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All Journal IAES International Journal of Robotics and Automation (IJRA) Indonesian Journal of Electrical Engineering and Computer Science
Singh, Vivek Kumar
Unknown Affiliation
Automotive Engineering Electrical & Electronics Engineering

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No-reference image quality assessment based on visual explanation images and deep transfer learning Ahmed, Basma; Omer, Osama A.; Singh, Vivek Kumar; Rashed, Amal; Abdel-Nasser, Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1521-1531

Abstract

Quantifying image quality in the absence of a reference image continues to be a challenge despite the introduction of numerous no-reference image quality assessments (NR-IQA) in recent years. Unlike most existing NRIQA methods, this paper proposes an efficient NR-IQA method based on deep visual interpretations. Specifically, the main components of the proposed method are: i) generating a pseudo-reference image (PRI) for the input distorted images, ii) employing a pretrained convolutional network to extract feature maps from the distorted image and the corresponding PRI, iii) producing visual explanation images (VEIs) by using the feature maps of the distorted image and the corresponding PRI, iv) measuring the similarity between the two VEIs using an image similarity metric, and v) employing a non-linear mapping function for quality score alignment. In our experiments, we evaluated the efficacy of the proposed method across various forms of distortion using four benchmark datasets (LIVE, SIQAD, CSIQ, and TID2013). The proposed approach demonstrates parity with the latest methods, as evidenced by comparisons with both hand-crafted NR-IQA and deep learning-based approaches.
Long-range radio and Internet of things-inspired smart road reflectors for smart highways Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Singh, Vivek Kumar; Iqbal, Mohammed Ismail; Mahala, Rahul
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 1: March 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i1.pp113-120

Abstract

The Internet of things (IoT) has been proven as an efficient technology for real-time monitoring of physical things through the Internet from any location. With the advancement in sensors and communication technologies, the implementation of IoT is adopted in wide extensions. Road reflectors on highway roads need to be automated and also powered with intelligence. With this motivation, we have proposed and implemented IoT and long range (LoRa) based architecture for the realization of smart road reflectors on the highway. To realize the proposed architecture, the hardware of the smart reflector and gateway is implemented on the university campus. During our implementation of the hardware, we observed the light intensity values that are sensed by smart reflectors on the server through LoRa and internet connectivity. In the future, we will be integrating additional sensors and also power the smart reflector with artificial intelligence to predict the fog status of a particular road.
LoRa-enabled remote-controlled surveillance robot for monitoring and navigation in disaster response missions Gehlot, Anita; Singh, Rajesh; Mahala, Rahul; Gupta, Mahim Raj; Singh, Vivek Kumar
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp311-321

Abstract

Rescue missions must be conducted within a strict timeframe, and the safety of all rescuers and civilians is prioritized. The proposed system aims to design a remote-operated aerial surveillance robot for disaster-affected areas for search and rescue missions. Real-time video transmission and RS-232 long-range communication enable operators to navigate rough environments and monitor data collected in real-time. This powerful tool ensures the protection of human life while collecting accurate and meaningful data. Cloud storage for data and surveillance strengthens the system, preventing part failure and fostering collaboration among users. This is a significant step towards using Internet of Things systems alongside remote-controlled robots in disaster response. The robot's key contribution to disaster management is identifying the environment, addressing issues of no visibility, complicated terrains, and speed. Its modification and expansion capabilities make it useful in armed surveillance, industrial monitoring, and environmental studies, making it an important innovation for many other fields.
Design and implementation of Internet of Things-enabled long-range autonomous surveillance bot for LPG leak detection and environmental safety monitoring Singh, Rajesh; Gehlot, Anita; Mahala, Rahul; Singh, Vivek Kumar
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 3: September 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i3.pp361-369

Abstract

Liquefied petroleum gas (LPG) accidents pose significant safety risks, requiring continuous monitoring and Internet of Things (IoT) technology to prevent gas leakage and ensure human safety. This work proposes distributed field-oriented IoT gas sensing robots for detecting dangerous flammable gases like Ammonia, Sulphur Dioxide, Nitrogen Dioxide, and Carbon Dioxide. The SnoLURk solution enables cost-effective IoT gas leak detection in indoor and outdoor robots using budget-friendly casings and sensors. The study also discusses a robotic system for gas leak detection, aiming to detect and combat burglary using ZigBee and GSM modules. Cloud support allows Wi-Fi zone residents to receive alerts and send investigators via email, enabling remote data analytics monitoring. The IoT-based Worker's Health Monitoring System improves health and safety practices in industrial environments by monitoring workers' health 24/7. It allows on-site and off-site monitoring, enabling quick intervention and avoiding complications. The system's applications include construction, mining, manufacturing, and healthcare. Future versions may include improved sensors and machine learning.
Internet of Things-enabled smart robotic baggage monitoring and tracking system for enhanced traveler convenience and security Gehlot, Anita; Singh, Rajesh; Mahala, Rahul; Singh, Vivek Kumar; Gupta, Mahim Raj
IAES International Journal of Robotics and Automation (IJRA) Vol 14, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v14i2.pp290-300

Abstract

Baggage travel is a significant issue, causing inconveniences and financial losses for travellers. The rise in efficient international and domestic travel has led to the need for live baggage tracking systems. Traditional methods, such as manual tracking and locks, are inefficient and counterproductive due to power limitations. IoT has revolutionized baggage management by providing real-time tracking feedback and enhancing security. IoT-enhanced smart luggage systems use biometric locks, GPS tracking, and smart locking mechanisms to prevent theft and unauthorized usage. Geofencing allows users to draw boundaries for luggage, and smart luggage systems can adapt to airport security requirements. Some smart suitcases also have self-following features, allowing travellers to have better control over their bags. IoT-enabled baggage solutions also improve airport and travel centre efficiency. RFID and barcode identification devices enable airline employees to quickly recognize, monitor, and manage luggage, reducing waiting times and loss risks. Cloud-based systems allow users to remember their luggage and receive travel suggestions based on predicted frequency of use. IoT-enabled baggage management systems have the potential to transform airport ecosystems into smarter ones through automated tracking with minimal human involvement and errors. AI and machine learning can also proactively address concerns and improve the overall customer journey.
Co-Authors Abdel-Nasser, Mohamed Ahmed, Basma Akram, Shaik Vaseem Anita Gehlot Gupta, Mahim Raj Iqbal, Mohammed Ismail Mahala, Rahul Omer, Osama A. Rajesh Singh Rashed, Amal