Survivor detection approach for post earthquake search and rescue missions based on deep learning inspired algorithms.

Rapid and reliable detection of human survivors trapped under debris is crucial for effective post-earthquake search and rescue (SAR) operations. This paper presents a novel approach to survivor detection using a snake robot equipped with deep learning (DL) based object identification algorithms. We evaluated the performance of three main algorithms: Faster R-CNN, Single Shot MultiBox Detector (SSD), and You Only Look Once (YOLO).

Optimization of Novel 2D Material Based SPR Biosensor Using Machine Learning.

Biosensors are needed for today's health monitoring system for detecting different biomolecules. Graphene is a monolayer material that can be utilized to sense biomolecules and design biosensors. We have proposed a Graphene-Gold-Silver hybrid structure design based on Zinc Oxide which gives sensitive performance to detect hemoglobin biomolecules.

Thin-Film Solar Energy Absorber Structure for Window Coatings for Self-Sufficient Futuristic Buildings.

Energy-efficient buildings are a new demand in the current era. In this paper, we present a novel metamaterial design aimed at achieving efficient solar energy absorption through a periodic MMA structure composed of a W-GaAs-W. The proposed structure can be implemented as the window coating and in turn it can absorb the incident solar energy and, then, this energy can be used to fulfill the energy demand of the building.

SARS-CoV-2 detecting rapid metasurface-based sensor.

We have proposed a novel approach to detect COVID-19 by detecting the ethyl butanoate which high volume ratio is present in the exhaled breath of a COVID-19 infected person. We have employed a refractive index sensor (RIS) with the help of a metasurface-based slotted T-shape perfect absorber that can detect ethyl butanoate present in exhaled breath of COVID-19 infected person with high sensitivity and in-process SARS-CoV-2. The optimized structure of the sensor is obtained by varying several structure parameters including structure length and thickness, slotted T-shape resonator length, width, and thickness.

Terahertz Metasurface-Based Refractive Index Sensor for Amino Acid Detection: A Numerical Approach.

A graphene disk metasurface-inspired refractive index sensor (RIS) with a subwavelength structure is numerically investigated to enhance the functionality of flexible metasurface in the biosensor sector. The main aim behind the sensor development is to detect amino acids with high sensitivity. The results in form of transmittance and the electric field intensity are carried out to verify the sensor's performance.

Recent Advances in Biosensors for Detection of COVID-19 and Other Viruses.

This century has introduced very deadly, dangerous, and infectious diseases to humankind such as the influenza virus, Ebola virus, Zika virus, and the most infectious SARS-CoV-2 commonly known as COVID-19 and have caused epidemics and pandemics across the globe. For some of these diseases, proper medications, and vaccinations are missing and the early detection of these viruses will be critical to saving the patients. And even the vaccines are available for COVID-19, the new variants of COVID-19 such as Delta, and Omicron are spreading at large.

Graphene-Based Metasurface Refractive Index Biosensor for Hemoglobin Detection: Machine Learning Assisted Optimization.

Machine learning is the latest approach to optimize the performance of absorbers, sensors, etc. A sensor with behavior prediction using polynomial regression is presented. Three different variations of metasurfaces namely double split-ring resonator, single split ring resonator, split ring resonator with thin wire are analyzed.

Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices.

Antenna design has evolved from bulkier to small portable designs but there is a need for smarter antenna design using machine learning algorithms that can meet today's high growing demand for smart and fast devices. Here in this research, main focus is on developing smart antenna design using machine learning applicable in 5G mobile applications and portable Wi-Fi, Wi-MAX, and WLAN applications. Our design is based on the metamaterial concept where the patch is truncated and etched with a split ring resonator (SRR).