Characterization of Cervical-Cranial Muscle Network in Correlation with Vocal Features

Objective evaluation of physiological responses using non-invasive methods has attracted great interest regarding the assessment of vocal performance and disorders. This paper, for the first time, demonstrates that the topographical features of the cervical-cranial intermuscular coherence network generated using surface electromyography (sEMG) have a strong potential for detecting subtle changes in vocal performance. For this purpose, in this paper, 12 sEMG signals were collected from six cervical and cranial muscles bilaterally. Data were collected from four subjects without a history of a voice disorder performing a series of vocal tasks. The vocal tasks were varied phonation (an /a/ sustained for the maximal duration with combinations of two levels of loudness and two levels of pitch), a pitch glide from low to high, singing a familiar song, spontaneous speech, and reading with different loudness levels. The varied phonation tasks showed the median degree, and weighted clustering coefficient of the coherence-based intermuscular network ascends monotonically, with a high effect size (|rrb| = 0.52). The set of tasks, including pitch glide, singing, and speech, was significantly distinguishable using the network features as both degree and weighted clustering coefficient had a very high effect size (|rrb| > 0.83) across these tasks. Also, pitch glide has the highest degree and weighted clustering coefficient among all tasks (degree > 0.6, weighted clustering coefficient > 0.6). Spectrotemporal features performed far less effective than the proposed functional muscle network metrics to differentiate the vocal tasks. The highest effect size for spectrotemporal features was only |rrb| = 0.19. In this paper, for the first time, the power of a cervical-cranial muscle network has been demonstrated as a neurophysiological window to vocal performance. The results also shed light on the tasks with the highest network involvement, which may be potentially used in monitoring vocal disorders and tracking rehabilitation progress.

Cluster Formation and Maintenance Techniques for Mobile Adhoc Networks with Improved Quality of Service

— In an Adhoc Network, every node is mobile and self-contained. As these networks lack infrastructure, highly adaptive algorithms are required to deal with frequent mobility changes by member nodes as well as Cluster Head (CH) nodes. The weighted clustering algorithms contribute significantly to cluster-based routing. In these algorithms, the selection of cluster heads is the most important task. In weighted clustering methods, the selected CH did their best to serve the network. However, the CH may become overloaded due to the arrival of nodes greater than their desired threshold value. In this case, the CH can become a bottleneck as it is unable to cope with rapidly increasing loads which ultimately degrade the network performance. In this paper, we address three network issues (i) Member Node movement (ii) Cluster head Node movement, and (iii) Overload at the Cluster head node caused due to mobility of nodes. Our proposed method Cluster Formation and Maintenance Techniques for Mobile Adhoc Networks with Improved Quality of Service (CFMIQS) include various adaptive algorithms to provide solutions to deal with these network issues and improve network Quality of Service (QoS). The Simulated Results are compared with the K-means AODV algorithm, the results showed better Packet Delivery Fraction (PDF) and Throughput values. Keywords— Cluster partition, MANET, Primary Cluster head, QoS, Secondary Cluster head

Micro, Small, and Medium Enterprises’ Business Vulnerability Cluster in Indonesia: An Analysis Using Optimized Fuzzy Geodemographic Clustering

The COVID-19 pandemic has caused effects in many sectors, including in businesses and enterprises. The most vulnerable businesses to COVID-19 are micro, small, and medium enterprises (MSMEs). Therefore, this paper aims to analyze the business vulnerability of MSMEs in Indonesia using the fuzzy spatial clustering approach. The fuzzy spatial clustering approach had been implemented to analyze the social vulnerability to natural hazards in Indonesia. Moreover, this study proposes the Flower Pollination Algorithm (FPA) to optimize the Fuzzy Geographically Weighted Clustering (FGWC) in order to cluster the business vulnerability in Indonesia. We performed the data analysis with the dataset from Indonesia’s national socioeconomic and labor force survey (SUSENAS and SAKERNAS). We first compared the performance of FPA with traditional FGWC, as well as several known optimization algorithms in FGWC such as Artificial Bee Colony, Intelligent Firefly Algorithm, Particle Swarm Optimization, and Gravitational Search Algorithm. Our results showed that FPAFGWC has the best performance in optimizing the FGWC clustering result in the business vulnerability context. We found that almost all of the regions in Indonesia outside Java Island have vulnerable businesses. Meanwhile, in most of Java Island, particularly the JABODETABEK area that is the national economic backbone, businesses are not vulnerable. Based on the results of the study, we provide the recommendation to handle the gap between the number of micro and small enterprises (MSMEs) in Indonesia.

Energy Efficient Cluster based Routing Protocol for WSN based on Hybrid BSO-TLBO Optimization Model

The most famous Wireless Sensor Networks(WSN) is one of the cheapest and rapidly evolving networks in modern communication. It can be used to sense various substantial and environmental specifications by providing cost-effective sensor devices. The development of these sensor networks is exploited to provide an energy-efficient weighted clustering method to increase the lifespan of the network. We propose a novel energy-efficient method which utilizes the Brainstorm algorithm in order to adopt the ideal CH to reduce energy-draining. Further, the effectiveness of the BrainStorm Optimization (BSO) algorithm is enhanced with the incorporation of the Modified Teacher-Learner Optimized(MTLBO) algorithm with it. The modified BSO-MTLBO algorithm can be used to attain an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The performance of our proposed work is analyzed with other existing approaches andinference that our approach performs better than all the other approaches.

Energy Efficient Cluster based Routing Protocol for WSN based on Hybrid BSO-TLBO Optimization Model

The most famous Wireless Sensor Networks (WSN) is one of the cheapest and rapidly evolving networks in modern communication. It can be used to sense various substantial and environmental specifications by providing cost-effective sensor devices. The development of these sensor networks is exploited to provide an energy-efficient weighted clustering method to increase the lifespan of the network. We propose a novel energy-efficient method which utilizes the Brainstorm algorithm in order to adopt the ideal CH to reduce energy-draining. Further, the effectiveness of the Brain Storm Optimization (BSO) algorithm is enhanced with the incorporation of the Modified Teacher-Learner Optimized (MTLBO) algorithm with it. The modified BSO-MTLBO algorithm can be used to attain an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The performance of our proposed work is analyzed with other existing approaches and inference that our approach performs better than all the other approaches.