Miracle-dz/UniFAR-FTUs huggingface dataset:
A 0.25–0.4-V, Sub-0.11-mW/GHz, 0.15–1.6-GHz PLL Using an Offset Dual-Path Architecture With Dynamic Charge Pumps

This article presents an ultra-low-voltage phase-locked loop (ULVPLL) with the minimum supply voltage at 0.25 V. The offset dual-path loop architecture is proposed to relax the charge pump (CP) current matching requirement. Thus, no current mismatch suppression technique is required. This significantly mitigates the CP design challenges at such low supply voltage. In the two proposed dynamic CPs (DCPs), all the current sources, current mirrors, and op amps in the prior CPs are eliminated. Hence, the CP voltage headroom requirement is relaxed to make the DCP be suitable for sub-0.3-V operation; meanwhile, the CP power consumption and circuit design complexity are simultaneously reduced. Implemented in 40-nm CMOS with core area of 0.00873 mm2, the 0.15–1.6-GHz ULVPLL is capable of operation under a 0.25–0.4-V supply voltage. The power efficiency is 0.106 mW/GHz from 1.6-GHz output (at 0.4-V supply) and 0.048 mW/GHz from 0.2 GHz (at 0.25-V supply). Measured spur level is −58.3 dBc at 100-MHz offset from 1.6-GHz output (at 0.4-V supply) and −48.5 dBc at 12.5-MHz offset from 200-MHz output (at 0.25-V supply).
Construction Process Quality Control Assisted Mobile Application

Digital transformation plays a big role in many industries, including construction. One of the obstacles to manage large-scale and complex construction projects is inefficient communication between involving personnel. Most problems are such as misunderstanding contexts, incomplete information, ignorance, missing multimedia files, etc. In Thailand, organizations adopt digital transformation by utilizing a variety of applications and software to complete their projects, such as LINE, G-Suite, Asana, etc. Users have to spend a lot of time to learn each application and software to be able to achieve maximum performance. Construction process quality control assisted application is the new tool developed for uniting all needs and solving communication problems. Requirements are gathered by five interviews with three major organizations, the Thailand Department of Highway, Team Built Co., Ltd., and one major construction company. Requirements were analyzed to extract the most useful features that are beneficial to all users. React Native and the Expo framework were used to develop the frontend part of the application. For the backend part, the application used microservice architecture with various cloud services, like Amazon AWS, Expo Server, and Firebase Cloud Messaging. In the end, the application was put through hand-on tests by 13 on-site personnel. The final results showed the average scores of the user interface, hands-on experience, and benefits for the users were 4, 4.16, and 3.92 (out of 5), respectively, in which the total satisfaction score is 4.03(out of 5).
Knowledge of and Competence in Artificial Intelligence: Perspectives of Vietnamese Digital-Native Students

Artificial Intelligence (AI) and related technologies have been employed to simulate human decision-making processes to improve people’s lives. Accordingly, AI knowledge and related competence are crucial, especially for students pursuing information technology (IT) and computer-related degrees, since they will eventually be the next generation of AI designers or users. While the importance of AI technology and its applications have been widely discussed and explored, AI technology in Vietnam is in a nascent stage due to the shortage of skilled experts and the reluctance to adopt AI applications by businesses. Thus, the primary objective of this study was to explore the knowledge of and competence in AI among Vietnamese university IT students. A total of 206 university students from software engineering and computer science programs participated in this study. The results indicate the need for a focused effort to establish a strong foundation for a comprehensive and accessible AI mandatory course plan for IT students.
Latent Variable Models Reconstruct Diversity of Neuronal Response to Drifting Gratings in Murine Visual Cortex

Decades of experimentation in visual neuroscience have been conducted with limited sample sizes of simultaneously recorded neurons. Past experiments have characterized the visual system based on statistical properties of individual neurons. Yet, no neuron functions in isolation: neural networks code information through their coordinated behavior. Calcium imaging can now simultaneously record larger populations than electrophysiological techniques could previously, allowing single-neuron analyses to be expanded to the network level. Larger datasets demand the development of computational approaches for network-level analysis. Our work validates a population modelling approach, utilizing two latent variable models and a large dataset of simultaneously recorded neurons. Rectified Latent Variable Model (RLVM) and Non-Negative Matrix Factorization (NNMF) were able to be fit to a large calcium imaging dataset without deconvolving the fluorescence signal into action potentials. These models show high reconstruction accuracy, with the RLVM outperforming NNMF. Both models’ reconstruction performance can be predicted via regression with node density and cell behavior metrics as predictors, showing that RLVM and NNMF are more effective on networks with high node density. Neurons and estimated latent variables show a spectrum of functional groups in response to simple stimulus features, with groups ranging from orientation and frequency specific, or orientation specific frequency agnostic, to orientation specific frequency modulated. Clustering of behavior profiles reveals some similar response profile groups between neurons and estimated latent variables in response to drifting gratings stimuli.
Neuron Segmentation using Incomplete and Noisy Labels via Adaptive Learning with Structure Priors

Recent advances in machine learning have shown significant success in biomedical image segmentation. Most existing high-quality segmentation algorithms rely on supervised learning with full training labels. However, such methods are more susceptible to label quality; besides, generating accurate labels in biomedical data is a labor- and time-intensive task. In this paper, we propose a novel neuron segmentation method that uses only incomplete and noisy labels. The proposed method employs a noise-tolerant adaptive loss that handles partially annotated labels. Moreover, the proposed reconstruction loss leverages prior knowledge of neuronal cell structures to reduce false segmentation near noisy labels. The proposed loss function outperforms several widely used state-of-the-art noise-tolerant losses, such as reverse cross entropy, normalized cross entropy and noise-robust dice losses.
Economic Correlation between social responsibility information disclosure and enterprise value: Empirical Evidence based on Logistic Model

The relationship between social responsibility information disclosure and corporate value is controversial. This article uses financial indexes to construct a logistic regression economic model to measure the ratio between social responsibility information disclosure and corporate financial indicators, and to empirically test the synergy between corporate performance and social responsibility information disclosure degree. This paper sorted out a total of 1189 sample companies by hand and studied the logistic model with SPSS software. It was found that, compared with companies that do not disclose social responsibility information, the disclosure of social responsibility information promotes the increase of corporate value. This paper further introduces earnings management variables into the model to test. Listed companies with higher earnings management are more motivated to disclose social responsibility information. Listed companies with lower earnings management have greater corporate value than listed companies with higher earnings management. This article has enriched the research in the field of corporate value, and constructed a specific economic model between social responsibility information disclosure, earnings management and corporate value, and provided a reference for the supervision of the capital market by relevant departments.
Popularity Prediction of Music Based on Factor Extraction and Model Blending

The purpose of this research article is to explore different factors that can affect the popularity of a song and to predict the potential popularity of one song with the most related factors. Since there are too many factors that can affect a song's popularity and high dimensions caused by large amounts of variables, they may result in significant errors in analysis. To avoid these errors, the methods used in this research are Principal components analysis (PCA) and model blending. By using PCA, we can get lower dimensions; and then model blending to model the relationship between a scalar response and one or more explanatory variables. Different weights to random forest, decision tree and knn are set to get a new model, with mean squared error (MSE) 4.96, which is a comparatively low one. The results provide a relatively precise prediction of the popularity of Spotify top music. And they can be used in music company’s commercial analysis and enrich the field of popularity prediction of the song market.
Fabrication of Nanopores Using the Controlled Dielectric Breakdown Technique

Controlled dielectric breakdown is becoming the main solid-state nanopore fabrication technique worldwide. This paper presents the construction of a system for the fabrication of a single solid-state nanopore in a membrane using this technique. We include a basic introduction to nanopore theory. We also detail the building process of the low-cost equipment for nanopore fabrication, so that this paper can be used as a guide for building a similar system. Finally, we include the results of the creation of two nanopores and their characterization.
Hand-movement Prediction from EMG with LSTM-Recurrent Neural Networks

In this paper we present an approach based on shallow recurrent long short-term memory neural networks for the prediction of hand kinematics for hand-prosthesis control from data acquired via high-density surface electromyography (HD-sEMG). We used 134-channel HD-sEMG recordings from seven participants while performing multiple repetitions of 13 hand movements. A CyberGlove II was used to simultaneously record 18 degrees of freedom (joint angles) used as ground truth for predicting the hand movements. Traditional features were calculated over 100 ms windows and fed to the network. Specifically we used: Mean Absolute Value (MAV), variance, and number of zero-crossings. Our results indicate that: (a) a small number of channels is sufficient to make accurate predictions, (b) many features are redundant, and MAV is sufficient for the job, (c) the simple neural network architecture we propose is effective in this task. These findings have important implications in terms of computational efficiency and memory storage, which are important considerations in relation to implementability in the typically very low-power and low-resources computers on-board of hand prostheses.
Uniform DC Compact Model for Schottky Barrier and Reconfigurable Field-Effect Transistors

This paper presents a closed-form, physics-based compact model which is used to calculate the DC characteristics of double gate Schottky barrier field-effect transistors (SBFETs) and reconfigurable field-effect transistors (RFETs). Therefore, the model calculates the drain current which consists of field emission through the Schottky barrier and thermionic emission over the barrier. In order to validate the model, this paper shows results for the calculated current in SBFETs and RFETs compared to transfer characteristics of simulated devices and measurements, which show a good agreement.
Multi-Module DC-DC Converter-based Fast Chargers for Neighborhood Electric Vehicles

To allow a wide adoption of Neighborhood Electric Vehicles (NEVs), low-speed EVs, the EV battery's fast recharging is necessary. This will further increase the utilization of EVs due to the reduced charging duration. To achieve fast recharging, several studies have introduced advanced power electronics technologies to allow wider acceptance for EVs by the public. Among these power electronics conversion systems, the DC-DC conversion stage plays an essential role in supplying energy to the EV via charging the EV's battery. Accordingly, in this paper, a multi-module Input-Series Output-Parallel (ISOP) DC-DC converter is presented for NEVs. The multi-module ISOP DC-DC converter is Dual Active Bridge (DAB) based topology. Unlike the conventional ISOP DC-DC converter, only one H-bridge is employed at the input side, while the intermediate high-frequency transformer and the H-bridge employed at the output side are of modular structure. Since only one bridge with a single capacitor is used at the input side, the power balance between the employed DAB units is accomplished via direct output current sharing, where stability is ensured without using the input voltage sharing control approach. Two Matlab/Simulink model is presented to verify the presented concept using reflex charging algorithm, where the first model uses the small-signal model to present the behavior of the converter under transient condition, while the other Matlab/Simulink model is time-domain based simulation. The controller is tested using three DAB units with parameter mismatch where each unit is rated at 1. 5 kW to achieve an overall power of 4. 5 kW.
The Prediction on the Change of the Electoral Map Based on the Demographic Structure and Data Analysis

American politics seemed to be polarized by drawing a clear line between education, gender, and race instead of the policies and advocations by the governor. Since the time of Nixon, the republicans have initialized the "southern strategy" to absorb the votes of the white majority in the southern states. This strategy was considered to be the most successful strategy in history of modern politics by Cornell Belcher [1]. Following the same pattern, Ronald Reagan and George H.W. Bush were successfully get elected and the Donald Trump is not an exception. Therefore, from Nixon to Trump, it can be seen that this strategy, which represented the interest of the white majority, has gradually been weakened. In contrast, the identity politics of Democrats party started to sway the power even in some southern states and gradually dominated the recent popular votes. Since two parties both have their strategies when it comes to the identity politics, the change of population structure will be significant in predicting the politics trend, which is the purpose of this essay. That is to use the simple formula, the linear regression, to predict the demographics in different states, reflecting the American politics in the future. It turns out that if the republican party insists on using the “southern strategy” and assume that these two parties will not alter their policy in the future, the states voting for the republican will continuously “shrink” into the inlands, where the white population are still increasing and hardly have minority immigrations. Most states either near the coastline or near the border of Mexico will start to turn blue (the symbol of democratic party) and Texas, the base of republicans, is not an exception, which signals that the republican should try to absorb at least one minority race as soon as possible.
Intelligent Blowgun Game Scoring Recognition System Based on Computer Vision

In this manuscript, we present score recognition techniques for blowgun game based on computer vision. First, the algorithm detects the position of the target, and calibrates the camera’s parameters. Then the score is calculated with the detection of the dart tip on the target for real-time applications. To improve the robustness, the initial calibration is proposed to record N points as references at the edges of circles, to correct the camera position and angle. This approach can overcome the problems of lighting changes and the camera viewing angle deviation. The fast segmentation and orientation decision is proposed to find the blowgun tip accurately. The two cameras are presented to solve the overlapping problem of multiple darts to improve the detection accuracy. Based on calibration parameters, the distance weighting method is proposed to calculate the score precisely. Experiments result that the accuracy can achieve about 97% to recognize the score, and the processing speed can meet the real-time requirement with software implementation.
Traffic Sign Detection by Image Preprocessing and Deep Learning*

Detection of traffic sign is quite important in autonomous driving and driver assistance systems. In this study, Faster R-CNN as a very powerful object detection system is integrated with a feature extractor Inception Resnet V2 system, and it is evaluated and tested for detection and recognition of traffic sign. In addition, to enhance the effectiveness of this presented architecture, a new procedure of preprocessing the image to augment the detection performance of a traffic sign detector is also proposed. This new image preprocessing is introduced to reduce noise by blurring statistically some regions in an image, in which existence possibility of a traffic sign is lower. Hence, a more efficient training of object detector approach is presented. In the experiments, the publicly available Faster R-CNN Inception Resnet V2 model pre-trained on Microsoft COCO dataset is imported from Tensorflow model zoo on Github and is trained again on German Traffic Sign Detection Benchmark dataset. Also, to recognize detected traffic signs, German Traffic Sign Recognition Benchmark is used to train a CNN classifier. The experimental study illustrates the effectiveness of the presented approach while benchmarking with existing methodologies.
The development of a High data rate atom interferometric gravimeter (HIDRAG) for gravity map matching navigation

This paper reports on the development of a transportable high data rate quantum absolute gravimeter for gravity map matching navigation. Its target sensitivity is 10−7 g/ ✓ Hz at 100 Hz data rate with a stability of 10−9 g over a month. The design issues, sensitivity analysis and an evaluation of noises are discussed.
CCX-rayNet: A Class Conditioned Convolutional Neural Network For Biplanar X-Rays to CT Volume

Despite the advancement of the deep neural network, the 3D CT reconstruction from its correspondence 2D X-ray is still a challenging task in computer vision. To tackle this issue here, we proposed a new class-conditioned network, namely CCX-rayNet, which is proficient in recapturing the shapes and textures with prior semantic information in the resulting CT volume. Firstly, we propose a Deep Feature Transform (DFT) module to modulate the 2D feature maps of semantic segmentation spatially by generating the affine transformation parameters. Secondly, by bridging 2D and 3D features (Depth-Aware Connection), we heighten the feature representation of the X-ray image. Particularly, we approximate a 3D attention mask to be employed on the enlarged 3D feature map, where the contextual association is emphasized. Furthermore, in the biplanar view model, we incorporate the Adaptive Feature Fusion (AFF) module to relieve the registration problem that occurs with unrestrained input data by using the similarity matrix. As far as we are aware, this is the first study to utilize prior semantic knowledge in the 3D CT reconstruction. Both qualitative and quantitative analyses manifest that our proposed CCX-rayNet outperforms the baseline method.
Design and Analysis of a Novel Flat Pneumatic Artificial Muscle

Existing pneumatic artificial muscles are often manufactured in complex ways, and they are challenging to meet up with the requirements of high contraction ratio, small size, and flexibility of parts at the same time. To address these problems, we developed a flat pneumatic artificial muscle (FPAM). The FPAM is woven by a flat balloon and a constrained layer. The two parts are manufactured in a highly efficient and simple laser cutting process. Experimental results show that the FPAM's maximum contraction ratio is 22.4%, and the maximum output force is 273 N. Dynamic experiments illustrated that they have consistent position tracking performance at 0.5 Hz and 0.25 Hz. What's more, the FPAM's weight and thickness are only 4 g and 0.6 mm, respectively, making it adaptable to various situations, especially in soft wearable devices.
Lossless Compression and Implementation for Medical Signals Using Verilog

In this paper, a lossless bio signal compression method known as Log2 sub band compression is implemented. A xor-Log2 sub-band compression where two data samples are compared to identify differences between them. This compression technique is applied for two random 24-bit bio medical signals. One block is current data which has data to be compressed and previous data has no value initially. There are four cases where the number of bits to be compressed are 8, 14, 20 and 26 bits. Compression occurs on certain count taken as 10, 16, 22 and 28. The compressed data is transmitted serially. Simulation and synthesis are performed on Xilinx ISE. Area occupied by the compressor is 83% of the total LUT-FF pairs which is quite considerable, and power consumed is 0.014 W which can make the overall compressor optimized. The delay taken by the compressor is 3.324 ns. Lesser the delay more fast the compression takes place.
Retinal Vessel Segmentation Via Context Guide Attention Net With Joint Hard Sample Mining Strategy

Retinal vessel segmentation is of great significance for clinical diagnosis of eye-related diseases and diabetic retinopathy. However, due to the imbalance of retinal vessel thickness distribution and the existence of a large number of capillaries, it is difficult to segment the retinal vessels correctly. To better solve this problem, we propose a novel Context Guided Attention Net (CGA-Net) with Joint hard sample mining strategy. Specifically, we propose a Context Guided Attention Module (CGAM) which can utilize both the surrounding context information and spatial attention information to promote the precision of segmentation results. As the CGAM is flexible and lightweight, it can be easily integrated into CNN architecture. To solve the problem of retinal vessel pixel imbalance, we further propose a novel Joint hard sample mining strategy (JHSM) in network training, which combines both the pixel-wise and patch-wise hard mining to largely improve the network’s robustness for hard samples. Experiments on publicly DRIVE and CHASE DB1 datasets show that our model outperforms state-of-the-art methods. Our code is available at https://github.com/vignywang/Medical_Seg.