Human-Machine Interaction and IoT Applications for a Smarter World

Gupta, Nishu; Gottapu, Srinivas Kiran; Nayak, Rakesh; Gupta, Anil Kumar; Derawi, Mohammad; Khakurel, Jayden

doi:10.1201/9781003268796

Cited by 4 publications

(7 citation statements)

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“…In this section, a numerical simulation study of the proposed distributed robust optimal control algorithm for Van der Pol oscillator agents is conducted. The comparison results between the ET control algorithm and the time-triggered (TT) control algorithm [1,Ch. 6] are performed.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…Internet of Things (IoT) technology has recently received significant attention from research communities and industrial societies due to the communication ability among devices and intelligent selection ability of perception and execution [1], [2], [3]. The controller for each device in the IoT can interact through the network to exchange data, generate control signals, and send feedback to others [4], [5], [6], [7], thanks to machine learning, distributed control algorithms for devices/plants that have been widely studied for recent years [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, the Van der Pol oscillators, an original model of an electrical circuit with a triode valve [18], have been interconnected in IoT networks due to the requirement of industrial applications [1,Ch. 6], [19].…”

Section: Introductionmentioning

confidence: 99%

“…Experimentally, a sliding-mode observer was used to estimate the states of the oscillators [28]. In optimal control, the oscillators were presented by the strict-feedback nonlinear systems [1,Ch. 6], [29], [30] and nonlinear systems with input constraints [1,Ch.…”

Section: Introductionmentioning

confidence: 99%

“…In optimal control, the oscillators were presented by the strict-feedback nonlinear systems [1,Ch. 6], [29], [30] and nonlinear systems with input constraints [1,Ch. 6], [31].…”

Section: Introductionmentioning

confidence: 99%

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H∞ Control for Oscillator Systems With Event-Triggering Signal Transmission of Internet of Things

2023

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This article proposes to design a distributed H ∞ optimal control algorithm for Van der Pol oscillators with unknown internal dynamics, input constraints and external disturbances, via event-triggering signal transmission of the Internet of Things (IoT). First, the graph theory for the IoT is introduced. Second, the dynamics of Van der Pol oscillators are transformed into the tracking dynamics which cooperate via the IoT network. Third, unlike the existing online optimal control algorithms using adaptive dynamic programming, we design an H ∞ optimal control algorithm employing an event-triggering signal transmission mechanism to reduce the burden of communication resource and computation bandwidth of the IoT network. As the triggering condition and approximation parameter update policies are appropriately designed, the algorithm guarantees that the Zeno phenomenon is free, the consensus errors are uniformly ultimately bounded, and the external disturbance is compensated. Finally, numerical simulation results with comparison to the time-triggering algorithms confirm the effectiveness of the proposed algorithm.

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Section: Numerical Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In optimal control, the oscillators were presented by the strict-feedback nonlinear systems [1,Ch. 6], [29], [30] and nonlinear systems with input constraints [1,Ch. 6], [31].…”

Section: Introductionmentioning

confidence: 99%

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H∞ Control for Oscillator Systems With Event-Triggering Signal Transmission of Internet of Things

2023

Self Cite

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Cross-Corpus Training Strategy for Speech Emotion Recognition Using Self-Supervised Representations

et al. 2023

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Speech Emotion Recognition (SER) plays a crucial role in applications involving human-machine interaction. However, the scarcity of suitable emotional speech datasets presents a major challenge for accurate SER systems. Deep Neural Network (DNN)-based solutions currently in use require substantial labelled data for successful training. Previous studies have proposed strategies to expand the training set in this framework by leveraging available emotion speech corpora. This paper assesses the impact of a cross-corpus training extension for a SER system using self-supervised (SS) representations, namely HuBERT and WavLM. The feasibility of training systems with just a few minutes of in-domain audio is also analyzed. The experimental results demonstrate that augmenting the training set with EmoDB (German), RAVDESS, and CREMA-D (English) datasets leads to improved SER accuracy on the IEMOCAP dataset. By combining a cross-corpus training extension and SS representations, state-of-the-art performance is achieved. These findings suggest that the cross-corpus strategy effectively addresses the scarcity of labelled data and enhances the performance of SER systems.

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Publish/Subscribe Method for Real-Time Data Processing in Massive IoT Leveraging Blockchain for Secured Storage

Ataei,

Eghmazi,

Shakerian

et al. 2023

Sensors

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In the Internet of Things (IoT) era, the surge in Machine-Type Devices (MTDs) has introduced Massive IoT (MIoT), opening new horizons in the world of connected devices. However, such proliferation presents challenges, especially in storing and analyzing massive, heterogeneous data streams in real time. In order to manage Massive IoT data streams, we utilize analytical database software such as Apache Druid version 28.0.0 that excels in real-time data processing. Our approach relies on a publish/subscribe mechanism, where device-generated data are relayed to a dedicated broker, effectively functioning as a separate server. This broker enables any application to subscribe to the dataset, promoting a dynamic and responsive data ecosystem. At the core of our data transmission infrastructure lies Apache Kafka version 3.6.1, renowned for its exceptional data flow management performance. Kafka efficiently bridges the gap between MIoT sensors and brokers, enabling parallel clusters of brokers that lead to more scalability. In our pursuit of uninterrupted connectivity, we incorporate a fail-safe mechanism with two Software-Defined Radios (SDR) called Nutaq PicoLTE Release 1.5 within our model. This strategic redundancy enhances data transmission availability, safeguarding against connectivity disruptions. Furthermore, to enhance the data repository security, we utilize blockchain technology, specifically Hyperledger Fabric, known for its high-performance attributes, ensuring data integrity, immutability, and security. Our latency results demonstrate that our platform effectively reduces latency for 100,000 devices, qualifying as an MIoT, to less than 25 milliseconds. Furthermore, our findings on blockchain performance underscore our model as a secure platform, achieving over 800 Transactions Per Second in a dataset comprising 14,000 transactions, thereby demonstrating its high efficiency.

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Human-Machine Interaction and IoT Applications for a Smarter World

Cited by 4 publications

References 0 publications

H∞ Control for Oscillator Systems With Event-Triggering Signal Transmission of Internet of Things

H∞ Control for Oscillator Systems With Event-Triggering Signal Transmission of Internet of Things

Cross-Corpus Training Strategy for Speech Emotion Recognition Using Self-Supervised Representations

Publish/Subscribe Method for Real-Time Data Processing in Massive IoT Leveraging Blockchain for Secured Storage

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