Advances In Hardware Research Articles

The Application of Deep Learning to Accurately Identify the Dimensions of Spinal Canal and Intervertebral Foramen as Evaluated by the IoU Index.

Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. For excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthened the accuracy of results. Indicators such as area and Intersection over Union (IoU) are also provided for assessment. Among them, the mean Average Precision (mAP) for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.

Rotation-invariant image recognition using interconnected floating-gate phototransistor

Rotational invariance is fundamental for robust image recognition systems, ensuring accurate analysis irrespective of image orientation. However, existing systems predominantly reliant on software often encounter challenges such as increased computational demands and compromises between processing speed and accuracy. In this study, we propose leveraging the interconnected floating-gate (FG) structure as an effective hardware-level solution to achieve rotational invariance in image recognition. Our design features a reconfigurable two-dimensional material FG phototransistor array, where each processing unit integrates four sensory devices sharing a common FG. This configuration facilitates uniform distribution of stored charges across the interconnected FG layer, which is typically made of metal, enabling consistent application of a single weight matrix to images across varied rotational conditions. The photoactive material, tungsten diselenide (WSe2), possesses a distinctive bipolar property that facilitates both hole and electron tunneling into the FG layer. This property directly contributes to the efficiency of state transition within the setup and improves its overall adaptability. In this manner, our design achieves stable and predictable outputs in recognizing identical digital numbers regardless of their rotation, while also demonstrating variable performance essential for accurately distinguishing between different digital numbers. This dual capability guarantees both the adaptability and precision required for rotation-invariant image recognition, suggesting that our work may open up a promising venue for exploring advanced hardware designs, such as optimized interconnected FG architectures, tailored for enhancing recognition accuracy and efficiency in the field of intelligent visual systems.

Advances in molecular imaging for early detection of lung cancer

Lung cancer remains the second most commonly diagnosed cancer globally and the leading cause of cancer-related deaths, a trend consistent in the United States as of 2023. One of the key reasons for the high mortality rate of lung cancer is its poor prognosis, with 75% of patients diagnosed at middle and advanced stages. Early detection of subclinical lung cancer, metastases, and their fibrotic stroma is crucial for enabling timely treatment, reducing reoccurrence, and stratifying patients. Current diagnostic methods, such as lung biopsy for patients with small nodules, are highly invasive and technically challenging. The radiological gold standard, computed tomography (CT), is associated with ionizing radiation. However, positron emission tomography (PET) and magnetic resonance imaging (MRI) have emerged as promising methodologies for lung cancer diagnosis. PET tracers with a variety of targeting mechanisms are currently under development in human trials. With advancements in hardware and software over the past decades, radiation-free MRI has been clinically and preclinically validated as an alternative to CT. Moreover, novel-targeted MRI contrast agents have been tested in animal models and show strong translational potential. In this review, we summarize the state-of-the-art progress in molecular imaging for the early detection of lung cancer and its potential biomarkers.

Enhancing the temporal resolution of water levels from altimetry using D-InSAR: A case study of 10 Swedish Lakes

Lakes provide societies and natural ecosystems with valuable services such as freshwater supply and flood control. Water level changes in lakes reflect their natural responses to climatic and anthropogenic stressors; however, their monitoring is costly due to installation and maintenance requirements. With its advanced hardware and computational capabilities, altimetry has become a popular alternative to conventional in-situ gauging, although subject to the temporal availability of altimetric observations. To further improve the temporal resolution of altimetric measurements, we here combine radar altimetry data with Differential Interferometric Synthetic Aperture Radar (D-InSAR), using ten lakes in Sweden as a testing platform. First, we use Sentinel-1A and Sentinel-1B SAR images to generate consecutive six-day baseline interferograms across 2019. Then, we accumulate the phase change of coherent pixels to construct the time series of InSAR-derived water level anomalies. Finally, we retrieve altimetric observations from Sentinel-3, estimate their mean and standard deviation, and apply them to the D-InSAR standardized anomalies. In this way, we build a water-level time series with more temporal observations. In general, we find a strong agreement between water level estimates from the combination of D-InSAR and Satellite Altimetry (DInSAlt) and in-situ observations in eight lakes (Concordance Correlation Coefficient - CCC >0.8) and moderate agreement in two lakes (CCC >0.57). The applicability of DInSAlt is limited to lakes with suitable conditions for double-bounce scattering, such as the presence of trees or marshes. The accuracy of the water level estimates depends on the quality of the altimetry observations and the lake's width. These findings are important considering the recently launched Surface Water and Ocean Topography (SWOT) satellite, whose capabilities could expand our methodology's geographical applicability and reduce its reliance on ground measurements.

How does obsolescence risk influence consumer resistance to smartwatches?

PurposeDue to advances in both software and hardware, obsolescence risk refers to the fear that a product will soon become obsolete, which can be very high for technological products such as smartwatches or smartphones. Drawing on the perceived risk theory and innovation resistance, this study examines the effects of different obsolescence risks on consumer resistance to smartwatches.Design/methodology/approachA sequential explanatory approach using a mixed method was adopted in this study. In Study 1, we conducted semi-structured and in-depth face-to-face interviews with 16 individuals to identify the most important obsolescence risks affecting consumers’ resistance to smartwatches. This qualitative study develops a novel theoretical model based on interpretive results, including technological, economic, functional, and aesthetic obsolescence risks. In Study 2, we tested our theoretical model by applying partial least-squares structural equation modeling to a sample of 298 smartwatch users.FindingsThe results show that consumer resistance to smartwatches is affected by technological, economic, functional, and aesthetic obsolescence risks.Originality/valueAlthough most extant studies have focused on the factors influencing the adoption and use of consumer electronics, little is known about the role of obsolescence risk in consumers’ resistance to these products.

Advanced quantum and semiclassical methods for simulating photoinduced molecular dynamics and spectroscopy

AbstractMolecular‐level understanding of photoinduced processes is critically important for breakthroughs in transformative technologies utilizing light, ranging from photomedicine to photoresponsive materials. Theory and simulation play a crucial role in this task. Despite great advances in hardware and computational methods, the theoretical description of photoinduced phenomena in the presence of complex environments and external photoexcitation conditions still poses formidable challenges for theoreticians and there are numerous formal and computational difficulties that must be overcome. The development of predictive, accurate, and at the same time, computationally efficient theoretical approaches to describe complex problems in photochemistry and photophysics is an active field of research in contemporary theoretical and computational chemistry. In this advanced review, we discuss modern computational advances and novel approaches that have been recently developed in excited‐electronic structure methods, and multiscale modeling, with a special emphasis on coupled electron‐nuclear dynamics and spectroscopy, from fully quantum to semi‐classical methodologies—including dissipative effects, the explicit light field interaction, femtosecond time‐resolved spectroscopy, and software infrastructure.This article is categorized under: Software > Quantum Chemistry Electronic Structure Theory > Combined QM/MM Methods Theoretical and Physical Chemistry > Spectroscopy Software > Molecular Modeling

Smart parallel automated cryo-electron tomography.

In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an experienced operator and appreciable microscope time to carefully select targets for high-throughput tilt series acquisition. Here, we developed smart parallel automated cryo-electron tomography (SPACEtomo), a workflow using machine learning approaches to fully automate the entire cryo-electron tomography process, including lamella detection, biological feature segmentation, target selection and parallel tilt series acquisition, all without the need for human intervention. This degree of automation will be essential for obtaining statistically relevant datasets and high-resolution structures of macromolecules in their native context.

Transforming Computer Practice Teaching through Virtual Reality: Trends and Future Directions

The integration of Virtual Reality (VR) in computer education has acquired significant momentum due to its ability to provide immersive and interactive learning experiences. This paper analyzes current trends, challenges, and potential advancements in the utilization of virtual reality for practical computer science education. We aim to investigate how virtual reality (VR) enhances experiential learning through key characteristics such as immersion, engagement, and presence, while also examining the influence of emerging technologies like artificial intelligence (AI) and VR-augmented reality (AR) hybrid systems on the progression of personalized learning. We assess the benefits of VR in developing practical skills and critical thinking through a comprehensive analysis of recent advancements in VR hardware, software, and their applications inside a safe, virtual environment. Additionally, we assess the integration of virtual reality (VR) with learning management systems (LMS) and its trans disciplinary applications, as well as ethical concerns regarding accessibility and data protection.

Edge AI: Revolutionizing Real-Time Intelligence at the Network Periphery

Abstract: Edge AI, which transfers computation from centralized data centers to devices at the edge of the network, is a revolutionary development in artificial intelligence that offers advantages including real-time processing, decreased latency, increased privacy, and increased bandwidth efficiency. This paradigm is especially important for industrial IoT, smart city, and autonomous car applications where quick data processing and decision making are critical. Even with obstacles like scalability and hardware constraints, edge AI capabilities are being significantly expanded by continuing research and technical developments. Its revolutionary influence is demonstrated by current applications in a variety of domains, from improving industrial operations to urban infrastructure. Edge AI promises to revolutionize many industries, changing how we interact with technology and influencing the development of the next wave of AI applications as it develops further thanks to advancements in hardware and AI algorithms. With enormous potential for the future, edge AI represents a major advancement toward ubiquitous AI with broad ramifications for both technology and society

Optimizing Plant Watering Efficiency via IoT: Fuzzy Sugeno Method with ESP8266 Microcontroller

This research explores automatic plant sprinklers utilizing fuzzy logic with the fuzzy Sugeno method to overcome optimal watering schedules. Developed with ESP8266 microcontroller hardware and Arduino IDE software, the WSN prototype focuses on spinach plants. Watering in the nursery was done twice a day for seedling growth, then reduced to once a day. Soil moisture levels were assessed using a soil analyzer, as well as temperature variations with the HTC-1 device. Python facilitates data analysis, generating fuzzy inference graphs on temperature sensors (10-40°C) and soil moisture values (1-1024 RH). This innovative approach, which integrates fuzzy logic and advanced hardware, offers a promising solution for effective plant care.

Multiple deep learning by majority-vote to classify haploid and diploid maize seeds

The maize plant is a crucial global staple, integral to food security. To ensure sustainable maize production, the development of high-yielding and resilient maize varieties is essential. This study proposes a majority voting-based decision support system for classifying haploid and diploid maize seeds using deep features from Convolutional Neural Networks (CNNs). Key variables include the accuracy, sensitivity, specificity, F-score, and Matthew's correlation coefficient (MCC) of the classification models. Experimental results showed impressive performance with accuracy, sensitivity, specificity, F-score, and MCC values of 90.96 %, 94.53 %, 86.40 %, 92.15 %, and 81.96 %, respectively. These results underscore the efficiency of the proposed method in accurately distinguishing between haploid and diploid seeds. The implementation of this decision support system in agricultural practices can significantly reduce the labour-intensive and time-consuming task of manual seed classification by experts. This system provides a cost-effective solution compared to existing expensive and complex methods, enhancing productivity, quality, and sustainability in maize breeding programmes. The ability to rapidly and accurately identify haploid seeds accelerates the breeding process, contributing to the development of new maize varieties with desirable traits such as higher yields and disease resistance. Future research should explore the integration of this decision support system with automation and robotics to further streamline the seed classification process. Additionally, investigating the applicability of this approach to other crops could broaden its impact. Further studies should also focus on enhancing the resolution of maize seed images and utilising more advanced hardware to improve processing performance. Finally, expanding the dataset with diverse maize varieties could refine the model's accuracy and generalisability.

Dual-mode neuron design with deterministic and non-deterministic operations using adiabatic superconductor devices

Abstract In this research, we unveil an innovative strategy in neuromorphic computing by developing a neuron model tailored for the energy-efficient Adiabatic Quantum-Flux-Parametron (AQFP) logic. This model is particularly aimed at enhancing neural network accelerators. Our design of the AQFP-based neuron operates effectively in both deterministic and non-deterministic modes. In deterministic mode, the design relies on superconducting inductive coupling to activate neurons by comparing the sum of AQFP signal currents against a tunable threshold. For non-deterministic operation, we demonstrate how altering specific circuit parameters can correlate these aggregated currents with the non-deterministic operational range of an AQFP current comparator. We verified its versatility and functionality by fabricating varied circuits and conducting extensive tests, confirming its practical application potential. Our work not only showcases the practical implementation of AQFP in neuromorphic computing but also sets a foundation for future advancements in energy-efficient AI hardware.

Whole-Body SPECT/CT: Protocol Variation and Technical Consideration-A Narrative Review.

Introducing a hybrid imaging approach, such as single-photon emission computerized tomography with X-ray computed tomography (SPECT)/CT, improves diagnostic accuracy and patient management. The ongoing advancement of SPECT hardware and software has resulted in the clinical application of novel approaches. For example, whole-body SPECT/CT (WB-SPECT/CT) studies cover multiple consecutive bed positions, similar to positron emission tomography-computed tomography (PET/CT). WB-SPECT/CT proves to be a helpful tool for evaluating bone metastases (BM), reducing equivocal findings, and enhancing user confidence, displaying effective performance in contrast to planar bone scintigraphy (PBS). Consequently, it is increasingly utilized and might substitute PBS, which leads to new questions and issues concerning the acquisition protocol, patient imaging time, and workflow process. Therefore, this review highlights various aspects of WB-SPECT/CT acquisition protocols that need to be considered to help understand WB-SPECT/CT workflow processes and optimize imaging protocols.

Clinical Advances in Cardiovascular Computed Tomography: From Present Applications to Promising Developments.

This review aims to provide a profound overview on most recent studies on the clinical significance of Cardiovascular Computed Tomography (CCT) in diagnostic and therapeutic pathways. Herby, this review helps to pave the way for a more extended but yet purposefully use in modern day cardiovascular medicine. In recent years, new clinical applications of CCT have emerged. Major applications include the assessment of coronary artery disease and structural heart disease, with corresponding recommendations by major guidelines of international societies. While CCT already allows for a rapid and non-invasive diagnosis, technical improvements enable further in-depth assessments using novel imaging parameters with high temporal and spatial resolution. Those developments facilitate diagnostic and therapeutic decision-making as well as improved prognostication. This review determined that recent advancements in both hardware and software components of CCT allow for highly advanced examinations with little radiation exposure. This particularly strengthens its role in preventive care and coronary artery disease. The addition of functional analyses within and beyond coronary artery disease offers solutions in wide-ranging patient populations. Many techniques still require improvement and validation, however, CCT possesses potential to become a "one-stop-shop" examination.

Wavelength-multiplexed multi-mode EUV reflection ptychography based on automatic differentiation

Ptychographic extreme ultraviolet (EUV) diffractive imaging has emerged as a promising candidate for the next generationmetrology solutions in the semiconductor industry, as it can image wafer samples in reflection geometry at the nanoscale. This technique has surged attention recently, owing to the significant progress in high-harmonic generation (HHG) EUV sources and advancements in both hardware and software for computation. In this study, a novel algorithm is introduced and tested, which enables wavelength-multiplexed reconstruction that enhances the measurement throughput and introduces data diversity, allowing the accurate characterisation of sample structures. To tackle the inherent instabilities of the HHG source, a modal approach was adopted, which represents the cross-density function of the illumination by a series of mutually incoherent and independent spatial modes. The proposed algorithm was implemented on a mainstream machine learning platform, which leverages automatic differentiation to manage the drastic growth in model complexity and expedites the computation using GPU acceleration. By optimising over 200 million parameters, we demonstrate the algorithm's capacity to accommodate experimental uncertainties and achieve a resolution approaching the diffraction limit in reflection geometry. The reconstruction of wafer samples with 20-nm high patterned gold structures on a silicon substrate highlights our ability to handle complex physical interrelations involving a multitude of parameters. These results establish ptychography as an efficient and accurate metrology tool.

(Invited) Chemical Mechanical Planarization Integrated Data Collaboration: Uncover Hidden Interactions for Predictive Maintenance and PCN

This article introduces an innovative analytical methodology that employs machine learning to optimize and predict consumable impacts for Chemical Mechanical Planarization (CMP). By harnessing fleet CMP equipment performance and individual equipment characteristics, our proposed approach aims to elevate wafer-level quality, maximize equipment utilization rates, and minimize unnecessary downtime. CMP, a multifaceted process, its performance is often influenced by strong interactions from its critical components including slurry, pad, machine recipe, and dresser usage. This requires control beyond hardware advancements, especially when equipment is pushed to its limits. Therefore, the demand for higher precision on-wafer performance mandates a data collaboration with sophisticated analytical strategy. In addition, a digitalized product change notification (PCN) concept is also introduced to demonstrate effectiveness of applying integrated data analytics collaboratively across semiconductor chain to accelerate material manufacturing process changes, minimizing impacts on wafer results and shortening the learning cycle and ramp time to full production.Our study leverages data analytics to explore differences between equipment within the same fleet of similar processing. Engineers can deploy a process based on the historical performance of specific equipment, supported by a model trained on data from the same fleet. Furthermore, our approach enables each equipment to predict its own fingerprinted consumable maintenance, accurately forecasting the maintenance time for consumable changes. This concept is equally applicable for consumable PCN migrations.The first use case is to demonstrate the use of integrated data for fleet matching of CMP equipment and/or consumable batches running similar processes. This is particularly crucial as the demand for process precision increases and the need to reduce post-CMP result variability in removal rates and wafer-level uniformity intensifies. This approach could add proactive data driven strategy for process engineers who traditionally concentrate on the removal rate, a highly influential factor, with the standard deviation of platen and machine data identified as a key parameter.The second use case employs heatmaps analysis and proposes scatter plots as an intuitive method to comprehend intricate connections between average removal rates and various parameters. Machine learning is particularly advantageous in this context, as no single parameter plays a dominant role. Preliminary observations suggested, for example that dresser and pad characteristics may have compounding impacts to the wafer level performance.In summary, this article provides a roadmap for in-depth CMP data analysis fed into machine learning, aiming to unravel trends and influential factors that can enhance process optimization and shorten the PCN qualification timeline to meet the increasing demands for CMP process precision and efficiency in High Volume Manufacturing (HVM).

Virtual reality as a treatment for chronic musculoskeletal pain syndromes

Chronic musculoskeletal pain syndromes, including fibromyalgia, are often resistant to conventional medications and invasive therapies. Central hypersensitization, neurotransmitter dysregulation, and autonomic nervous system abnormalities are key pathomechanisms, frequently resulting in widespread pain and a variety of psychosomatic symptoms. Virtual Reality (VR) applications have demonstrated effectiveness in reducing pain, both during and after interventions, and in chronic conditions such as fibromyalgia and back pain. The proposed mechanisms behind VR's effectiveness include distraction and immersion, coupled with cognitive behavioral therapy, which promote neuroplasticity and alter pain perceptions. Functional MRI studies have shown the impact of VR interventions on specific brain regions. Advances in hardware and software, potentially combined with treatments like biofeedback, could enhance VR's role in managing chronic pain. Currently, VR for musculoskeletal pain syndromes is primarily used within multimodal programs, but it is also available for home use as a standalone health application. Future research should focus on the ‘drug-like’ effects of VR, requiring controlled trials with comparable study populations and appropriate sham interventions.

Confidential Computing in the Cloud: An Overview

Major financial institutions like Goldman Sachs and JP Morgan have employed these hardware-based trusted execution environments (TEEs) and reported a 50% reduction in data breaches and a 40% increase in customer trust. Daily, these companies do billions of transactions in the cloud, leveraging confidentiality computing to ensure the privacy and integrity of their sensitive data. Over the years, confidential computing has evolved significantly, and the emergence of technology to safeguard sensitive information from malicious insiders and external threats now encompasses advanced and complex cryptographic techniques and hardware innovations, offering robust security assurances for cloud-based operations. In this paper, we will talk about foundational technologies and implementation strategies for the core of confidential computing. We will explore benefits, including performance trade-offs and integration complexities. Furthermore, the paper will highlight real-world applications and use cases, showcasing how industries such as finance, healthcare, and government leverage confidential computing to enhance data security and complicate cloud environments.

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main .

Development of Ground Control Station for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have emerged as important tools in various industries, from military research to civilian applications such as agriculture, disaster management and research. This paper examines the technological development of GCS and current state-of-the-art developments, including advances in hardware, software, and human-machine interfaces. Through a systematic review of recent research, we identify key policy considerations, performance criteria, and challenges associated with GCS development. In addition, we explore autonomy, artificial intelligence integration, cybersecurity, interoperability standards, and other development trends that are changing the UAV industry landscape. Combining insights from academic literature, industry reports, and technology developments, this review provides a comprehensive understanding and new idea of the state of GCS technology and valuable perspectives on future research directions and practical applications in UAV systems.