Miracle-dz/UniFAR-FTUs huggingface dataset:
The C-Band All-Sky Survey (C-BASS): constraining diffuse Galactic radio emission in the North Celestial Pole region

The C-Band All-Sky Survey (C-BASS) is a high sensitivity all-sky radio survey at an angular resolution of 45 arcmin and a frequency of 4.7 GHz. We present a total intensity map of the North Celestial Pole (NCP) region of sky, above declination >+80°, which is limited by source confusion at a level of ≈0.6 mK rms. We apply the template-fitting (cross-correlation) technique to WMAP and Planck data, using the C-BASS map as the synchrotron template, to investigate the contribution of diffuse foreground emission at frequencies ∼20–40 GHz. We quantify the anomalous microwave emission (AME) that is correlated with far-infrared dust emission. The AME amplitude does not change significantly (${\lt }10\, {{\ \rm per\ cent}}$) when using the higher frequency C-BASS 4.7 GHz template instead of the traditional Haslam 408 MHz map as a tracer of synchrotron radiation. We measure template coefficients of 9.93 ± 0.35 and $9.52\pm 0.34\,$ K per unit τ353 when using the Haslam and C-BASS synchrotron templates, respectively. The AME contributes $55\pm 2\, \mu$K rms at 22.8 GHz and accounts for ${\approx } 60{{\ \rm per\ cent}}$ of the total foreground emission. Our results show that a harder (flatter spectrum) component of synchrotron emission is not dominant at frequencies ≳5 GHz; the best-fitting synchrotron temperature spectral index is β = −2.91 ± 0.04 from 4.7 to 22.8 GHz and β = −2.85 ± 0.14 from 22.8 to 44.1 GHz. Free–free emission is weak, contributing ${\approx } 7\, \mu$K rms (${\approx } 7{{\ \rm per\ cent}}$) at 22.8 GHz. The best explanation for the AME is still electric dipole emission from small spinning dust grains.
A Fully Analog Autonomous QRS Complex Detection and Low-Complexity Asystole, Extreme Bradycardia, and Tachycardia Classification System

This article presents a power-efficient fully analog classifier architecture for the detection of critical cardiac abnormalities, i.e., asystole, extreme bradycardia, and tachycardia. To reduce power consumption and hardware complexity, an analog QRS complex detection circuit and arithmetic counter-based classification modules are introduced. The QRS detection circuit is autonomous and consumes an average current of 34 nA only, vis-à-vis state-of-the-art QRS detection designs that are digital signal processor (DSP) assisted and consume tens of microwatts of power. Furthermore, a heart-rate estimator provides the number of QRS complexes per minute. Each of the proposed modules is successfully validated through real electrocardiogram (ECG) test signals taken from the PhysioNet database. The proposed beat detector circuit exhibits a sensitivity of 97.85% and a positive prediction of 98.3%. Experimental results based on bedside monitor data show that the proposed classification module provides an overall sensitivity of 96.25% and a positive prediction of 96.97%. The complete classification architecture, implemented fully on analog platform, is simulated in the UMC 0.18- $\mu \text{m}$ CMOS process and consumes 119-nW power with an active silicon area of $450\times 800~\mu \text{m}$ . Moreover, the low-power implementation makes it suitable for long-term battery-operated remote ECG monitoring systems.
Wearable Elastic Networks for Customized Multijoint Torque Fields

We describe a theoretical framework for wearable ExoNET devices that use multiple tension elements to render torque. Passive spring elements are used to demonstrate they can act as mathematical basis functions to deliver any arbitrary torque-angle relationship. Several example applications reveal design considerations for functional prototypes that can supplement muscle action, provide gravity compensation, create attractors that can pull the limb towards a desired configuration, or provide therapeutic training forces. ExoNETs have the potential to provide users with a variety of solutions quickly and inexpensively. They shift control intelligence from the software to physical hardware, establishing an efficient and inexpensive instrument for neurorehabilitation, military, manual labor, and performance enhancement.
Airline baggage classification/recognition and measurement based on computer vision

The current airline baggage handling is mainly by manual, which exist serious problems such as crucial handling, baggage loss, low efficiency, high human labor cost, and so on. To solve these problems, an automatic baggage handling process is more and more needed within current airport operation. To this end, high-accuracy classification and high-precision measurement of airline baggage are essential. In this paper, three works are reported: a baggage classification recognition method based on Convolutional Neural Network (CNN) model, a baggage measurement algorithm using a combination of two-dimensional(2D) image and three-dimensional(3D) point cloud, and their realizations in an embedded platform. Firstly, gray feature of image of an airline baggage was fused with height and gradient features of point cloud of the same baggage to construct a baggage information sample. Two thousand fused baggage information samples were fed into two CNNs (vgg16 and mobilenetv3) for training. The best one was selected as the final predictor. Secondly, three-dimensional size, centroid point position and deflection angle of a baggage were measured in 3D point cloud with help of edge information extracted from the 2D image of the same baggage by Scharr operator. Finally, the proposed recognition method and measurement algorithm were transplanted into an embedded platform for efficiency purpose. Experimental results show that average classification accuracy of the proposed 2D image and 3D point cloud fused baggage information CNN model increased 10% at the best shot compared to former reported models. The proposed 2D-3D combined measurement algorithm also obtained comparable precision versus three former jobs. Most importantly, total processing time of the proposed classification and measurement program takes 86 milliseconds, which is one fifth to one tenth of the best result of former works. Plus, a lightweight version in an embedded platform took 54 milliseconds, 200 times faster than PC terminal's 13 seconds including time of data transmission. Considering a distance of dozens of kilometers in airport remote baggage handling system, the proposed embedded platform version of classification and measurement program is promising in the future's automatic scenarios, such as baggage self-service check-in, baggage tracking, automatic baggage palletization, and so on.
HSmartBPM: A modular web platform for tailored management of hypertension

Hypertension is a serious disorder which contributes to an increased risk of cardiovascular disease and death. However, digital health systems dealing with the complexity of long-term hypertension self-management and remote medical management have been scarce. HSmartBPM provides a modular web platform for tailored management of hypertension. The HSmartBPM components include a virtual agent for patient guidance, a Decision Support System (DSS) for individualized monitoring of health parameters, risk prediction for cardiovascular disease, and shared care plan activities for patient treatment, contributing to a personalized approach for the therapeutic management of hypertension. Overall, the HSmartBPM solution aims to assist both patients and healthcare professionals with the everyday management of hypertension through the provision of an intelligent and tailored system.
Improve the trustwortiness of medical text interpretations

Currently, how to make a concrete and correct disease prediction is a popular research trend. Researchers made more efforts to develop various models to provide interpretations of medical area, however, there is still lack of human understandable explanations provided due to the non-transparency structure of some machine learning and deep learning models. According to this work, there is one combined model application we would like to adopt. After comparison experiments of classification and interpretation, it is found the combination model can address the issues from the latest interpretation models, and try to improve the trustworthiness of medical text interpretations.
Road Damage Intelligent Detection with Deep Learning Techniques

Transportation safety and cost heavily rely on good road surface conditions. Road damage detection is a critical task for road maintenance so the agencies can make timely repairments to keep roads in optimal conditions. With the fast development of deep learning techniques in recent years, researchers have quickly embraced object detection/classification deep learning models for road damage detection. Object detection algorithms generally include two categories: two-stage and one-stage algorithms. The two-stage solutions first search potential object regions on an image and then classify objects. The representative models include support vector machines and regional proposal deep learning models based on convolutional neural networks. The one-stage solutions treat the object detection problem as a regression problem by simultaneously predicting optimal object location and class. The representative algorithms are YOLO and SSD, which try to locate the best anchor boxes on an image containing target objects. This study reviews the recent progress of object detection algorithms in the two categories and their representative applications in road damage detection.
A Novel Method for Frequency Selective Surface Design Using Deep Learning with Improved Particle Swarm Algorithm

This paper presents a design method for frequency selective surface (FSS) based on the deep neural network and improved particle swarm algorithm (IPSO). In the proposed method, the forward prediction network (FPN) based on the fully connected network is established to fast predict the transmission coefficient of FSS. Combined with the FPN, the IPSO is used to optimize the structural parameters of FSS. Compared with the traditional iterative optimization method based on full-wave simulation, this method greatly improves the optimization efficiency of FSS. For example, a band-stop FSS is optimized with the proposed method in 210.6s, and the optimization efficiency increases by more than 99%. Simulation results show that the transmission coefficient errors of key frequency points between optimization results and objectives are less than 1 dB. And the deviation of the center frequency and the bandwidth of the target frequency bands is less than 0.81% and 4.1%, respectively.
BYOZ: Protecting BYOD Through Zero Trust Network Security

As the COVID-19 pandemic scattered businesses and their workforces into new scales of remote work, vital security concerns arose surrounding remote access. Bring Your Own Device (BYOD) also plays a growing role in the ability of companies to support remote workforces. As more enterprises embrace concepts of zero trust in their network security posture, access control policy management problems become a more significant concern as it relates to BYOD security enforcement. This BYOD security policy must enable work from home, but enterprises have a vested interest in maintaining the security of their assets. Therefore, the BYOD security policy must strike a balance between access, security, and privacy, given the personal device use. This paper explores the challenges and opportunities of enabling zero trust in BYOD use cases. We present a BYOD policy specification to enable the zero trust access control known as BYOZ. Accompanying this policy specification, we have designed a network architecture to support enterprise zero trust BYOD use cases through the novel incorporation of continuous authentication & authorization enforcement. We evaluate our architecture through a demo implementation of BYOZ and demonstrate how it can meet the needs of existing enterprise networks using BYOD.
Transient Analysis in Dynamic Reconfigurable Battery System

With the fast-paced deployment of battery energy storage systems (BESSs), efficiency and safety issues of BESS, caused by the notorious “bucket effect”, have become prominent. Therefore, dynamic reconfigurable battery system (DRB) provides a promising approach to overcome the “bucket effect” by integrating batteries with power electronics switches in a systematic fashion and dynamically changing battery cell topology based on battery status. However, since the battery energy flow is digitized within milliseconds, there is no theoretical study of the transient behavior of DRB. In this paper, we will fill this gap by establishing a hybrid system model with considerations of the dynamic behaviors of battery cell and power electronics switch, and then to study the transient response of the hybrid system at different working frequencies under constant discharging current profile. Both analytical study and simulation analysis are carried out, which providing design insights for such hybrid system design.
Towards Realising Value of Internet of Energy (IoE) Application for Peer-to-Peer (P2P) Energy Trading: A PETRONAS Case Study

The Internet of Energy (IoE) is a technology space where domains across power systems and end customers are connected via ubiquitous communication and digital technologies. One of the emerging technologies in IoE is Peer to Peer (P2P) energy trading, which is based on a sharing economy concept enabled by digital systems. PETRONAS through its Group Technical Solution (GTS) in collaboration with TNBX Sdn. Bhd. Berhad (TNBX) is planning to commence a Proof of Concept (PoC) for the IoE P2P Project using an actual case study in PETRONAS's Petrochemical Complex. IoE P2P aspired to enable real-time energy generation and demand matching towards enriching the electricity market focusing on Renewable Energy (RE). This paper discusses the challenges in terms of legislative, technical, and commercial aspects prior to the implementation of the PoC at PETRONAS Petrochemical Complex and highlights the expected value creation from the project.
Theoretical Simulation of the Optical Properties of the Colloidal Solutions of the Nanoparticles as the Light Absorption by the Nanocomposite

In the work it has been shown that the theory of the light absorption of the nanocomposite in the framework of the local field theory can be used for simulation of the absorbance spectra of the colloidal solutions of the nanoparticles. Results of calculations have demonstrated that the proposed method demonstrates insufficient differences in the light absorption by the nanoparticles of different sized, but also reflect the difference in the absorbance spectra of the nanoparticles with different stabilizers. The proposed method may be useful for the simulation of the nanosystems used in nanosized optoelectronic devices.
On the Limits of Distributed Agreement between Correlated Sources

The work of Witsenhausen explores conditions under which two non-interactive users observing different coordinates of an i.i. d random process, can reach asymptotic agreement. Witsenhausen considers two scenarios: one in which both users observe a finite sequence with an error probability in the limit, and the second in which both users observe infinite-length sequences. In both cases, it turns out that perfect agreement is possible if and only if the joint distribution has a special decomposable structure known as a Gács-Körner common part. This paper revisits Witsenhausen's work and makes two contributions. First, we show that both results are equivalent, that each implies the other. Second, we offer a new proof of the second result, that unlike the others, avoids any tensorizing arguments or manipulations of multi-letter information measures. We explain how this new converse might overcome some of the obstacles commonly encountered in the classical converse arguments.
A Multiquark Approach to Excited Hadrons and Regge Trajectories

We propose a novel approach to construction of hadron spectroscopy. The case of light nonstrange mesons is considered. By assumption, all such mesons above 1 GeV appear due to creation of constituent quark-antiquark pairs inside π or ρ(ω) mesons. These spin-singlet or triplet pairs dictate the quantum numbers of formed resonance. The resulting classification of light mesons turns out to be in a better agreement with the experimental observations than the standard quark model classification. It is argued that the total energy of quark components should be proportional to the hadron mass squared rather than the linear mass. As a byproduct a certain relation expressing the constituent quark mass via the gluon and quark condensate is put forward. We show that our approach leads to an effective mass counting scheme for meson spectrum and results in the linear Regge and radial Regge trajectories by construction. An experimental observation of these trajectories might thus serve as evidence not for string but for multiquark structure of highly excited hadrons.
Dielectric Microwave Components with Magnetic Films for Communications

Thick films of $\mathbf{Ni}_{1\text{-}\mathbf{x}}\mathbf{Zn}_{\mathbf{x}}\mathbf{Fe}_{2}\mathbf{O}_{4}$ ferrites were polymerized on the dielectric $\mathbf{BaTi}_{4}\mathbf{O}_{9}\text{-}\mathbf{ZnO}$ substrates using suspensions consisting of photopolymer and weakly agglomerated ferrimagnetic particles. Structures including magnetic and nonmagnetic components make it possible to control the energy transfer and operating frequencies while maintaining the relatively high quality factor of the structure. The optimal composition of film and other features of film obtaining were revealed. The maximal controlled by magnetic field change in resonance frequency for obtained elements was around 71 MHz. The value of the decoupling was around 60 dB. Such elements can operate as microwave valves, phase shifters, and resonators and be used in GSM, 5G networks.
Study on the Relationship Between Speech Intelligibility and Quality Estimates in University Classrooms

In this paper, correlation coefficients between the five objective estimates of speech quality, on the one hand, and the Speech Transmission Index as speech intelligibility measure, on the other hand, were estimated. This comparison was performed using binaural room impulse responses corresponded to different points of the three university auditoriums of different sizes. Speech quality was assessed using intrusive speech quality measures: Segmental Signal-to-Noise Ratio, Logarithmic Spectral Distortion, Frequency-Weighted Segmental Signal-to-Noise Ratio, Bark Spectral Distortion, and Perceptual Evaluation of Speech Quality. The formation of signals distorted by reverberation was performed by convolving of pure signals with binaural room impulse responses of the premises. A high level of correlation (0.6-0.99) of Bark Spectral Distortion estimates with estimates of the Speech Transmission Index for rooms of different sizes was revealed. Correlation of estimates (0.65-0.98) of Frequency-Weighted Segmental Signal-to-Noise ratio with Speech Transmission Index estimates was observed for medium and large rooms. Significant correlation (0.96-0.99) of Perceptual Evaluation of Speech Quality with Speech Transmission Index estimates was observed only for large audiences. At the same time, estimates of the Segmental Signal-to-Noise Ratio and Logarithmic Spectral Distortion turned out to be practically uncorrelated with Speech Transmission Index estimates for all studied premises.
Towards Modular Digital Twins of Robot Systems

Robots are used in various types of applications in manufacturing, ranging from assembly to machine tending. Changes in production lines are becoming more common, either due to product changes, requiring changing manufacturing processes, as well as faults, caused by wear and tear of the robots. In such cases it is crucial that the robot system can adapt to the new circumstances. Recently the concept of Digital Twins (DTs) has gained a large amount of interest in both academia and industry. The idea of a DT is to have a digital copy of a physical system, where both systems exchange information which can be used to adapt the system to unforeseen changes in itself or its environment. This enables a DT to perform self-adaptation when problems arise during operation. However, development of a DT, whether for existing or future robots, is cumbersome. We therefore propose a development of modular DTs and outline our methodology for creating DTs for robot systems, their shortcomings and open challenges. Such a modular DT can be utilised to safely explore the potential adaptations of the physical system and its performance in changing environmental conditions.
A Modular and Composable Approach to Develop Trusted Artificial Intelligence

Trustworthy artificial intelligence (Trusted AI) is of utmost importance when learning-enabled components (LECs) are used in autonomous, safety-critical systems. When reliant on deep learning, these systems need to address the reliability, robustness, and interpretability of learning models. In addition to developing specific strategies to address each of these concerns, appropriate software architectures are needed to coordinate LECs and ensure they deliver acceptable behavior under uncertain conditions. This work proposes a model-driven framework of loosely-coupled modular services designed to monitor and control LECs with respect to Trusted AI assurance concerns. The proposed framework is composable, deploying independent services to improve the resilience and robustness of AI systems. The overarching objective of this framework is to support software engineering principles focusing on modularity, composability, and reusability in order to facilitate development and maintenance tasks, while also increasing stakeholder confidence in Trusted AI systems. To demonstrate this framework, it has been implemented to manage the operation of an autonomous rover’s vision-based LEC while exposed to uncertain environmental conditions.
Development of a Soft Inflatable Exosuit for Knee Flexion Assistance

Wearable robotics has shown to be effective for assisting in activities of daily living and restoring motor functions. The objective of this research is to develop a soft robotic exosuit for knee flexion assistance during normal walking and validate its ability to reduce the efforts of the knee flexor muscles: biceps femoris (BF) and semitendinosus (SM). The exosuit is powered by an inflatable curved fabric actuator with the capability to generate flexion torques at the knee joint. An analytical model to characterize the torque of the proposed actuator is derived and validated experimentally. It is found that the analytical torque model precisely matches the experimental results such that the highest root mean square error (RMSE) obtained is 1.237 Nm while the lowest is 0.188 Nm. In addition, the derived model outperformed a benchmark torque model such that its minimum and maximum RMSEs are approximately 90% and 70% less than the benchmark model respectively. A prototype of the knee exosuit is fabricated and tested on one healthy subject with different operating conditions to assist knee flexion during normal walking. The results show that by choosing the appropriate timing of inflation, the exosuit can reduce the electromyography activity of the BF and the SM by 32% and 23%, respectively, without impeding the knee extensor muscle or reducing the knee's range of motion.