Low-cost Hardware Research Articles

In-situ acoustic monitoring of direct energy deposition process with deep learning-assisted signal denoising

In-situ monitoring is crucial for detecting process anomalies and ensuring part quality in additive manufacturing. Acoustic-based monitoring techniques offer extra benefits such as adjustable sensor setup and lower hardware costs. In the direct energy deposition (DED) process, acoustic signals generated by laser-material interactions carry information about underlying complex physical mechanisms such as melting, solidification, crack propagation, and pore formation. This paper presents a novel acoustic-based in-situ monitoring method for the DED process. The raw acoustic signal is made up of laser-material interaction sound as well as noise from machine movement, inert gas flow, and powder flow. A deep learning model is developed to build an end-to-end signal denoising framework to minimize environmental noise and extract the laser-material interaction sound. Audio equalization, bandpass filtering, and Harmonic-Percussive Source Separation algorithm are used to produce a cleaned laser-material interaction sound as the model's ground truth target. Acoustic data is collected from experiments using different DED machines, materials, and varied process parameters to train the deep learning model. The proposed deep learning-assisted signal denoising strategy lays the groundwork for acoustic-based in-situ defect detection of the DED process.

Applying t-SNE to Estimate Image Sharpness of Low-cost Nailfold Capillaroscopy

Machine learning can classify the image clarity of low-cost nailfold capillaroscopy (NC) and can be applied to the design verification for other medical devices. The method can be beneficial for systems that require a large number of image datasets. This investigation covers the design, integration, image sharpness estimation, and deconvolution sharpening of the NC. The study applies this device to record two videos and extract 600 photos, including blurry and sharp images. It then uses the Laplace operator method for blur detection of the pictures. Statistics are recorded for each image’s Laplace score and the distribution of clear photos in NC. In this investigation, an algorithm called t-distributed stochastic neighbor embedding (t-SNE) is introduced into the NC image sharpness estimation issue. The t-SNE is employed as a data visualization tool to determine the cluster distribution of sharp images. The result shows the t-SNE method to classify sharp images of NC is close to the method combine statistic and Laplace operator for sharp image distribution. And the image captured from the low-cost NC that applying deconvolution can sharpen the image. It shows that the NC equipment combines with low-cost hardware and software computing advantages has broader, powerful, and complex applications. For long-term purposes, it is conducive for detecting people who have Raynaud’s phenomenon in rural areas of developing countries to realize the vision of health equity.

MonkeyTrail: A scalable video-based method for tracking macaque movement trajectory in daily living cages.

Behavioral analysis of macaques provides important experimental evidence in the field of neuroscience. In recent years, video-based automatic animal behavior analysis has received widespread attention. However, methods capable of extracting and analyzing daily movement trajectories of macaques in their daily living cages remain underdeveloped, with previous approaches usually requiring specific environments to reduce interference from occlusion or environmental change. Here, we introduce a novel method, called MonkeyTrail, which satisfies the above requirements by frequently generating virtual empty backgrounds and using background subtraction to accurately obtain the foreground of moving animals. The empty background is generated by combining the frame difference method (FDM) and deep learning-based model (YOLOv5). The entire setup can be operated with low-cost hardware and can be applied to the daily living environments of individually caged macaques. To test MonkeyTrail performance, we labeled a dataset containing >8 000 video frames with the bounding boxes of macaques under various conditions as ground-truth. Results showed that the tracking accuracy and stability of MonkeyTrail exceeded that of two deep learning-based methods (YOLOv5 and Single-Shot MultiBox Detector), traditional frame difference method, and naïve background subtraction method. Using MonkeyTrail to analyze long-term surveillance video recordings, we successfully assessed changes in animal behavior in terms of movement amount and spatial preference. Thus, these findings demonstrate that MonkeyTrail enables low-cost, large-scale daily behavioral analysis of macaques.

Construction of dPCR and qPCR integrated system based on commercially available low-cost hardware.

Fluorescent quantitative PCR (qPCR) and digital PCR (dPCR) are two mainstream nucleic acid quantification technologies. However, commercial dPCR and qPCR instruments have a low integration, a high price, and a large footprint. To solve these shortcomings, we introduce a compound PCR system with both qPCR and dPCR functions. All the hardware used in this compound PCR system is commercially available and low-cost, and free software was used to realize the absolute quantification of nucleic acids. The compound PCR provides two working modes. In the qPCR mode, thermal cycling is realized by controlling the reciprocating motion of the x axis. The heating rate is 1.25 °C s-1 and the cooling rate is 1.75 °C s-1. We performed amplification experiments of the PGEM-3zf (+)1 gene. The performance level was similar to commercial qPCR instruments. In the dPCR mode, the heating rate is 0.5 °C s-1 and the cooling rate is 0.6 °C s-1. We performed the UPE-Q gene amplification and used the sequential actions of the two-dimensional mechanical sliders to scan the reaction products and used the method of regional statistics and back-inference threshold to get test results. The result we got was 1208 copies per μL-1, which was similar to expectations.

Design of Multihop Communication Systems Under Hardware Manufacturing Defects and Self Interference

Most studies on multi-hop communication systems are actually based on the assumption that hardware parts are built with high quality transmit/receive radio frequency chains, which are expensive and power-hungry. In practice, low cost hardware components are employed, which are prone to hardware manufacturing defects (e.g., phase noise, nonlinear power amplifier, inphase/quadrature imbalance, nonlinear low noise amplifier and analog to digital converter impairments). This paper focuses on the design of the transmitter, relay stations (RSs) and receiver for multi-hop communication systems under hardware defects. This paper analyzes the hardware defects and their impacts on the performance of multi-hop communication system. In addition, the impact of self-interference at the RSs is considered. The effect of hardware defects is modeled using distortion noises. A closed-form expression for the signal to noise and distortion ratios (SNDRs) is mathematically derived, then the performance metrics (i.e., SNDR, outage probability (OP) and ergodic capacity) are calculated, accounting for hardware defects. In addition, exact form expression of the average bit error probability is derived. To facilitate comparison, performance metrics of the proposed multi-hop relaying system and ideal system are illustrated. We also propose a linear minimum mean square error (LMMSE) and self-interference cancellation technique to mitigate the hardware defects. The results reveal that hardware defects can degrade the system performance. In addition, SNDR ceiling is inversely proportional to the summation of the square of hardware defect lev el. Also, the proposed mitigation technique can enhance the system performance.

Lt: A Resource to Future-Proof the Laboratory in Uncertain Times.

The COVID-19 pandemic abruptly challenged educators to transition previously in-person courses to an online environment. This has been especially difficult for laboratory courses where students must experience the process of science to develop lab skills and scientific competencies. Due to the uncertainty caused by the pandemic, it is essential that instructional resources are flexible and robust for use in various potential learning environments. The Lt software platform (ADInstruments) is a resource designed to support in-person, online, and hybrid learning environments. Lt supports the in-person lab experience by integrating with data collection hardware and facilitating collaboration through group-based activity. In addition, the platform also provides several avenues for teaching online labs using the same experiments that would be done on campus. At home, students can analyze Lt's built-in example data, or be supplied with low-cost hardware to complete labs remotely. In conjunction with other online tools, Lt can support online group work and student collaboration. Lt hosts a wide range of pre-built lab experiments and activities covering neuroscience, anatomy, physiology, clinical health science, biology, and chemistry. Although the material can be used "out-of-the-box", the content is completely editable and new labs can be created. Feedback from students suggests that Lt has proved valuable for supporting flexible instructional practices during the pandemic.

A High-Speed Low-Cost Hardware Implementation for Depth Estimation Using Disparity Fusion Method

Depth estimation using stereo images can be achieved by calculating the disparity values between the left and the right images captured by two parallel cameras. Reconstructing depth information from 2D images is crucial in many applications, such as self-driving vehicles and robot navigation. Furthermore, most of these applications are employed with resource-constrained devices and have real-time requirements. In this paper, a high-speed, low-cost hardware implementation for disparity estimation is proposed. We adopted the novel disparity fusion method in our architecture, which can significantly reduce the number of calculations in the overall process. A refinement method is also designed to reduce the error rate of the resulting depth map and improve the tolerance to light noise. The proposed algorithm was implemented with the Kintex-7 field-programmable gate array. Its performance was tested by using the Middlebury-Version 2 and -Version 3 datasets. The proposed algorithm provides an operating speed of 118 fps with an error rate of only 6.36%. Compared with other state-of-the-art designs used for similar applications, the proposed method can achieve a 34.6% reduction in the error rate while providing the highest speed with competitive hardware cost.

Unsupervised Learning for Non-intrusive Load Monitoring in Smart Grid Based on Spiking Deep Neural Network

This paper investigates the intelligent load monitoring problem with applications to practical energy management scenarios in smart grids. As one of the critical components for paving the way to smart grids' success, an intelligent and feasible non-intrusive load monitoring (NILM) algorithm is urgently needed. However, most recent researches on NILM have not dealt with practical problems when applied to power grid, i.e., ① limited communication for slow-change systems; ② requirement of low-cost hardware at the users' side; and ③ inconvenience to adapt to new households. Therefore, a novel NILM algorithm based on biology-inspired spiking neural network (SNN) has been developed to overcome the existing challenges. To provide intelligence in NILM, the developed SNN features an unsupervised learning rule, i.e., spike-time dependent plasticity (STDP), which only requires the user to label one instance for each appliance while adapting to a new household. To upgrade the feasibility in NILM, the designed spiking neurons mimic the mechanism of human brain neurons that can be constructed by a resistor-capacitor (RC) circuit. In addition, a distributed computing system has been designed that divides the SNN into two parts, i.e., smart outlets and local servers. Since the information flows as sparse binary vectors among spiking neurons in the developed SNN-based NILM, the high-frequency data can be easily compressed as the spike times, and are sent to the local server with limited communication capability, whereas it is unable to handle the traditional NILM. Finally, a series of experiments are conducted using a benchmark public dataset. Meanwhile, the effectiveness of developed SNN-based NILM can be demonstrated through comparisons with other emerging NILM algorithms such as the convolutional neural networks.

An Informal Introduction to Numerical Weather Models with Low-Cost Hardware

Abstract Weather, climate, and other Earth system models are growing in complexity as computing resources and technologies continue to evolve with time. Thus, models are and will remain a vital tool for scientific research. Exposure and education on the workings of such models can generate interest toward atmospheric science, and it can increase scientific literacy among the general public. Additionally, studies have suggested that early exposure to these models can affect the career trajectory of students. However, gaining exposure and experience remains difficult outside of internships, research settings, and other professional endeavors. Some of these barriers can include hardware and computing costs, curriculum structure, and access to instructors. As a means of addressing these barriers, the goal of this work is to utilize low-cost hardware and abstract away some of the complexities of running a numerical weather model without sacrificing fidelity. The approach is to create a graphical user interface (GUI) where users can quickly configure the model, run it, and analyze the output without knowledge of model configuration, system architecture, or navigation via a command line interface. The Pi-WRF application is packaged such that users can download and run the model within a matter of minutes. The application is designed to promote informal learning through hands-on experience. It is targeted toward lower secondary level students, but it can scale across grade levels, and it can be adapted for general audiences.

A Robust Area-Efficient Physically Unclonable Function With High Machine Learning Attack Resilience in 28-nm CMOS

Strong physically unclonable function (PUF) offers a promising solution to low-cost hardware identification and authentication for Internet of Things (IoT) applications. The continuous advancement of machine learning (ML) technology makes the PUF resilience to ML attacks a major design priority. This paper presents a robust and area-efficient strong PUF design with high ML attack resilience. The proposed PUF architecture based on inverter amplifiers operating in the subthreshold region achieves both low energy consumption and high supply and temperature scalability. The proposed nonlinearity topology effectively enhances PUF resilience to various ML attacks with low implementation area and cost. The proposed strong PUF design was designed and implemented using a 28-nm CMOS process. The measurement results show that the proposed PUF design achieves a nearly ideal ML attack resilience of 49.96 &#x0025; with a small area of 239,857 F², and demonstrates a stable operation across a wide range of supply voltage from 0.5&#x2013;1.4 V and temperature from &#x2212;40&#x2013;100 &#x00B0;C. This represents <inline-formula> <tex-math notation="LaTeX">$3\times $ </tex-math></inline-formula> improvement in area efficiency, <inline-formula> <tex-math notation="LaTeX">$2.25\times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$1.08\times $ </tex-math></inline-formula> improvement in operating voltage and temperature range, respectively, compared to the state-of-the-art results.

A new truncation algorithm of low hardware cost multiplier

Multiplier is one of the most inevitable arithmetic circuit in digital signal design. Multipliers dissipate high power and occupy significant amount of the die area. In this paper, a low-error architecture design of the pre-truncated parallel multiplier is presented. The coefficients word length has been truncated to reduce the multiplier size. This truncation scaled down the gate count and shortened the critical paths of partial product array. The statistical errors of the designed multiplier are calculated for different pre-truncate values and compared. The multiplier is implemented using Stratix III, FPGA device. The post fitting report is presented in this paper, which shows a saving of 36.9 % in resources usage, and a reduction of 17 % in propagation time delay.

VLCIoT: design and implementation of a visible light communication system for indoor Internet of Things applications.

In this paper, a visible light communication (VLC) system for indoor Internet of Things (IoT) applications, called VLCIoT, is proposed. The proposed system is based on type I of the IEEE 802.15.7 standard physical (PHY) layer. The PHY I is provided for low data rate applications from 10 to 100 kb/s, which looks suitable for the typical IoT applications. The on-off keying suggested modulation scheme by the PHY I that is simple and requires low-cost hardware for implementation is considered. The implemented VLCIoT system is robust against indoor ambient light interference. Using the frequency division multiple access, several VLC networks can operate at different frequencies in the vicinity of each other without interference. The data rate of VLCIoT is up to 115.2 kb/s, and the bit error ratio of the system is very low. This system is designed for indoor, which for this purpose operates well up to 7 m distances. In this paper, a figure of merit (FoM) is proposed, in which the most important parameters for IoT applications are considered. A comprehensive comparison of VLCIoT to other suitable VLC systems for IoT applications is performed. The results show that the VLCIoT achieves the best FoM and is suitable for indoor IoT applications.

A Current-Switching Technique for Intra-Body Communication With Miniaturized Electrodes.

Medical implants are required to be minimized in size to alleviate surgical pains. Battery and antenna are often the main bottlenecks in system miniaturization. Wireless power transfer (WPT) is a possible way to minimize or eliminate the battery. Medical implants with WPT often use backscattering for data communication due to its low power consumption and low hardware cost. However, the conventional backscattering approach with WPT requires a large implanted antenna to ensure a relatively high efficiency and enough signal-to-noise ratio (SNR) for demodulation. In this work, we propose a current-switching technique for intra-body communication to achieve a high SNR and data rate with a pair of small implanted electrodes. Instead of the conventional electric-field based WPT and communication, a current loop is configured in the body tissue for WPT, where a new passive-communication scheme is implemented at the same time. A prototype is implemented to validate the proposed technique, in which the implanted electrodes are designed to be as small as 200 μm × 200 μm, located 13 mm deep in the tissue. The system achieves a communication rate of 10 Mbps with a bit error rate (BER) of 8.4 ×10-4 over the 406 MHz MedRadio band, while the signal-to-blocker ratio and SNR are measured to be -35.7 dB and 12.4dB, respectively.

A Low-Cost Hardware Design of Learning-Based One-Dimensional Interpolation for Real-Time Video Applications at the Edge

Gradual advances have occurred in the design of micro-artificial intelligence for resource-limited hardware. A high-resolution (HR) image reconstruction module is indispensable for video analytics chips or devices at the edge. This paper proposes a low-cost and learning-based interpolation method for HR image reconstruction. The proposed method generates reconstructed pixels by processing reference pixels with optimal weights, which are pre-trained by solving the minimum mean square error problem for real images. To reduce the number of computation units and the usage of learned weights, a cross-directional interpolation architecture, which includes a vertical kernel and a horizontal kernel, is adopted. Moreover, a one-dimensional feature discriminator is proposed to improve the quality of up-scaled images efficiently. The main benefit of the proposed method is that it requires a small number of computation units but can still produce high-quality images. The hardware architecture of the proposed method was implemented on a field-programmable gate array (FPGA) by using Xilinx UltraScale+ ZCU102 and an application-specific integrated circuit (ASIC) by using TSMC’s 0.13-μm technology. On the ASIC, the proposed hardware required only approximately 60K gate counts and 50 KBytes of memory. The experimental results indicate that the average peak signal-to-noise ratio of the up-scaled images reached 35.92 dB for the Set-5 dataset. The throughput of the proposed hardware was at least 1000 Mpixels/s on the FPGA and 1200 Mpixels/s on the ASIC, which indicates that the proposed hardware can handle a target resolution higher than 4K in real time.

ECG AND PULSE OXYGEN LEVEL MONITORING AND ARRHYTHMIA CLASSIFICATION USING CNN

Early Diagnosis of disease has always been a boon for the treatment of any disease. However, for people living in remote areas, health facilities have not been easily accessible. A few of the critical parameters for determining whether the person is healthy or not depend on a few health parameters such as Heart Rate, electrocardiogram (ECG), oxygen saturation (SpO2), and Body Temperature. To avoid overcrowding hospitals, we have developed a remote monitoring and prediction system. We have developed Arduino-based low-cost hardware that can be used for telemedicine service and remote monitoring of patients in India directly from expert doctors in the field. The system was successfully tested and got around 97 percent successful transmission of data without latency. The model includes a web app that directly reports the data to the doctors and has been trained with many machine learning models to predict abnormal patterns. Ultimately the web app gives a composite score based on heart rate and arrhythmia to facilitate subjects for heart health. Created a system, which is the dynamic web app to display real-time data and plot the graph of the variation dynamically. Using this system to check our health periodically makes it possible to reduce the chances of deteriorating health conditions

An Efficient Digital Realization of Retinal Light Adaptation in Cone Photoreceptors

In recent years, hardware modeling for various parts of the body’s sensitive organs, including the brain and nervous system, heart and eyes, has been considered for the treatment of diseases and rehabilitation, as well as for moving towards the construction of artificial prostheses. The retina is a thin layer that is the innermost layer of the human eye. In this paper, low-cost hardware implementation for retinal cone cells is performed. Existing mathematical models for implementing the behavior of these cells include a series of nonlinear functions that, if implemented directly, would require a large amount of hardware and, in addition, would not have the desired speed. The proposed model uses multi-linear functions to approximate the nonlinear terms and eliminate the multiplication expressions. The simulation results show that the proposed model tracks the behavior of the original model with high precision. There is also a good match between the main model and the proposed model in terms of dynamic behaviors. The results of hardware implementation using the virtex5 XC5VLX20T (2FF323) reconfigurable board (FPGA) show that the proposed model is fully valid and has a lower hardware volume as well as a 4 times higher frequency, and 22% less power consumption than the original model.

Implementation of non-pharmaceutical intervention of COVID-19 in MRT through engineering controlled queue line using participatory ergonomics approach

The viral transmission in public places and transportations can be minimized by following the world health organization (WHO) guideline. However, the uncertainty in a dynamic system complicates the social engagement to the physical distancing regulation. This study aims to overcome this obstacle in MRT stations and train by developing an adaptive queue line system. The system was developed using low-cost hardware and open-source software to guide passengers using visual information. The system works by capturing seat images and identify the presence of humans using a cloud machine learning service. The physical representation of MRT was translated to data representation using the internet of things (IoT). The data then streamed using an asynchronous API with a representative endpoint. The endpoint is then accessed by a display computer in the destination station platform to provide visual information. The visual information was ergonomically designed with visual display principles, including the minimum content load, layout, color combination, and dimension of contents. The design of the system was evaluated by Markov simulation of virus transmission in train and usability testing of the visual design. The implementation of the system has balanced the queue line capacity in station and crowd spots distribution in MRT. The system was effective due to the visual cortex manipulation by visual information. Consequently, the aerosol and falling droplets' viral transmission radius can be reduced. Accordingly, the chance for airborne transmission can be lowered. Therefore, the adaptive queue line system is a non-pharmaceutical intervention of viral transmission diseases in public transportation

QoS Strategies for Wireless Multimedia Sensor Networks with Energy- Efficient Routing Techniques &amp; QoS Assurances

In recent years, for delivering multimedia information such as images and videos, wireless multimedia sensor networks (WMSNs) have emerged as an outstanding technique. Due to the fast Advancements in sensor technology and the availability of low-cost hardware, the development of Wireless Multimedia Sensor Networks (WMSNs) has emerged. WMSNs are composed of resourceconstrained wireless nodes through which both the scalar data and multimedia data (i.e., audio, still images, and video) can be sensed and acquired from the environment. However, the resource-constrained nature of multimedia sensing devices has made the WMSNs face several challenges. To tackle these challenges, different authors developed different methods. In this paper, we have surveyed all such kinds of methods. Initially, we study the basic architecture of multimedia senor nodes followed by the characteristics and applications of WMSNs. Next, we have conducted a detailed survey over different methods and all those methods are segregated into three categories. Under the segregation, we have considered different aspects and segregated them as Data-Aware methods, QoS Aware methods, and Energy-Aware methods. In the end, we also summarized the existing solutions and outlined several pros and cons. In this paper, recent developments in techniques for designing highly energy-efficient and QoScapable WMSNs are surveyed.

Small-signal stability analysis in smart grids: An approach based on distributed decision trees

Accurate information on the dynamic behavior of a smart grid is essential for its effective control and reliable operation. Specifically, ensuring that the electromechanical oscillations are properly damped is needed for the dynamic security operation of an interconnected system. Since the model-based method to evaluate these oscillations is subject to uncertainties, the data from a wide-area measurement system can assist in this challenging task. In this context, this paper proposes a decentralized approach based on decision trees to classify the interarea oscillations through data measured in the generation buses. For each generation bus, there is an individual decision tree capable of evaluating the interarea oscillations damping. The decision trees presented a low computational burden and, consequently, they can be embedded in phasor measurement units with low-cost hardware. The results obtained with the 68-bus test system show that the classifications provided by individual decision trees are accurate, even when contingency scenarios were evaluated, reaching accuracies greater than 0.93. Thus, the possibility of analyzing the power system stability to small disturbances from local information is one of the main contributions of the proposed approach to advance the state-of-the-art, since the use of distributed decision trees is robust even when there is loss of information from a specific generator.

One-Bit Spectrum Sensing Based on Statistical Covariances: Eigenvalue Moment Ratio Approach

One-bit analog-to-digital converter (ADC), performing signal sampling as an extreme simple comparator, is an overwhelming technology for spectrum sensing due to its low-cost, low-power consumptions and high sampling rate. In this letter, we propose a novel one-bit sensing approach based on the eigenvalue moment ratio (EMR), which has been proved to be highly efficient for conventional multi-antenna spectrum sensing in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> -bit situation. The statistical covariances of the one-bit samples inherent the statistical correlation properties, thus allow us to detect the existence of primary user (PU) though the test for sphericity. Particularly, we verify the element-by-element independence of one-bit sample covariance matrix (SCM) in the absence of signal, which allows us to asymptotically determine the null distributions of statistical covariance based spectrum sensing techniques. On this basis, we formulate the asymptotic distribution of one-bit EMR under null hypothesis via the central limited theorem (CLT) and perform spectrum sensing with one-bit samples directly. Theoretical and simulation results show the new approach can provide superior sensing performance at a low hardware cost.