Metal-Organic Framework Reinforced Highly Stretchable and Durable Conductive Hydrogel-Based Triboelectric Nanogenerator for Biomotion Sensing and Wearable Human-Machine Interfaces

Flexible triboelectric nanogenerators (TENGs) with multifunctional sensing capabilities offer an elegant solution to address the growing energy supply challenges for wearable smart electronics. Herein, a highly stretchable and durable electrode for wearable TENG is developed using ZIF-8 as a reinforcing nanofiller in a hydrogel with LiCl electrolyte. ZIF-8 nanocrystals improve the hydrogel's mechanical properties by forming hydrogen bonds with copolymer chains, resulting in 2.7 times greater stretchability than pure hydrogel. The hydrogel electrode is encapsulated by microstructured silicone layers that act as triboelectric materials and prevent water loss from the hydrogel. Optimized ZIF-8-based hydrogel electrodes enhance the output performance of TENG through the dynamic balance of electric double layers (EDLs) during contact electrification. Thus, the as-fabricated TENG delivers an excellent power density of 3.47 Wm^–2, which is 3.2 times higher than pure hydrogel-based TENG. The developed TENG can scavenge biomechanical energy even at subzero temperatures to power small electronics and serve as excellent self-powered pressure sensors for human-machine interfaces (HMIs). The nanocomposite hydrogel-based TENG can also function as a wearable biomotion sensor, detecting body movements with high sensitivity. This study demonstrates the significant potential of utilizing ZIF-8 reinforced hydrogel as an electrode for wearable TENGs in energy harvesting and sensor technology.     

Enhanced Metaheuristics with Trust Aware Route Selection for Wireless Sensor Networks

Recently, a trust system was introduced to enhance security and cooperation between nodes in wireless sensor networks (WSN). In routing, the trust system includes or avoids nodes related to the estimated trust values in the routing function. This article introduces Enhanced Metaheuristics with Trust Aware Secure Route Selection Protocol (EMTA-SRSP) for WSN. The presented EMTA-SRSP technique majorly involves the optimal selection of routes in WSN. To accomplish this, the EMTA-SRSP technique involves the design of an oppositional Aquila optimization algorithm to choose safe routes for data communication. For the clustering process, the nodes with maximum residual energy will be considered cluster heads (CHs). In addition, the OAOA technique gets executed to choose optimal routes based on objective functions with multiple parameters such as energy, distance, and trust degree. The experimental validation of the EMTA-SRSP technique is tested, and the results exhibited a better performance of the EMTA-SRSP technique over other approaches.     

Suicide Ideation Detection of Covid Patients Using Machine Learning Algorithm

During Covid pandemic, many individuals are suffering from suicidal ideation in the world. Social distancing and quarantining, affects the patient emotionally. Affective computing is the study of recognizing human feelings and emotions. This technology can be used effectively during pandemic for facial expression recognition which automatically extracts the features from the human face. Monitoring system plays a very important role to detect the patient condition and to recognize the patterns of expression from the safest distance. In this paper, a new method is proposed for emotion recognition and suicide ideation detection in COVID patients. This helps to alert the nurse, when patient emotion is fear, cry or sad. The research presented in this paper has introduced Image Processing technology for emotional analysis of patients using Machine learning algorithm. The proposed Convolution Neural Networks (CNN) architecture with DnCNN preprocessing enhances the performance of recognition. The system can analyze the mood of patients either in real time or in the form of video files from CCTV cameras. The proposed method accuracy is more when compared to other methods. It detects the chances of suicide attempt based on stress level and emotional recognition.     

Rotation,Translation and Scale Invariant Sign Word Recognition Using Deep Learning

Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task. One of the main functions of sign language is to communicate with each other through hand gestures. Recognition of hand gestures has become an important challenge for the recognition of sign language. There are many existing models that can produce a good accuracy, but if the model test with rotated or translated images, they may face some difficulties to make good performance accuracy. To resolve these challenges of hand gesture recognition, we proposed a Rotation, Translation and Scale-invariant sign word recognition system using a convolutional neural network (CNN). We have followed three steps in our work: rotated, translated and scaled (RTS) version dataset generation, gesture segmentation, and sign word classification. Firstly, we have enlarged a benchmark dataset of 20 sign words by making different amounts of Rotation, Translation and Scale of the original images to create the RTS version dataset. Then we have applied the gesture segmentation technique. The segmentation consists of three levels, i) Otsu Thresholding with YCbCr, ii) Morphological analysis: dilation through opening morphology and iii) Watershed algorithm. Finally, our designed CNN model has been trained to classify the hand gesture as well as the sign word. Our model has been evaluated using the twenty sign word dataset, five sign word dataset and the RTS version of these datasets. We achieved 99.30% accuracy from the twenty sign word dataset evaluation, 99.10% accuracy from the RTS version of the twenty sign word evolution, 100% accuracy from the five sign word dataset evaluation, and 98.00% accuracy from the RTS version five sign word dataset evolution. Furthermore, the influence of our model exists in competitive results with state-of-the-art methods in sign word recognition.     

cFEM: a cluster based feature extraction method for network intrusion detection

International Journal of Information Security - The recent trend in network intrusion detection leverages key features of machine learning (ML) algorithms to detect network traffic anomalies....     

Fabrication of aluminium doped zinc oxide piezoelectric thin film on a silicon substrate for piezoelectric MEMS energy harvesters

Thin film piezoelectric materials play an essential role in micro electro mechanical system (MEMS) energy harvesting due to its low power requirement and high available energy densities. Non-ferroelectric piezoelectric materials such as ZnO and AlN are highly silicon compatible making it suitable for MEMS energy harvesters in self-powered microsystems. This work primarily describe the design, simulation and fabrication of aluminium doped zinc oxide (AZO) cantilever beam deposited on <100> silicon substrate. AZO was chosen due its high piezoelectric coupling coefficient, ease of deposition and excellent bonding with silicon substrate. Doping of ZnO with Al has improved the electrical properties, conductivity and thermal stability. The proposed design operates in transversal mode (d ₃₁ mode) which was structured as a parallel plated capacitor using Si/Al/AZO/Al layers. The highlight of this work is the successful design and fabrication of Al/AZO/Al on <100> silicon as the substrate to make the device CMOS compatible for electronic functionality integration. Design and finite element modeling was conducted using COMSOL? software to estimate the resonance frequency. RF Magnetron sputtering was chosen as the deposition method for aluminium and AZO. Material characterization was performed using X-ray diffraction and field emission scanning electron microscopy to evaluate the piezoelectric qualities, surface morphology and the cross section. The fabricated energy harvester generated 1.61?V open circuit output voltage at 7.77?MHz resonance frequency. The experimental results agreed with the simulation results. The measured output voltage is sufficient for low power wireless sensor nodes as an alternative power sources to traditional chemical batteries.     

A simple void node and loop‐free three‐dimensional routing protocol for multi‐hop wireless networks

Wireless networks are now very essential part for modern ubiquitous communication systems. The design of efficient routing and scheduling techniques for such networks have gained importance to ensure reliable communication. Most of the currently proposed geographic routing protocols are designed for 2D spatial distribution of user nodes, although in many practical scenarios user nodes may be deployed in 3D space also. In this paper, we propose 3D routing protocols for multihop wireless networks that may be implemented in two different ways depending on how the routing paths are computed. When the routing paths to different user nodes from the base station in the wireless network are computed by the base station, we call it centralized protocol (3DMA‐CS). A distributed routing (3DMA‐DS) protocol is implemented when respective routing path of each user node to the base station is computed by the user node. In both of these protocols, the user (base station) selects the relay node to forward packets in the direction of destination, from the set of its neighbours, which makes minimum angle with the reference line drawn from user (base station) to the base station (user), within its transmission range. The proposed protocols are free from looping problem and can solve the void node problem (VNP) of multihop wireless networks. Performance analysis of the proposed protocol is shown by calculating end‐to‐end throughput, average path length, end‐to‐end delay, and energy consumption of each routing path through extensive simulation under different network densities and transmission ranges.     

Private key agreement and secure communication for heterogeneous sensor networks

Key management is an important building block for all security operations in sensor networks. Most existing key management schemes try to establish shared keys for all pairs of neighbor sensors; hence, a large number of keys need to be preloaded on each sensor, which necessitates a large key space for the nodes in the network. The recent trend in research is to mainly consider homogeneous sensor networks, and to a lesser degree heterogeneous sensor networks, for key management. In this paper, we propose a novel key agreement protocol which is based on pairing-based cryptography over an elliptic curve. Using this protocol, any two nodes that need to communicate can independently compute the same secret key by using pairing and identity-based encryption properties. The proposed protocol significantly reduces the key space of a node. Additionally, the security analysis of the proposed protocol shows that it is robust against a number of attacks including wormhole attack, masquerade attacks, reply attacks, and message manipulation attacks.     

A study on controllable aluminium doped zinc oxide patterning by chemical etching for MEMS application

This present work reports on the study of controllable aluminium doped zinc oxide (AZO) patterning by chemical etching for MEMS application. The AZO thin film was prepared by RF magnetron sputtering as it is capable of producing uniform thin film at high deposition rates. X-Ray diffraction (XRD) and atomic force microscopy (AFM) characterization were done to characterize AZO thin film. The sputtered AZO thin film shows c-axis (002) orientation, low surface roughness and high crystalline quality. To pattern AZO thin film for MEMS application, wet etching was chosen due to its ease of processing with few controlling parameters. Four etching solutions were used namely: 10 % Nitric acid, 10 % Phosphoric acid, 10 % Acetic acid and Molybdenum etch solutions. For the first time, chemical etching using Molybdenum etch that consist of a mixture of CH₃COOH, HNO₃ and H₃PO₄ was characterized and reported. The effect of these acidic solutions on the undercut etching, vertical and lateral etch rate were studied. The etched AZO were characterized by scanning electron microscopy (SEM) and stylus profilometer. The investigations showed that the Molybdenum etch has the lowest undercut etching of 7.11 µm, and is highly effective in terms of lateral and vertical etching with an etch ratio of 1.30. Successful fine patterning of AZO thin films was demonstrated at device level on a surface acoustic wave resonator fabricated in 0.35 μm CMOS technology. The AZO thin film acts as the piezoelectric thin film for acoustic wave generation. Patterning of the AZO thin film is necessary for access to measurement probe pads. The working acoustic resonator showed resonance peak at 1.044 GHz at 45.28 dB insertion loss indicating that the proposed Molybdenum etch method does not adversely affect the device’s operating characteristics.

     

Gonokendra model: a response to “information poverty” in rural areas of Bangladesh

We argue that there is a need to understand impacts of information and communication technologies (ICT) projects in their local context, considering the participants' perspectives at the micro (community) level. Hence, this paper reports on the development and refinement of an extended framework to investigate ICT impact on development in three village areas in the developing country, Bangladesh. Through an interpretive study, we argue that previous studies failed to encapsulate many aspects such as mobility restriction, negative attitude towards female group and religious influences of ICT impact, especially at the micro level. Our extended framework demonstrates that ICT projects can lead to development, but only when social constraints are addressed.