This article proposes a neural network based traffic signal controller, which eliminates most of the problems associated with the Traffic Responsive Plan Selection (TRPS) mode of the closed loop system. Instead of storing timing plans for different traffic scenarios, which requires clustering and threshold calculations, the proposed approach uses an Artificial Neural Network (ANN) model that produces optimal plans based on optimized weights obtained through its learning phase. Clustering in a closed loop system is root of the problems and therefore has been eliminated in the proposed approach. The Particle Swarm Optimization (PSO) technique has been used both in the learning rule of ANN as well as generating training cases for ANN in terms of optimized timing plans, based on Highway Capacity Manual (HCM) delay for all traffic demands found in historical data. The ANN generates optimal plans online to address real time traffic demands and thus is more responsive to varying traffic conditions.

Blind people face multiple challenges in performing their daily activities, such as localization, navigation, and communication with other people following the social status of the ambient environment. The outdoor navigation and localization are mitigated through assistive technologies, such as white cane, smartphones, and Global Positioning System (GPS). However, little attention has been paid to assisting a blind person in judging the room environment, occupants in a room, communicating with an intended person, social status, and so on. This study attempts to provide cognitive assistance to blind people in predicting room types and finding the intended person to communicate their message by understanding their ambient environment and social status in terms of their age, gender, and the number of people inside a room. The proposed solution uses the microphone and speaker to recognize room types and a camera for understanding the ambient environment and social status. The information is conveyed to the blind person through haptic feedback. We analyzed different evaluation metrics, including the movement of people, ambient sounds, orientation, and position. We conducted an extensive user study to validate the proposed solution in real-world scenarios and achieved 87.64% accuracy in room type recognition in 11 rooms, 64.57% gender recognition, 61.73% age group, and 61.71% correctly identified the number of people.

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Imaging and Multimedia Analytics in a Web and Mobile World proceedings.

This paper proposes a neural network based traffic signal controller, which eliminates most of the problems associated with the Traffic Responsive Plan Selection (TRPS) mode of the closed loop system. Instead of storing timing plans for different traffic scenarios, which requires clustering and threshold calculations, the proposed approach uses an Artificial Neural Network (ANN) model that produces optimal plans based on optimized weights obtained through its learning phase. Clustering in a closed loop system is root of the problems and therefore has been eliminated in the proposed approach. The Particle Swarm Optimization (PSO) technique has been used both in the learning rule of ANN as well as generating training cases for ANN in terms of optimized timing plans, based on Highway Capacity Manual (HCM) delay for all traffic demands found in historical data. The ANN generates optimal plans online to address real time traffic demands and thus is more responsive to varying traffic conditions.