Standard Hardware Research Articles

Flexible STEM with Simultaneous Phase and Depth Contrast.

Recent advances in scanning transmission electron microscopy (STEM) have rekindled interest in multi-channel detectors and prompted the exploration of unconventional scan patterns. These emerging needs are not yet addressed by standard commercial hardware. The system described here incorporates a flexible scan generator that enables exploration of low-acceleration scan patterns, while data are recorded by a scalable eight-channel array of nonmultiplexed analog-to-digital converters. System integration with SerialEM provides a flexible route for automated acquisition protocols including tomography. Using a solid-state quadrant detector with additional annular rings, we explore the generation and detection of various STEM contrast modes. Through-focus bright-field scans relate to phase contrast, similarly to wide-field TEM. More strikingly, comparing images acquired from different off-axis detector elements reveals lateral shifts dependent on defocus. Compensation of this parallax effect leads to decomposition of integrated differential phase contrast (iDPC) to separable contributions relating to projected electric potential and to defocus. Thus, a single scan provides both a computationally refocused phase contrast image and a second image in which the signed intensity, bright or dark, represents the degree of defocus.

Design of a BIST implemented AES crypto-processor ASIC.

This paper presents the design of a Built-in-self-Test (BIST) implemented Advanced Encryption Standard (AES) cryptoprocessor Application Specific Integrated Circuit (ASIC). AES has been proved as the strongest symmetric encryption algorithm declared by USA Govt. and it outperforms all other existing cryptographic algorithms. Its hardware implementation offers much higher speed and physical security than that of its software implementation. Due to this reason, a number of AES cryptoprocessor ASIC have been presented in the literature, but the problem of testability in the complex AES chip is not addressed yet. This research introduces a solution to the problem for the AES cryptoprocessor ASIC implementing mixed-mode BIST technique, a hybrid of pseudo-random and deterministic techniques. The BIST implemented ASIC is designed using IEEE industry standard Hardware Description Language(HDL). It has been simulated using Electronic Design Automation (EDA)tools for verification and validation using the input-output data from the National Institute of Standard and Technology (NIST) of the USA Govt. The simulation results show that the design is working as per desired functionalities in different modes of operation of the ASIC. The current research is compared with those of other researchers, and it shows that it is unique in terms of BIST implementation into the ASIC chip.

Cold-Start Modeling and On-Line Optimal Control of the Three-Way Catalyst

We present a three-way catalyst (TWC) cold-start model, calibrate the model based on experimental data from multiple operating points, and use the model to generate a Pareto-optimalcold-start controller suitable for implementation in standard engine control unit hardware. The TWC model is an extension of a previously presented physics-based model that predicts carbon monoxide, hydrocarbon, and nitrogen oxides tailpipe emissions. The model axially and radially resolves the temperatures in the monolith using very few state variables, thus allowing for use with control-policy based optimal control methods. In this paper, we extend the model to allow for variable axial discretization lengths, include the heat of reaction from hydrogen gas generated from the combustion engine, and reformulate the model parameters to be expressed in conventional units. We experimentally measured the temperature and emission evolution for cold-starts with ten different engine load points, which was subsequently used to tune the model parameters (e.g. chemical reaction rates, specific heats, and thermal resistances). The simulated cumulative tailpipe emission modeling error was found to be typically − 20% to + 80% of the measured emissions. We have constructed and simulated the performance of a Pareto-optimal controller using this model that balances fuel efficiency and the cumulative emissions of each individual species. A benchmark of the optimal controller with a conventional cold-start strategy shows the potential for reducing the cold-start emissions.

Stabilization of energy level sets of underactuated mechanical systems exploiting impulsive braking

Recent investigations of underactuated systems have demonstrated the benefits of control inputs that are impulsive in nature. Here we consider the problem of stabilization of energy level sets of underactuated systems exploiting impulsive braking. We consider systems with one passive degree-of-freedom (DOF) and the energy level set is a manifold where the active coordinates are fixed and the mechanical energy equals some desired value. A control strategy comprised of continuous inputs and intermittent impulsive braking inputs is presented. The generality of the approach is shown through simulation of a three-DOF Tiptoebot; the feasibility of implementation of impulsive control using standard hardware is demonstrated using a rotary pendulum.

Chemputation and the Standardization of Chemical Informatics.

The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture—the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation—that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.

Resolution-based distillation for efficient histology image classification

Developing deep learning models to analyze histology images has been computationally challenging, as the massive size of the images causes excessive strain on all parts of the computing pipeline. This paper proposes a novel deep learning-based methodology for improving the computational efficiency of histology image classification. The proposed approach is robust when used with images that have reduced input resolution, and it can be trained effectively with limited labeled data. Moreover, our approach operates at either the tissue- or slide-level, removing the need for laborious patch-level labeling. Our method uses knowledge distillation to transfer knowledge from a teacher model pre-trained at high resolution to a student model trained on the same images at a considerably lower resolution. Also, to address the lack of large-scale labeled histology image datasets, we perform the knowledge distillation in a self-supervised fashion. We evaluate our approach on three distinct histology image datasets associated with celiac disease, lung adenocarcinoma, and renal cell carcinoma. Our results on these datasets demonstrate that a combination of knowledge distillation and self-supervision allows the student model to approach and, in some cases, surpass the teacher model's classification accuracy while being much more computationally efficient. Additionally, we observe an increase in student classification performance as the size of the unlabeled dataset increases, indicating that there is potential for this method to scale further with additional unlabeled data. Our model outperforms the high-resolution teacher model for celiac disease in accuracy, F1-score, precision, and recall while requiring 4 times fewer computations. For lung adenocarcinoma, our results at 1.25× magnification are within 1.5% of the results for the teacher model at 10× magnification, with a reduction in computational cost by a factor of 64. Our model on renal cell carcinoma at 1.25× magnification performs within 1% of the teacher model at 5× magnification while requiring 16 times fewer computations. Furthermore, our celiac disease outcomes benefit from additional performance scaling with the use of more unlabeled data. In the case of 0.625× magnification, using unlabeled data improves accuracy by 4% over the tissue-level baseline. Therefore, our approach can improve the feasibility of deep learning solutions for digital pathology on standard computational hardware and infrastructures.

Enhanced Multiplexing of Immunofluorescence Microscopy Using a Long-Stokes-Shift Fluorophore.

Immunofluorescence labeling and microscopy offer a highly specific means to visualize proteins or other molecular species in a sample by labeling target antigens with fluorescent probes. These fluorescent probes can then be visualized using a fluorescence microscope, allowing their relative spatial relationships to be determined. Due to spectral overlap of common fluorophores, however, it can be challenging to analyze more than three antigens in a single sample with standard imaging approaches. This article describes multiplexed labeling and imaging of four target antigens through the use of a long-Stokes-shift fluorophore-a fluorophore with an unusually large gap between its excitation and emission maxima-in tandem with three conventional fluorophores. This combination allows for multiplexed imaging of four antigens in a single sample with excellent spectral discrimination suitable for sensitive analyses using standard imaging hardware. Particular advantages of this approach are its flexibility in terms of target antigens and the lack of any specialized procedures, reagents, or equipment beyond the commercially available labeling reagent coupled to the long-Stokes-shift fluorophore. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Four-probe immunofluorescence labeling Basic Protocol 2: Four-probe immunofluorescence imaging.

KBoost: a new method to infer gene regulatory networks from gene expression data

Reconstructing gene regulatory networks is crucial to understand biological processes and holds potential for developing personalized treatment. Yet, it is still an open problem as state-of-the-art algorithms are often not able to process large amounts of data within reasonable time. Furthermore, many of the existing methods predict numerous false positives and have limited capabilities to integrate other sources of information, such as previously known interactions. Here we introduce KBoost, an algorithm that uses kernel PCA regression, boosting and Bayesian model averaging for fast and accurate reconstruction of gene regulatory networks. We have benchmarked KBoost against other high performing algorithms using three different datasets. The results show that our method compares favorably to other methods across datasets. We have also applied KBoost to a large cohort of close to 2000 breast cancer patients and 24,000 genes in less than 2 h on standard hardware. Our results show that molecularly defined breast cancer subtypes also feature differences in their GRNs. An implementation of KBoost in the form of an R package is available at: https://github.com/Luisiglm/KBoost and as a Bioconductor software package.

Rapid optimization of stationary tokamak plasmas in RAPTOR: demonstration for the ITER hybrid scenario with neural network surrogate transport model QLKNN

This work presents a fast and robust method for optimizing the stationary radial distribution of temperature, density and parallel current density in a tokamak plasma and its application to first-principle-based modeling of the ITER hybrid scenario. A new solver is implemented in the RAPTOR transport code, enabling direct evaluation of the stationary solution to which the radial plasma profiles evolve. Coupled to a neural network emulation of the quasi-linear gyrokinetic QuaLiKiz transport model (QLKNN-hyper-10D), a first-principle-based estimate of the stationary state of the core plasma can be found at unprecedented computational speed (typically a few seconds on standard hardware). The stationary state solver is then embedded in a numerical optimization scheme, allowing the optimization of tokamak plasma scenarios in only a few minutes. The proposed method is applied to investigate the performance of ITER hybrid scenarios at different values of total plasma current, plasma density and pedestal height and for different power contributions in a heating mix consisting of electron cyclotron and neutral beam heating. Optimizing the radial distribution of electron cyclotron current drive (ECCD) deposition, the q profile is tailored to maximize the fusion gain Q, by maximizing the energy confinement predicted through the first-principles-based transport model, while satisfying q > 1, avoiding sawtooth oscillations. It is found that optimal use of ECCD in ITER hybrid scenarios is to deposit power as close to the core as possible, while maintaining sufficient off-axis current drive to keep q above 1. Upper limits for the fusion gain Q are shown to be constrained either by minimum power requirements for the separatrix power flow to maintain H-mode or by minimum current drive requirements for q profile tailoring. Finally, it is shown that the ITER hybrid scenario operating window is significantly extended by an upgrade of the electron cyclotron power to 40 MW.

INVESTIGATION OF A MILLIMETER-WAVE RADIO LINK CHARACTERISTICS OF IEEE 802.11AD STANDARD IN URBAN AREAS

Background. The explosive growth in the use of mobile broadband is significantly increasing the bandwidth requirements. Millimeter-wave spectrum is necessary for 5G networks to achieve data transfer rates of the order of Gb/s, in particular, for the provision of 3D video services, and the use radio modules for millimeter-wave frequencies as picocells in the streets will expand the capabilities of existing cellular networks and provide an increase in bandwidth. Therefore, the study of the characteristics of this spectrum is an urgent task today.
 Objective. The purpose of the paper is to present the results of studying the characteristics of a millimeter-wave radio link to ensure high-speed user access to IP data transmission networks and the possibility of using the IEEE 802.11ad standard in open areas.
 Methods. Structural and functional methods of constructing a millimeter-wave wireless network in urban areas based on IEEE 802.11ad standard hardware are investigated.
 Results. The studies were carried out using a test bench with a point-to-point topology deployed in an urban environment (Kiev) with the line of sight without significant obstacles. The studies tested the possibility of using for millimeter-wave hardware technologies of the IEEE 802.11ad standard, which is used indoors, for applications in urban areas.
 The use of a narrow beam antenna based on an antenna array allows adaptive control of the radiation pattern to bypass small obstacles blocking direct transmission, which allows reducing interference and receive/transmit a signal.
 Conclusions. Experimental testing of the hardware capabilities of the IEEE 802.11ad standard has been carried out. Scenarios for constructing a millimeter-wave radio link under various weather conditions have been worked out.
 Keywords: millimeter-wave; bandwidth; IEEE 802.11ad standard; 5G networks

Improved Redundant Binary Adder Realization in FPGA

This paper presents the design of improved redundant binary adder (IRBA) by utilizing positive–negative encoding rules in FPGA platform. The proposed design deals with inverted encoding of negative binary (IEN) and positive binary number to get addition result using readily available standard hardware module. The Verilog hardware description language is used as design entry for synthesis of the proposed architecture in Xilinx ISE Desisn Suite 14.4 software. This structure is realized on Vertex-4 xc4vfx12-12sf363 FPGA device. The proposed IRBA is found to be time efficient in comparison with the performance parameters such as propagation delay and area over previous reported architecture.

Predicting the fiber orientation in glass fiber reinforced polymers using the moment of inertia and convolutional neural networks

The mechanical properties of glass fiber reinforced polymers (GFRP) are significantly governed by the orientation of the fibers in the composite. Micro X-ray computed tomography (CT) imaging offers a way of determining the fiber’s orientation in a non-destructive fashion. Various approaches have been presented to compute the direction of the fibers based on fiber tracking or weighted volume algorithms. In this work we present two novel approaches, one employing convolutional neural networks (CNNs) and the other directional analysis by inertia tensor evaluation (ITE). We establish a workflow based on molecular dynamics simulations to efficiently create synthetic training data for the CNN. The two methods are applied to two experimental CT scans of the GFRP polyamide 66 of two different components, featuring different CT resolutions, fiber lengths and volume fractions.The CNN model trained by synthetic data predicts fiber orientations consistently and with similar accuracy as best-in-class commercially available products. We observe an increase in computational speeds of at least a factor of 4 on CPUs or about a factor of 50 on GPUs, respectively. A striking feature of this approach is that the ground truth of our training data is perfectly known and no time-consuming manual labeling of fibers for training is needed. The proposed ITE method is very robust and particularly suited to lower resolution CT scans, as the evaluation of gradients is not necessary.Both methods extend the toolbox of weighted volume approaches and are well-suited to predict orientations of densely-packed fibers that are often encountered in industrial practice. In addition, predictions by the trained CNN model can be run on standard office hardware which makes them particularly interesting for industrial environments.

Personalized Pre- and Post-Operative Hemodynamic Assessment of Aortic Coarctation from 3D Rotational Angiography.

Coarctation of Aorta (CoA) is a congenital disease consisting of a narrowing that obstructs the systemic blood flow. This proof-of-concept study aimed to develop a framework for automatically and robustly personalizing aortic hemodynamic computations for the assessment of pre- and post-intervention CoA patients from 3D rotational angiography (3DRA) data. We propose a framework that combines hemodynamic modelling and machine learning (ML) based techniques, and rely on 3DRA data for non-invasive pressure computation in CoA patients. The key features of our framework are a parameter estimation method for calibrating inlet and outlet boundary conditions, and regional mechanical wall properties, to ensure that the computational results match the patient-specific measurements, and an improved ML based pressure drop model capable of predicting the instantaneous pressure drop for a wide range of flow conditions and anatomical CoA variations. We evaluated the framework by investigating 6 patient datasets, under pre- and post-operative setting, and, since all calibration procedures converged successfully, the proposed approach is deemed robust. We compared the peak-to-peak and the cycle-averaged pressure drop computed using the reduced-order hemodynamic model with the catheter based measurements, before and after virtual and actual stenting. The mean absolute error for the peak-to-peak pressure drop, which is the most relevant measure for clinical decision making, was 2.98 mmHg for the pre- and 2.11 mmHg for the post-operative setting. Moreover, the proposed method is computationally efficient: the average execution time was of only [Formula: see text] minutes on a standard hardware configuration. The use of 3DRA for hemodynamic modelling could allow for a complete hemodynamic assessment, as well as virtual interventions or surgeries and predictive modeling. However, before such an approach can be used routinely, significant advancements are required for automating the workflow.

Can Visual Acuity Be Reliably Measured at Home? Validation of Telemedicine Remote Computerised Visual Acuity Measurements.

Purpose:The recent pandemic has identified the need for telemedicine assessment of ophthalmology patients. A vital component of such assessment is visual acuity (VA) measurement. The aim of this study was to determine the feasibility and reliability of computerised ‘at home’ VA measurements using COMPlog software.Methods:A Bland Altman method comparison study of worse eye ‘in clinic’ and ‘at home’ orthoptist-supervised COMPlog computerised VA measurements. Subjects underwent gold standard semi-automated computerised test and retest logMAR VA measurements on their habitually corrected worse eye both ‘in clinic’ and ‘at home.’ The orthoptist ran the test from the eye clinic with the patient viewing a secondary PC monitor either in the same clinic room or at home. A screen sharing voice and video conferencing application and standard consumer IT hardware were employed to present the test optotypes in the patient’s home.Results:23 paediatric and 13 adult patients with a range of ocular diseases and worse eye visual acuities were included (range –0.14 to 1.06 logMAR). No significant bias was found between ‘in clinic’ and ‘at home’ measurements (mean –0.01 logMAR and 95% confidence interval –0.03, 0.00 logMAR). Test-retest variability of ‘in clinic,’ ‘at home’ and ‘in clinic’ versus ‘at home’ measurements were within normal reported ranges at 0.12 logMAR (6 ETDRS letters) or less.Conclusion:Remote home VA testing performed by an eye care professional using a semi-automated VA measurement program and video conferencing application provided unbiased measurements with acceptable test-retest reliability. Home testing was both feasible and acceptably reliable in appropriately equipped patients.

A deep learning algorithm for 3D cell detection in whole mouse brain image datasets.

Understanding the function of the nervous system necessitates mapping the spatial distributions of its constituent cells defined by function, anatomy or gene expression. Recently, developments in tissue preparation and microscopy allow cellular populations to be imaged throughout the entire rodent brain. However, mapping these neurons manually is prone to bias and is often impractically time consuming. Here we present an open-source algorithm for fully automated 3D detection of neuronal somata in mouse whole-brain microscopy images using standard desktop computer hardware. We demonstrate the applicability and power of our approach by mapping the brain-wide locations of large populations of cells labeled with cytoplasmic fluorescent proteins expressed via retrograde trans-synaptic viral infection.

In-sensor classification with boosted race trees

When extremely low-energy processing is required, the choice of data representation makes a tremendous difference. Each representation (e.g., frequency domain, residue coded, and log-scale) embodies a different set of tradeoffs based on the algebraic operations that are either easy or hard to perform in that domain. We demonstrate the potential of a novel form of encoding, race logic, in which information is represented as the delay in the arrival of a signal. Under this encoding, the ways in which signal delays interact and interfere with one another define the operation of the system. Observations of the relative delays (for example, the outcome of races between signals) define the output of the computation. Interestingly, completely standard hardware logic elements can be repurposed to this end and the resulting embedded systems have the potential to be extremely energy efficient. To realize this potential in a practical design, we demonstrate two different approaches to the creation of programmable tree-based ensemble classifiers in an extended set of race logic primitives; we explore the trade-offs inherent to their operation across sensor, hardware architecture, and algorithm; and we compare the resulting designs against traditional state-of-the-art hardware techniques.

A low-cost, high-performance video-based binocular eye tracker for psychophysical research.

We describe a high-performance, pupil-based binocular eye tracker that approaches the performance of a well-established commercial system, but at a fraction of the cost. The eye tracker is built from standard hardware components, and its software (written in Visual C++) can be easily implemented. Because of its fast and simple linear calibration scheme, the eye tracker performs best in the central 10 degrees of the visual field. The eye tracker possesses a number of useful features: (1) automated calibration simultaneously in both eyes while subjects fixate four fixation points sequentially on a computer screen, (2) automated realtime continuous analysis of measurement noise, (3) automated blink detection, (4) and realtime analysis of pupil centration artifacts. This last feature is critical because it is known that pupil diameter changes can be erroneously registered by pupil-based trackers as a change in eye position. We evaluated the performance of our system against that of a wellestablished commercial system using simultaneous measurements in 10 participants. We propose our low-cost eye tracker as a promising resource for studies of binocular eye movements.

Work Envelope Expansion and Parametric Optimization in WAAM with Relative Density and Surface Aspect as Quality Constraints: The Case of Al5Mg Thin Walls with Active Cooling

The successful and efficient production of parts with specific features by Wire + Arc Additive Manufacturing (WAAM) strongly depends on the selection of proper and typically interrelated deposition parameters. This task might be particularly challenging in the making of thin walls, which might be highly impacted by processing conditions and heat accumulation. In this context, this study aims at expanding the work envelope and optimizing the parametric conditions in WAAM with relative density and surface aspects of the preforms as quality constraints. The experimental approach was based on the deposition of thin Al5Mg walls by the CMT process on its standard welding setup and with an active cooling technique to enhance the deposition robustness. Internal voids were estimated by Archimedes’ method. The surface quality of the walls was assessed through the visual aspect and the surface waviness by cross-section analysis. All the conditions presented relative density higher than 98%. The upgrade of the standard welding hardware to WAAM purposes through the addition of a supplementary shielding gas nozzle to the torch and the intensity of the heat sinking from the part significantly expanded the process work envelope, with its applicability being successfully demonstrated with multi-objective optimization. To sum up, a decision-making procedure is presented towards achieving intended preform quality.

Reprogrammable and high-precision holographic optical addressing of trapped ions for scalable quantum control

High-precision, individually programmable manipulation of quantum particles is crucial for scaling up quantum information processing (QIP) systems such as laser-cooled trapped-ions. However, restricting undesirable “crosstalk” in optical manipulation of ion qubits is fundamentally challenging due to micron-level inter-ion separation. Further, inhomogeneous ion spacing and high susceptibility to aberrations at UV wavelengths suitable for most ion-species pose severe challenges. Here, we demonstrate high-precision individual addressing (λ = 369.5 nm) of Yb+ using a reprogrammable Fourier hologram. The precision is achieved through in-situ aberration characterization via the trapped ion, and compensating (to λ/20) with the hologram. Using an iterative Fourier transformation algorithm (IFTA), we demonstrate an ultra-low (<10−4) intensity crosstalk error in creating arbitrary pair-wise addressing profiles, suitable for over fifty ions. This scheme relies on standard commercial hardware, can be readily extended to over a hundred ions, and adapted to other ion-species and quantum platforms.

Accelerating 3D single-molecule localization microscopy using blind sparse inpainting.

.Significance: Single-molecule localization-based super-resolution microscopy has enabled the imaging of microscopic objects beyond the diffraction limit. However, this technique is limited by the requirements of imaging an extremely large number of frames of biological samples to generate a super-resolution image, thus requiring a longer acquisition time. Additionally, the processing of such a large image sequence leads to longer data processing time. Therefore, accelerating image acquisition and processing in single-molecule localization microscopy (SMLM) has been of perennial interest.Aim: To accelerate three-dimensional (3D) SMLM imaging by leveraging a computational approach without compromising the resolution.Approach: We used blind sparse inpainting to reconstruct high-density 3D images from low-density ones. The low-density images are generated using much fewer frames than usually needed, thus requiring a shorter acquisition and processing time. Therefore, our technique will accelerate 3D SMLM without changing the existing standard SMLM hardware system and labeling protocol.Results: The performance of the blind sparse inpainting was evaluated on both simulation and experimental datasets. Superior reconstruction results of 3D SMLM images using up to 10-fold fewer frames in simulation and up to 50-fold fewer frames in experimental data were achieved.Conclusions: We demonstrate the feasibility of fast 3D SMLM imaging leveraging a computational approach to reduce the number of acquired frames. We anticipate our technique will enable future real-time live-cell 3D imaging to investigate complex nanoscopic biological structures and their functions.