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JLPEA
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Advanced Researches in Embedded Systems
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Advanced Researches in Embedded Systems

A special issue of Journal of Low Power Electronics and Applications (ISSN 2079-9268).

Deadline for manuscript submissions: closed (15 March 2022) | Viewed by 19467

Special Issue Editors

Prof. Dr. Nicu Bizon

E-Mail Website
Guest Editor
Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitesti, Romania
Interests: electrical engineering; power electronics; power converters; inverters; renewable energy; energy efficiency; energy storage; fuel cell; hybrid power systems; control; optimization; MATLAB simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Mihai Oproescu

E-Mail Website
Guest Editor
Faculty of Electronics, Communication and Computers, University of Pitesti, 110040 Pitesti, Romania
Interests: measurements in electronics and telecommunications; power electronics; industrial electronics; renewable energy sources; programming in C, C ++, C #
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid growth of computing systems embedded in various satellite toy applications has been possible due to the exponential increase in the processing power of on-chip systems simultaneously with a decrease in the cost for processors and memory.

Thus, to highlight the latest solutions in embedded systems, this Special Issue, entitled “Advanced researches in Embedded Systems,” is organized in collaboration with the 13th Edition of INTERNATIONAL CONFERENCE on Electronics, Computers and Artificial Intelligence (ECAI 2021). Contributions from ECAI 2021 are welcome, as well as papers from other fields of application of Embedded Systems and researchers outside the conference. Relevant topics, methods, and applications are included in (but not limited to) the list below.

  • Embedded hardware architectures
  • Embedded software architectures (Kernel and RTOS, Device Drivers, Driver Development, software tests, etc.)
  • Solutions for communication protocols (GPS, AGPS, RF, WiFi, LiFi, Bluetooth, Zigbee etc.)
  • Computer boards in engineering application
  • ASIC and FPGA solutions
  • Embedded systems for peripherals
  • Tools for debugging and testing embedded systems
  • Hardware & software-based optimizations for saving energy consumption
  • Solutions to improve the protection and reliability of embedded systems
  • Solutions to reduce the volume of embedded systems
  • Embedded applications (for industry, automotive, medical equipment, telecommunications equipment (mobiles, 4G and 5G, LTE, satellite communications, Wireless Sensor Network (WSN), Wireless Power Transfer (WPT), WLAN (Wireless Local Area Network), WANET (Wireless ad-hoc Network), WiMax, IoT, etc.), testing and measurement equipment, agriculture, airplanes, household appliances, etc.).

Papers received are subject to a rigorous, but fast, peer review procedure, ensuring wide dissemination of research results accepted for this Special Issue. 

Prof. Dr. Nicu Bizon
Dr. Mihai Oproescu
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. Journal of Low Power Electronics and Applications is an international peer-reviewed open access quarterly 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 1800 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

  • Hardware architectures
  • Software architectures
  • Communications protocols
  • Computer boards
  • ASIC
  • FPGA
  • Peripherals
  • Debugging
  • Testing
  • Energy consumption
  • Protection
  • Reliability
  • Engineering applications

Published Papers (6 papers)

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Research

14 pages, 1937 KiB  
Article
Efficiency of Priority Queue Architectures in FPGA
by Lukáš Kohútka
J. Low Power Electron. Appl. 2022, 12(3), 39; https://doi.org/10.3390/jlpea12030039 - 14 Jul 2022
Cited by 3 | Viewed by 2647
Abstract
This paper presents a novel SRAM-based architecture of a data structure that represents a set of multiple priority queues that can be implemented in FPGA or ASIC. The proposed architecture is based on shift registers, systolic arrays and SRAM memories. Such architecture, called [...] Read more.
This paper presents a novel SRAM-based architecture of a data structure that represents a set of multiple priority queues that can be implemented in FPGA or ASIC. The proposed architecture is based on shift registers, systolic arrays and SRAM memories. Such architecture, called MultiQueue, is optimized for minimum chip area costs, which leads to lower energy consumption too. The MultiQueue architecture has constant time complexity, constant critical path length and constant latency. Therefore, it is highly predictable and very suitable for real-time systems too. The proposed architecture was verified using a simplified version of UVM and applying millions of instructions with randomly generated input values. Achieved FPGA synthesis results are presented and discussed. These results show significant savings in FPGA Look-Up Tables consumption in comparison to existing solutions. More than 63% of Look-Up Tables can be saved using the MultiQueue architecture instead of the existing priority queues. Full article
(This article belongs to the Special Issue Advanced Researches in Embedded Systems)
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17 pages, 1632 KiB  
Article
Real-Time Embedded Implementation of Improved Object Detector for Resource-Constrained Devices
by Niranjan Ravi and Mohamed El-Sharkawy
J. Low Power Electron. Appl. 2022, 12(2), 21; https://doi.org/10.3390/jlpea12020021 - 13 Apr 2022
Cited by 12 | Viewed by 3545
Abstract
Artificial intelligence (A.I.) has revolutionised a wide range of human activities, including the accelerated development of autonomous vehicles. Self-navigating delivery robots are recent trends in A.I. applications such as multitarget object detection, image classification, and segmentation to tackle sociotechnical challenges, including the development [...] Read more.
Artificial intelligence (A.I.) has revolutionised a wide range of human activities, including the accelerated development of autonomous vehicles. Self-navigating delivery robots are recent trends in A.I. applications such as multitarget object detection, image classification, and segmentation to tackle sociotechnical challenges, including the development of autonomous driving vehicles, surveillance systems, intelligent transportation, and smart traffic monitoring systems. In recent years, object detection and its deployment on embedded edge devices have seen a rise in interest compared to other perception tasks. Embedded edge devices have limited computing power, which impedes the deployment of efficient detection algorithms in resource-constrained environments. To improve on-board computational latency, edge devices often sacrifice performance, creating the need for highly efficient A.I. models. This research examines existing loss metrics and their weaknesses, and proposes an improved loss metric that can address the bounding box regression problem. Enhanced metrics were implemented in an ultraefficient YOLOv5 network and tested on the targeted datasets. The latest version of the PyTorch framework was incorporated in model development. The model was further deployed using the ROS 2 framework running on NVIDIA Jetson Xavier NX, an embedded development platform, to conduct the experiment in real time. Full article
(This article belongs to the Special Issue Advanced Researches in Embedded Systems)
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13 pages, 798 KiB  
Article
Implementation of a Fuel Estimation Algorithm Using Approximated Computing
by Imed Ben Dhaou
J. Low Power Electron. Appl. 2022, 12(1), 17; https://doi.org/10.3390/jlpea12010017 - 16 Mar 2022
Cited by 2 | Viewed by 2431
Abstract
The rising concerns about global warming have motivated the international community to take remedial actions to lower greenhouse gas emissions. The transportation sector is believed to be one of the largest air polluters. The quantity of greenhouse gas emissions is directly linked to [...] Read more.
The rising concerns about global warming have motivated the international community to take remedial actions to lower greenhouse gas emissions. The transportation sector is believed to be one of the largest air polluters. The quantity of greenhouse gas emissions is directly linked to the fuel consumption of vehicles. Eco-driving is an emergent driving style that aims at improving gas mileage. Real-time fuel estimation is a critical feature of eco-driving and eco-routing. There are numerous approaches to fuel estimation. The first approach uses instantaneous values of speed and acceleration. This can be accomplished using either GPS data or direct reading through the OBDII interface. The second approach uses the average value of the speed and acceleration that can be measured using historical data or through web mapping. The former cannot be used for route planning. The latter can be used for eco-routing. This paper elaborates on a highly pipelined VLSI architecture for the fuel estimation algorithm. Several high-level transformation techniques have been exercised to reduce the complexity of the algorithm. Three competing architectures have been implemented on FPGA and compared. The first one uses a binary search algorithm, the second architecture employs a direct address table, and the last one uses approximation techniques. The complexity of the algorithm is further reduced by combining both approximated computing and precalculation. This approach helped reduce the floating-point operations by 30% compared with the state-of-the-art implementation. Full article
(This article belongs to the Special Issue Advanced Researches in Embedded Systems)
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15 pages, 6120 KiB  
Article
CondenseNeXtV2: Light-Weight Modern Image Classifier Utilizing Self-Querying Augmentation Policies
by Priyank Kalgaonkar and Mohamed El-Sharkawy
J. Low Power Electron. Appl. 2022, 12(1), 8; https://doi.org/10.3390/jlpea12010008 - 03 Feb 2022
Viewed by 2604
Abstract
Artificial Intelligence (AI) combines computer science and robust datasets to mimic natural intelligence demonstrated by human beings to aid in problem-solving and decision-making involving consciousness up to a certain extent. From Apple’s virtual personal assistant, Siri, to Tesla’s self-driving cars, research and development [...] Read more.
Artificial Intelligence (AI) combines computer science and robust datasets to mimic natural intelligence demonstrated by human beings to aid in problem-solving and decision-making involving consciousness up to a certain extent. From Apple’s virtual personal assistant, Siri, to Tesla’s self-driving cars, research and development in the field of AI is progressing rapidly along with privacy concerns surrounding the usage and storage of user data on external servers which has further fueled the need of modern ultra-efficient AI networks and algorithms. The scope of the work presented within this paper focuses on introducing a modern image classifier which is a light-weight and ultra-efficient CNN intended to be deployed on local embedded systems, also known as edge devices, for general-purpose usage. This work is an extension of the award-winning paper entitled ‘CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems’ published for the 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). The proposed neural network dubbed CondenseNeXtV2 utilizes a new self-querying augmentation policy technique on the target dataset along with adaption to the latest version of PyTorch framework and activation functions resulting in improved efficiency in image classification computation and accuracy. Finally, we deploy the trained weights of CondenseNeXtV2 on NXP BlueBox which is an edge device designed to serve as a development platform for self-driving cars, and conclusions will be extrapolated accordingly. Full article
(This article belongs to the Special Issue Advanced Researches in Embedded Systems)
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11 pages, 2719 KiB  
Article
LoRaWAN Base Station Improvement for Better Coverage and Capacity
by Filip Turčinović, Gordan Šišul and Marko Bosiljevac
J. Low Power Electron. Appl. 2022, 12(1), 1; https://doi.org/10.3390/jlpea12010001 - 30 Dec 2021
Cited by 2 | Viewed by 3196
Abstract
Low Power Wide Area Network (LPWAN) technologies provide long-range and low power consumption for many battery-powered devices used in Internet of Things (IoT). One of the most utilized LPWAN technologies is LoRaWAN (Long Range WAN) with over 700 million connections expected by the [...] Read more.
Low Power Wide Area Network (LPWAN) technologies provide long-range and low power consumption for many battery-powered devices used in Internet of Things (IoT). One of the most utilized LPWAN technologies is LoRaWAN (Long Range WAN) with over 700 million connections expected by the year 2023. LoraWAN base stations need to ensure stable and energy-efficient communication without unnecessary repetitions with sufficient range coverage and good capacity. To meet these requirements, a simple and efficient upgrade in the design of LoRaWAN base station is proposed, based on using two or more concentrators. The development steps are outlined in this paper and the evaluation of the enhanced base station is done with a series of measurements conducted in Zagreb, Croatia. Through these measurements we compared received messages and communication parameters on novel and standard base stations. The results showed a significant increase in the probability of successful reception of messages on the novel base station which corresponds to the increase of base station capacity and can be very beneficial for the energy consumption of most LoRaWAN end devices. Full article
(This article belongs to the Special Issue Advanced Researches in Embedded Systems)
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17 pages, 7992 KiB  
Article
Low-Power FPGA Architecture Based Monitoring Applications in Precision Agriculture
by Amine Saddik, Rachid Latif and Abdelhafid El Ouardi
J. Low Power Electron. Appl. 2021, 11(4), 39; https://doi.org/10.3390/jlpea11040039 - 30 Sep 2021
Cited by 11 | Viewed by 3425
Abstract
Today’s on-chip systems technology has grounded impressive advances in computing power and energy consumption. The choice of the right architecture depends on the application. In our case, we were studying vegetation monitoring algorithms in precision agriculture. This study presents a system based on [...] Read more.
Today’s on-chip systems technology has grounded impressive advances in computing power and energy consumption. The choice of the right architecture depends on the application. In our case, we were studying vegetation monitoring algorithms in precision agriculture. This study presents a system based on a monitoring algorithm for agricultural fields, an electronic architecture based on a CPU-FPGA SoC system and the OpenCL parallel programming paradigm. We focused our study on our own dataset of agricultural fields to validate the results. The fields studied in our case are in the Guelmin-Oued noun region in the south of Morocco. These fields are divided into two areas, with a total surface of 3.44 Ha2 for the first field and 3.73 Ha2 for the second. The images were collected using a DJI-type unmanned aerial vehicle and an RGB camera. Performance evaluation showed that the system could process up to 86 fps versus 12 fps or 20 fps in C/C++ and OpenMP implementations, respectively. Software optimizations have increased the performance to 107 fps, which meets real-time constraints. Full article
(This article belongs to the Special Issue Advanced Researches in Embedded Systems)
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