Exploring Challenges in Developing a Smart and Effective Assistive System for Improving the Experience of the Elderly Drivers

As the overall population ages, driving-related accidents and injuries, associated with elderly drivers, have risen. Existing research about elderly drivers mainly focuses on factual data collection and analysis, indicating the elderly＇s growing fatal accident rates and their different behaviours compared to younger drivers. However, few research has focused on design-led practical solutions to mitigate the elderly＇s growing fatal accidents, by consid- ering their usability and body conditions, afflicting the elderly, such as decreased vision, hearing, and reaction times. In this paper, first, current worldwide situations on growing fatal accident rates for elderly drivers is reviewed and the key impact factors are identified and discussed with regarding to usability and design trend in the automotive technology for elderly. Second, existing smart vehicle technology-based solutions to promote safe driving are explored and their pros and cons are discussed and anal- ysed. Most of solutions are not created by people with driving difficulties, which are caused by health problems most commonly afflicting the elderly. Thirdly, diverse design-led research activities are taken, such as a survey, observation, and interviews to gain new understanding of what kinds of driving problems elderly drivers have and demonstrate how new system concepts could be developed for the elderly＇s benefits. Finally, it is found that the elderly＇s low vision and late reaction are main factors causing their driving difficulties. Based on this finding, usable vehicle system design ideas have been proposed, by utilising facial expression sensing technology as a solution. The proposed solutions would ensure reducing both the elderly＇s driving problems and high fatal accident rates and provide a more enjoyable driving environment for the elderly population.

RSSI Based Localization of Bluetooth Devices for Visually Impaired

Localization for visually impaired people in dynamically changing environments with unexpected hazards and obstacles is a current need. Many techniques have been discussed in the literature with respect to location-based services and techniques used for the positioning of devices. Time difference of arrival (TDOA), time of arrival (TOA) and received signal strength (RSS) have been widely used for the positioning but narrow band signals such as Bluetooth cannot efficiently utilize TDOA or TOA. Received signal strength indicator (RSSI) to measure RSS, has been found to be more reliable. RSSI measurement estimations depend heavily on the environmental interference. RSSI measurement estimations of Bluetooth systems can be improved either by improving the existing methodologies used to implement them or by using fusion techniques that employ Kalman filters to combine more than one RSSI method to improve the results significantly. This paper focuses on improving the existing methodology of measuring RSSI by proposing a new method using trilateration for localization of Bluetooth devices for visually impaired people. To validate the new method, class 2 Bluetooth devices (Blue Giga WT-12) were used with an evaluation board. The software required was developed in National Instruments LabView. The PCB was designed and manufactured as well. Experiments were then conducted, and surface plots of Bluetooth modules were obtained to show the signal interference and other environmental effects. Lastly, the results were discussed, and relevant conclusions were drawn.

Multi-Scale Image Segmentation Model for Fine-Grained Recognition of Zanthoxylum Rust

Zanthoxylum bungeanum Maxim,generally called prickly ash,is widely grown in China.Zanthoxylum rust is the main disease affecting the growth and quality of Zanthoxylum.Traditional method for recognizing the degree of infection of Zanthoxylum rust mainly rely on manual experience.Due to the complex colors and shapes of rust areas,the accuracy of manual recognition is low and difficult to be quantified.In recent years,the application of artificial intelligence technology in the agricultural field has gradually increased.In this paper,based on the DeepLabV2 model,we proposed a Zanthoxylum rust image segmentation model based on the FASPP module and enhanced features of rust areas.This paper constructed a fine-grained Zanthoxylum rust image dataset.In this dataset,the Zanthoxylum rust image was segmented and labeled according to leaves,spore piles,and brown lesions.The experimental results showed that the Zanthoxylum rust image segmentation method proposed in this paper was effective.The segmentation accuracy rates of leaves,spore piles and brown lesions reached 99.66%,85.16%and 82.47%respectively.MPA reached 91.80%,and MIoU reached 84.99%.At the same time,the proposed image segmentation model also had good efficiency,which can process 22 images per minute.This article provides an intelligent method for efficiently and accurately recognizing the degree of infection of Zanthoxylum rust.

MFPL:Multi-Frequency Phase Difference Combination Based Device-Free Localization

With the popularity of indoor wireless network,device-free indoor localization has attracted more and more attention.Unlike device-based localization where the target is required to carry an active transmitter,their frequent signal scanning consumes a large amount of energy,which is inconvenient for devices with limited energy.In this work,we propose the MFPL,device-free localization(DFL)system based on WiFi distance measurement.First,we combine multi-subcarrier characteristic of Channel State Information(CSI)with classical Fresnel reflection model to get the linear relationship between the change of the length of reflection path and the subcarrier phase difference.Then we calculate the Fresnel phase difference between subcarrier pairs with different spacing from CSI amplitude time series.Finally,we get the change of the length of the reflection path caused by target moving to achieve distance measurement and localization.Using a combination of subcarriers with different spacing to achieve distance measurement effectively broadens the maximum unambiguous distance of the system.To solve the complex non-linear problem of the intersection of two elliptic equations,we introduce Newton's method to transform the non-linear problem into a linear one.The effectiveness of our approach is verified using commodity WiFi infrastructures.The experimental results show our method achieves a median error of 0.87 m in actual indoor environment.

A Time-Domain Comparator Based Skipping-Window SAR ADC

This paper presents an energy efficient successive-approximation register(SAR)analog-to-digital converter(ADC)for low-power applications.To improve the overall energy-efficiency,a skipping-window technique is used to bypass corresponding conversion steps when the input falls in a window indicated by a time-domain comparator,which can provide not only the polarity of the input,but also the amount information of the input.The timedomain comparator,which is based on the edge pursing principle,consists of delay cells,two NAND gates,two D-flip-flop register-based phase detectors and a counter.The digital characteristic of the comparator makes the design more flexible,and the comparator can achieve noise and power optimization automatically by simply adjusting the delay cell number.An energy efficient digital-to-analog converter(DAC)control scheme suitable for the skipping window technique is also developed to reduce the switching energy during SAR conversion.Together with the skipping-window technique,the linearity and the power consumption of the SAR ADC are improved.The impact of different window sizes on comparison cycles,DAC switching energy and the overall energy efficiency is analyzed.Simulation results show that the proposed skipping-window technique can improve the overall energy-efficiency of the SAR ADC,as well as the linearity,and the optimized window size for the overall energy efficiency will vary with the DAC switching energy.

A Two-Dimension Time-Domain Comparator for Low Power SAR ADCs

This paper presents a two-dimension time-domain comparator suitable for low power successive-approximation register(SAR)analog-to-digital converters(ADCs).The proposed two-dimension time-domain comparator consists of a ring oscillator collapse-based comparator and a counter.The propagation delay of a voltage controlled ring oscillator depends on the input.Thus,the comparator can automatically change the comparison time according to its input difference,which can adjust the power consumption of the comparator dynamically without any control logic.And a counter is utilized to count the cycle needed to finish a comparison when the input difference is small.Thus,the proposed comparator can not only provide the polarity of the input,but also the amount information of the input,which helps to skip most of the SAR cycles when the initial input is small.Thus,most energy can be saved when the initial input is small.The proposed time-domain comparator is designed in 0.18μm CMOS technology.Simulation results demonstrate that the comparator can not only save power consumption,but also give the design flexibility,and the current is only nA level when the supply voltage is 0.6 V.

Power System Transients Analysis by Wavelet Transforms

In contrast to Fourier transform, wavelet transform is especially suitable for transient analysis because of its time frequency characteristics with automatically adjusted window lengths. Research shows that wavelet transform is one of the most powerful tools for power system transient analysis. The basic ideas of wavelet transform are presented in the paper together with several power system applications. It is clear that wavelet transform has some clear advantages over other transforms in detecting, analyzing, and identifying various types of power system transients.

Research on Indoor Positioning Technology of WSN based on T-RL Partition Path Model

To address the issues of unstable received signal strength indicator(RSSI)and low indoor positioning accuracy caused by walls and obstacles,the propagation conditions of the wireless communication system are categorized into two distinct environments:line-of-sight(LOS)and non-line-of-sight(NLOS).In the LOS environment,the traditional logarithmic path loss model is applied.For the NLOS environment,the impact of walls on signal transmission is considered,leading to the development of a multi-wall path loss model based on the T-RL method,with improvements made to the key parameter,the Fresnel coefficient R.The breakpoint value d=2.3 m in the partitioned model is determined,and the positional coordinates of the unknown nodes are calculated using the trilateration algorithm.Experimental results indicate that the T-RL based multi-wall model improves localization accuracy by 47%in NLOS environments compared to the traditional logarithmic path loss model.The average localization error using the T-RL partitioned path loss model is 0.7021 m,representing a 55.9%improvement over the logarithmic path loss model and a 16.8%enhancement over the T-RL attenuation multi-wall model,thereby providing better environmental adaptability.