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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 (June 2023)
Deep Learning for Patient-Independent Epileptic Seizure Prediction Using Scalp EEG Signals
Author: test
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Epilepsy is one of the most prevalent neurological diseases among humans. It can lead to severe brain injuries, strokes, and brain tumors. Early detection of seizures is critical to improving the day-to-day lives of patients. The researchers propose two deep learning approaches, using EEG data as input, which can detect epileptic seizures one hour before they occur.
Colon Cancer Detection by Designing and Analytical Evaluation of a Water-Based THz Metamaterial Perfect Absorber
Author: Zohreh Vafapour, William Troy, Ali Rashidi
Published in: IEEE Sensors Journal (Volume: 21, Issue: 17, September 2021)
Summary Contributed by: Anupama
Early detection of colon cancer is vital for patient survival. The researchers propose a novel device for the detection of colon cancer using terahertz electromagnetic waves. The device detects differences in water concentration within healthy and cancerous colon tissues by using surface plasmon polaritons (SPP). It shows potential as a less invasive, safer, faster, and early detection procedure for colon cancer.
IoT Enabled, Leaf Wetness Sensor on the Flexible Substrates for In-Situ Plant Disease Management
Author: Kamlesh S. Patle, Riya Saini, Ahlad Kumar, Sandeep G. Surya, Vinay S. Palaparthy, Khaled N. Salama
Published in: IEEE Sensors Journal (Volume: 21, Issue: 17, September 2021)
Summary Contributed by: Margi Hingrajia
Early detection of plant diseases can prevent crop failure. The Leaf wetness duration (LWD) that leads to fungal infections is a major concern among farmers. Using LWD as a parameter, researchers developed disease detection models with an Internet of Things (IoT)-enabled leaf wetness sensor (LWS) prototype fabricated on flexible substrates. Researchers tested the prototypes on medicinal plants. The prototype made comparatively accurate, precise, and early disease predictions.
Graphene and graphene derivative-based field effect transistor (FET) gas sensors are the most recent inclusion in the gas sensor family, having enormous potential to detect a wide variety of oxidizing and reducing target species with high sensitivity. The researchers adopted various strategies to improve the different performance index of the sensor either at the fabrication/device stage or at the operational/measurement technique level.
Determination of Salinity and Sugar Concentration by Means of a Circular-Ring Monopole Textile Antenna-Based Sensor
Author: Mariam El Gharbi, Marc Martinez-Estrada, Raúl Fernández-García, and Ignacio Gil
Published in: IEEE Sensors Journal (Volume: 21, Issue: 21, November 2021)
Summary Contributed by: Kamalesh Tripathy
Excessive sugar and salt in the diet are harmful to health. A wearable antenna sensor with real-time sugar and salt monitoring could help monitor salt and sugar intake. The researchers here propose an e-textile-based monopole antenna sensor prototype for it. The simple fabrication process, compact size, and high sensitivity make the prototype ideal for a blood sugar or sodium sensor.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 13, July 2021)
Summary Contributed by: Vahid Khojasteh Lazarjan (Author)
The portable Point-of-care (POC) devices that can detect diseases will revolutionize healthcare. The researchers propose a miniature wireless cell fiber-spectrophotometer prototype for real-time bio-markers detection and diagnostic support outside the laboratories. The low-power and lightweight spectrometer can identify biomarkers, such as tagged proteins or genetic materials, even in small samples. It promises effective point-of-care solutions for rapid testing of infectious diseases like SARS-CoV-2 or cancer.
False-Alarm-Controllable Radar Detection for Marine Target Based on Multi Features Fusion via CNNs
Author: Xiaolong Chen, Ningyuan Su, Yong Huang, Jian Guan
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Summary Contributed by: Shradha Makhija
Sea surface target detection is vital for maritime security, surveillance, and rescue operations. The complexity of the marine environment makes it difficult to achieve robust, reliable, and adaptive target detection. Deep learning methods show better feature extraction ability and classification accuracy. The study proposes a new approach to marine target detection in complex background conditions using marine dual-channel convolutional neural networks (MDCCNN) with a false-alarm controllable classifier (FACC).
Multiplexed Silicon Nanowire Tunnel FET-Based Biosensors With Optimized Multi-Sensing Currents
Author: Sihyun Kim, Ryoongbin Lee, Daewoong Kwon, Tae-Hyeon Kim, Tae Jung Park, Sung-Jin Choi, Hyun-Sun Mo, Dae Hwan Kim, Byung-Gook Park
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Summary Contributed by: Sihyun Kim (author)
Multiplexed sensing is an efficient approach for diagnosing and monitoring progressive diseases like cancer. This paper implements the independent detection of two different biomolecules in a novel single tunnel field-effect transistor (TFET) sensor. Since this new sensor can be perfectly co-integrated with complementary metal–oxide–semiconductor (CMOS) read-out circuits by state-of-art industry CMOS process, the mass production of CMOS-based biochips for multiplexed sensing is possible.
Micro-Doppler Based Target Recognition With Radars: A Review
Author: Ali Hanif, Muhammad Muaz, Azhar Hasan, Muhammad Adeel
Published in: IEEE Sensors Journal (Volume: 22, Issue: 4, February 2022)
Summary Contributed by: Anupama
Radar detection of smaller targets requires lowering the radar cross-section and velocity thresholds. With it, an abundance of target signatures gets generated, making it necessary to classify only relevant targets. Micro-motions of targets are significant characteristics. Micro-Doppler signatures have emerged as an effective method of classifying such targets. The study presents a systematic review of various micro-Doppler-based radar target signature analysis and classification techniques.
Published in: IEEE Sensors Journal (Volume: 21, Issue: 7, April 2021)
Summary Contributed by: Sidrah Liaqat (Author)
Posture detection is important for monitoring health status. Incorrect postures may cause muscle weaknesses and body pain. The novel hybrid remote posture detection method developed by integrating machine learning (ML) and deep learning (DL) has produced promising results with 98% accuracy. Remote posture monitoring has the edge over camera-based, and wearable devices due to its privacy preserved feature.
A robust pavement crack detection network is imperative to mitigate traffic accidents and minimize maintenance costs. The paper proposed an efficient hybrid model by merging YOLOv5 and Transformer, utilizing one-stage architecture and long-range dependency capture for reliable crack detection. The network's performance is further improved using test time augmentation (TTA) for crack detection. An efficient solution for urban pavement damage detection, it paves the way for expanding datasets to tackle diverse pavement issues.
Implantable medical devices (IMD) are developed to control and report acquired biological data from an implanted device in the body or brain to an external stage for biomedical purposes. They receive power from batteries or wireless power transmissions (WPT). Due to their simplicity and safety, magnetic waves are extensively studied and developed for powering in biomedical applications.
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