Miracle-dz/UniFAR-FTUs huggingface dataset:
Avoiding Dynamic Time Error by Implementing Prediction Logic

In a flip-flop, the amount of time that elapses between the transitions of the data input and the active edge of the clock is referred to as the device's setup time. In the event that the data being submitted undergoes any changes while this time frame is active, the storage will not be accurate. This is referred to be a breach of the setup time. The minimal amount of time following the active edge of the clock during which data must remain steady is referred to as hold time. Hold time violations will result in the storing of inaccurate data. Techniques known as “time borrowing” are often used in the mitigation of timing mistakes in high performance architectures. The process of changing a flip-flop into a transparent latch on the fly so that time may be taken up by the subsequent stage and setup time violations can be avoided. Nonetheless, timing violations on continuous critical path (CCP) and critical feedback path (CFP) architectures might still occur as a result of their use. An example of a unique dynamic timing error avoidance (DTEA) approach is provided by a static timing analysis. This method begins by making an effort to eliminate timing errors by using the time borrowing technique. including characteristics such as dynamic clock stretching to make it possible for the circuit to run at a high-performance level. According to the results of the FPGA synthesis, the digital implementation of the structure has exceptionally outstanding performance.
Preparation of Miniature Thermionic Cathode to be Used in THz Devices

A kind of miniature thermionic cathode is prepared by depositing an anti-emission Ta-Zr composite coating on impregnated scandate cathode surface via dual Ion-Beam-Assisted Deposition (Dual IBAD) and then etching in the center of the anti-emission coating a mini electron emission zone with a diameter of $\mathbf{100}\boldsymbol{\mu} \mathbf{m}$ via Focus Ion Beam(FIB). Experimental results show that dense coating prepared by Dual IBAD is more effective than loose coating prepared by magnetron sputtering in suppressing electron emission, that Ta/Zr coating is more effective than Zr coating in suppressing electron emission, and that the average current density of the cathode can reach 43A/cm2 at 950 °C.
Design of a 2-W Wideband Power Amplifier MMIC in 0.15μm PHEMT Technology

This paper presents a 2-W wideband 6-18GHz monolithic microwave integrated circuit(MMIC) power amplifier(PA) in $\boldsymbol{0.15}\boldsymbol{\upmu}\mathbf{m}$ GaAs PHEMT technology. This PA adopts power combining technology and passive consumptive network matching technology to achieve a broadband impedance matching network with low loss. These techniques enable power amplifier to exhibit the advantages of high power, high efficiency and wide bandwidth. The three-stage power amplifier accomplishes an average output power of 34dBm and the peak output power at 17GHz is 34.7dBm. The average power additive efficiency(PAE) is 28% and the peak PAE at 17GHz is 30.2%. The average small signal gain S21 is 20dB and the average input and output reflection coefficient S11, S22 is less than -10dB. The power amplifier layout area is $\boldsymbol{7.2}\mathbf{mm}\times \boldsymbol{3.4}\mathbf{mm}$. The proposed design has advantages in bandwidth, output power and power additive efficiency, compared with the power amplifier design which uses the same process.
Bayesian-Based Classification Confidence Estimation for Enhancing SSVEP Detection

The brain–computer interface (BCI) enables paralyzed people to directly communicate with and operate peripheral equipment. The steady-state visual evoked potential (SSVEP)-based BCI system has been extensively investigated in recent years due to its fast communication rate and high signal-to-noise ratio. Many present SSVEP recognition methods determine the target class via finding the largest correlation coefficient. However, the classification performance usually degrades when the largest coefficient is not significantly different from the rest of the values. This study proposed a Bayesian-based classification confidence estimation method to enhance the target recognition performance of SSVEP-based BCI systems. In our method, the differences between the largest and the other values generated by a basic target identification method are used to define a feature vector during the training process. The Gaussian mixture model (GMM) is then employed to estimate the probability density functions of feature vectors for both correct and wrong classifications. Subsequently, the posterior probabilities of being an accurate and false classification are calculated via Bayesian inference in the test procedure. A classification confidence value (CCValue) is presented based on two posterior probabilities to estimate the classification confidence. Finally, the decision-making rule can determine whether the present classification result should be accepted or rejected. Extensive evaluation studies were performed on an open-access benchmark dataset and a self-collected dataset. The experimental results demonstrated the effectiveness and feasibility of the proposed method for improving the reliability of SSVEP-based BCI systems.
Game Engines as a Performance-Aware Participatory and Interactive Design Platform: a Prototypical Workflow

This paper explores human-building interaction as an active factor in the early stages of the design process. Current design methods allow a range of highly sophisticated tools to measure and analyze quantitative design parameters such as structural, thermal, lighting performance and more. However, interpreting human preferences and occupant behaviour as metrics to inform early design stages remains largely unexplored. Game engines’ are examined for their capacity to steer the design process and embed performance data. Furthermore, the virtual environments they enable are investigated qualitatively and quantitatively as tools for engaging with user preferences. The resultant metrics are then exported into architectural design software to be used as tools by the designer. Finally, the present context in which game engines are applied in architectural design is examined. The paper proposes a framework that enables non-designers to participate in a data-driven, performance-aware participatory environment actively is created and ready for data collection. Components of the platform include direct and indirect user feedback that enable designers to understand user preferences and quantify its qualitative features. Finally, a case study is presented using the developed platform to design high-performance facade panels.
Retinal Vessel Segmentation using UNet++

In this proposed Paper a novel, simple lightweight structured Deep Learning method to solve the problem of Retinal Vessel Segmentation. Such kind of problem in the retinal vessel segmentation is very common in the field of medical image segmentation moreover which has present in the human eyes a computer-aided diagnosis (CAD) based solution to allow easier, quicker, and more effective diagnosis of pathological diseases. This problem will be solved through the analysis of the morphological properties of the blood vessels present in the human retina. There have been many approaches using Deep Learning to solve the problem of retinal vessel segmentation in the earlier few years and the performance of these models have kept increasing consistently. Our proposed model is a multiresolution pathway U-Net which is a modified U-Net with intermediate nodes which perform multi-resolution aggregation of features. Our design was find to achieve comparable results in the comparison of the state in the DRIVE and STARE datasets.
Evaluation of Industry 4.0 familiarity at SMEs in Central-Eastern Europe using Machine Learning Algorithms

Familiarity with Industry 4.0 (I4.0) and its components is inevitable in business development, since it supports digitalization among the various sectors of businesses SMEs in Central-Eastern Europe, namely in the V4 countries, Serbia and Bulgaria have been surveyed; how familiar they are with I4.0 and its components. This paper presents how machine learning (ML) methods, namely supervised algorithms help to determine which I4.0 components contribute positively to SMEs’ familiarity with I4.0 and which of them contradict to its familiarity. The ML algorithm Vector Support Machine (VSM) and Neural Network (NN) proved to be the most accurate methods to predict the familiarity with I4.0 based on its components while Decision Tree (DT) gave the highest precision and specificity rates, therefore, the predictions of these three methods are compared. The results imply that Cloud Computing Services, Big Data analysis, VR and 3D Printing and Robotics do not contribute to the familiarity with I4.0 as much as expected. SMEs need further information on I4.0 developments, especially on its components’ beneficial impact on business performance and operations.
Point2PartVolume: Human Body Volume Estimation From a Single Depth Image

Human body volume (BV) is a useful biometric feature for human identification and an important medical indicator for monitoring body health. Traditional BV estimation techniques such as underwater weighing and air displacement demand a lot of equipment and are difficult to be performed under some circumstances, for example, in clinical environments when dealing with bedridden patients. In this contribution, a novel vision-based method dubbed Point2PartVolume based on deep learning is proposed to rapidly and accurately predict the part-aware BVs from a single-depth image of the dressed body. First, a novel multitask neural network is proposed for jointly completing the partial body point clouds, predicting the body shape under clothing and semantically segmenting the reconstructed body into parts. Next, the estimated body segments are fed into the proposed volume regression network to estimate the partial volumes. A simple yet efficient two-step training strategy is proposed for improving the accuracy of volume prediction regressed from point clouds. Compared to existing methods, the proposed method addresses several major challenges in vision-based human BV estimation, including shape completion, pose estimation, body shape estimation under clothing, body segmentation, and volume regression from point clouds. Experimental results on both the synthetic data and public real-world data show that our method achieved average 90% volume prediction accuracy and outperformed the relevant state-of-the-art.
Arbitrary Termination Complex Impedances Tunable Bandpass Filter Using Single and Dual-Mode Resonators

This paper presents a design of microstrip line tunable bandpass filter (BPF) with a transmission zero (TZ) that can accommodate arbitrarily terminated input and output port impedances (<inline-formula> <tex-math notation="LaTeX">$Z_{s}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$Z_{L}\ne ~50~\Omega )$ </tex-math></inline-formula>. We extracted a new coupling matrix to design the proposed tunable BPF and presented an intuitive circuit-level implementation using a single dc-bias controlled single and dual-mode resonators. The proposed tunable BPF integrates the function of tunable filter and matching network into a single circuit. To validate the design, three filter prototypes with different input and output port termination impedances (filter A: 50-to-<inline-formula> <tex-math notation="LaTeX">$50~\Omega $ </tex-math></inline-formula>, filter B: 20-to-<inline-formula> <tex-math notation="LaTeX">$50~\Omega $ </tex-math></inline-formula>, and filter C: (25+j10)-to-<inline-formula> <tex-math notation="LaTeX">$50~\Omega )$ </tex-math></inline-formula> are manufactured and their performances were measured. The experimental results are consistent with the simulation showing that the passband frequency of the BPF can be tuned from 1.65 GHz to 2.08 GHz (23.06%) with the insertion loss variation from 3.40 dB to 4.69 dB. The proposed tunable BPFs achieved frequency selectivity characteristics by locating the TZ at a lower stopband. The proposed arbitrary terminated tunable BPF can allow direct connection between power amplifier (PA)/low noise amplifier (LNA) and antenna without extra matching networks, resulting in smaller circuit size and can enhance overall performance of RF front-end.
A Multiobjective Incremental Heuristic Search Algorithm

A multiobjective incremental heuristic search algorithm which combines heuristic search with incremental search is put forward.When the state space of the multiobjective problem changes,the algorithm will not resolve the new problem from scratch,but reuse the parts of the information of the previous search to find the set of optimal solutions of the new problem and thus the efficiency of resolution is improved.The experiment results of the Gridworld benchmark problem show that the algorithm can solve a series of similar multiobjective problems very efficiently when the state space changes continuously.
Decision Support Framework for Automating the Optimization of Edge Computing Federations

Modern IoT applications present demanding Quality of Service (QoS) requirements that can no longer rely on a centralized solution to offload compute-intensive tasks. Edge Computing tackles these limitations by extending the Cloud to the edge of the network. However, the distributed and complex nature of Edge infrastructures introduces new technical challenges that must be addressed to develop a successful solution. Modeling, Simulation, and Optimization (M&S&O) tools can aid in the automatic design of modern Edge deployments. Here we present a decision support module that allows practitioners to automatically improve aspects of their Edge infrastructure by applying different optimization algorithms, such as simulated annealing, tabu search, and stochastic hill climbing. Furthermore, the structure of this module allows us to extend the available optimization methods if needed. We illustrate how this tool can help us to improve energy consumption policies of Edge Computing facilities and reduce their operational costs.
On the impact of population density and mobility restrictions in the control of epidemic spreading

Modern computational epidemiology uses compartmental models and agent-based modeling to provide cutting-edge computational tools to track and control epidemic outbreaks. In this sense, many recent global studies have underlined the critical role of population density and mobility restrictions in reducing the spread of an epidemic. In this study, we define a web-based simulation framework based on an original agent-based model and the SICARS epidemic model to investigate the relationship between population density and individual mobility restrictions in terms of reducing the intensity of an infectious outbreak. By analyzing the results of discrete event simulations, we provide a perspective on how these two factors influence the spread of the disease and reduce its impact on the population. We conclude that both density and mobility play a crucial, yet equivalent, role in the dynamics of epidemic spreading, determining a logarithmic increase in the amplitude of the highest infectious wave. The results of the study are expected to provide valuable insights to policy makers and public health officials on how to manage future outbreaks.
IRL-PM: An Inverse Reinforcement Learning-based Power Minimization in Multi-User MISO Networks

This paper aims to efficiently save energy consumption of multiple-input-single-output (MISO) networks. To this end, an optimization problem is formulated to minimize the transmit power of the base station (BS) while ensuring the minimal required information rate of each user. Reinforcement learning (RL) is a significant solution to tackle this non-convex problems. Nevertheless, designing efficient reward functions that accurately describe the involved optimization problems for RL is a challenging problem, which may result in low learning efficiency and even inaccurate learning. To cope with such an issue, this paper proposes an inverse reinforcement learning (IRL)-based approach to minimize the transmit power of MISO networks, in which the generative adversarial imitation learning (GAIL) is employed as the IRL framework and the reward function is represented by an artificial neural network (ANN) model, whose parameters are learned automatically in terms of an expert policy based on the semidefinite relaxation (SDR)-based approach. Since SDR is able to achieve the near optimal solution, the learned reward function is more accurate than traditional artificially designed one. Simulations show that, the proposed IRL-based approach reaches similar energy consumption to the SDR-based approach, and reduces about 50% transmit power compared to traditional RL-based one.
Performance Analysis of Binary Chirp Decoding

Binary Chirp (BC) codebooks consist of ${N^{\left( {{{\log }_2}N + 3} \right)/2}}$ lines in ${\mathbb{C}^N}$, equivalent up to overall phase rotations. Exploiting the underlying algebraic structure, the BCs allow suboptimal decoders with complexity N(logN)2, based on autocorrelations between the received signal and its permuted versions. We analyze the performance of these decoders in additive white Gaussian noise channels, providing lower bounds of decoding error probability, which are tight in the limits of low and high signal-to-noise ratio. Due to the autocorrelation nature of the receiver, the error probability becomes a function of order statistics of χ2-distributed random variables. Our results can be used when dimensioning communication systems where BCs are used as component codes.
Vapor-Phase Hydrogen Peroxide Uptake by Silicone Tubing and Primary Packaging Components during Protein Drug Product Aseptic Filling: Impact of Pretreatment and Sterilization Process

In the vapor-phase hydrogen peroxide (VPHP)-sanitized environment, VPHP uptake by product-contacting components could eventually lead to undesired oxidation of biological drug products. Silicone tubing and primary packaging materials are prominent examples of such product-contacting surfaces that are typically processed/sterilized prior to use. This study investigated the VPHP-uptake tendency of these components and how their respective processing/sterilization methods affect the uptake behaviors. Silicone tubing that was sterilized via autoclave or gamma irradiation exhibited different VPHP uptake patterns—decreased uptake rates post autoclaving vs. increased uptake rates post gamma irradiation. The reduced uptake tendency of autoclaved tubing is maintained for 14 days after sterilization, whereas the uptake tendency of irradiated tubing was mostly reversed to normal levels 1 month after irradiation. Empty glass vials adsorbed hydrogen peroxide via the diffusion of VPHP into the vial with high vial-to-vial variability. Vial pretreatment (i.e., depyrogenation) and surface hydrophilicity/hydrophobicity impacted the uptake tendency. Stoppers and empty syringes also adsorbed hydrogen peroxide but at a relatively low level. The uptake behavior of these components appeared to correlate with water levels at the surface (i.e., hydrophilicity). This study provides process development scientists and engineers an in-depth understanding of the VPHP uptake by critical product-contacting surfaces so that they can mitigate the impact on drug product quality. LAY ABSTRACT: This study investigated vapor-phase hydrogen peroxide (VPHP) absorption by biopharmaceutical drug products via VPHP uptake by critical product-contacting components during the aseptic manufacturing process with a focus on various pretreatments and processing of these components. Sterilization of silicone tubing by gamma irradiation or autoclaving resulted in different VPHP uptake profiles with different effect durations. Primary packaging components, such as vials, syringes, and stoppers, also showed different levels of VPHP uptake with surface hydrophilicity/hydrophobicity playing a critical role. These outcomes suggested that VPHP uptake is a complex phenomenon and should be carefully considered to minimize its impact on product quality. The approach and outcome of this study can benefit scientists and engineers who develop biological product manufacturing processes by providing an in-depth understanding of drug product process risks.
Sensorless Force Estimation of a Lever-Based Variable Stiffness Actuator Using a Current-Deflection Fusion Method

Sensorless force estimation of a variable stiffness actuator (VSA) is crucial for safe physical human–robot interaction. The current-based method suffers from inaccurate model parameters and susceptibility to external disturbances, whereas the deflection-based method lacks a reliable estimation of dynamic stiffness cases. This article presents a sensorless force estimation scheme using a current-deflection fusion (CDF) method for a lever-based VSA. First, unified dynamic models of the driving systems are established, and the unknown parameters are identified before employing the current information. Taking into account the frictions of all the transmission components, the developed unified dynamic models enhance the performance of parameter identification. Then, the CDF-based force estimation is developed with an adaptive coefficient. It effectively improves the estimation accuracy in both quasi-static and dynamic stiffness situations and degrades sensitivity to external disturbances. Extensive experiments are conducted to demonstrate the effectiveness of parameter identification with the developed unified dynamic models and the estimation quality of the proposed CDF method, through evaluating the absolute error of estimated force.
Overlapping Fusion Estimation for Discrete Time-Varying Interconnected Systems

This letter is concerned with the distributed estimation problem for discrete time-varying interconnected systems with overlapping states. A fusion strategy, which weights neighbors’ overlapping state estimates into a better result, is used to exploit the redundant information from sensors. Under this fusion strategy, an overlapping fusion estimator is proposed with only the information from each subsystem and its neighbors. By the idea of constructing and minimizing an upper bound of the square error of the estimate, convex optimization problems are established in terms of linear matrix inequalities to obtain the fusion weights and estimator gains successively. Notice that these optimization problems can be directly solved by standard software packages, and the computational complexity of each optimization problem is low, even for large-scale systems. Moreover, conditions on coupling structures are provided to guarantee the stability of the overlapping fusion estimator. Finally, an illustrative example is employed to show the effectiveness of the proposed methods.
Physical Human-Aerial Robot Interaction and Collaboration: Exploratory Results and Lessons Learned

In this work, we present, a first of its kind, physical human-aerial robot interaction (pHARI) experiment, with an articulated aerial manipulator (AM). The robotic platform is a fully-actuated multi-rotor aerial vehicle (MRAV) with fixedly-tilted propellers endowed with a 3degree of freedom (DoF) robotic arm. We implemented a state-of-the-art control architecture composed of a feedback linearization motion controller, an admittance filter and a hybrid wrench observer. The experiments prove the viability of a new use case in aerial robotics, namely pHARI. The experimental results also shed light on the limitations of the current state-of-the-art and provide insights into possible research directions. The video of the experiments, which is available at https://youtu.be/LrQxXbQ5IHc, shows an experiment simulating work at height, where a human manually guides an AM and then attaches a tool to its end effector (EE).