Miracle-dz/UniFAR-FTUs huggingface dataset:
Equivalent Capacitance and Multilayer Models for Effective Roughness Dielectric in PCBs

Effective Roughness Dielectric (ERD) is used to substitute copper foil roughness in printed circuit board (PCB) interconnects. In this work, the the equivalent capacitance approach is used to get the ERD parameters based on the understanding that there is a gradual variation of concentration of metallic inclusions in the transition layer between the dielectric and foil. The metallic concentration profile can be extracted from scanning electron microscopy (SEM) or high-resolution optical microscopy. The proposed model of equivalent capacitance with gradient dielectric is applied to standard (STD), very-low-profile (VLP), and hyper-very-low profile (HVLP) foils, and the frequency-dependent dielectric parameters of the homogenized ERD are calculated. The analytically calculated ERD parameters are used in 2D-FEM and 3D full-wave numerical models of the stripline structures with various types of foils. There are two types of 3D numerical models: with homogeneous ERD parameters and multilayer “space map” model. All the models show excellent agreement with measurements, and the analysis of the results of different models is provided.
Cone‐Beam CT image contrast and attenuation‐map linearity improvement (CALI) for brain stereotactic radiosurgery procedures

Abstract A Contrast and Attenuation‐map Linearity Improvement (CALI) framework is proposed for cone‐beam CT (CBCT) images used for brain stereotactic radiosurgery (SRS). The proposed framework is tailored to improve soft tissue contrast of a new point‐of‐care image‐guided SRS system that employs a challenging half cone beam geometry, but can be readily reproduced on any CBCT platform. CALI includes a pre‐ and post‐processing step. In pre‐processing we apply a shading and beam hardening artifact correction to the projections, and in post‐processing step we correct the dome/capping artifact on reconstructed images caused by the spatial variations in X‐ray energy generated by the bowtie‐filter. The shading reduction together with the beam hardening and dome artifact correction algorithms aim to improve the linearity and accuracy of the CT‐numbers (CT#). The CALI framework was evaluated using CatPhan to quantify linearity, contrast‐to‐noise (CNR), and CT# accuracy, as well as subjectively on patient images acquired on a clinical system. Linearity of the reconstructed attenuation‐map was improved from 0.80 to 0.95. The CT# mean absolute measurement error was reduced from 76.1 to 26.9 HU. The CNR of the acrylic insert in the sensitometry module was improved from 1.8 to 7.8. The resulting clinical brain images showed substantial improvements in soft tissue contrast visibility, revealing structures such as ventricles which were otherwise undetectable in the original clinical images obtained from the system. The proposed reconstruction framework also improved CT# accuracy compared to the original images acquired on the system. For frameless image‐guided SRS, improving soft tissue visibility can facilitate evaluation of MR to CBCT co‐registration. Moreover, more accurate CT# may enable the use of CBCT for daily dose delivery measurements.
A 3D printed Two DoF Soft Robotic Finger With Variable Stiffness

Grippers based on fluid elastomer actuators have shown their ability to manipulate fragile and delicate objects. However, they cannot resist large external force due to their soft material and compliant structure, leading to an unstable or even failed grasp. Low stiffness has limited their applicability. In this paper, we propose a soft robotic finger based on multimaterial 3D printing and layer jamming to achieve varied finger bending motion and variable stiffness. The proposed finger has two DoF, including two pneumatic actuators (top actuator and bottom actuator) and each with an integrated layer jamming unit. A prototype of the proposed finger is directly printed out by an Object350Connex 3D printer without requiring an additional casting process. The finger’s bending angle and stiffness can be controlled due to the novel actuator design. Experiments were carried out to exam the finger’s bending angles. Four finger motion can be achieved and the stiffness of the finger is increased with the aid of the layer jamming mechanism. Multi-material 3D printing technique can provide flexibility to the design of soft robotics, shorten the fabrication process and offer a range of materials to achieve desired mechanical properties.
Cognitive enhancing and antioxidant effects of tetrahydroxystilbene glucoside in Aβ1-42-induced neurodegeneration in mice.

Polyhydroxy stilbenes have been reported to possess various biological activities and have potential for treatment of Alzheimer's disease. Tetrahydroxystilbene glucoside is one of the major polyhydroxy stilbenes, which provides the underlying therapeutic activities for neuroprotective actions in various experimental conditions. This study investigates the impact of a tetrahydroxystilbene glucoside remedy for cognitive disorder and oxidative stress in Aβ1-42-induced Alzheimer's disease mice and clarifies the mechanisms of action through the Kelch-like erythroid cell-derived protein with conserved non-coding homology-associated protein 1/nuclear factor erythroid 2-related factor pathway. It was found that the swimming time of Aβ1-42-induced mice treated with tetrahydroxystilbene glucoside (30, 60, and 120 mg/kg) was significantly increased in the target quadrant of a Morris water maze experiment and the number of avoidances was increased during a passive avoidance experiment, Moreover, tetrahydroxystilbene glucoside attenuated Aβ1-42-induced memory impairment. However, the locomotor and exploratory activity of mice were not affected. Tetrahydroxystilbene glucoside clearly decreased the levels of malondialdehyde and oxidized glutathione in both hippocampus and cortex compared with the Aβ1-42-treated group, and also clearly increased the level of glutathione and activities of catalase and superoxide dismutase in those tissues. The results of this study demonstrated that tetrahydroxystilbene glucoside increased nuclear factor erythroid 2-related factor and heme oxygenase-1 protein expression and decreased Kelch-like erythroid cell-derived protein with conserved non-coding homology-associated protein 1 protein expression in a concentration-dependent manner in Aβ1-42-treated mice, which involved the Kelch-like erythroid cell-derived protein with conserved non-coding homology-associated protein 1/nuclear factor erythroid 2-related factor antioxidant pathway in hippocampus and cerebral cortex tissue. These results demonstrate that tetrahydroxystilbene glucoside as a natural drug that may provide a potential treatment for Alzheimer's disease.
Constrained Optimization for Image Reshaping With Soft Conditions

Conventional image resizing problems demand hard conditions on size and aspect ratio, which must be met with no tolerance. In this paper, a generalized optimization framework is presented, which can handle soft conditions as well as the hard ones. The soft condition can be given by an allowable range of the image parameter, which is incorporated as an inequality condition in the constrained optimization framework. Given the soft constraints, the proposed framework seeks to find the set of image parameters that minimize the cost function. A constrained optimization via a linear programming framework is employed to manage a diverse combination of soft and hard conditions for the target image. The optimization is based on the image line, which optimally selects a set of image lines (columns and rows) to be deleted for size reduction in accordance with the cost function and the constraints. As a case study, the line-based optimal image resizing method based on the linear programming framework is applied for the pre-processing of VGG-19 convolutional neural network (CNN). Although the target input size is a hard condition of $224\times 224$ for the VGG-19 CNN, the proposed optimization framework with a soft condition on the image size firstly finds an optimal near-square image with a tradeoff against the saliency level of image features. Then, the optimal near-square image is linearly scaled to the final image size to meet the hard condition.
Study of Aerosol Distribution over the Potential Sink Region During Central Portugal Forest Fire Event

On 18th June, 2017 an ample amount of smoke plume had been dispersed into the atmosphere during the raging forest fire event which had taken place in Central Portugal, thereby affecting the air quality of the surrounding regions. The detection of smoke aerosols has been carried out by using the aerosol data from MODIS and OMI. The transport of air parcels containing smoke particles have been observed using NOAA HYSPLIT forward trajectory model which determines the potential sink area in the nearby region of the source area. This work is aimed to provide a comparative study of aerosol loadings derived from different passive sensors over the source and potential sink area, before and after the fire event happened in the source area.
MILP Formulations for Generator Maintenance Scheduling in Hydropower Systems

Maintenance activities help prevent costly generator breakdowns but because generators under maintenance are typically unavailable, the impact of maintenance schedules is significant and their cost must be accounted for when planning maintenance. In this paper, we address the generator maintenance scheduling problem in hydropower systems. We propose a mixed-integer programing model that considers the time windows of the maintenance activities, as well as the nonlinearities and disjunctions of the hydroelectric production functions. Because the resulting model is hard to solve, we also propose an extended formulation, a set reduction approach that uses logical conditions for excluding unnecessary set elements from the model, and valid inequalities. We performed computational experiments using a variety of instances adapted from a real hydropower system in Canada, and the extended formulation with set reduction achieved the best results in terms of computational time and optimality gap.
Chronological Time-Period Clustering for Optimal Capacity Expansion Planning With Storage

To reduce the computational burden of capacity expansion models, power system operations are commonly accounted for in these models using representative time periods of the planning horizon such as hours, days, or weeks. However, the validity of these time-period aggregation approaches to determine the capacity expansion plan of future power systems is arguable, as they fail to capture properly the mid-terms dynamics of renewable power generation and to model accurately the operation of electricity storage. In this paper, we propose a new time-period clustering method that overcomes the aforementioned drawbacks by maintaining the chronology of the input time series throughout the whole planning horizon. Thus, the proposed method can correctly assess the economic value of combining renewable power generation with interday storage devices. Numerical results from a test case based on the European electricity network show that our method provides more efficient capacity expansion plans than existing methods while requiring similar computational needs.
Design of a Cooperative Lane Change Protocol for a Connected and Automated Vehicle Based on an Estimation of the Communication Delay

Connected and automated vehicles (CAVs) have recently attracted a great deal of attention. Various studies have been conducted to improve vehicle and traffic safety through vehicle to vehicle (V2V) communication. In the field of CAVs, lane change research is considered a very challenging subject. This paper presents a cooperative lane change protocol, considering the impact of V2V communication delay. When creating a path for a lane change in the local path planning module, V2V communication delay occurs. Each vehicle was represented, in our study, by an oriented bounding box (OBB) to determine the risk of collision. We set up a highway driving simulation environment and verified the improved protocol by implementing a longitudinal and lateral controller.
Sag Estimation of Real Transmission Systems for Faults Along the Lines in the Presence of Distributed Generation

In this paper, the influence of the distributed generation on the severity of the voltage sags (dips) caused by the three phase short circuits along the feeders was considered. For obtaining the voltage sags, we used the fault position method (FPM). The paper demonstrates that the FPM involves acceptable accuracy with reduced computational efforts. This paper, in particular, deals with the effects of the units of the distributed generation on the quality of the voltage in the HV networks. The considered units are connected to the transmission system by means of HV/MV stations, and the severity of the dips through the transmission system was assessed in different generation scenarios. The numerical applications refer to a part of a real transmission system using actual data of the transmission system operator (TSO).
Dynamic Wireless Charging of Electric Vehicles: Multi-Channel Modeling

This study presents an equivalent model of dynamic wireless charging system of electric vehicles to analyze switch on and switch off processes. The effect of switch on and switch off processes on system currents is thoroughly studied. Firstly, circuit model is proposed and resonant circuit analysis is performed with resonant frequency 85 kHz. In addition, within the switching state analysis, the mathematical modeling is developed for the system with different receiver positions. Three transmitter coils connected to one inverter and one receiver coil are selected to observe the switch on and switch off states. Mutual inductance between transmitter and receiver units is assumed to vary in stepwise manner. Proposed analytical modeling results are further compared and verified by simulation. Lastly, modeling and analysis of variable mutual inductance is provided and its effect on the proposed system is examined.
Exploring Ontologies to Improve the Empathy of Interactive Bots

Bots are virtual agents that people can interact with text messages. They are mostly made with the aim of mimicking a person in conversations. Although several studies have devised natural language processing techniques for the creation of bots, few studies explore the use of ontologies in the development of novel context-aware interactive bots. In this article, we propose a software architecture that allows ontology-based interpretation of several types of data (audio, video, and text) from the bot's environment. We define formal concept-based rules to express affective behavior aiming to improve the empathy of bots. The proposed technique relies on Semantic technologies such as OWL and SWRL languages. This technique is illustrated in an interaction scenario.
Regulatory sRNAs in Cyanobacteria

As the transcriptional and post-transcriptional regulators of gene expression, small RNAs (sRNAs) play important roles in every domain of life in organisms. It has been discovered gradually that bacteria possess multiple means of gene regulation using RNAs. They have been continuously used as model organisms for photosynthesis, metabolism, biotechnology, evolution, and nitrogen fixation for many decades. Cyanobacteria, one of the most ancient life forms, constitute all kinds of photoautotrophic bacteria and exist in almost any environment on this planet. It is believed that a complex RNA-based regulatory mechanism functions in cyanobacteria to help them adapt to changes and stresses in diverse environments. Although lagging far behind other model microorganisms, such as yeast and Escherichia coli, more and more non-coding regulatory sRNAs have been recognized in cyanobacteria during the past decades. In this article, by focusing on cyanobacterial sRNAs, the approaches for detection and targeting of sRNAs will be summarized, four major mechanisms and regulatory functions will be generalized, eight types of cis-encoded sRNA and four types of trans-encoded sRNAs will be reviewed in detail, and their possible physiological functions will be further discussed.
Semantic image segmentation using an improved hierarchical graphical model

Hierarchical graphical models can incorporate jointly several tasks in a unified framework. By applying this approach, information exchange among tasks would improve the results. A hierarchical conditional random field (CRF) is proposed here to improve the semantic image segmentation. Although this newly proposed model applies the information of several tasks, its run time is comparable with the contemporary approaches. This method is evaluated on MSRC dataset and has shown similar or better segmentation accuracy in comparison with models where CRFs or hierarchical models are adopted.
A Hybrid Approach to Reduce Cone-Beam Artifacts for a Circular Orbit Cone-Beam CT System

In a cone-beam computed tomography (CT) system, the Feldkamp, Davis, and Kress (FDK) algorithm produces cone-beam artifacts due to insufficient object sampling in the missing cone region of frequency space. While total variation minimization-based iterative reconstruction (TV-IR) may reduce cone-beam artifacts by filling in the missing cone region, it introduces image blurring or noise increase depending on the regularization parameter. In this paper, we propose a method to reduce cone-beam artifacts through an optimal combination of FDK and TV-IR images. The method utilizes FDK (TV-IR) data outside (inside) the missing cone region, which enables to keep the original image quality of the FDK image and preserve the advantages of the TV-IR image for cone-beam artifact reduction. To validate the proposed method, we used numerical phantoms composed of Defrise disks, vertical plates, and star objects and assessed the image quality of FDK, TV-IR, and the proposed method using the mean squared error, contrast to noise ratio, and structural similarity with different TV-IR regularization parameters. Experimental validation was also conducted using a spine phantom with a bench-top cone-beam CT system. The results showed that the performance of the cone-beam artifacts reduction in TV-IR depended on the value of the regularization parameter, which often produced suboptimal image quality for different imaging tasks. However, the proposed method provided good image quality regardless of the regularization parameter values.
A pilot study of neuromuscular ultrasound as a biomarker for amyotrophic lateral sclerosis

Introduction: This study explores the reliability and responsiveness of neuromuscular ultrasound in amyotrophic lateral sclerosis (ALS). Methods: Investigations were conducted with 10 healthy controls, 10 patients with ALS (single point in time), and 10 different patients with ALS (followed over 6 months; 4 completed follow‐up). Ultrasound was used to measure the thickness of the geniohyoid, bilateral biceps/brachialis, bilateral tibialis anterior, and bilateral hemidiaphragms (at inspiration and expiration). Interrater and intrarater reliability and change in muscle thickness over 6 months were measured. Results: Interrater correlation coefficients ranged between 0.80 and 0.99 in healthy controls and between 0.78 and 0.97 in patients with ALS. Intrarater correlation coefficients ranged between 0.83 and 0.98 in healthy controls. The mean percentage decline in muscle thickness over 6 months was 20.25%. Discussion: Muscle ultrasound appears to be a reliable technique for measuring important muscles in patients with ALS. Larger studies with age‐matched controls should be conducted to assess further the responsiveness of this biomarker in ALS. Muscle Nerve 59:181–186, 2019
SIFRS: Spoof Invariant Facial Recognition System (A Helping Hand for Visual Impaired People)

Facial recognition is the fast growing and challenging field in biometric applications. Variety of improvements has been suggested considering the feature extraction and matching techniques for accurate and efficient facial recognition. In this paper we proposed an approach which uses facial recognition methodologies to implement a computational efficient technique for facial biometric application to help visually impaired people. In this system we used LBP for feature extraction to classify image either valid or invalid person using SVM classification so that it helps visual impaired people in improving their lifestyle and safety. The architecture has been verified with both in a real environment Actual users and printed images have achieved very good results.
A Model-Based Approach to Visual Reasoning on CNLVR Dataset

Visual Reasoning requires an understanding of complex compositional images and common-sense reasoning about sets of objects, quantities, comparisons, and spatial relationships. This paper presents a semantic parser that combines Computer Vision (CV), Natural Language Processing (NLP) and Knowledge Representation & Reasoning (KRR) to automatically solve visual reasoning problems from the Cornell Natural Language Visual Reasoning (CNLVR) dataset. Unlike the data-driven approaches applied to the same dataset, our sys-tem does not require any training but is guided by the knowledge base that is manually constructed. The system demonstrates robust overall performance which is also time and space efﬁcient. Our system achieves 87.3% accuracy, which is 17.6% higher over the state-of-the-art method on raw image representations.
Automatic Segmentation of Cervical Nuclei Based on Deep Learning and a Conditional Random Field

Automatic and accurate cervical nucleus segmentation is important because nuclei carry substantial diagnostic information for automatic computer-assisted cervical cancer screening and diagnosis systems. In this paper, we propose a cervical nucleus segmentation method in which pixel-level prior information is utilized to provide the supervisory information for the training of a mask regional convolutional neural network (Mask-RCNN), which is then employed to extract the multi-scale features of the nuclei, and the coarse segmentation and bounding box of the nuclei are obtained by forward propagation of the Mask-RCNN. To refine the segmentation, a local fully connected conditional random field (LFCCRF) that contains unary and pairwise energy terms is employed. The nuclear region of interest is determined by extending the bounding box, the coarse segmentation in the nuclear region is used to construct the unary energy, and the pairwise energy is contributed by the position and intensity information of all of the pixels in the nuclear region. By minimizing the energy of the LFCCRF, the final segmentation is realized. We evaluated our method by using cervical nuclei from the Herlev Pap smear data set in this paper, and the precision, recall, and Zijdenbos similarity index were all found to be greater than 0.95 with low standard deviations, demonstrating that our method enables more accurate and stable cervical nucleus segmentation than the current state-of-the-art methods.
A 0.1nW −1µW All-Dynamic Capacitance-to-Digital Converter with Power/Speed/Capacitance Scalability

A versatile, low power and energy efficient Capacitance-to-Digital Converter (CDC) for Internet-of-Things (IoT) is presented, based on an all-dynamic architecture with adaptable speed, resolution and range. Sampling rates from 1S/s up to 100kS/s are supported and capacitances from 1.23 to 24.59pF can be digitized while the power scales inherently from 0.1nW to 1µW. This makes the design versatile to efficiently deal with a variety of sensors with different speed requirements and different capacitance values. The 0.1nW lowest absolute power is >20x smaller than prior-art, and the Figure-of-Merit (FoM) from 18 to 59fJ/conv-step is also the lowest among prior designs. As example, by connecting a MEMS pressure sensor, this chip can measure environmental pressure with only 0.8nW at a speed of 100S/s.