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Algorithms
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Artificial Intelligence and Signal Processing: Circuits and Systems
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Artificial Intelligence and Signal Processing: Circuits and Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (15 August 2024) | Viewed by 5225

Special Issue Editor

Prof. Dr. Sergey Y. Yurish

E-Mail Website
Guest Editor
International Frequency Sensor Association (IFSA), 08860 Castelldefels, Spain
Interests: smart sensors; optical sensors; frequency measurements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue contains extended papers from the 6th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2024), 17–19 April 2024, Funchal (Madeira Island), Portugal (https://aspai-conference.com). Advances in artificial intelligence (AI) and signal processing are driving the growth of the artificial intelligence market as improved appropriate technologies are critical to offer enhanced drones, self-driving cars, robotics, etc. Today, more and more sensor manufacturers are using machine learning to sensors and signal data for analysis. Hardware is becoming smaller and sensors are becoming cheaper, making Internet of things devices widely available for a variety of applications ranging from predictive maintenance to user behavior monitoring.  The artificial intelligence market size was USD 428.00 billion in 2022 and is projected to grow from USD 515.31 billion in 2023 to USD 2025.12 billion by 2030, exhibiting a CAGR of 21.6% However, the increased number of sensors in devices will inherently generate higher data throughput, which poses a serious challenge in managing and processing the tremendous amount of sensory information. Furthermore, traditional processing techniques in conventional sensing devices are no longer suitable for systematically labeling, processing, and analyzing the exuberant amount of information. The publication of the journal’s Special Issue will fill in this gap and help to answer to the coming challenges.

You may choose our Joint Special Issue in Electronics.

Dr. Sergey Y. Yurish
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • signal processing
  • microelectronics
  • industrial electronics

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Published Papers (4 papers)

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13 pages, 1056 KiB  
Article
A Framework for Evaluating Dynamic Directed Brain Connectivity Estimation Methods Using Synthetic EEG Signal Generation
by Zoran Šverko, Saša Vlahinić and Peter Rogelj
Algorithms 2024, 17(11), 517; https://doi.org/10.3390/a17110517 - 9 Nov 2024
Viewed by 1168
Abstract
This study presents a method for generating synthetic electroencephalography (EEG) signals to test dynamic directed brain connectivity estimation methods. Current methods for evaluating dynamic brain connectivity estimation techniques face challenges due to the lack of ground truth in real EEG signals. To [...] Read more.
This study presents a method for generating synthetic electroencephalography (EEG) signals to test dynamic directed brain connectivity estimation methods. Current methods for evaluating dynamic brain connectivity estimation techniques face challenges due to the lack of ground truth in real EEG signals. To address this, we propose a framework for generating synthetic EEG signals with predefined dynamic connectivity changes. Our approach allows for evaluating and optimizing dynamic connectivity estimation methods, particularly Granger causality (GC). We demonstrate the framework’s utility by identifying optimal window sizes and regression orders for GC analysis. The findings could guide the development of more accurate dynamic connectivity techniques. Full article
(This article belongs to the Special Issue Artificial Intelligence and Signal Processing: Circuits and Systems)
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30 pages, 2669 KiB  
Article
Fuzzy Multi-Agent Simulation for Collective Energy Management of Autonomous Industrial Vehicle Fleets
by Juliette Grosset, Ouzna Oukacha, Alain-Jérôme Fougères, Moïse Djoko-Kouam and Jean-Marie Bonnin
Algorithms 2024, 17(11), 484; https://doi.org/10.3390/a17110484 - 28 Oct 2024
Cited by 2 | Viewed by 951
Abstract
This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV [...] Read more.
This paper presents a multi-agent simulation implemented in Python, using fuzzy logic to explore collective battery recharge management for autonomous industrial vehicles (AIVs) in an airport environment. This approach offers adaptability and resilience through a distributed system, taking into account variations in AIV battery capacity. Simulation scenarios were based on a proposed charging/discharging model for an AIV battery. The results highlight the effectiveness of adaptive fuzzy multi-agent models in optimizing charging strategies, improving operational efficiency, and reducing energy consumption. Dynamic factors such as workload variations and AIV-infrastructure communication are taken into account in the form of heuristics, underlining the importance of flexible and collaborative approaches in autonomous systems. In particular, an infrastructure capable of optimizing charging according to energy tariffs can significantly reduce consumption during peak hours, highlighting the importance of such strategies in dynamic environments. An optimal control model is established to improve the energy consumption of each AIV during its mission. The energy consumption depends on the speed, as demonstrated via numerical simulations using realistic data. The speed profile of each AIV is adjusted according to the various constraints within an airport. Overall, the study highlights the potential of incorporating adaptive fuzzy multi-agent models for AIV energy management to boost efficiency and sustainability in industrial operations. Full article
(This article belongs to the Special Issue Artificial Intelligence and Signal Processing: Circuits and Systems)
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14 pages, 15389 KiB  
Article
Impact of Sliding Window Variation and Neuronal Time Constants on Acoustic Anomaly Detection Using Recurrent Spiking Neural Networks in Automotive Environment
by Shreya Kshirasagar, Andre Guntoro and Christian Mayr
Algorithms 2024, 17(10), 440; https://doi.org/10.3390/a17100440 - 1 Oct 2024
Cited by 1 | Viewed by 1283
Abstract
Acoustic perception of the automotive environment has the potential to advance driving potentials with enhanced safety. The challenge arises when these acoustic perception systems need to perform under resource and power constraints on edge devices. Neuromorphic computing has introduced spiking neural networks in [...] Read more.
Acoustic perception of the automotive environment has the potential to advance driving potentials with enhanced safety. The challenge arises when these acoustic perception systems need to perform under resource and power constraints on edge devices. Neuromorphic computing has introduced spiking neural networks in the context of ultra-low power sensory edge devices. Spiking architectures leverage biological plausibility to achieve computational capabilities, accurate performance, and great compatibility with neuromorphic hardware. In this work, we explore the depths of spiking neurons and feature components with the acoustic scene analysis task for siren sounds. This research work aims to address the qualitative analysis of sliding windows’ variation on the feature extraction front of the preprocessing pipeline. Optimization of the parameters to exploit the feature extraction stage facilitates the advancement of the performance of the acoustics anomaly detection task. We exploit the parameters for mel spectrogram features and FFT calculations, prone to be suitable for computations in hardware. We conduct experiments with different window sizes and the overlapping ratio within the windows. We present our results for performance measures like accuracy and onset latency to provide an insight on the choice of optimal window. The non-trivial motivation of this research is to understand the effect of encoding behavior of spiking neurons with different windows. We further investigate the heterogeneous nature of membrane and synaptic time constants and their impact on the accuracy of anomaly detection. On a large scale audio dataset comprising of siren sounds and road traffic noises, we obtain accurate predictions of siren sounds using a recurrent spiking neural network. The baseline dataset comprising siren and noise sequences is enriched with a bird dataset to evaluate the model with unseen samples. Full article
(This article belongs to the Special Issue Artificial Intelligence and Signal Processing: Circuits and Systems)
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21 pages, 15522 KiB  
Article
Efficient and Robust Arabic Automotive Speech Command Recognition System
by Soufiyan Ouali and Said El Garouani
Algorithms 2024, 17(9), 385; https://doi.org/10.3390/a17090385 - 2 Sep 2024
Viewed by 1160
Abstract
The automotive speech recognition field has become an active research topic as it enables drivers to activate various in-car functionalities without being distracted. However, research in Arabic remains nascent compared to English, French, and German. Therefore, this paper presents a Moroccan Arabic automotive [...] Read more.
The automotive speech recognition field has become an active research topic as it enables drivers to activate various in-car functionalities without being distracted. However, research in Arabic remains nascent compared to English, French, and German. Therefore, this paper presents a Moroccan Arabic automotive speech recognition system. Our system aims to enhance the driving experience to make it comfortable and safe while assisting individuals with disabilities. We created a speech dataset comprising 20 commonly used car commands. It consists of 5600 instances collected from Moroccan contributors and recorded in clean and noisy environments to increase its representativity. We used MFCC, weighted MFCC, and Spectral Subband Centroids (SSC) for feature extraction, as they demonstrated promising results in noisy settings. For classifier construction, we proposed a hybrid architecture, consisting of Bidirectional Long Short-Term Memory (Bi-LSTM) and the Convolutional Neural Network (CNN). Training our proposed model with WMFCC and SSC features achieved an accuracy of 98.48%, outperforming all baseline models we trained and outperforming the existing solutions in the state-of-the-art literature. Moreover, it shows promising results in a clean and noisy environment and maintains resilience to additive Gaussian noise while using few computational resources. Full article
(This article belongs to the Special Issue Artificial Intelligence and Signal Processing: Circuits and Systems)
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