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Electronics
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Neural Network Applications to Digital Signal Processing
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Neural Network Applications to Digital Signal Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 8449

Special Issue Editors

Dr. Antonio Rios Navarro

E-Mail Website
Guest Editor
Robotics and Technology of Computers Lab, University of Seville, 41012 Seville, Spain
Interests: neuromorphic engineering; digital design; signal processing; machine learning; deep learning
Dr. Gianvito Urgese

E-Mail Website
Guest Editor
Politecnico di Torino, DIST, 10125 Torino, Italy
Interests: neuromorphic engineering; bioinformatics; machine learning; parallel algorithms

Special Issue Information

Dear Colleagues,

Biology offers system models that process information more efficiently than current technology. Neuromorphic computing systems offer a new computing paradigm for emerging scientific and engineering applications by mimicking the processing of neurobiological architectures. This computing paradigm requires a melding of novel engineering principles with knowledge gleaned from neuroscience. Artificial intelligence is increasingly present in applications that we use in our daily lives, especially in visual and audio processing tasks, but they require high computational power and are not as efficient compared to biological systems.

The scope of this Special Issue will be broadly interpreted to include but not be limited to:

  • Neuromorphic sensory fusion;
  • Event-based algorithms for visual and audio processing;
  • Neuromorphic control algorithms;
  • Neuromorphic vision sensing and processing;
  • New hardware architectures for neuromorphic edge computing;
  • Neuromorphic audio sensing and processing;
  • Neuromorphic sensory integration.

Dr. Antonio Rios Navarro
Dr. Gianvito Urgese
Guest Editors

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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • sensory fusion
  • neuromorphic engineering
  • spiking neural networks
  • event-based algorithm
  • neuromorphic control
  • neuromorphic edge computing
  • neuromorphic computing

Published Papers (3 papers)

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Research

12 pages, 2269 KiB  
Article
Temple Recommendation Engine for Route Planning Based on TPS Clustering CNN Method
by Dasarada Rajagopalan Thirupurasundari, Annadurai Hemlathadhevi, Amit Kumar Gupta, Ruchi Rani Garg and Mangal Sain
Electronics 2022, 11(16), 2630; https://doi.org/10.3390/electronics11162630 - 22 Aug 2022
Cited by 1 | Viewed by 2367
Abstract
There are no restrictions on religious or cultural practices in India. India’s temples are becoming an ideal platform for Hindu groups to express their ideals in a global context. For the sake of devotees, temples now require widespread accessibility and participation by a [...] Read more.
There are no restrictions on religious or cultural practices in India. India’s temples are becoming an ideal platform for Hindu groups to express their ideals in a global context. For the sake of devotees, temples now require widespread accessibility and participation by a wide range of individuals on major holidays. A pilgrim may be unable to determine which site to visit, or where to stay, due to a variety of considerations such as cost, location, and the interests of each individual user. A user’s preferences are taken into consideration when a personalized recommendation list is generated. A large number of systems use Collaborative Filtering to produce user recommendations. In order to generate user-specific recommendations, this system uses a filtering method dubbed the “hybrid approach”. The Proposed OTPS Cluster technique is used to determine TPS (Time, Place, and Service). Users’ interests and TPA recommendations are taken into account. Users can forecast the location of the temple based on the temple’s history. Collaborative Filtering and Material-Based Filtering were used to propose sites based on comparable users and content, respectively. Testing shows that the algorithm is capable of solving difficulties in standard tour routing and providing a temple visit route that is tailored to each individual’s preferences. This study makes use of data from the South Indian city of Temple in the form of temples. Full article
(This article belongs to the Special Issue Neural Network Applications to Digital Signal Processing)
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13 pages, 2775 KiB  
Article
Segmentation of Aorta 3D CT Images Based on 2D Convolutional Neural Networks
by Simone Bonechi, Paolo Andreini, Alessandro Mecocci, Nicola Giannelli, Franco Scarselli, Eugenio Neri, Monica Bianchini and Giovanna Maria Dimitri
Electronics 2021, 10(20), 2559; https://doi.org/10.3390/electronics10202559 - 19 Oct 2021
Cited by 12 | Viewed by 2944
Abstract
The automatic segmentation of the aorta can be extremely useful in clinical practice, allowing the diagnosis of numerous pathologies to be sped up, such as aneurysms and dissections, and allowing rapid reconstructive surgery, essential in saving patients’ lives. In recent years, the success [...] Read more.
The automatic segmentation of the aorta can be extremely useful in clinical practice, allowing the diagnosis of numerous pathologies to be sped up, such as aneurysms and dissections, and allowing rapid reconstructive surgery, essential in saving patients’ lives. In recent years, the success of Deep Learning (DL)-based decision support systems has increased their popularity in the medical field. However, their effective application is often limited by the scarcity of training data. In fact, collecting large annotated datasets is usually difficult and expensive, especially in the biomedical domain. In this paper, an automatic method for aortic segmentation, based on 2D convolutional neural networks (CNNs), using 3D CT (computed axial tomography) scans as input is presented. For this purpose, a set of 153 CT images was collected and a semi-automated approach was used to obtain their 3D annotations at the voxel level. Although less accurate, the use of a semi-supervised labeling technique instead of a full supervision proved necessary to obtain enough data in a reasonable amount of time. The 3D volume was analyzed using three 2D segmentation networks, one for each of the three CT views (axial, coronal and sagittal). Two different network architectures, U-Net and LinkNet, were used and compared. The main advantages of the proposed method lie in its ability to work with a reduced number of data even with noisy targets. In addition, analyzing 3D scans based on 2D slices allows for them to be processed even with limited computing power. The results obtained are promising and show that the neural networks employed can provide accurate segmentation of the aorta. Full article
(This article belongs to the Special Issue Neural Network Applications to Digital Signal Processing)
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17 pages, 1677 KiB  
Article
Semi-Supervised Machine Condition Monitoring by Learning Deep Discriminative Audio Features
by Iordanis Thoidis, Marios Giouvanakis and George Papanikolaou
Electronics 2021, 10(20), 2471; https://doi.org/10.3390/electronics10202471 - 11 Oct 2021
Cited by 6 | Viewed by 2178
Abstract
In this study, we aim to learn highly descriptive representations for a wide set of machinery sounds and exploit this knowledge to perform condition monitoring of mechanical equipment. We propose a comprehensive feature learning approach that operates on raw audio, by supervising the [...] Read more.
In this study, we aim to learn highly descriptive representations for a wide set of machinery sounds and exploit this knowledge to perform condition monitoring of mechanical equipment. We propose a comprehensive feature learning approach that operates on raw audio, by supervising the formation of salient audio embeddings in latent states of a deep temporal convolutional neural network. By fusing the supervised feature learning approach with an unsupervised deep one-class neural network, we are able to model the characteristics of each source and implicitly detect anomalies in different operational states of industrial machines. Moreover, we enable the exploitation of spatial audio information in the learning process, by formulating a novel front-end processing strategy for circular microphone arrays. Experimental results on the MIMII dataset demonstrate the effectiveness of the proposed method, reaching a state-of-the-art mean AUC score of 91.0%. Anomaly detection performance is significantly improved by incorporating multi-channel audio data in the feature extraction process, as well as training the convolutional neural network on the spatially invariant front-end. Finally, the proposed semi-supervised approach allows the concise modeling of normal machine conditions and accurately detects system anomalies, compared to existing anomaly detection methods. Full article
(This article belongs to the Special Issue Neural Network Applications to Digital Signal Processing)
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