Hardware Platforms

2006 ◽  
pp. 239-269
Keyword(s):  
Hardware Platforms

Development of hardware and software system for access control and management

Ergodesign ◽  
2020 ◽  
Vol 2020 (1) ◽  
pp. 19-24
Author(s):  
Igor Pestov ◽  
Polina Shinkareva ◽  
Sofia Kosheleva ◽  
Maxim Burmistrov
Keyword(s):  
Access Control ◽  
High Mobility ◽  
Raspberry Pi ◽  
Software System ◽  
Hardware System ◽  
Software And Hardware ◽  
Arduino Uno ◽  
Control And Management ◽  
Hardware Platforms ◽  
Hardware And Software System

This article aims to develop a hardware-software system for access control and management based on the hardware platforms Arduino Uno and Raspberry Pi. The developed software and hardware system is designed to collect data and store them in the database. The presented complex can be carried and used anywhere, which explains its high mobility.

scholarly journals Virtualizing AI at the Distributed Edge Towards Intelligent IoT Applications

2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro
Keyword(s):  
Resource Constraints ◽  
Smart Cities ◽  
Traditional Approach ◽  
Low Cost ◽  
Iot Applications ◽  
Software And Hardware ◽  
Iot Devices ◽  
Consumer Devices ◽  
Hardware Platforms ◽  
Smart Factories

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.

scholarly journals A Model-Based Approach for Adaptable Middleware Evolution in WSN Platforms

2021 ◽  
Vol 10 (1) ◽  
pp. 20
Author(s):  
Walter Tiberti ◽  
Dajana Cassioli ◽  
Antinisca Di Marco ◽  
Luigi Pomante ◽  
Marco Santic
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Embedded System ◽  
Operating Systems ◽  
Parallel Evolution ◽  
Wireless Sensor ◽  
New Techniques ◽  
Model Based ◽  
And Performance ◽  
Hardware Platforms

Advances in technology call for a parallel evolution in the software. New techniques are needed to support this dynamism, to track and guide its evolution process. This applies especially in the field of embedded systems, and certainly in Wireless Sensor Networks (WSNs), where hardware platforms and software environments change very quickly. Commonly, operating systems play a key role in the development process of any application. The most used operating system in WSNs is TinyOS, currently at its TinyOS 2.1.2 version. The evolution from TinyOS 1.x and TinyOS 2.x made the applications developed on TinyOS 1.x obsolete. In other words, these applications are not compatible out-of-the-box with TinyOS 2.x and require a porting action. In this paper, we discuss on the porting of embedded system (i.e., Wireless Sensor Networks) applications in response to operating systems’ evolution. In particular, using a model-based approach, we report the porting we did of Agilla, a Mobile-Agent Middleware (MAMW) for WSNs, on TinyOS 2.x, which we refer to as Agilla 2. We also provide a comparative analysis about the characteristics of Agilla 2 versus Agilla. The proposed Agilla 2 is compatible with TinyOS 2.x, has full capabilities and provides new features, as shown by the maintainability and performance measurement presented in this paper. An additional valuable result is the architectural modeling of Agilla and Agilla 2, missing before, which extends its documentation and improves its maintainability.

scholarly journals ARINC653 Channel Robustness Verification Using LeonViP-MC, a LEON4 Multicore Virtual Platform

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1179
Author(s):  
Jonatan Sánchez ◽  
Antonio da Silva ◽  
Pablo Parra ◽  
Óscar R. Polo ◽  
Agustín Martínez Hellín ◽  
...  
Keyword(s):  
Fault Tolerance ◽  
Architectural Design ◽  
Fault Injection ◽  
Control Unit ◽  
Tolerance Mechanisms ◽  
Efficient Data ◽  
Instrument Control ◽  
Virtual Platform ◽  
Hardware Platforms ◽  
The University

Multicore hardware platforms are being incorporated into spacecraft on-board systems to achieve faster and more efficient data processing. However, such systems lead to increased complexity in software development and represent a considerable challenge, especially concerning the runtime verification of fault-tolerance requirements. To address the ever-challenging verification of this kind of requirement, we introduce a LEON4 multicore virtual platform called LeonViP-MC. LeonViP-MC is an evolution of a previous development called Leon2ViP, carried out by the Space Research Group of the University of Alcalá (SRG-UAH), which has been successfully used in the development and testing of the flight software of the instrument control unit (ICU) of the energetic particle detector (EPD) on board the Solar Orbiter. This paper describes the LeonViP-MC architectural design decisions oriented towards fault-injection campaigns to verify software fault-tolerance mechanisms. To validate the simulator, we developed an ARINC653 communications channel that incorporates fault-tolerance mechanisms and is currently being used to develop a hypervisor level for the GR740 platform.

scholarly journals A Review of Algorithms and Hardware Implementations for Spiking Neural Networks

2021 ◽  
Vol 11 (2) ◽  
pp. 23
Author(s):  
Duy-Anh Nguyen ◽  
Xuan-Tu Tran ◽  
Francesca Iacopi
Keyword(s):  
Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Training Algorithms ◽  
Current State ◽  
Hardware Implementations ◽  
Neuromorphic Hardware ◽  
High Level ◽  
Event Based ◽  
Hardware Platforms

Deep Learning (DL) has contributed to the success of many applications in recent years. The applications range from simple ones such as recognizing tiny images or simple speech patterns to ones with a high level of complexity such as playing the game of Go. However, this superior performance comes at a high computational cost, which made porting DL applications to conventional hardware platforms a challenging task. Many approaches have been investigated, and Spiking Neural Network (SNN) is one of the promising candidates. SNN is the third generation of Artificial Neural Networks (ANNs), where each neuron in the network uses discrete spikes to communicate in an event-based manner. SNNs have the potential advantage of achieving better energy efficiency than their ANN counterparts. While generally there will be a loss of accuracy on SNN models, new algorithms have helped to close the accuracy gap. For hardware implementations, SNNs have attracted much attention in the neuromorphic hardware research community. In this work, we review the basic background of SNNs, the current state and challenges of the training algorithms for SNNs and the current implementations of SNNs on various hardware platforms.

scholarly journals Generation and robustness of quantum entanglement in spin graphs

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jan Riegelmeyer ◽  
Dan Wignall ◽  
Marta P. Estarellas ◽  
Irene D’Amico ◽  
Timothy P. Spiller
Keyword(s):  
Quantum Information Processing ◽  
Bell State ◽  
Spin Chains ◽  
Coupling Scheme ◽  
High Fidelity ◽  
Coupling Ratio ◽  
Entanglement Generation ◽  
Fabrication Errors ◽  
Coupling Strengths ◽  
Hardware Platforms

AbstractEntanglement is a crucial resource for quantum information processing, and so protocols to generate high-fidelity entangled states on various hardware platforms are in demand. While spin chains have been extensively studied to generate entanglement, graph structures also have such potential; however, only a few classes of graphs have been explored for this specific task. In this paper, we apply a particular coupling scheme involving two different coupling strengths to a graph of two interconnected $$3\times 3$$ 3 × 3 square graphs such that it effectively contains three defects. We show how this structure allows generation of a Bell state whose fidelity depends on the chosen coupling ratio. We apply partitioned graph theory in order to reduce the dimension of the graph and show that, using a reduced graph or a reduced chain, we can still simulate the same protocol with identical dynamics. Finally, we investigate how fabrication errors affect the entanglement generation protocol and how the different equivalent structures are affected, finding that for some specific coupling ratios they are extremely robust.

AsTAR: Sustainable Energy Harvesting for the Internet of Things through Adaptive Task Scheduling

2022 ◽  
Vol 18 (1) ◽  
pp. 1-34
Author(s):  
Fan Yang ◽  
Ashok Samraj Thangarajan ◽  
Gowri Sankar Ramachandran ◽  
Wouter Joosen ◽  
Danny Hughes
Keyword(s):  
Energy Harvesting ◽  
Internet Of Things ◽  
A Priori ◽  
Optimum Level ◽  
Energy Aware ◽  
Energy Demands ◽  
Sustainable Operation ◽  
The Face ◽  
Execution Rates ◽  
Hardware Platforms

Battery-free Internet-of-Things devices equipped with energy harvesting hold the promise of extended operational lifetime, reduced maintenance costs, and lower environmental impact. Despite this clear potential, it remains complex to develop applications that deliver sustainable operation in the face of variable energy availability and dynamic energy demands. This article aims to reduce this complexity by introducing AsTAR, an energy-aware task scheduler that automatically adapts task execution rates to match available environmental energy. AsTAR enables the developer to prioritize tasks based upon their importance, energy consumption, or a weighted combination thereof. In contrast to prior approaches, AsTAR is autonomous and self-adaptive, requiring no a priori modeling of the environment or hardware platforms. We evaluate AsTAR based on its capability to efficiently deliver sustainable operation for multiple tasks on heterogeneous platforms under dynamic environmental conditions. Our evaluation shows that (1) comparing to conventional approaches, AsTAR guarantees Sustainability by maintaining a user-defined optimum level of charge, and (2) AsTAR reacts quickly to environmental and platform changes, and achieves Efficiency by allocating all the surplus resources following the developer-specified task priorities. (3) Last, the benefits of AsTAR are achieved with minimal performance overhead in terms of memory, computation, and energy.

Optimal path planning of a disinfection mobile robot against COVID-19 in a ROS-based research platform

2021 ◽  
Vol 16 (4) ◽  
pp. 405-417
Author(s):  
L. Banjanovic-Mehmedovic ◽  
I. Karabegovic ◽  
J. Jahic ◽  
M. Omercic
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Pso Algorithm ◽  
Optimal Path Planning ◽  
Localization And Mapping ◽  
Increasing Demand ◽  
Window Approach ◽  
Path Planner ◽  
Hardware Platforms ◽  
Reactive Collision

Due to COVID-19 pandemic, there is an increasing demand for mobile robots to substitute human in disinfection tasks. New generations of disinfection robots could be developed to navigate in high-risk, high-touch areas. Public spaces, such as airports, schools, malls, hospitals, workplaces and factories could benefit from robotic disinfection in terms of task accuracy, cost, and execution time. The aim of this work is to integrate and analyse the performance of Particle Swarm Optimization (PSO) algorithm, as global path planner, coupled with Dynamic Window Approach (DWA) for reactive collision avoidance using a ROS-based software prototyping tool. This paper introduces our solution – a SLAM (Simultaneous Localization and Mapping) and optimal path planning-based approach for performing autonomous indoor disinfection work. This ROS-based solution could be easily transferred to different hardware platforms to substitute human to conduct disinfection work in different real contaminated environments.

Large Neural Network Simulations on Multiple Hardware Platforms

1997 ◽  
pp. 919-923 ◽  
Author(s):  
Per Hammarlund ◽  
Örjan Ekeberg ◽  
Tomas Wilhelmsson ◽  
Anders Lansner
Keyword(s):  
Neural Network ◽  
Network Simulations ◽  
Hardware Platforms
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