Hiroshi Saito

A device-free human detection system based on a received signal strength indicator (RSSI) monitors and analyzes the change of RSSI signals to detect human movements in a wireless network. This study proposes and implements a real-time, device-free human detection system based on an RSSI distribution-based detection method with an autonomous threshold. The novelty and contribution of our solution is that the RSSI distribution concept is considered and used to calculate the optimal threshold setting for human detection, while thresholds can be automatically determined from RSSI data streams gathered from test environments. The proposed system can efficiently work without requiring an offline phase, as introduced in many existing works in the research literature. Experiments using 2.4 GHz IEEE 802.15.4 technology have been carried out in indoor environments in two laboratory rooms with different numbers of wireless links, human movement patterns, and movement speeds. Experimental results show that, in all test scenarios, the proposed method can monitor and detect human movement in a wireless network in real time. It outperforms a comparative method and achieves high accuracy (i.e., 100% detection accuracy) with a low computational complexity requirement.

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 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.

Binarized Convolutional Neural Network (BCNN) is a promising technology to realize fast and energy-efficient inference in various applications such as robots and virtual reality (VR) in edge AI devices. A low-bit quantized CNN approximation method using random forest (RF) models has been proposed, which can significantly reduce the number of required multiply-accumulate (MAC) operations and the area of a BCNN accelerator. However, the accuracy reduction is a problem. In this paper, we propose two methods to realize computationally-cheaper BCNN models while maintaining sufficient accuracy with random forest approximation (RFA). The first method is approximate-aware training for BCNN models. The second method is a channel-wise RFA method based on the imbalance rate of the output feature map’s values. We demonstrate that for binarized VGG16, the proposed approximate-aware training enables the approximation of intermediate layers that are computationally heavy and achieves 13.9% reduction in the computational cost of feature extraction by RFA while maintaining the original inference accuracy. Moreover, we demonstrate that channel-wise RFA approximation can select channels that are more suitable for RFA approximation compared with channel selection based on the output prediction accuracy of random forest models.

In this paper, we propose a design method to design asynchronous circuits with bundled-data implementation on commercial Field Programmable Gate Arrays using placement constraints. The proposed method uses two types of placement constraints to reduce the number of delay adjustments to fix timing violations and to improve the performance of the bundled-data implementation. We also propose a floorplan algorithm to reduce the control-path delays specific to the bundled-data implementation. Using the proposed method, we could design the asynchronous circuits whose performance is close to and energy consumption is small compared to the synchronous counterparts with less delay adjustment.

In this paper, we propose four optimization methods during the Register Transfer Level (RTL) conversion from synchronous RTL models into asynchronous RTL models. The modularization of data-path resources and the use of appropriate D flip-flops reduce the circuit area. Fixing the control signal of the multiplexers and inserting latches for the data-path resources reduce the dynamic power consumption. In the experiment, we evaluated the effect of the proposed optimization methods. The combination of all optimization methods could reduce the energy consumption by 21.9% on average compared to the ones without the proposed optimization methods.

In this paper, to make asynchronous circuit design easy, we propose a conversion method from synchronous Register Transfer Level (RTL) models to asynchronous RTL models with bundled-data implementation. The proposed method consists of the generation of an intermediate representation from a given synchronous RTL model and the generation of an asynchronous RTL model from the intermediate representation. This allows us to deal with different representation styles of synchronous RTL models. We use the eXtensible Markup Language (XML) as the intermediate representation. In addition to the asynchronous RTL model, the proposed method generates a simulation model when the target implementation is a Field Programmable Gate Array and a set of non-optimization constraints for the control circuit used in logic synthesis and layout synthesis. In the experiment, we demonstrate that the proposed method can convert synchronous RTL models specified manually and obtained by a high-level synthesis tool to asynchronous ones.