Hardware Improvements Research Articles

Role of eXtended Reality use in medical imaging interpretation for pre-surgical planning and intraoperative augmentation.

eXtended Reality (XR) technology, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), is a growing field in healthcare. Each modality offers unique benefits and drawbacks for medical education, simulation, and clinical care. We review current studies to understand how XR technology uses medical imaging to enhance surgical diagnostics, planning, and performance. We also highlight current limitations and future directions. We reviewed the literature on immersive XR technologies for surgical planning and intraoperative augmentation, excluding studies on telemedicine and 2D video-based training. We cited publications highlighting XR's advantages and limitations in these categories. A review of 556 papers on XR for medical imaging in surgery yielded 155 relevant papers reviewed utilizing the aid of chatGPT. XR technology may improve procedural times, reduce errors, and enhance surgical workflows. It aids in preoperative planning, surgical navigation, and real-time data integration, improving surgeon ergonomics and enabling remote collaboration. However, adoption faces challenges such as high costs, infrastructure needs, and regulatory hurdles. Despite these, XR shows significant potential in advancing surgical care. Immersive technologies in healthcare enhance visualization and understanding of medical conditions, promising better patient outcomes and innovative treatments but face adoption challenges such as cost, technological constraints, and regulatory hurdles. Addressing these requires strategic collaborations and improvements in image quality, hardware, integration, and training.

Strategic Integration: A Cross-Disciplinary Review of the fNIRS-EEG Dual-Modality Imaging System for Delivering Multimodal Neuroimaging to Applications.

Background: Recent years have seen a surge of interest in dual-modality imaging systems that integrate functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) to probe brain function. This review aims to explore the advancements and clinical applications of this technology, emphasizing the synergistic integration of fNIRS and EEG. Methods: The review begins with a detailed examination of the fundamental principles and distinctive features of fNIRS and EEG techniques. It includes critical technical specifications, data-processing methodologies, and analysis techniques, alongside an exhaustive evaluation of 30 seminal studies that highlight the strengths and weaknesses of the fNIRS-EEG bimodal system. Results: The paper presents multiple case studies across various clinical domains-such as attention-deficit hyperactivity disorder, infantile spasms, depth of anesthesia, intelligence quotient estimation, and epilepsy-demonstrating the fNIRS-EEG system's potential in uncovering disease mechanisms, evaluating treatment efficacy, and providing precise diagnostic options. Noteworthy research findings and pivotal breakthroughs further reinforce the developmental trajectory of this interdisciplinary field. Conclusions: The review addresses challenges and anticipates future directions for the fNIRS-EEG dual-modal imaging system, including improvements in hardware and software, enhanced system performance, cost reduction, real-time monitoring capabilities, and broader clinical applications. It offers researchers a comprehensive understanding of the field, highlighting the potential applications of fNIRS-EEG systems in neuroscience and clinical medicine.

Integer Factorization with Stochastic Spiking in Percolating Networks of Nanoparticles.

As growth in global demand for computing power continues to outpace ongoing improvements in transistor-based hardware, novel computing solutions are required. One promising approach employs stochastic nanoscale devices to accelerate probabilistic computing algorithms. Percolating Networks of Nanoparticles (PNNs) exhibit stochastic spiking, which is of particular interest as it meets criteria for criticality which is associated with a range of computational advantages. Here, we show several ways in which spiking PNNs can be used as the core stochastic components of coupled networks that allow successful factorization of integers up to 945. We demonstrate asynchronous operation and show that a single device is sufficient to solve all factorization tasks and to generate multiple solutions simultaneously.

X-Tracker: Automated Analysis of Xenopus Tadpole Visual Avoidance Behavior.

Xenopus laevis tadpoles exhibit an avoidance behavior when they encounter a moving visual stimulus. A visual avoidance event occurs when a moving object approaches the eye of a free-swimming animal at an approximately 90-degree angle and the animal turns in response to the encounter. Analysis of this behavior requires tracking both the free-swimming animal and the moving visual stimulus both prior to and after the encounter. Previous automated tracking software does not discriminate the moving animal from the moving stimulus, requiring time-consuming manual analysis. Here we present X-Tracker, an automated behavior tracking code that can detect and discriminate moving visual stimuli and free-swimming animals and score encounters and avoidance events. X-Tracker is as accurate as human analysis without the human time commitment. We also present software improvements to our previous visual stimulus presentation and image capture that optimize videos for automated analysis, and hardware improvements that increase the number of animal-stimulus encounters. X-Tracker is a high throughput, unbiased, and significant time-saving analysis system that will greatly facilitate visual avoidance behavior analysis of Xenopus laevis tadpoles, and potentially other free-swimming organisms. The tool is available at https://github.com/ClineLab/Tadpole-Behavior-Automation.

A Survey of Hardware Improvements to Secure Program Execution

Hardware has been constantly augmented for security considerations since the advent of computers. There is also a common perception among computer users that hardware does a relatively better job on security assurance compared with software. Yet, the community has long lacked a comprehensive study to answer questions such as how hardware security support contributes to security, what kind of improvements have been introduced to improve such support and what its advantages/disadvantages are. By generalizing various security goals, we taxonomize hardware security features and their security properties that can aid in securing program execution, considered as three aspects, i.e., state correctness, runtime protection and input/output protection. Based on this taxonomy, the survey systematically examines (1) the roles: how hardware is applied to achieve security; and (2) the problems: how reported attacks have exploited certain defects in hardware. We see that hardware’s unique advantages and problems co-exist and it highly depends on the desired security purpose as to which type to use. Among the survey findings are also that code as part of hardware (aka. firmware) should be treated differently to ensure security by design; and how research proposals have driven the advancement of commodity hardware features.

Generic FPGA Pre-Processing Image Library for Industrial Vision Systems.

Currently, there is a demand for an increase in the diversity and quality of new products reaching the consumer market. This fact imposes new challenges for different industrial sectors, including processes that integrate machine vision. Hardware acceleration and improvements in processing efficiency are becoming crucial for vision-based algorithms to follow the complexity growth of future industrial systems. This article presents a generic library of pre-processing filters for execution in field-programmable gate arrays (FPGAs) to reduce the overall image processing time in vision systems. An experimental setup based on the Zybo Z7 Pcam 5C Demo project was developed and used to validate the filters described in VHDL (VHSIC hardware description language). Finally, a comparison of the execution times using GPU and CPU platforms was performed as well as an evaluation of the integration of the current work in an industrial application. The results showed a decrease in the pre-processing time from milliseconds to nanoseconds when using FPGAs.

Automatic source code generation for deterministic global optimization with parallel architectures

Trends over the past two decades indicate that much of the performance gains of commercial optimization solvers is due to improvements in x86 hardware. To continue making progress, it is critical to consider alternative/specialized massively parallel computing architectures. In this work, we detail the development of an open-source source code transformation approach built using Symbolics.jl to construct McCormick-based relaxations of functions that enables their effective parallelized evaluation. We then apply this approach in a novel parallelized branch-and-bound routine that offloads lower- and upper-bounding problems to a GPU. The effectiveness of this new approach is demonstrated on three nonconvex problems of interest, where it yields convergence time improvements of 22–118x compared to an equivalent serial CPU implementation and in two cases outperforms vanilla branch-and-bound versions of existing state-of-the-art solvers that use tighter bounding techniques. This work exemplifies how deterministic global optimizers using alternative hardware architectures can compete with—or eventually outclass—even the most powerful serial CPU implementations, and to the best of the authors' knowledge, represents the first successful demonstration of deterministic global optimization using a GPU.

Transformer for low concentration image denoising in magnetic particle imaging.

Objective.Magnetic particle imaging (MPI) is an emerging tracer-basedin vivoimaging technology. The use of MPI at low superparamagnetic iron oxide nanoparticle concentrations has the potential to be a promising area of clinical application due to the inherent safety for humans. However, low tracer concentrations reduce the signal-to-noise ratio of the magnetization signal, leading to severe noise artifacts in the reconstructed MPI images. Hardware improvements have high complexity, while traditional methods lack robustness to different noise levels, making it difficult to improve the quality of low concentration MPI images.Approach.Here, we propose a novel deep learning method for MPI image denoising and quality enhancing based on a sparse lightweight transformer model. The proposed residual-local transformer structure reduces model complexity to avoid overfitting, in which an information retention block facilitates feature extraction capabilities for the image details. Besides, we design a noisy concentration dataset to train our model. Then, we evaluate our method with both simulated and real MPI image data.Main results.Simulation experiment results show that our method can achieve the best performance compared with the existing deep learning methods for MPI image denoising. More importantly, our method is effectively performed on the real MPI image of samples with an Fe concentration down to 67μgFeml-1.Significance.Our method provides great potential for obtaining high quality MPI images at low concentrations.

Strategies for minimizing repeated exposures in diagnostic radiography

Advancements in diagnostic radiography have significantly improved medical imaging, yet the associated radiation exposure poses health risks, particularly with repeated examinations. Technological innovations, including the transition from analog to digital radiography, have led to substantial reductions in radiation doses while enhancing image quality. Digital systems, coupled with automatic exposure control (AEC), dynamically adjust radiation levels based on patient size and diagnostic requirements, minimizing unnecessary exposure. Iterative reconstruction algorithms in CT imaging further reduce radiation doses by efficiently processing images and reducing noise. Hardware improvements, such as high-efficiency detectors, capture high-quality images with lower radiation levels, contributing to dose reduction. Standardizing radiographic protocols across healthcare settings ensures consistent practices, reducing variability in radiation doses. Techniques such as beam collimation and appropriate shielding protect surrounding tissues and enhance image quality, while continuous education for technologists maintains proficiency in dose-reduction strategies. Patient education is crucial for minimizing repeated exposures. Informing patients about the risks of ionizing radiation and the importance of adhering to follow-up schedules can reduce unnecessary examinations. Electronic health records (EHRs) facilitate the tracking of cumulative radiation doses, enabling informed decision-making regarding additional imaging studies. Adherence to the ALARA principle (As low as reasonably achievable) in clinical practice ensures that radiation exposure is minimized while achieving diagnostic goals. Special considerations for vulnerable populations, such as children and pregnant women, further enhance safety. Implementing radiation safety measures and maintaining a commitment to ongoing professional development for healthcare providers fosters a culture of safety within radiology departments. By integrating these strategies, healthcare providers can balance the diagnostic benefits of radiography with the imperative to protect patients from unnecessary radiation exposure, ultimately enhancing patient safety and improving diagnostic outcomes. This comprehensive approach ensures that radiographic procedures remain both effective and safe, optimizing patient care in medical imaging.

Transitioning practices of water utilities from reactive to proactive: Leveraging Australian best practices in digital technologies and data analytics

Developments in technology and data analytics bring opportunities for water utilities to proactively adapt their practices to better manage pressing challenges, such as water losses, ageing assets, and urban water scarcity due to climate change. Despite the increase in commercial digital technologies for monitoring urban water, wastewater and stormwater systems, digital transformation in water utilities has been occurring much slower than in other businesses (e.g., the energy sector). Leveraging from Australian best practices, this study aims to facilitate water utility’s digital transformation by establishing a targeted research agenda and a continuous improvement process for the successful implementation, operation, and improvement of smart water technologies. Four case studies on 1) automated and digital water consumption metering, 2) early warning of water mains breaks, 3) early detection of sewerage blockages and spills, and 4) smart rainfall reuse and flood mitigation are used to exemplify the potential benefits and hurdles of adopting digital technologies in water utilities, while highlighting pressing areas needing further research and development. Key research areas identified include: 1) development frameworks to map water utilities capacity and needs for ranking priority areas of investment, 2) improvements in sensor hardware, 3) integration of big, multi-sourced data and cybersecurity, 4) increase in generality of smart data applications for various field conditions (i.e., reduce the need for or level of customization), and 5) data-informed proactive asset management and water pricing. Key barriers to be overcome include 1) lack of internal capacity to develop, operate and integrate smart applications, 2) the risk conservative culture of water utilities when facing innovation, and 3) lack of operation standards among water utilities to promote benchmarking. It is imperative that water utilities actively drive the digital transformation process by perpetuating collaborations with research centres and the private sector, while simultaneously increasing in-house capacity through investments in skilled personnel and training.

Reducing hardware requirements for entanglement distribution via joint hardware-protocol optimization

We conduct a numerical investigation of fiber-based entanglement distribution over distances of up to 1600 km using a chain of processing-node quantum repeaters. We determine minimal hardware requirements while simultaneously optimizing over protocols for entanglement generation and entanglement purification, as well as over strategies for entanglement swapping. Notably, we discover that through an adequate choice of protocols the hardware improvement cost scales linearly with the distance covered. Our results highlight the crucial role of good protocol choices in significantly reducing hardware requirements, such as employing purification to meet high-fidelity targets and adopting a swap as soon as possible policy for faster rates. To carry out this analysis, we employ an extensive simulation framework implemented with NetSquid, a discrete-event-based quantum-network simulator, and a genetic-algorithm-based optimization methodology to determine minimal hardware requirements.

Water, Sanitation, and Hygiene (WASH) in Schools: A Catalyst for Upholding Human Rights to Water and Sanitation in Anápolis, Brazil

Ensuring access to water, sanitation, and hygiene (WASH) facilities, along with behavior change education in schools, is essential for fostering a conducive learning environment and promoting principles of inclusion, dignity, and equality. This study focuses on 12 primary schools in Anápolis, Brazil, with a total of 4394 students and 248 teachers. WASH assessments were conducted using a questionnaire, observations of water and sanitation facilities, and microbiological analysis. Between 2018 and 2020, the proportion of schools with safely managed drinking water increased from 42% to 92%. However, sanitation conditions in the schools were categorized as basic, with hygiene services rated as limited in 92% of the schools due to the absence of soap. While this study shows that Anápolis schools generally meet WASH guidelines for basic/advanced drinking water services, there is a crucial need for improvements in both hardware and software facilities to address sanitation and hygiene challenges.

Regressor cascading for time series forecasting

Time series forecasting is the process of predicting future values of a time series based on its historical data patterns. It is a critical task in many domains, including finance, supply chain management, the environment, and more as accurate forecasts can help businesses and organizations make better decisions and improve their metrics. Although there have been significant advances in time series forecasting systems, thanks to the development of new machine learning algorithms, hardware improvements, and the increasing availability of data, it remains a challenging task. Common pitfalls, especially of single-model approaches include susceptibility to noise and outliers and inability to handle non-stationary data, which can lead to inaccurate and non-robust forecasts. Model-combining approaches, such as averaging the results of multiple predictors to produce a final forecast, are commonly used to mitigate such issues. This work introduces a novel application of Cascade Generalization or Cascading for time series forecasting, where multiple predictors are used sequentially, with each predictor’s output serving as additional input for the next. This methodology aims to overcome the limitations of single-model forecasts and traditional ensembles by incorporating a progressive learning mechanism. We adapt Cascade Generalization specifically for time series data, detailing its implementation and potential for handling complex, dynamic datasets. Our approach was systematically evaluated against traditional two-model averaging ensembles across ten diverse datasets, employing the Root Mean Square Error (RMSE) metric for performance assessment. The results revealed that cascading tends to outperform voting ensembles in most cases. This consistent trend suggests that cascading can be considered a reliable alternative to voting ensembles, showcasing its potential as an effective strategy for improving time series forecasting across a wide range of scenarios.

Toward Multiscale, Multimaterial 3D Printing.

Biological materials and organisms possess the fundamental ability to self-organize, through which different components are assembled from the molecular level up to hierarchical structures with superior mechanical properties and multifunctionalities. These complex composites inspire material scientists to design new engineered materials by integrating multiple ingredients and structures over a wide range. Additive manufacturing, also known as 3D printing, has advantages with respect to fabricating multiscale and multi-material structures. The need for multifunctional materials is driving 3D printing techniques toward arbitrary 3D architectures with the next level of complexity. In this paper, the aim is to highlight key features of those 3D printing techniques that can produce either multiscale or multimaterial structures, including innovations in printing methods, materials processing approaches, and hardware improvements. Several issues and challenges related to current methods are discussed. Ultimately, the authors also provide their perspective on how to realize the combination of multiscale and multimaterial capabilities in 3D printing processes and future directions based on emerging research.

Current Applications of PET/MR: Part I: Technical Basics and Preclinical/Clinical Applications.

Positron emission tomography/magnetic resonance (PET/MR) imaging has gone through major hardware improvements in recent years, making it a reliable state-of-the-art hybrid modality in clinical practice. At the same time, image reconstruction, attenuation correction, and motion correction algorithms have significantly evolved to provide high-quality images. Part I of the current review discusses technical basics, pre-clinical applications, and clinical applications of PET/MR in radiation oncology and head and neck imaging. PET/MR offers a broad range of advantages in preclinical and clinical imaging. In the preclinic, small and large animal-dedicated devices were developed, making PET/MR capable of delivering new insight into animal models in diseases and facilitating the development of methods that inform clinical PET/MR. Regarding PET/MR's clinical applications in radiation medicine, PET and MR already play crucial roles in the radiotherapy process. Their combination is particularly significant as it can provide molecular and morphological characteristics that are not achievable with other modalities. In addition, the integration of PET/MR information for therapy planning with linear accelerators is expected to provide potentially unique biomarkers for treatment guidance. Furthermore, in clinical applications in the head and neck region, it has been shown that PET/MR can be an accurate modality in head and neck malignancies for staging and resectability assessment. Also, it can play a crucial role in diagnosing residual or recurrent diseases, reliably distinguishing from oedema and fibrosis. PET/MR can furthermore help with tumour characterization and patient prognostication. Lastly, in head and neck carcinoma of unknown origin, PET/MR, with its diagnostic potential, may obviate multiple imaging sessions in the near future.

A two-capillary viscometer for temperatures up to 473 K and pressures up to 100 MPa—operation and verification at low pressure

In this paper, we described the design and construction of a new two-capillary viscometer with several novel technical solutions for viscosity and density measurements. Our design, which is based on the low-pressure principle, featured numerous improvements in hardware and procedure that allowed the greatly extended range of pressure. The new design adopted a (2 × 2) capillary configuration, utilizing different combinations of four capillaries to enable viscosity measurements with a wide range of flow rates, temperatures, and pressures. The design temperature range is 213 K–473 K, and the pressure range is up to 100 MPa. The viscometer was specifically designed for measuring the viscosity of pure CO2 and CO2-rich mixtures, addressing the scarcity of data in conditions relevant to carbon capture, transport, and storage. Our facility is capable of viscosity measurements in different thermodynamic states; gaseous, liquid, supercritical, and critical regions. A commercial densimeter is integrated to measure density under the same temperatures and pressures. We aimed for a total uncertainty target of better than 0.03%. The performance of the viscometer was validated by measurements with pure CO2 at 298.15 K and zero density. We observed a deviation of less than 0.03% between the reference viscosity of CO2 of this work and accurately calculated data using ab initio quantum mechanics with a standard uncertainty of 0.2%. Our primary focus in this paper was to provide a detailed description of the design and construction of the apparatus, emphasizing improvements and introducing new solutions to other research groups in constructing similar instruments suitable for low- and high-pressure viscosity measurements with high accuracy.

Validating Joint Acoustic Emissions Models as a Generalizable Predictor of Joint Health.

Joint acoustic emissions (JAEs) have been used as a non-invasive sensing modality of joint health for different conditions such as acute injuries, osteoarthritis (OA), and rheumatoid arthritis (RA). Recent hardware improvements for sensing JAEs have made at-home sensing to supplement clinical visits a possibility. To complement these advances, models must be improved for JAEs to function as generalizable predictors of joint health. Addressing this need, this work investigates the effects of recording setup, location-specific factors, and participant population on previously validated JAE models. The effect of recording setup is first investigated by testing a model developed previously for a wearable brace to predict erythrocyte sedimentation rate (ESR) in participants with RA on benchtop data, resulting in an area under the receiver-operating characteristic curve (AUC), sensitivity, and specificity of 0.79, 0.73, and 0.81 respectively. Investigating the effects of participant population type and location-specific factors, a feature-based model and a convolutional neural network (CNN) were both trained with healthy and RA data to predict ESR level, and then tested on a new dataset containing healthy, pre-radiographic osteoarthritis (Pre-OA), and OA data. The feature-based model had an AUC of 0.69 and 0.94, a sensitivity of 0.38 and 0.80, and a sensitivity of 1, while the CNN had an AUC of 0.85 and 0.99, a sensitivity of 0.50 and 1, and a specificity of 0.90 for detecting Pre-OA and OA respectively. The ability to generalize models across setup, location, and participant population provides a foundation for using JAEs as a measure of joint health.

3SAT on an all-to-all-connected CMOS Ising solver chip

This work solves 3SAT, a classical NP-complete problem, on a CMOS-based Ising hardware chip with all-to-all connectivity. The paper addresses practical issues in going from algorithms to hardware. It considers several degrees of freedom in mapping the 3SAT problem to the chip—using multiple Ising formulations for 3SAT; exploring multiple strategies for decomposing large problems into subproblems that can be accommodated on the Ising chip; and executing a sequence of these subproblems on CMOS hardware to obtain the solution to the larger problem. These are evaluated within a software framework, and the results are used to identify the most promising formulations and decomposition techniques. These best approaches are then mapped to the all-to-all hardware, and the performance of 3SAT is evaluated on the chip. Experimental data shows that the deployed decomposition and mapping strategies impact SAT solution quality: without our methods, the CMOS hardware cannot achieve 3SAT solutions on SATLIB benchmarks. Under the assumption of some hardware improvements, our chip-based 3SAT solver demonstrates a remarkable 250×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} acceleration compared to Tabu search in dwave-hybrid on a CPU.

Bragg Spot Finder (BSF): a new machine-learning-aided approach to deal with spot finding for rapidly filtering diffraction pattern images

Macromolecular crystallography contributes significantly to understanding diseases and, more importantly, how to treat them by providing atomic resolution 3D structures of proteins. This is achieved by collecting X-ray diffraction images of protein crystals from important biological pathways. Spotfinders are used to detect the presence of crystals with usable data, and the spots from such crystals are the primary data used to solve the relevant structures. Having fast and accurate spot finding is essential, but recent advances in synchrotron beamlines used to generate X-ray diffraction images have brought us to the limits of what the best existing spotfinders can do. This bottleneck must be removed so spotfinder software can keep pace with the X-ray beamline hardware improvements and be able to see the weak or diffuse spots required to solve the most challenging problems encountered when working with diffraction images. In this paper, we first present Bragg Spot Detection (BSD), a large benchmark Bragg spot image dataset that contains 304 images with more than 66 000 spots. We then discuss the open source extensible U-Net-based spotfinder Bragg Spot Finder (BSF), with image pre-processing, a U-Net segmentation backbone, and post-processing that includes artifact removal and watershed segmentation. Finally, we perform experiments on the BSD benchmark and obtain results that are (in terms of accuracy) comparable to or better than those obtained with two popular spotfinder software packages (Dozor and DIALS), demonstrating that this is an appropriate framework to support future extensions and improvements.

Will AI’s growth create an explosion of energy consumption?

Further improvements in hardware and software efficiencies may counteract an expected surge in demand for electricity needed to power the new large language models.