Acquisition Hardware Research Articles

Low-Cost IoT-Based Sensors Dashboard for Monitoring the State of Health of Mobile Harbor Cranes: Hardware and Soft-ware Description

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality Control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server. The novelty of the QC-DAS design is that each function is encapsulated in a predefined module that is "immersed" in a message transmission medium. Modules interact by sending and receiving various signals through this medium. Modularity makes system design simpler, faster, and flexible. Thanks to modularity, users may incorporate their data processing modules when new sensors are added to match the system's needs. Thanks to modularity the DC-DAS can operate quality control hardware for any mobile cranes. There are several constraints in the quality control data acquisition system used by the Damietta Port Authority in Damietta, Egypt, SESCO TRANS company which cause the loading and unloading process to be slowed down. As a result, the SESCO TRANS company upgraded its quality control data acquisition system using the QC-DAS. The hardware was deployed for six months, during which the collected data was used to verify the crane's performance. The vibration produced by the slewing of the crane was monitored and compared with the bearing fault frequency limits, during the operation the wind speed was monitored and compared with the critical wind speed to stop the crane operation automatically, and the payloads data of the six months was collected and was used to calculate the working efficiency of the load and unload process of the crane. The results demonstrated that while maintenance costs were decreased, the crane load/unload procedure was improved. The SESCO TRANS company crane operators approved the developed approach and appreciated the achieved results.

Synthetic data in generalizable, learning-based neuroimaging

Abstract Synthetic data has emerged as an attractive option for developing machine learning methods in human neuroimaging, particularly in magnetic resonance imaging (MRI)— a modality where image contrast depends enormously on acquisition hardware and parameters. This retrospective paper reviews a family of recently proposed methods, based on synthetic data, for generalizable machine learning in brain MRI analysis. Central to this framework is the concept of domain randomization, which involves training neural networks on a vastly diverse array of synthetically generated images with random contrast properties. This technique has enabled robust, adaptable models that are capable of handling diverse MRI contrasts, resolutions, and pathologies, while working out-of-the-box, without retraining. We have successfully applied this method to tasks such as whole brain segmentation (SynthSeg), skull-stripping (SynthStrip), registration (SynthMorph, EasyReg), super-resolution and MR contrast transfer (SynthSR). Beyond these applications, the paper discusses other possible use cases and future work in our methodology. Neural networks trained with synthetic data enable the analysis of clinical MRI, including large retrospective datasets, while greatly alleviating (and sometimes eliminating) the need for substantial labeled datasets, and offer enormous potential as robust tools to address various research goals.

Design and research of serial communication system based on virtual instrument technology

Abstract In the measurement and automation control system, serial communication has been connected between the acquisition hardware (lower computer) and the upper computer between the easiest to realize and the most economical way. In view of the traditional instrument itself is relatively bulky, inconvenient to carry, this paper adopts the virtual instrument development environment to realize the realization of serial communication, in order to complete the subsequent data acquisition. When compared with other software development environment, LabWindows/CVI platform comes with a function library to make the program writing simple and flexible. In this paper, we designed the realization method of serial communication based on virtual instrument Labwindows/CVI environment, completed the design and realization of serial communication between PC and lower computer (microcontroller) based on Labwindows/CVI environment, and applied it to the example of serial communication with microcontroller to realize the data communication function. Practical results prove that the serial communication system based on virtual instrument technology has the advantages of friendly user interface, high stability, convenience and reliability, and can reliably and accurately complete the communication, and provide strong technical support for the system to follow up data acquisition.

Optimizing the Transition: Strategies for Migrating On-Premise Storage to the Cloud

In recent years, Cloud Computing has had great relevance and has aroused interest in the Telecommunications industry, due to the reduction of costs in hardware acquisition, maintenance and operation personnel. One of the great advantages of cloud computing is the automation of processes that makes systems more robust and secure by limiting the margin of human and operational errors. It is of utmost importance to consider the large storage capacity management of video files that can provide solutions to media companies or related industries, this is key to think about a cost benefit analysis that can generate the migration and rental of these services in the cloud, with more reliable and accessible systems, this will help us to study hybrid technologies that offer on premise solutions. In addition to the above, when talking about companies that seek to digitally transform themselves to achieve agility and resilience, as well as to support future applications, it is necessary to mention their current infrastructures that have the capacity to host new technologies that require high performance, security and an adequate digital interconnection that allows the correct access to the acquired applications. In this research, an analysis of the existing process models for migration from on-premise storage to the cloud has been made with the objective of identifying the most important challenges, from planning, execution, factors to consider when assessing the complexity of applications and data before migrating to the cloud.

Exploring feasibility of vision-based automated evaluation during laboratory courses in manufacturing

Manual assessment of laboratory exercises and assignments by human instructors is subjective and time-consuming, introducing errors, inconsistencies, and biases. A preliminary study developing an automated evaluation system utilizing computer vision has been proposed in this article to address these challenges. It utilizes computer vision to assess the accuracy and quality of components machined by students enrolled during the manufacturing laboratory course. The system includes image acquisition hardware, algorithms for objective decisions, and an interface for evaluating student performance. The system is implemented during one of the laboratory classes, and a comparative assessment is carried out with manual evaluations followed by student feedback. It has been shown that the system can address subjectivity concerns, reducing the workload on course instructors and teaching assistants. The research also broadens the utility of computer vision in manufacturing education and creates interest among enrolled students to appreciate the role of newer technologies in a core discipline.

The development and structure of the HEALthy Brain and Child Development (HBCD) Study EEG protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. Electroencephalography (EEG) is one of two brain imaging modalities central to the HBCD Study. EEG records electrical signals from the scalp that reflect electrical brain activity. In addition, the EEG signal can be synchronized to the presentation of discrete stimuli (auditory or visual) to measure specific cognitive processes with excellent temporal precision (e.g., event-related potentials; ERPs). EEG is particularly helpful for the HBCD Study as it can be used with awake, alert infants, and can be acquired continuously across development. The current paper reviews the HBCD Study’s EEG/ERP protocol: (a) the selection and development of the tasks (Video Resting State, Visual Evoked Potential, Auditory Oddball, Face Processing); (b) the implementation of common cross-site acquisition parameters and hardware, site setup, training, and initial piloting; (c) the development of the preprocessing pipelines and creation of derivatives; and (d) the incorporation of equity and inclusion considerations. The paper also provides an overview of the functioning of the EEG Workgroup and the input from members across all steps of protocol development and piloting.

Study of a detector prototype for detecting special nuclear materials based on differential die-away technology

Differential die-away Analysis (DDAA) technology is a powerful analytical technique to detect fissile materials such as 235U and 239Pu. It can reveal the presence of nuclear materials even when shielded or embedded in non-fissile matrices by inducing fission through externally applied pulsed neutrons. An innovative modular system that is convenient for transportation and suitable for various application scenarios is proposed. The detection sensitivity of the system, with different structures, is studied. The data acquisition hardware, along with real-time online measurement and alerting software, have been developed. The results indicate that a detection system with a bilaterally arranged array of detectors offers enhanced sensitivity, enabling rapid detection of a single nuclear fuel rod (200.4g) under heavy shielding and at standard distances.

A High-Speed ANN-Based Data Acquisition Hardware Accelerator Targeting Electrical Impedance Tomography

A High-Speed ANN-Based Data Acquisition Hardware Accelerator Targeting Electrical Impedance Tomography

Evaluation of a standard analytical model and definition of efficiency metrics for lunar and planetary excavation

To sustainably live on and explore the lunar and other planetary surfaces, in situ regolith will need to be used for resource extraction and infrastructure development. To evaluate and ensure such safety and efficiency of excavation operations in variable, site-specific regolith terranes with given equipment designs, reliable computational models are necessary. The work here evaluates the accuracy and reliability of a standard, well-established excavation force prediction model using published data from a previous planetary excavation experimental study and establishes efficiency metrics for force reduced excavation as a proof-of-concept. It is shown that the established excavation force prediction model is not able to simulate planetary excavation mechanics sufficiently and hence more advanced models of lunar and planetary excavation are needed to enable safer, faster, and more economical resource acquisition hardware development. The case study of the efficiency metrics being applied to the published data shows informative trends that can be used in future excavation hardware and tool path optimizations. These are recommended for use in future planetary excavation modeling and planning workflows.

Design and systematic evaluation of an under-canopy robotic spray system for row crops

Despite having made much improvement in sensing, automation, and control, the current broadcast spraying system has several drawbacks, such as uneven coverage, excessive chemical use, and deviation from recommended dosage. Typically, farmers use large self-propelled sprayers to spray the entire field without knowledge of spatial pest severity, potentially resulting in an unintentional application. However, application errors and the extent of chemical use can be optimized by utilizing an intelligent site-specific decision-based sprayer to control pests more efficiently. Hence, the initial project goal was to design a robotic liquid application system for row crops (e.g., sorghum and corn) and validate sprayer system performance. The critical design considerations for the spray application system were modularity; the ability to be mounted on an autonomous platform to go within 76.2-cm spaced crops; spray on either side of the crop row using spray booms; onboard hardware and software for control and data acquisition; and record as-applied data. A system with desired design requirements was built and individual sub-systems were tested under simulated lab scenarios to quantify the response time and accuracy of the spray system. The results showed that the sprayer could maintain an average system pressure within ±5% of the target under different duty cycles for each of the six nozzles. At 40% duty cycle, the nozzle pressure settling time at an error margin of ±5% from the mean was 13 ms, 20 ms, and 19 ms, for one, three, and six nozzles, respectively. Also, no substantial pressure difference was observed between nozzles installed at different heights in two different booms. Therefore, this application system could be a viable solution for autonomous platforms to site-specifically apply pesticides only on critically infested plants, has the potential to decrease the overall input costs on chemicals and reduce the negative environmental impacts.

Integrating sEMG and IMU Sensors in an e-Textile Smart Vest for Forward Posture Monitoring: First Steps.

Currently, the market for wearable devices is expanding, with a growing trend towards the use of these devices for continuous-monitoring applications. Among these, real-time posture monitoring and assessment stands out as a crucial application given the rising prevalence of conditions like forward head posture (FHP). This paper proposes a wearable device that combines the acquisition of electromyographic signals from the cervical region with inertial data from inertial measurement units (IMUs) to assess the occurrence of FHP. To improve electronics integration and wearability, e-textiles are explored for the development of surface electrodes and conductive tracks that connect the different electronic modules. Tensile strength and abrasion tests of 22 samples consisting of textile electrodes and conductive tracks produced with three fiber types (two from Shieldex and one from Imbut) were conducted. Imbut's Elitex fiber outperformed Shieldex's fibers in both tests. The developed surface electromyography (sEMG) acquisition hardware and textile electrodes were also tested and benchmarked against an electromyography (EMG) gold standard in dynamic and isometric conditions, with results showing slightly better root mean square error (RMSE) values (for 4 × 2 textile electrodes (10.02%) in comparison to commercial Ag/AgCl electrodes (11.11%). The posture monitoring module was also validated in terms of joint angle estimation and presented an overall error of 4.77° for a controlled angular velocity of 40°/s as benchmarked against a UR10 robotic arm.

Mapping short association fibre connectivity up to V3 in the human brain in vivo.

Short association fibres (SAF) are the most abundant fibre pathways in the human white matter. Until recently, SAF could not be mapped comprehensively in vivo because diffusion weighted magnetic resonance imaging with sufficiently high spatial resolution needed to map these thin and short pathways was not possible. Recent developments in acquisition hardware and sequences allowed us to create a dedicated in vivo method for mapping the SAF based on sub-millimetre spatial resolution diffusion weighted tractography, which we validated in the human primary (V1) and secondary (V2) visual cortex against the expected SAF retinotopic order. Here, we extended our original study to assess the feasibility of the method to map SAF in higher cortical areas by including SAF up to V3. Our results reproduced the expected retinotopic order of SAF in the V2-V3 and V1-V3 stream, demonstrating greater robustness to the shorter V1-V2 and V2-V3 than the longer V1-V3 connections. The demonstrated ability of the method to map higher-order SAF connectivity patterns in vivo is an important step towards its application across the brain.

A Review of Enhancement Techniques for Cone Beam Computed Tomography Images

Cone Beam Computed Tomography (CBCT) has emerged as a valuable imaging modality for various medical applications due to its ability to provide three-dimensional information with minimal radiation exposure. However, CBCT images often suffer from inherent limitations, such as increased noise, artifacts, and reduced spatial resolution. This paper presents a comprehensive review of image processing techniques employed to enhance the quality of CBCT images, addressing the challenges posed by acquisition hardware and image reconstruction algorithms. The review covers a range of preprocessing and post-processing methods, including denoising, artifact correction, and resolution improvement techniques. These methods encompass various mathematical algorithms, machine learning approaches, and hybrid models, which aim to mitigate the imperfections present in CBCT data while preserving diagnostically relevant information. Additionally, this paper discusses the application of deep learning methods, convolutional neural networks, and generative adversarial networks in CBCT image enhancement. These advanced techniques have shown promise in tackling the complex nature of CBCT data and optimizing image quality.

Generalisation capabilities of machine-learning algorithms for the detection of the subthalamic nucleus in micro-electrode recordings.

Micro-electrode recordings (MERs) are a key intra-operative modality used during deep brain stimulation (DBS) electrode implantation, which allow for a trained neurophysiologist to infer the anatomy in which the electrode is placed. As DBS targets are small, such inference is necessary to confirm that the electrode is correctly positioned. Recently, machine learning techniques have been used to augment the neurophysiologist's capability. The goal of this paper is to investigate the generalisability of these methods with respect to different clinical centres and training paradigms. Five deep learning algorithms for binary classification of MER signals have been implemented. Three databases from two different clinical centres have also been collected with differing size, acquisition hardware, and annotation protocol. Each algorithm has initially been trained on the largest database, then either directly tested or fine-tuned on the smaller databases in order to estimate their generalisability. As a reference, they have also been trained from scratch on the smaller databases as well in order to estimate the effect of the differing database sizes and annotation systems. Each network shows significantly reduced performance (on the order of a 6.5% to 16.0% reduction in balanced accuracy) when applied out-of-distribution. This reduction can be ameliorated through fine-tuning the network on the new database through transfer learning. Although, even for these small databases, it appears that retraining from scratch may still offer equivalent performance as fine-tuning with transfer learning. However, this is at the expense of significantly longer training times. Generalisability is an important criterion for the success of machine learning algorithms in clinic. We have demonstrated that a variety of recent machine learning algorithms for MER classification are negatively affected by domain shift, but that this can be quickly ameliorated through simple transfer learning procedures that can be readily performed for new centres.

Multiplexed neurochemical sensing with sub-nM sensitivity across 2.25 mm2 area

Multichannel arrays capable of real-time sensing of neuromodulators in the brain are crucial for gaining insights into new aspects of neural communication. However, measuring neurochemicals, such as dopamine, at low concentrations over large areas has proven challenging. In this research, we demonstrate a novel approach that leverages the scalability and processing power offered by microelectrode array devices integrated with a functionalized, high-density microwire bundle, enabling electrochemical sensing at an unprecedented scale and spatial resolution. The sensors demonstrate outstanding selective molecular recognition by incorporating a selective polymeric membrane. By combining cutting-edge commercial multiplexing, digitization, and data acquisition hardware with a bio-compatible and highly sensitive neurochemical interface array, we establish a powerful platform for neurochemical analysis. This multichannel array has been successfully utilized in vitro and ex vivo systems. Notably, our results show a sensing area of 2.25 mm2 with an impressive detection limit of 820 pM for dopamine. This new approach paves the way for investigating complex neurochemical processes and holds promise for advancing our understanding of brain function and neurological disorders.

Comparative performance analysis of different types of k-nearest neighbor (k-NN) classifiers for fault diagnosis of air compressor setup

This paper presents a method for identifying Reciprocating Air Compressor (RAC) faults using acoustic signals obtained from both healthy and unhealthy conditions. The entire procedure is carried out with Uni-directional microphones using a LABVIEW-based data collection interface and data acquisition (DAQ) hardware unit that has several ports. Accumulated one healthy and seven unhealthy signals of RAC setup processed using signal processing technique called Local Mean Decomposition (LMD). Further, six Statistical Properties (SPs) have been evaluated in order to extract fault features namely: mean (US), variance (σ s2), root square of mean (Mrms), root amplitude of mean (Mrma), absolute amplitude of mean (Mama), and Kurtosis index (Ki). Extracted fault features are classified using various types of k-NN classifiers namely: fine (f-kNN), medium (m-kNN), coarse (c-kNN) and weighted (w-kNN). It has been found that LMD along with 6 statistical properties and form different type of k-NN classifiers, the weighted k-NN classifier has a greater accuracy of 86.74%, which is quite accurate as compared to other k-NN classifiers.

Live Intersection Data Acquisition for Traffic Simulators (LIDATS).

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments.

Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study

BackgroundDeep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the generalizability of object detection models, using sperm analysis as a pilot example.MethodsAblation studies were performed using state-of-the-art models for detecting human sperm to quantitatively assess how model precision (false-positive detection) and recall (missed detection) were affected by imaging magnification, imaging mode, and sample preprocessing protocols. The results led to the hypothesis that the richness of image acquisition conditions in a training dataset deterministically affects model generalizability. The hypothesis was tested by first enriching the training dataset with a wide range of imaging conditions, then validated through internal blind tests on new samples and external multi-center clinical validations.ResultsAblation experiments revealed that removing subsets of data from the training dataset significantly reduced model precision. Removing raw sample images from the training dataset caused the largest drop in model precision, whereas removing 20x images caused the largest drop in model recall. by incorporating different imaging and sample preprocessing conditions into a rich training dataset, the model achieved an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94-0.99) for precision, and an ICC of 0.97 (95% CI: 0.93-0.99) for recall. Multi-center clinical validation showed no significant differences in model precision or recall across different clinics and applications.ConclusionsThe results validated the hypothesis that the richness of data in the training dataset is a key factor impacting model generalizability. These findings highlight the importance of diversity in a training dataset for model evaluation and suggest that future deep learning models in andrology and reproductive medicine should incorporate comprehensive feature sets for enhanced generalizability across clinics.

Border militarization and domestic institutions

AbstractThe objective of this paper is to examine the impact of border policies and the increased militarization of border control practices on the expansion of police powers, the erosion of constitutional constraints and on other institutional spillovers. In recent decades, the Border Patrol has been integrated with the broader national security state as part of the war on drugs and the war on terror. This has entailed the acquisition of military hardware, the incorporation of organizational structures and training originally developed in the military, and increased interaction with the military, intelligence agencies, and defense contractors. The Border Patrol has frequently lent their equipment, personnel, and powers to domestic policing that has only a tangential relationship with border security. Border militarization therefore contributes to expansions in the scope of police powers and the erosion of constitutional constraints. This process undermines functional polycentricity, and thereby alters the incentives and knowledge of political decision-makers.

Precision and Test-Retest Repeatability of Stiffness Measurement with MR Elastography: A Multicenter Phantom Study.

Background MR elastography (MRE) has been shown to have excellent performance for noninvasive liver fibrosis staging. However, there is limited knowledge regarding the precision and test-retest repeatability of stiffness measurement with MRE in the multicenter setting. Purpose To determine the precision and test-retest repeatability of stiffness measurement with MRE across multiple centers using the same phantoms. Materials and Methods In this study, three cylindrical phantoms made of polyvinyl chloride gel mimicking different degrees of liver stiffness in humans (phantoms 1-3: soft, medium, and hard stiffness, respectively) were evaluated. Between January 2021 and January 2022, phantoms were circulated between five different centers and scanned with 10 MRE-equipped clinical 1.5-T and 3-T systems from three major vendors, using two-dimensional (2D) gradient-recalled echo (GRE) imaging and/or 2D spin-echo (SE) echo-planar imaging (EPI). Similar MRE acquisition parameters, hardware, and reconstruction algorithms were used at each center. Mean stiffness was measured by a single observer for each phantom and acquisition on a single section. Stiffness measurement precision and same-session test-retest repeatability were assessed using the coefficient of variation (CV) and the repeatability coefficient (RC), respectively. Results The mean precision represented by the CV was 5.8% (95% CI: 3.8, 7.7) for all phantoms and both sequences combined. For all phantoms, 2D GRE achieved a CV of 4.5% (95% CI: 3.3, 5.7) whereas 2D SE EPI achieved a CV of 7.8% (95% CI: 3.1, 12.6). The mean RC of stiffness measurement was 5.8% (95% CI: 3.7, 7.8) for all phantoms and both sequences combined, 4.9% (95% CI: 2.7, 7.0) for 2D GRE, and 7.0% (95% CI: 2.9, 11.2) for 2D SE EPI (all phantoms). Conclusion MRE had excellent in vitro precision and same-session test-retest repeatability in the multicenter setting when similar imaging protocols, hardware, and reconstruction algorithms were used. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Tang in this issue.