Low-cost Hardware Platform Research Articles

Processing techniques for metrological improvement of low-cost smart meter hardware solution for IEC 61000-4-7 Class I harmonics measurements

The work investigates the feasibility of harmonic analysis implementation on smart meter microcontroller devices, according to IEC 61000-4-30 Class A and IEC 61000-4-7 Class I requirements. The final aim was to evaluate to what extent Class I harmonic analysis can be integrated into existing low-cost hardware platforms for smart metering, which normally have limited hardware features, especially concerning the ADC and the possibility of varying the sampling frequency with high resolution, according to the power system signal frequency. An extended experimental characterization is carried out on a case study device, aimed at analyzing its performances in terms of both measurement accuracy and computational burden. To increase metrological ADC behavior and decrease computational costs, sampling strategies and optimized interpolation algorithm have been implemented and tested verifying the feasibility of harmonic analysis implementation on smart meter microcontroller devices, according to IEC 61000-4-30 Class A and IEC 61000-4-7 Class I requirements.

Wind Power Forecasting with Machine Learning Algorithms in Low-Cost Devices

The urgent imperative to mitigate carbon dioxide (CO2) emissions from power generation poses a pressing challenge for contemporary society. In response, there is a critical need to intensify efforts to improve the efficiency of clean energy sources and expand their use, including wind energy. Within this field, it is necessary to address the variability inherent to the wind resource with the application of prediction methodologies that allow production to be managed. At the same time, to extend its use, this clean energy should be made accessible to everyone, including on a small scale, boosting devices that are affordable for individuals, such as Raspberry and other low-cost hardware platforms. This study is designed to evaluate the effectiveness of various machine learning (ML) algorithms, with special emphasis on deep learning models, in accurately forecasting the power output of wind turbines. Specifically, this research deals with convolutional neural networks (CNN), fully connected networks (FC), gated recurrent unit cells (GRU), and transformer-based models. However, the main objective of this work is to analyze the feasibility of deploying these architectures on various computing platforms, comparing their performance both on conventional computing systems and on other lower-cost alternatives, such as Raspberry Pi 3, in order to make them more accessible for the management of this energy generation. Through training and a rigorous benchmarking process, considering accuracy, real-time performance, and energy consumption, this study identifies the optimal technique to accurately model such real-time series data related to wind energy production, and evaluates the hardware implementation of the studied models. Importantly, our findings demonstrate that effective wind power forecasting can be achieved on low-cost hardware platforms, highlighting the potential for widespread adoption and the personal management of wind power generation, thus representing a fundamental step towards the democratization of clean energy technologies.

A Fuzzy-PI Clock Servo with Window Filter for Compensating Queue-Induced Delay Asymmetry in IEEE 1588 Networks.

Clock synchronization is one of the popular research topics in Distributed Measurement and Control Systems (DMCSs). In most industrial fields, such as Smart Grid and Flight Test, the highest requirement for synchronization accuracy is 1 μs. IEEE 1588 Precision Time Protocol-2008 (PTPv2) can theoretically achieve sub-microsecond accuracy, but it relies on the assumption that the forward and backward delays of PTP packets are symmetrical. In practice, PTP packets will experience random queue delays in switches, making the above assumption challenging to satisfy and causing poor synchronization accuracy. Although using switches supporting the Transparent Clock (TC) can improve synchronization accuracy, these dedicated switches are generally expensive. This paper designs a PTP clock servo for compensating Queue-Induced Delay Asymmetry (QIDA), which can be implemented based on ordinary switches. Its main algorithm comprises a minimum window filter with drift compensation and a fuzzy proportional-integral (PI) controller. We construct a low-cost hardware platform (the cost of each node is within USD 10) to test the performance of the clock servo. In a 100 Mbps network with background (BG) traffic of less than 70 Mbps, the maximum absolute time error (max |TE|) does not exceed 0.35 μs, and the convergence time is about half a minute. The accuracy is improved hundreds of times compared with other existing clock servos.

Autonomous mobile robots for outdoor tasks

This paper presents a low-cost hardware platform for outdoor robots, being suitable for education, industrial prototyping and private use. The choice of components isdiscussed, including platform, sensors and controller as well as GPS and image processing hardware. Furthermore, a software approach is proposed, allowing students and researchers to easily implement own algorithms for localization, navigation and the tasks to fulfill. Several robots can be integrated in a framework which connects various different hardware platforms, called the BOSPORUS network. Together with other components, they form an intelligent network for gathering sensor and image data, sensor data fusion, navigation and control of mobile platforms. The architecture of a reference platform on the campus of the Brandenburg University of Applied Sciences is presented and evaluated.

Low-Complexity P Class to M Class PMU Model for Distribution Systems

The paper presents a new PMU (Phasor Measurement Unit) signal processing model compliant with the IEC/IEEE 60255-118-1 standard. The disturbances are rejected by a cascade digital prefilter made of sections, each with one nominal cycle long rectangular impulse response. The frequency is next estimated by the discrete-time frequency-gain transducer (DTFGT). A single section prefilter with the DTFGT is a 1.5 nominal cycle long P class compliant PMU. M class compliance requires 13 sections prefilter what, along with the DTFGT, gives a 13.4 cycle long PMU. By the elongation of the prefilter cascade, with one cycle increment, the transition from the 1.5 cycle P class to the 13.4 cycle M class PMU is obtained. Each cascade section filter is implemented recursively with only two additions and one multiplication independently of the assumed sampling frequency. The phasor is precisely estimated thanks to the proposed correction of amplitude attenuation and phase shift caused by the prefilter. The main advantages of the proposed PMU model are: full compliance with the standard, low computational complexity, superior rejection of harmonics, and possibility of phasor quality assessment based on different length PMU estimations. The proposed PMU model is primary meant for the distribution systems because of high immunity to disturbances and low computational complexity that makes it suitable for implementation in a low-cost hardware platform or as a software module in a virtual instrumentation and large-scale deployment.

Acoustic Spectral Analysis for Emergency Vehicle Detection: Symbolic and CNN Approaches

During emergencies, ambulances on city streets face delays due to traffic obstacles. This paper addresses two efficient emergency vehicle detection (EVD) methods for restricted hardware implementation considering noisy conditions: a symbolic processing-based algorithm and a convolutional neural network (CNN) model, both of which utilize Mel spectrogram representations of Hi-Lo siren audio records. The symbolic method employs regular expressions and acceptance criteria to process text-pattern features extracted from spectrograms, offering a self-explanatory, easily tunable, and resource-efficient solution suitable for lowcost hardware platforms. On the other hand, the CNN model directly processes spectrogram representations, leveraging spatial correlation for classification with a streamlined architecture consisting of very few layers. The experimental results demonstrate that both approaches achieve high accuracy (97-98%) in classifying Hi-Lo sirens, with the CNN model exhibiting slightly better performance. Challenges such as signal noise and harmonics are addressed through iterative algorithms and signal reconstruction considerations. Future directions include identifying additional siren effects and conducting performance measurements on constrained hardware devices. Overall, this study presents viable EVD solutions suitable for real-time implementation and underscores the importance of adaptable and explainable AI methods in enhancing road safety.

Control System for Indoor Safety Measures Using a Faster R-CNN Architecture

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system’s robustness and scalability to larger indoor environments with more complex safety hazards.

A Cooperative Relative Localization System for Distributed Multi-Agent Networks

While absolute position information is inaccessible for multi-agent networks due to the lack of global reference in GNSS-challenged or infrastructure-free scenarios, relative pose information among the agents can be obtained via agent cooperation. In this paper, we develop a cooperative relative localization system for distributed multi-agent networks. In particular, we propose a node activation strategy to select the backbone agents that minimize the information loss caused by malformed topology, and then the listener agents localize themselves based on the backbone topology. The scheme also incorporates a back calibration step that utilizes the position estimates of the listener agents to further improve the localization accuracy of the backbone topology. Moreover, a multi-sensory information fusion scheme is developed for dynamic scenes, where a coordinate transformation method is proposed to reduce the accumulated error caused by the coordinate misalignment. Finally, the system is implemented on a low-cost hardware platform with the ultra-wideband radios, and extensive simulations and experiments demonstrate that the proposed system can achieve decimeter-level relative localization accuracy.

Low latency BCH decoder using the affine polynomial over the finite field

The paper proposes a low latency BCH decoder with low complexity using parallel computation and simplifying locating errors by finding the roots of the affine polynomial over finite fields. The proposed design can be implemented on low-cost hardware platforms while applicable in very low latency information systems.

Design and Evaluation of an Electrodermal Activity Sensor (EDA) With Adaptive Gain

Electrodermal activity (EDA) is a physiological signal that can be measured non-intrusively and associated with the sympathetic nervous system, hence being widely used nowadays. Although multiple sensor designs have been proposed over the years, practical hardware limitations still persist, due to differences in the electrode materials used to interface with the body, sweat gland density at the recording sites, and variability in the overall electrical properties of the skin in-between users. This is particularly challenging when low resolution Analog-to-Digital Converters (ADC) are used, which, due to the proliferation of open source and low-cost hardware platforms, are currently widespread. In this article we present a design and experimental evaluation of a new circuit for EDA measurement with adaptive gain control, allowing the dynamic adaptation of the measurement range. The proposed circuit has shown comparable results to those obtained with a reference sensor, obtaining a mean correlation coefficient of 0.967, and proved to have superior performance in cases where the reference sensor would saturate.

Low-Cost Precision Monitoring System of Machine Tools for SMEs

Digital manufacturing technology promotes the application of digital information to manufacturing processes and machine tools. However, the high entry cost and complexity of existing offerings limit the wide application of digital manufacturing in small and medium-sized enterprises (SMEs). For CNC machine tools, precision parameters can be acquired either by a visual inspection by the operator or by third-party connection software or hardware. To decrease human error, cost and time in data recording, this research proposes a machine tool data acquisition process for monitoring purposes using a novel monitoring system based on low-cost hardware (Arduino and low-cost camera) and open-source computation platforms (Node-RED). Remote data processing in a tablet is rendered possible by wireless data communication. Prototypes were tested with a HU40-T Five-axis machine tool. Tests show that the proposed system is capable of acquiring precision parameters such as the pitch error and backlash error of machine tools at a system cost of less than $200 CAD.

Efficient Implementation of Stochastic Computing Based Deep Neural Network on Low Cost Hardware with Saturation Arithmetic

This study presents an efficient and rapid implementation of Stochastic Computing (SC) based Deep Neural Network (DNN) on a low-cost hardware platform. The proposed technique uses bipolar signal encoding in stochastic computing which relatively gives low hardware footprint compared to binary computing. Thereinafter, stochastic max function is presented and subsequently used to approximate the hyperbolic tangent activation function in SC. In addition, saturation arithmetic is proposed to reduce down scaling parameters that can further affect precision in computation. In this study, we demonstrate our SC-based DNN feasibility through a hardware accelerator prototype with the AXI Stream interface on a PYNQ Z2 board which is equipped with a XILINX ZYNQ XC7Z020-1CLG400C. The validity of this study is demonstrated through a MNIST handwritten digit recognition task. The experimental result shows our SC-based DNN model can be easily deployed on the embedded devices. The SC-based accelerator with AXI Stream interface performs at 1.877 GOP/s processing throughput, achieves higher accuracy with minimum area and energy consumption, consuming only 0.61 mm2 area and 1.89W power.

A Rotor Flux Observer of Permanent Magnet Synchronous Motors With Adaptive Flux Compensation

Due to the influence of current sampling and current reconstruction deviation, non-linearity of the inverters and parameter variation of the motors, DC offset component and high-order harmonic components exist in flux model, which makes the estimation of traditional rotor flux observer inaccurate and even divergent. In this paper, a rotor flux observer of permanent magnet synchronous motors (PMSM) with adaptive flux compensation is proposed. The DC offset component and the high-order harmonic components with the flux integrator observer can be eliminated by fed them back to the input of the flux integrator through PI regulator, so that the output of the flux integrator observer only contains the fundamental component, thus the rotor flux can be accurately estimated. Since the parameters of PI regulator are related with the estimated motor angular velocity, the control system has good robustness in a wide speed range. The flux observer is based on the voltage model in stationary coordinate system, so it is suitable for both surface and interior PMSMs. Moreover, the control method of the whole system has less computational complexity and fewer variables, it is easy to implement on a low-cost hardware platform. Finally, the experimental results verify that the flux observer has superior performance.

A Novel Digital Realization of AdEx Neuron Model

Realization of biological neuron models is an essential research area in the field of neuromorphic. This brief presents a novel implementation of adaptive-exponential (AdEx) neuron model based on a complete-matching method called Power-2 Based AdEx Model (PBAM). This model can precisely reproduce different spiking behaviors, similar to the biological neurons with lower implementation costs compared with previous works. To validate the PBAM neuron, the proposed model is physically realized on FPGA as a proof of concept. Experimental results demonstrate high similarity with the original model, high computational performance and lower hardware cost. The proposed PBAM neuron implementation on FPGA requires considerably lower hardware resources compared with the original AdEx neuron. In comparison with similar works, this model has a higher performance and can operate at higher frequencies. In this modification, time domain spiking and dynamical behaviors of the original model are regenerated with very low computational errors using low-cost fixed-point calculations. This makes the proposed model an ideal candidate for large scale neuromorphic and biologically inspired neural network implementations targeting low-cost hardware platforms.

Noncontact SpO2 Measurement Using Eulerian Video Magnification

Peripheral oxygen saturation (SpO2) monitoring is critical during surgery and in intensive care units (ICS). Over the last decades, several studies have addressed the noncontact monitoring of vital signs, but few reports have focused on SpO2. This paper presents an SpO2 monitoring method without physical contact with the patient using imaging photoplethysmography (iPPG). Eulerian video magnification (EVM) was employed in the proposed solution to amplify the changes in skin color due to the heart cycle. The developed software operates on a widely available, low-cost hardware platform. Experiments were performed with nine healthy individuals. When comparing the experimental SpO2 results to those obtained with a reference pulse oximeter, the proposed solution showed good accuracy, with differences of less than 2%.

Grid Load Reduction through Optimized PV Power Utilization in Intermittent Grids Using a Low-Cost Hardware Platform

Renewable energy incorporation in many countries takes different forms. In many developed countries, grid-tied solar photovoltaic (PV) installations are widely coupled with lucrative Feed-in-Tariffs (FiT). However, conventional grid-tied solutions are not readily viable in many developing countries mainly due to intermittent grids with load shedding and, in some cases, lack of net-metering or FiT. Load shedding refers to an intentional electrical power shutdown by the utility company where electricity delivery is stopped for non-overlapping periods of time over different parts of the distribution region. This results in a non-continuous availability of the utility grid for many consumers over the course of a day. In this work, the key challenges in the integration of solar energy explicitly in residential power back-up units are reviewed and system hardware level requirements to allow optimized solar PV utilization in such intermittent grid environments are analyzed. Further, based upon the low-cost sensing and real-time monitoring scheme, an online optimization framework enabling efficient solar incorporation in existing systems to achieve minimum grid dependence in intermittent grid environments is also provided. This work is particularly targeted for over 1.5 billion residents of semi-electrified regions in South Asia and Africa with the weak and intermittent grid.

Molecular drug susceptibility testing and strain typing of tuberculosis by DNA hybridization.

In Mycobacterium tuberculosis (Mtb) the detection of single nucleotide polymorphisms (SNPs) is of high importance both for diagnostics, since drug resistance is primarily caused by the acquisition of SNPs in multiple drug targets, and for epidemiological studies in which strain typing is performed by SNP identification. To provide the necessary coverage of clinically relevant resistance profiles and strain types, nucleic acid-based measurement techniques must be able to detect a large number of potential SNPs. Since the Mtb problem is pressing in many resource-poor countries, requiring low-cost point-of-care biosensors, this is a non-trivial technological challenge. This paper presents a proof-of-concept in which we chose simple DNA-DNA hybridization as a sensing principle since this can be transferred to existing low-cost hardware platforms, and we pushed the multiplex boundaries of it. With a custom designed probe set and a physicochemical-driven data analysis it was possible to simultaneously detect the presence of SNPs associated with first- and second-line drug resistance and Mtb strain typing. We have demonstrated its use for the identification of drug resistance and strain type from a panel of phylogenetically diverse clinical strains. Furthermore, reliable detection of the presence of a minority population (<5%) of drug-resistant Mtb was possible.

Experimental characterization and analysis of the BITalino platforms against a reference device.

Low-cost hardware platforms for biomedical engineering are becoming increasingly available, which empower the research community in the development of new projects in a wide range of areas related with physiological data acquisition. Building upon previous work by our group, this work compares the quality of the data acquired by means of two different versions of the multimodal physiological computing platform BITalino, with a device that can be considered a reference. We acquired data from 5 sensors, namely Accelerometry (ACC), Electrocardiography (ECG), Electroencephalography (EEG), Electrodermal Activity (EDA) and Electromyography (EMG). Experimental evaluation shows that ACC, ECG and EDA data are highly correlated with the reference in what concerns the raw waveforms. When compared by means of their commonly used features, EEG and EMG data are also quite similar across the different devices.

A High Spatial Resolution Depth Sensing Method Based on Binocular Structured Light.

Depth information has been used in many fields because of its low cost and easy availability, since the Microsoft Kinect was released. However, the Kinect and Kinect-like RGB-D sensors show limited performance in certain applications and place high demands on accuracy and robustness of depth information. In this paper, we propose a depth sensing system that contains a laser projector similar to that used in the Kinect, and two infrared cameras located on both sides of the laser projector, to obtain higher spatial resolution depth information. We apply the block-matching algorithm to estimate the disparity. To improve the spatial resolution, we reduce the size of matching blocks, but smaller matching blocks generate lower matching precision. To address this problem, we combine two matching modes (binocular mode and monocular mode) in the disparity estimation process. Experimental results show that our method can obtain higher spatial resolution depth without loss of the quality of the range image, compared with the Kinect. Furthermore, our algorithm is implemented on a low-cost hardware platform, and the system can support the resolution of 1280 × 960, and up to a speed of 60 frames per second, for depth image sequences.

Open loop active control of noise through open windows

Noise propagating through open windows is a big concern of liveability in metropolises, where residential buildings and main roads are built densely. Active noise control (ANC) is a convincing technique to improve the current situation. Classic ANC systems adopt the close loop control. The quietness after control is continuously monitored by error microphones. However, ANC systems that attenuate noise propagating through open windows should ideally avoid the use of error microphones for the ease of implementation and maintenance. Furthermore, as open windows are commonly large as compared to the wavelength of noise, multichannel ANC systems are necessary to be carried out. They result in huge computational burdens that cannot be handled by low-cost hardware platforms. This paper investigates acoustic characteristics of open windows and formulates the control strategies as a spatial sampling and reconstruction problem. This theoretical development prompts the fact that controlling noise propagating through open windows essentially leads to global noise reduction. Both centralized and decentralized open loop control are feasible to deal with broadband noise. The upper bound of the effective band is limited by the spacing between channels, while the lower bound is dependent on the frequency response of secondary sources.