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Intelligent Systems: Methods and Implementation
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Intelligent Systems: Methods and Implementation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 3786

Special Issue Editors

Dr. Ahmed Ben Atitallah

E-Mail Website
Guest Editor
Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Interests: embedded system; algorithm-architecture matching; SoC/SoPC architecture; VLSI; high-level synthesis; image and video processing; image denoising; medical image diagnostics; artificial intelligence
Dr. Anis Sahbani

E-Mail Website
Guest Editor
1. Institute for Intelligent Systems and Robotics (ISIR), Sorbonne University, CNRS, 75005 Paris, France
2. ENOVA Robotics S.A., Novation City, Sousse 4051, Tunisia
Interests: hybrid systems; manipulation planning using PRM; learning dexterous manipulation by imitation; visual servoing for manipulation; dexterous manipulation; co-manipulation systems in medical robotics

Special Issue Information

Dear Colleagues,

An intelligent system is a highly developed computer system with the ability to observe its environment, process that information, and act accordingly. It can cooperate and exchange information with other agents, such as humans and other computers. It has the capacity to acquire knowledge over time and adapt in response to new information.

This Special Issue emphasizes the following lines of investigation related to the use of intelligent processes, techniques, methods, and their implementation in hardware and/or software contexts that enable a wide variety of applications:

  • Human identification (visual surveillance, image and video processing, biometric monitoring, character or speech recognition);
  • Public health (medical diagnostics, violence pattern detection, biomedical engineering);
  • Transportation (traffic control systems, traffic flow analysis and congestion monitoring, autonomous cars, public transportation systems, accident prevention);
  • Aerospace (drones, mission planning, advanced guidance and navigation, air traffic control);
  • Robotics (advanced robotics and automation, autonomous systems, visual servoing).

Dr. Ahmed Ben Atitallah
Dr. Anis Sahbani
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. Applied Sciences 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

33 pages, 6045 KiB  
Article
A Display-Adaptive Pipeline for Dynamic Range Expansion of Standard Dynamic Range Video Content
by Gonzalo Luzardo, Asli Kumcu, Jan Aelterman, Hiep Luong, Daniel Ochoa and Wilfried Philips
Appl. Sci. 2024, 14(10), 4081; https://doi.org/10.3390/app14104081 - 11 May 2024
Viewed by 836
Abstract
Recent advancements in high dynamic range (HDR) display technology have significantly enhanced the contrast ratios and peak brightness of modern displays. In the coming years, it is expected that HDR televisions capable of delivering significantly higher brightness and, therefore, contrast levels than today’s [...] Read more.
Recent advancements in high dynamic range (HDR) display technology have significantly enhanced the contrast ratios and peak brightness of modern displays. In the coming years, it is expected that HDR televisions capable of delivering significantly higher brightness and, therefore, contrast levels than today’s models will become increasingly accessible and affordable to consumers. While HDR technology has gained prominence over the past few years, low dynamic range (LDR) content is still consumed due to a substantial volume of historical multimedia content being recorded and preserved in LDR. Although the amount of HDR content will continue to increase as HDR becomes more prevalent, a large portion of multimedia content currently remains in LDR. In addition, it is worth noting that although the HDR standard supports multimedia content with luminance levels up to 10,000 cd/m2 (a standard measure of brightness), most HDR content is typically limited to a maximum brightness of around 1000 cd/m2. This limitation aligns with the current capabilities of consumer HDR TVs but is a factor approximately five times brighter than current LDR TVs. To accurately present LDR content on a HDR display, it is processed through a dynamic range expansion process known as inverse tone mapping (iTM). This LDR to HDR conversion faces many challenges, including the inducement of noise artifacts, false contours, loss of details, desaturated colors, and temporal inconsistencies. This paper introduces complete inverse tone mapping, artifact suppression, and a highlight enhancement pipeline for video sequences designed to address these challenges. Our LDR-to-HDR technique is capable of adapting to the peak brightness of different displays, creating HDR video sequences with a peak luminance of up to 6000 cd/m2. Furthermore, this paper presents the results of comprehensive objective and subjective experiments to evaluate the effectiveness of the proposed pipeline, focusing on two primary aspects: real-time operation capability and the quality of the HDR video output. Our findings indicate that our pipeline enables real-time processing of Full HD (FHD) video (1920 × 1080 pixels), even on hardware that has not been optimized for this task. Furthermore, we found that when applied to existing HDR content, typically capped at a brightness of 1000 cd/m2, our pipeline notably enhances its perceived quality when displayed on a screen that can reach higher peak luminances. Full article
(This article belongs to the Special Issue Intelligent Systems: Methods and Implementation)
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30 pages, 2013 KiB  
Article
A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit
by Ohoud Almughram, Sami Abdullah ben Slama and Bassam A. Zafar
Appl. Sci. 2023, 13(9), 5539; https://doi.org/10.3390/app13095539 - 29 Apr 2023
Cited by 11 | Viewed by 2459
Abstract
These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems [...] Read more.
These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology. Full article
(This article belongs to the Special Issue Intelligent Systems: Methods and Implementation)
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