The programmable nature of smart textiles makes them an indispensable part of an emerging new technology field. Smart textile-integrated microelectronic systems (STIMES), which combine microelectronics and technology such as artificial intelligence and augmented or virtual reality, have been intensively explored. A vast range of research activities have been reported. Many promising applications in healthcare, the internet of things (IoT), smart city management, robotics, etc., have been demonstrated around the world. A timely overview and comprehensive review of progress of this field in the last five years are provided. Several main aspects are covered: functional materials, major fabrication processes of smart textile components, functional devices, system architectures and heterogeneous integration, wearable applications in human and nonhuman-related areas, and the safety and security of STIMES. The major types of textile-integrated nonconventional functional devices are discussed in detail: sensors, actuators, displays, antennas, energy harvesters and their hybrids, batteries and supercapacitors, circuit boards, and memory devices.

Smart Textile-Integrated Microelectronic Systems for Wearable Applications

This work presents and assesses the power of various volumetric, sentiment, and social network approaches to predict crucial decisions from online social media platforms. The views of individuals play a vital role in the discovery of some critical decisions. Social media has become a well-known platform for voicing the feelings of the general population around the globe for almost decades. Sentiment analysis or opinion mining is a method that is used to mine the general population’s views or feelings. In this respect, the forecasting of election results is an application of sentiment analysis aimed at predicting the outcomes of an ongoing election by gauging the mood of the public through social media. This survey paper outlines the evaluation of sentiment analysis techniques and tries to edify the contribution of the researchers to predict election results through social media content. This paper also gives a review of studies that tried to infer the political stance of online users using social media platforms such as Facebook and Twitter. Besides, this paper highlights the research challenges associated with predicting election results and open issues related to sentiment analysis. Further, this paper also suggests some future directions in respective election prediction using social media content.

The emergence of social media data and sentiment analysis in election prediction

Figurative language (FL) seems ubiquitous in all social media discussion forums and chats, posing extra challenges to sentiment analysis endeavors. Identification of FL schemas in short texts remains largely an unresolved issue in the broader field of natural language processing, mainly due to their contradictory and metaphorical meaning content. The main FL expression forms are sarcasm, irony and metaphor. In the present paper, we employ advanced deep learning methodologies to tackle the problem of identifying the aforementioned FL forms. Significantly extending our previous work (Potamias et al., in: International conference on engineering applications of neural networks, Springer, Berlin, pp 164–175, 2019), we propose a neural network methodology that builds on a recently proposed pre-trained transformer-based network architecture which is further enhanced with the employment and devise of a recurrent convolutional neural network. With this setup, data preprocessing is kept in minimum. The performance of the devised hybrid neural architecture is tested on four benchmark datasets, and contrasted with other relevant state-of-the-art methodologies and systems. Results demonstrate that the proposed methodology achieves state-of-the-art performance under all benchmark datasets, outperforming, even by a large margin, all other methodologies and published studies.

/pdf/a-transformer-based-approach-to-irony-and-sarcasm-detection-18dgd3e89c.pdf

A transformer-based approach to irony and sarcasm detection

In this communication, a compact two-element ultra-wideband (UWB) wearable multiple-input multiple-output (MIMO) antenna with high port isolation is presented. The proposed structure is composed of jeans material in which an `8' shaped stub is placed on the middle position of the antenna backside and connected to the partially suppressed ground structure to improve the port isolation characteristics. The antenna covers the frequency range from 2.74 to 12.33 GHz (about 127.27%) with the port isolation of >26 dB over the entire UWB frequency range. The envelope correlation co-efficient is found to be 9.9) throughout the complete operating band. The channel capacity loss for the proposed MIMO antenna is <;0.13 bit/s/Hz. The imprinted optimised UWB MIMO antenna covers the area size of 55 × 35 mm
 2
. The performances of the proposed antenna by the simulation and experimentation equally designated it a blameless candidate for the UWB applications.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-map.2018.5599

Compact wearable MIMO antenna with improved port isolation for ultra-wideband applications

The range of applications of Wireless Sensor Networks (WSNs) is increasing continuously despite of their serious constraints of the sensor nodes’ resources such as storage, processing capacity, communication range and energy. The main issues in WSN are the energy consumption and the delay in relaying data to the Sink node. This becomes extremely important when deploying a big number of nodes, like the case of industry pollution monitoring. We propose an artificial neural network based energy-efficient and robust routing scheme for WSNs called ELDC. In this technique, the network is trained on huge data set containing almost all scenarios to make the network more reliable and adaptive to the environment. Additionally, it uses group based methodology to increase the life-span of the overall network, where groups may have different sizes. An artificial neural network provides an efficient threshold values for the selection of a group's CN and a cluster head based on back propagation technique and allows intelligent, efficient, and robust group organization. Thus, our proposed technique is highly energy-efficient capable to increase sensor nodes’ lifetime. Simulation results show that it outperforms LEACH protocol by 42 percent, and other state-of-the-art protocols by more than 30 percent.

ELDC: An Artificial Neural Network Based Energy-Efficient and Robust Routing Scheme for Pollution Monitoring in WSNs

This paper proposes a guidance law for intercepting a stationary target in a three-dimensional engagement scenario with bounds on terminal impact angle and look-angle. In this work, a combined guidance law is derived, which consists of two Proportional Navigation (PN)-based guidance laws and can achieve any impact angle in the entire angular spectrum. The optimal PN-based guidance law proposed earlier was designed to achieve the same objective but without look-angle constraints. Here look-angle constraint is considered to take it one step ahead towards real-world applications. The proposed guidance law is also compared with a guidance law with look-angle bounds existed in the literature to demonstrate its efficiency. 

Look Angle Constrained Guidance Law for Stationary Target Interception in 3D

This paper describes the system description for the HinglishEval challenge at INLG 2022. The goal of this task was to investigate the factors influencing the quality of the code-mixed text generation system. The task was divided into two subtasks, quality rating prediction and annotators disagreement prediction of the synthetic Hinglish dataset. We attempted to solve these tasks using sentence-level embeddings, which are obtained from mean pooling the contextualized word embeddings for all input tokens in our text. We experimented with various classifiers on top of the embeddings produced for respective tasks. Our best-performing system ranked 1st on subtask B and 3rd on subtask A. 

niksss at HinglishEval: Language-agnostic BERT-based Contextual
  Embeddings with Catboost for Quality Evaluation of the Low-Resource
  Synthetically Generated Code-Mixed Hinglish Text

In this paper, flow over an elliptical cylinder located near a moving wall is investigated numerically to ascertain the effect of aspect ratio (AR) on flow characteristics in terms of wake pattern, separation and stagnation points, and hydrodynamic forces. The two-dimensional simulations span both steady and unsteady flow regimes, where the effect of AR is analyzed in conjunction with other important parameters, such as gap ratio (GR∈ [0.6, 1.2]), Reynolds number (Re ∈ [5, 150]), and angle of attack (AOA∈ [−45°, 45°]). At a low AR = 0.1, significant differences in the wake pattern and critical Re for steady to unsteady wake transition are observed in GR-Re space in comparison with previous results for high AR cylinders. This results from an influence of AR on vorticity convection into the wake. Additionally, the effect of variation in AR from a flat plate (AR = 0) to a circular cylinder (AR = 1) on the wake pattern is discussed and presented in AR-Re space at a low GR. The locations of separation and stagnation points are shown to result from an interplay of AR and wall suppression depicting a low sensitivity to Re with low AR or high wall suppression effect. Subsequently, the effect of AR in conjunction with AOA is analyzed where counterclockwise inclination is observed to reduce wall suppression, while a competing effect arising from the contrasting movements of cylinder bottom and rear side governs the wake pattern for clockwise inclination that is explicit at a certain AR. Eventually, the variations in drag and lift forces with AR for different AOA are characterized.

https://pubs.aip.org/aip/pof/article-pdf/doi/10.1063/5.0151879/17999671/063606_1_5.0151879.pdf

Aspect ratio effect on flow past an elliptical cylinder near a moving wall

In this chapter, bendable textile antenna with partial ground plane is proposed. The anticipated antenna is made of jeans substrate which acts as a flexible material. The anticipated antenna is compared with some earlier published papers. The CST microwave studio is used to model the anticipated antenna, and after simulation, various results are obtained. The anticipated antenna has been provided the bandwidth from 1.90 GHz to 6.81 GHz having wide bandwidth of 112.74%. The designed antenna is suitable for wideband and Bluetooth application.

Nikhil Kumar Singh

Papers

Look Angle Constrained Guidance Law for Stationary Target Interception in 3D

niksss at HinglishEval: Language-agnostic BERT-based Contextual Embeddings with Catboost for Quality Evaluation of the Low-Resource Synthetically Generated Code-Mixed Hinglish Text

Aspect ratio effect on flow past an elliptical cylinder near a moving wall

Optimization of Textile Antennas Using Flexible Dielectric Material