Design, Implementation, and Practical Evaluation of a Voice Recognition Based IoT Home Automation System for Low-Resource Languages and Resource-Constrained Edge IoT Devices: A System for Galician and Mobile Opportunistic Scenarios

Froiz-Míguez, Iván; Fraga‐Lamas, Paula; Fernández‐Caramés, Tiago M.

doi:10.1109/access.2023.3286391

Cited by 9 publications

(2 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After training the system, this will not require serious additional computing resources. We expect that the proposed combined application of algorithms and artificial intelligence methods will expand the possibilities surrounding distributed acoustic sensor utilization for the registration of voice commands [52][53][54] and their origin localization in smart home systems, smart production [55][56][57], and other Internet of Things applications [58].…”

Section: Discussionmentioning

confidence: 99%

Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

Turov,

Barkov,

Konstantinov

et al. 2023

Algorithms

View full text Add to dashboard Cite

This work studies the application of low-cost noise reduction algorithms for the data processing of distributed acoustic sensors (DAS). It presents an improvement of the previously described methodology using the activation function of neurons, which enhances the speed of data processing and the quality of event identification, as well as reducing spatial distortions. The possibility of using a cheaper radiation source in DAS setups is demonstrated. Optimal algorithms’ combinations are proposed for different types of the events recorded. The criterion for evaluating the effectiveness of algorithm performance was an increase in the signal-to-noise ratio (SNR). The finest effect achieved with a combination of algorithms provided an increase in SNR of 10.8 dB. The obtained results can significantly expand the application scope of DAS.

show abstract

Section: Discussionmentioning

confidence: 99%

Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

Turov,

Barkov,

Konstantinov

et al. 2023

Algorithms

View full text Add to dashboard Cite

show abstract

“…They adopted a robust combination of the SVM algorithm and Dynamic Time Warping (DTW) to accurately interpret commands from users' voice audio. Following this work, Froiz et al in [68] incorporated advanced technologies such as Wav2vec2 and Whisper models for speech recognition and the Bidirectional Encoder Representations from Transformers (BERT) model for NLP to enable seamless control of IoT devices through voice commands. Furthermore, Ali et al in [69] combined the Google Speech API, NLP model, and logistic regression to enable the recognition and execution of both structured and unstructured voice commands.…”

Section: Use Cases In Iot Applications and Characteristics Overviewmentioning

confidence: 99%

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

Panduman,

Funabiki,

Fajrianti

et al. 2024

Information

View full text Add to dashboard Cite

In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works.

show abstract

CNN-LSTM based deep learning application on Jetson Nano: Estimating electrical energy consumption for future smart homes

Gozuoglu,

Ozgonenel,

Gezegin

2024

Internet of Things

View full text Add to dashboard Cite

Design, Implementation, and Practical Evaluation of a Voice Recognition Based IoT Home Automation System for Low-Resource Languages and Resource-Constrained Edge IoT Devices: A System for Galician and Mobile Opportunistic Scenarios

Cited by 9 publications

References 55 publications

Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

Activation Function Dynamic Averaging as a Technique for Nonlinear 2D Data Denoising in Distributed Acoustic Sensors

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

CNN-LSTM based deep learning application on Jetson Nano: Estimating electrical energy consumption for future smart homes

Contact Info

Product

Resources

About