 XBee3 micro-modules and a communication protocol among nodes were developed. A wireless node deployment solution was introduced for practical testing. The proposed system’s communication reliability was tested in two different scenarios: a mountainous forest with sloping areas and trees and a watercourse, which referred to environmental and flooding monitoring applications. Wireless network performances were evaluated through the received signal strength indicator (RSSI) level of each wireless link, a packet delivery ratio (PDR), as the successful rate of packet transmission, and the end-to-end delay (ETED) of all data packets from the transmitter to the receiver. The experimental results demonstrate the success of the multi-hop WSN deployment and communication in both scenarios, where the RSSI of each link was kept at the accepted level and the PDR achieved the highest result. Furthermore, as a real-time response, the data from the source could be sent to the sink with a small ETED.

This work introduces a statistical analysis of knee range of motion (ROM) and surface electromyography (EMG) data gathered from a knee extension rehabilitation device. Real-time ROM and EMG signals of rehabilitation users are measured using a single angle sensor and a two-channel EMG device (for the vastus lateralis and vastus medialis muscles). These signals are collected by the NI-myRIO embedded device in accordance with the designed rehabilitation program. The main contribution and novelty of this study is that real-time signals are automatically processed and transformed into statistical data for use by users and medical experts. A solution for extracting raw signals is proposed, in which several statistical functions such as range, mean, standard deviation, skewness, percentiles, interquartile range, and total knee holding times above the threshold level, are implemented and applied. The proposed solution is tested using data acquired from healthy people, which includes gender, age, body size, knee side, exercise behavior, and surgical experience. Results indicated that real-time signals and related statistical data on the knee’s performance can be efficiently monitored. With this solution, rehabilitation users can practice and learn about their knee performance, while medical experts can evaluate the data and design the best rehabilitation program for users.

Autonomous vehicle systems have been significantly increasing in design complexity, including precise control, reliable communications, and data security. This paper presents a Model-Based Design (MBD) framework on MATLAB/Simulink to integrate the vehicle model, Vehicle-to-Vehicle (V2V) communication model, and autonomous driving scenario model. A vehicle-following control model is demonstrated to maneuver a follower vehicle using locations and velocities of the leader vehicle sent via V2V. The vehicle model consists of Time to Collision (TTC), velocity decision control, path-following control, and vehicle dynamics. The follower vehicle decision is modeled by MathWorks Stateflow considering the important factors including velocities, positions, lanes, obstacles, and buildings that effect V2V communication efficiency. Simulink Design Verifier which is a formal verification tool was then used to verifier the TTC, velocity decision, and path following control. The test coverage analysis and test harness were repeated to generate test patterns with 100% coverage results. The experiments were done under the following communications and environmental conditions: single-input-single-output (SISO) without buildings, SISO with buildings, and single-input-multiple-output (SIMO) with buildings. The resulting communication packet delivery ratios were 100%, 95.32%, and 99.91%, respectively. This reveals that the proposed method can effectively model the vehicle following control and autonomous driving scenario including the effects of V2V communications efficiencies.

As the automotive industry moves toward autonomous driving and ADAS (Advanced Driver Assistance Systems), Model-Based Design (MBD) is a practical design methodology. It can be used to develop rapid prototyping by using MATLAB and Simulink. The MBD method still has limitations for handling complex models. This paper uses the Control Data Flow Graph (CDFG), an intermediate representation for analyzing complex algorithms, so that suitable optimizations for image processing applications can be implemented on an FPGA. The experimental results show that the proposed CDFG method improved both the area and speed of the edge detection case study compared with the MathWorks Vision HDL toolbox.

In this paper, a real-time knee extension monitoring and rehabilitation system for people, such as patients, the elderly, athletes, etc., is developed and tested. The proposed system has three major functions. The first function is two-channel surface electromyography (EMG) signal measurement and processing for the vastus lateralis (VL) and vastus medialis (VM) muscles using a developed EMG device set. The second function is the knee extension range of motion (ROM) measurement using an angle sensor device set (i.e., accelerometer sensor). Both functions are connected and parallelly processed by the NI-myRIO embedded device. Finally, the third function is the graphical user interface (GUI) using LabVIEW, where the knee rehabilitation program can be defined and flexibly set, as recommended by physical therapists and physicians. Experimental results obtained from six healthy subjects demonstrated that the proposed system can efficiently work with real-time response. It can support multiple rehabilitation users with data collection, where EMG signals with mean absolute value (MAV) and root mean square value (RMS) results and knee extension ROM data can be automatically measured and recorded based on the defined rehabilitation program. Furthermore, the proposed system is also employed in the hospital for validation and evaluation, where bio-feedback EMG and ROM data from six patients, including (a) knee osteoarthritis, (b) herniated disc, (c) knee ligament injury, (d) ischemic stroke, (e) hemorrhagic stroke, and (f) Parkinson are obtained. Such data are also collected for one month for tracking, evaluation, and treatment. With our proposed system, results indicate that the rehabilitation people can practice themselves and know their rehabilitation progress during the time of testing. The system can also evaluate (as a primary treatment) whether the therapy training is successful or not, while experts can simultaneously review the progress and set the optimal treatment program in response to the rehabilitation users. This technology can also be integrated as a part of the Internet of Things (IoT) and smart healthcare systems.

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