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"IEEE Sensors Alert" is a pilot project of the IEEE Sensors Council. Started as one of its new initiatives, this weekly digest publishes teasers and condensed versions of our journal papers in layperson's language.
Articles Posted in the Month (December 2023)
MS-YOLO: Object Detection Based on YOLOv5 Optimized Fusion Millimeter-Wave Radar and Machine Vision
Author: Yunyun Song, Zhengyu Xie, Xinwei Wang, Yingquan Zou
Published in: IEEE Sensors Journal (Volume: 22, Issue: 15, August 2022)
Summary Contributed by: Yingquan Zou (Author)
Multi-sensor fusion is becoming an increasingly crucial part of the environmental perception system in autonomous driving. Experience the cutting-edge fusion of millimeter-wave radar and machine vision in the proposed MS-YOLO, powered by You Only Look Once (YOLOv5) algorithm. The proposed fusion model offers exceptional accuracy in detecting objects and provides immediate real-time insights regardless of light and weather conditions.
BabyPose: Real-Time Decoding of Baby’s Non-Verbal Communication Using 2D Video-Based Pose Estimation
Author: M. Mücahit Enes Yurtsever, Süleyman Eken
Published in: IEEE Sensors Journal (Volume: 22, Issue: 14, July 2022)
Summary Contributed by: Saurabh Dubey
Decoding or understanding non-verbal communication forms in babies is paramount to parents. These forms are expressed in various poses and body-language signals, which can be interpreted using a Human Pose Estimation method. Babies are tracked in real-time using 2D videos. Pose estimators that model these poses into keypoints are then employed to recognize and monitor these activities. This study delves into these estimation models that interpret baby poses with 99% accuracy.
Published in: IEEE Sensors Journal (Volume: 22, Issue: 22, November 2022)
Summary Contributed by: Shixin Zhang (Author)
Vision-based tactile sensors (VBTS) are attracting attention for their application in robotics. As an innovative optical sensor, VBTS leverages tactile sensing to enhance the interpretation and utilization tactile information. The paper presents an overview of the hardware aspects of VBTS, including their technology, capabilities, challenges, and potential solutions. It provides insightful guidelines for optimizing the design and fabrication processes of VBTS to improve their performance.
Smart Healthcare: RL-Based Task Offloading Scheme for Edge-Enable Sensor Networks
Author: Rahul Yadav, Weizhe Zhang, Ibrahim A. Elgendy, Guozhong Dong, Muhammad Shafiq, Asif Ali Laghari, Shiv Prakash
Published in: IEEE Sensors Journal (Volume: 21, Issue: 22, November 2021)
Summary Contributed by: Anupama
Smart healthcare systems produce massive data, which is challenging to manage. The Internet of Medical Things (IoMT) and Artificial intelligence (AI) based smart healthcare systems and applications have shown potential in intelligent and accurate data management and support healthcare. While the edge-enabled network provides necessary computational resources to deal with enormous data, the proposed Computation Offloading using Reinforcement Learning (CORL) algorithm minimizes total latency and energy consumption.
A Radar-Based Human Activity Recognition Using a Novel 3-D Point Cloud Classifier
Author: Zheqi Yu, Ahmad Taha, William Taylor, Adnan Zahid, Khalid Rajab, Hadi Heidari, Muhammad Ali Imran, Qammer H. Abbasi
Published in: IEEE Sensors Journal (Volume: 22, Issue: 19, October 2022)
Summary Contributed by: William Taylor (Author)
Wearable sensors for human activity recognition (HAR) have many uses, especially in health, surveillances and man-machine conversation. Technological advancements have enabled non-invasive, contactless sensing methods to detect human activities. However, insufficient training data severely affects the performance of HAR applications. The paper discusses the dataset collection using 3-D cloud point technology and deep learning algorithms to classify the dataset, which will enable a transition from wearable to contactless sensing.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 8, April 2021)
Summary Contributed by: Laxmeesha Somappa
Origami is the art of paper folding to create a two-dimensional and three-dimensional sculpture. Inspired by this art, the researchers developed a flexible pressure sensor, which finds various applications in wearable devices and healthcare products. The origami structure inherently offers higher sensitivity and measurement range. These pressure sensors can easily be fabricated with 3-D printing technology, making them low-cost and enabling mass production.
Data Fusion Based on Temperature Monitoring of Aquaculture Ponds With Wireless Sensor Networks
Author: Haohui Chen, Xinyuan Nan, Sibo Xia
Published in: IEEE Sensors Journal (Volume: 23, Issue: 1, January 2023)
Summary Contributed by: Haohui Chen (author)
Aquaculture is the farming of aquatic animals and plants in a controlled environment. The water temperature is a vital environmental factor that affects the water quality and life of the aquatic species. The paper proposes an effective real-time aquaculture temperature monitoring method through a layered and clustered wireless sensor networks (WSNs) framework. It is more effective in temperature monitoring with improved accuracy in comparison to the traditional monitoring methods.
Image-Based Force Estimation in Medical Applications: A Review
Author: Ali A. Nazari, Farrokh Janabi-Sharifi, Kourosh Zareinia
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Summary Contributed by: Anupama
Precise, real-time force estimation is still an ongoing challenge in minimally invasive robotic surgical (MIRS) interventions. The applied force depends on the size and deformity of the tissue. Advanced imaging techniques and deep-learning algorithms provide efficient object recognition and force estimation in MIRS. The researchers present a comprehensive review of image-based force estimation techniques for MIRS haptic force feedback.
Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron
Author: Umer Saeed, Syed Yaseen Shah, Adnan Zahid, Jawad Ahmad, Muhammad Ali Imran, Qammer H. Abbasi, Syed Aziz Shah
Published in: IEEE Sensors Journal (Volume: 21, Issue: 18, September 2021)
Summary Contributed by: Kamalesh Tripathy
The COVID-19 pandemic highlighted the need for contactless respiration monitoring systems. A software-defined radio frequency sensing technique integrated with a deep learning algorithm was proposed for the non-invasive monitoring of various breathing patterns. The system used variations in channel state information produced by human motions to identify six distinct respiratory patterns. The prototype could classify these respiratory patterns with up to 99% accuracy.
On the Detection of Unauthorized Drones—Techniques and Future Perspectives: A Review
Author: Muhammad Asif Khan, Hamid Menouar, Aisha Eldeeb, Adnan Abu-Dayya, Flora D. Salim
Published in: IEEE Sensors Journal (Volume: 22, Issue: 12, June 2022)
Summary Contributed by: Muhammad Asif Khan (Author)
The increasing number of commercial drones poses severe threats to the security of critical infrastructure and people’s privacy. A drone detection system thus becomes inevitable to detect unauthorized drones in the low altitude airspace. This paper delves into the various aspects of an efficient, reliable, robust, and scalable drone detection system by investigating the four fundamental technologies and the associated challenges and limitations.
The human desire for accuracy in exploration and guided navigation has brought inventive changes in inertial sensors technology. Integrating inertial sensors with external aids like Doppler, star tracker, or Global Positioning Systems (GPS) improves their accuracy, enhances reliability, and helps overcome inertial drift. Its vast applications in autonomous vehicles, military and space technology, etc., demand the need for extremely low-cost, small size, efficient and batch-producible sensors.
Humans are paying a heavy price for economic growth and overall development, whether infrastructure or industrial growth. The pollution and greenhouse gas emissions have led to environmental concerns and climate change, affecting health and life’s quality. However, a rise in environmental awareness created a demand for Environment monitoring systems (EMS) to detect the source and quantify the pollution level by providing a real-time data monitoring and alarm system.
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