Resistive random-access memory (RRAM) technology has been gaining importance due to scalability, low power, non-volatility, and the ability to perform in-memory computing. The RRAM sensing applications have also emerged to enable single RRAM technology platforms which include sensing, data storage, and computing. This paper reports on sol-gel drop coated low-power $\mu $ -thick Ag/TiO ₂ /Cu memristor, named MemSens, developed for radiation sensing. MemSens exhibits a bipolar memristive switching behavior within a small voltage window, ranging up to +0.7 V for the turn-ON, and down to −0.2 V for the turn-OFF. Under these operating conditions, MemSens has 67% less switching voltage, 20% drop in ON switching current, 75% reduced active area and > 3x improved device endurance, compared to the best characteristics reported in the literature for $\mu $ -thick memristors. The device is tested under direct exposure to ionizing Cs-137 662keV $\gamma $ -rays, during which a significant increase in the electrical conductivity of the device is observed. MemSens circuit is proposed to allow a relatively real time and cost-effective radiation detection. This provides a first insight to the advancement of reliable memristors that could potentially be deployed in future low-power radiation sensing technologies for medical, personal protection, and other field applications.

This paper proposes a modified hybrid method for WSN localization that combines Received Signal Strength (RSS) and Direction of Arrival (DOA) estimation based on MUSIC (Multiple Signal Classification) using circular antenna array with smoothing capability. Simulation results proves the enhanced performance obtained using the proposed hybrid method. This takes the form of successful detection of a mixture of correlated and uncorrelated signals at a higher accuracy and lower Root Mean Square Error (RMSE) in comparison with conventional method.

Machine learning and computer vision algorithms can provide a precise and automated interpretation of medical videos. The segmentation of the left ventricle of echocardiography videos plays an essential role in cardiology for carrying out clinical cardiac diagnosis and monitoring the patient’s condition. Most of the developed deep learning algorithms for video segmentation require an enormous amount of labeled data to generate accurate results. Thus, there is a need to develop new semi-supervised segmentation methods due to the scarcity and costly labeled data. In recent research, semi-supervised learning approaches based on graph signal processing emerged in computer vision due to their ability to avail the geometrical structure of data. Video object segmentation can be considered as a node classification problem. In this paper, we propose a new approach called GraphECV based on the use of graph signal processing for semi-supervised learning of video object segmentation applied for the segmentation of the left ventricle in echordiography videos. GraphECV includes instance segmentation, extraction of temporal, texture and statistical features to represent the nodes, construction of a graph using K-nearest neighbors, graph sampling to embed the graph with small amount of labeled nodes or graph signals, and finally a semi-supervised learning approach based on the minimization of the Sobolov norm of graph signals. The new algorithm is evaluated using two publicly available echocardiography videos, EchoNet-Dynamic and CAMUS datasets. The proposed approach outperforms other state-of-the-art methods under challenging background conditions.

This paper presents a fast approach for detecting coherent signals encountered in mul-tipath propagation environment. The developed approach utilizes a Toeplitz matrix reconstruction method along with DOA estimation using Root-MUSIC. The proposed approach exhibits several features such as robustness, high accuracy, and computational efficiency. Nevertheless, the primary advantage of the proposed Toeplitz-based de-correlation scheme is the fast detection of the maximum possible number of coherent signals, without the need of iteration and re-estimation.

The detection of natural disasters is a critical step in supporting Sustainable Development Goal 15. In this paper, we introduce a novel segmentation method to identify the areas affected by floods and landslides, using multiple sources such as multispectral imagery, Synthetic Aperture Radar data, Digital Elevation Models, slope, and hydrology maps. Traditional deep learning semantic segmentation algorithms encounter difficulties in handling multi-source data. In response to this challenge, we have refined the single-channel encoder-decoder architecture to better discern features derived from multiple sources. We introduce Multi-Source Semantic Segmentation with Channel and Spatial Attention Fusion (MSSCSAF-Net), a novel solution for semantic segmentation. MSSCSAF-Net incorporates a multi-source encoder designed to extract features from diverse inputs. This technique involves the addition of a Multi-Source Channel Spatial Attention Fusion Module to fuse the extracted features, and a Decoder Channel Spatial Attention Fusion Module to improve the fusion with attention of spatial and abstract features. The evaluation on the MMFlood and Landslide4Sense datasets finds that our approach exceeds the performance of current state-of-the-art segmentation techniques.