scholarly journals Chimera

2021 ◽  
Vol 2 (2) ◽  
pp. 1-25
Author(s):  
Emekcan Aras ◽  
Stéphane Delbruel ◽  
Fan Yang ◽  
Wouter Joosen ◽  
Danny Hughes
Keyword(s):  
Software Evolution ◽  
Low Cost ◽  
Hardware Platform ◽  
Battery Lifetime ◽  
Software Upgrades ◽  
Field Programmable ◽  
Industrial Monitoring ◽  
Computationally Intensive ◽  
Reconfigurable Software ◽  
The Internet Of Things

The Internet of Things (IoT) is being deployed in an ever-growing range of applications, from industrial monitoring to smart buildings to wearable devices. Each of these applications has specific computational requirements arising from their networking, system security, and edge analytics functionality. This diversity in requirements motivates the need for adaptable end-devices, which can be re-configured and re-used throughout their lifetime to handle computation-intensive tasks without sacrificing battery lifetime. To tackle this problem, this article presents Chimera, a low-power platform for research and experimentation with reconfigurable hardware for the IoT end-devices. Chimera achieves flexibility and re-usability through an architecture based on a Flash Field Programmable Gate Array (FPGA) with a reconfigurable software stack that enables over-the-air hardware and software evolution at runtime. This adaptability enables low-cost hardware/software upgrades on the end-devices and an increased ability to handle computationally-intensive tasks. This article describes the design of the Chimera hardware platform and software stack, evaluates it through three application scenarios, and reviews the factors that have thus far prevented FPGAs from being utilized in IoT end-devices.

scholarly journals RPPUF: An Ultra-Lightweight Reconfigurable Pico-Physically Unclonable Function for Resource-Constrained IoT Devices

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3039
Author(s):  
Zhao Huang ◽  
Liang Li ◽  
Yin Chen ◽  
Zeyu Li ◽  
Quan Wang ◽  
...  
Keyword(s):  
Low Cost ◽  
Resource Constrained ◽  
Security Issues ◽  
Resource Limited ◽  
Field Programmable ◽  
Hardware Resource ◽  
Inherent Nature ◽  
Iot Devices ◽  
Multiple Samples ◽  
The Internet Of Things

With the advancement of the Internet of Things (IoTs) technology, security issues have received an increasing amount of attention. Since IoT devices are typically resource-limited, conventional security solutions, such as classical cryptography, are no longer applicable. A physically unclonable function (PUF) is a hardware-based, low-cost alternative solution to provide security for IoT devices. It utilizes the inherent nature of hardware to generate a random and unpredictable fingerprint to uniquely identify an IoT device. However, despite existing PUFs having exhibited a good performance, they are not suitable for effective application on resource-constrained IoT devices due to the limited number of challenge-response pairs (CRPs) generated per unit area and the large hardware resources overhead. To solve these problems, this article presents an ultra-lightweight reconfigurable PUF solution, which is named RPPUF. Our method is built on pico-PUF (PPUF). By incorporating configurable logics, one single RPPUF can be instantiated into multiple samples through configurable information K. We implement and verify our design on the Xilinx Spartan-6 field programmable gate array (FPGA) microboards. The experimental results demonstrate that, compared to previous work, our method increases the uniqueness, reliability and uniformity by up to 4.13%, 16.98% and 10.5%, respectively, while dramatically reducing the hardware resource overhead by 98.16% when a 128-bit PUF response is generated. Moreover, the bit per cost (BPC) metric of our proposed RPPUF increased by up to 28.5 and 53.37 times than that of PPUF and the improved butterfly PUF, respectively. This confirms that the proposed RPPUF is ultra-lightweight with a good performance, making it more appropriate and efficient to apply in FPGA-based IoT devices with constrained resources.

scholarly journals Hardware Neural Networks Modeling for Computing Different Performance Parameters of Rectangular, Circular, and Triangular Microstrip Antennas

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Taimoor Khan ◽  
Asok De
Keyword(s):  
Neural Networks ◽  
Low Cost ◽  
Microstrip Antennas ◽  
Performance Parameters ◽  
Neural Models ◽  
Hardware Platform ◽  
Generalized Neural Networks ◽  
Field Programmable ◽  
The Neural Networks ◽  
Good Agreement

In the last one decade, neural networks-based modeling has been used for computing different performance parameters of microstrip antennas because of learning and generalization features. Most of the created neural models are based on software simulation. As the neural networks show massive parallelism inherently, a parallel hardware needs to be created for creating faster computing machine by taking the advantages of the parallelism of the neural networks. This paper demonstrates a generalized neural networks model created on field programmable gate array- (FPGA-) based reconfigurable hardware platform for computing different performance parameters of microstrip antennas. Thus, the proposed approach provides a platform for developing low-cost neural network-based FPGA simulators for microwave applications. Also, the results obtained by this approach are in very good agreement with the measured results available in the literature.

scholarly journals Design and Construction of a Low Cost All-Digital Phase Locked Loop Based on Field Programmable Gate Array

2021 ◽  
Vol 1972 (1) ◽  
pp. 012054
Author(s):  
Chunyu Zhang ◽  
Shouxiang Wang ◽  
Ruxun He ◽  
Qianyu Zhao ◽  
Kai Wang
Keyword(s):  
Field Programmable Gate Array ◽  
Low Cost ◽  
Phase Locked Loop ◽  
Digital Phase ◽  
Field Programmable ◽  
Gate Array ◽  
Design And Construction

scholarly journals A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2254
Author(s):  
Francisco Javier González-Cañete ◽  
Eduardo Casilari
Keyword(s):  
Low Cost ◽  
Fall Detection ◽  
The Elderly ◽  
Point Of View ◽  
False Alarms ◽  
Battery Life ◽  
Area Network ◽  
Battery Lifetime ◽  
Detection Systems ◽  
Operational Aspects

Over the last few years, the use of smartwatches in automatic Fall Detection Systems (FDSs) has aroused great interest in the research of new wearable telemonitoring systems for the elderly. In contrast with other approaches to the problem of fall detection, smartwatch-based FDSs can benefit from the widespread acceptance, ergonomics, low cost, networking interfaces, and sensors that these devices provide. However, the scientific literature has shown that, due to the freedom of movement of the arms, the wrist is usually not the most appropriate position to unambiguously characterize the dynamics of the human body during falls, as many conventional activities of daily living that involve a vigorous motion of the hands may be easily misinterpreted as falls. As also stated by the literature, sensor-fusion and multi-point measurements are required to define a robust and reliable method for a wearable FDS. Thus, to avoid false alarms, it may be necessary to combine the analysis of the signals captured by the smartwatch with those collected by some other low-power sensor placed at a point closer to the body’s center of gravity (e.g., on the waist). Under this architecture of Body Area Network (BAN), these external sensing nodes must be wirelessly connected to the smartwatch to transmit their measurements. Nonetheless, the deployment of this networking solution, in which the smartwatch is in charge of processing the sensed data and generating the alarm in case of detecting a fall, may severely impact on the performance of the wearable. Unlike many other works (which often neglect the operational aspects of real fall detectors), this paper analyzes the actual feasibility of putting into effect a BAN intended for fall detection on present commercial smartwatches. In particular, the study is focused on evaluating the reduction of the battery life may cause in the watch that works as the core of the BAN. To this end, we thoroughly assess the energy drain in a prototype of an FDS consisting of a smartwatch and several external Bluetooth-enabled sensing units. In order to identify those scenarios in which the use of the smartwatch could be viable from a practical point of view, the testbed is studied with diverse commercial devices and under different configurations of those elements that may significantly hamper the battery lifetime.

Based on FPGA Intelligent Physiological Parameters Acquisition

2013 ◽  
Vol 344 ◽  
pp. 107-110
Author(s):  
Shun Ren Hu ◽  
Ya Chen Gan ◽  
Ming Bao ◽  
Jing Wei Wang
Keyword(s):  
High Speed ◽  
Low Cost ◽  
Physiological Parameters ◽  
Sensor Data ◽  
Low Power Consumption ◽  
Physiological Signal ◽  
Pulse Sensor ◽  
Monitoring Applications ◽  
Field Programmable ◽  
Design Ideas

For the physiological signal monitoring applications, as a micro-controller based on field programmable gate array (FPGA) physiological parameters intelligent acquisition system is given, which has the advantages of low cost, high speed, low power consumption. FPGA is responsible for the completion of pulse sensor, the temperature sensor, acceleration sensor data acquisition and serial output and so on. Focuses on the design ideas and architecture of the various subsystems of the whole system, gives the internal FPGA circuit diagram of the entire system. The whole system is easy to implement and has a very good promotional value.

High Performance Low Cost Implementation of FPGA-Based Fractional-Order Operators

2005 ◽  
Author(s):  
Cindy X. Jiang ◽  
Tom T. Hartley ◽  
Joan E. Carletta
Keyword(s):  
Fractional Order ◽  
Word Length ◽  
High Performance ◽  
Low Cost ◽  
Careful Consideration ◽  
Order System ◽  
System Quality ◽  
Gate Arrays ◽  
Field Programmable ◽  
Programmable Gate Arrays

Hardware implementation of fractional-order differentiators and integrators requires careful consideration of issues of system quality, hardware cost, and speed. This paper proposes using field programmable gate arrays (FPGAs) to implement fractional-order systems, and demonstrates the advantages that FPGAs provide. As an illustration, the fundamental operators to a real power is approximated via the binomial expansion of the backward difference. The resulting high-order FIR filter is implemented in a pipelined multiplierless architecture on a low-cost Spartan-3 FPGA. Unlike common digital implementations in which all filter coefficients have the same word length, this approach exploits variable word length for each coefficient. Our system requires twenty percent less hardware than a system of comparable quality generated by Xilinx’s System Generator on its most area-efficient multiplierless setting. The work shows an effective way to implement a high quality, high throughput approximation to a fractional-order system, while maintaining less cost than traditional FPGA-based designs.

Smart Railway Track and Crossing Gate Security System Based on IoT

2021 ◽  
Vol 10 (2) ◽  
pp. 1346-1355
Keyword(s):  
Human Error ◽  
Low Cost ◽  
Railway Track ◽  
Sonar Sensors ◽  
Long Time ◽  
Railway System ◽  
Direct Input ◽  
Road Crossing ◽  
Track Monitoring ◽  
The Internet Of Things

The railway system is one of the most widely used modes of transportation due to its low cost. To keep the railway system running smoothly, continuous track monitoring is needed. These days, the railway system is manually supervised. As a result, there is a greater risk of disasters, such as fatalities, occurring as a result of human error while monitoring. The main problem with manual system monitoring is that it takes a long time to process all of the necessary data. Since railway tracks are built over thousands of miles, it is virtually impossible to manually control the device over such a longdistance. At railway crossings, a lot of accidents happen. Crossing gates are usually opened and closed after receiving direct input from the station. If there is a delay in obtaining information from the station, there is a risk of swearing incidents. The main goal of this research is to simplify and protect the railway system. The proposed system employs Force Sensitive Resistor (FSR) detectors for automatic side road crossing protection. Any type of breakage, as well as vibration, can be efficiently detected with a higher degree of precision using Light Dependent Resistor (LRR) and laser detectors. In the event of an unexpected situation, such as an accident, the GSM module will begin communicating via message with the nearest control room for assistance. Sonar sensors are often used for obstacle avoidance when something unexpectedly appears in front of the train. The Internet of Things (IoT) has been added to the system to allow it to be monitored from anywhere in the sphere. The Arduino UNO is a microcontroller that serves as the system's backbone. The framework has the potential to be extremely beneficial to our country's railway economic growth.

scholarly journals Development of an IoT System for Environmental Monitoring: Software

2021 ◽  
Author(s):  
Benjamin Secker
Keyword(s):  
Environmental Monitoring ◽  
Low Cost ◽  
Proof Of Concept ◽  
Embedded Devices ◽  
Data Loggers ◽  
Environmental Monitoring System ◽  
Use Of The Internet ◽  
High Level ◽  
Monitoring Software ◽  
The Internet Of Things

Use of the Internet of Things (IoT) is poised to be the next big advancement in environmental monitoring. We present the high-level software side of a proof-of-concept that demonstrates an end-to-end environmental monitoring system,<br><div>replacing Greater Wellington Regional Council’s expensive data loggers with low-cost, IoT centric embedded devices, and it’s supporting cloud platform. The proof-of-concept includes a Micropython-based software stack running on an ESP32 microcontroller. The device software includes a built-in webserver that hosts a responsive Web App for configuration of the device. Telemetry data is sent over Vodafone’s NB-IoT network and stored in Azure IoT Central, where it can be visualised and exported.</div><br>While future development is required for a production-ready system, the proof-of-concept justifies the use of modern IoT technologies for environmental monitoring. The open source nature of the project means that the knowledge gained can be re-used and modified to suit the use-cases for other organisations.

scholarly journals Internet of Trees

2021 ◽  
Vol 10 (2) ◽  
pp. 750-753
Keyword(s):  
Forest Fires ◽  
Low Cost ◽  
Web Server ◽  
Fire Risk ◽  
Fire Detection ◽  
Natural Habitats ◽  
The Public ◽  
Recorded Data ◽  
The Impact ◽  
The Internet Of Things

The frequency of the forest fires that have occurred in the different parts of the world, In recent decades significant population problems and causing the death if the wild animals as the impact of these fires extend beyond the destruction of the natural habitats. The proliferation of the Internet of Things industry, resolutions for initial fire detection should be developed. The valuation of the fire risk of an area and communication of this realities to the population could reduce the amount of fires originated by accident or due to carelessness of the public user. This paper proposes a low-cost network based on NXP Rapid IOT kit and Long Range (Lora) technology to autonomously estimate the level of fire risk in the forest. The system comprises of NXP Rapid IOT kit which humidity, air quality and detection of the tree fall. The data from each node stored and processed in a in a web server or the mobile application that sendsthe recorded data to a web server for graphical conception of collected data.

scholarly journals FPGA Implementation of Weighted Online Sequential Extreme Learning Machine for Data Classification

2019 ◽  
Vol 9 (1) ◽  
pp. 6551-6557
Keyword(s):  
Extreme Learning Machine ◽  
High Speed ◽  
Bench Mark ◽  
Hardware Platform ◽  
Learning Speed ◽  
Field Programmable ◽  
Artificial Neural Network Ann ◽  
Learning Machine ◽  
Elm Classifier ◽  
Short Event

To design an efficient embedded module field-programmable gate array (FPGA) plays significant role. FPGA, a high speed reconfigurable hardware platform has been used in various field of research to produce the throughput efficiently. A now-a-days artificial neural network (ANN) is the most prevalent classifier for many analytical applications. In this paper, weighted online sequential extreme learning machine (WOS-ELM) classifier is presented and implemented in hardware environment to classify the different real-world bench-mark datasets. The faster learning speed, remarkable classification accuracy, lesser hardware resources, and short-event detection time, aid the hardware implementation of WOS-ELM classifier to design an embedded module. Finally, the developed hardware architecture of the WOS-ELM classifier is implemented on a high speed reconfigurable Xilinx Virtex (ML506) FPGA board to demonstrate the feasibility, effectiveness, and robustness of WOS-ELM classifier to classify the data in real-time environment.

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