Miracle-dz/UniFAR-FTUs huggingface dataset:
AM PWM Driving Circuit for Mini-LED Backlight in Liquid Crystal Displays

This work proposes a mini-LED driving circuit that adopts the pulse-width modulation (PWM) driving method for use in a liquid-crystal display (LCD) backlight. The proposed circuit can compensate for the threshold voltage (<inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ TH}}$ </tex-math></inline-formula>) variation in a low-temperature poly-crystalline silicon thin-film transistor (LTPS TFT) and a VSS current-resistance (I-R) rise, to generate a stable driving current to power the mini-LED. Since the proposed circuit uses the PWM method, the mini-LED can be operated at the best luminance-efficacy point, minimizing the power consumption of the circuit. The electrical characteristic of fabricated LTPS TFTs are measured to establish a simulation model to demonstrate the feasibility of the proposed circuit. Simulation results demonstrate that the relative mini-LED current error rates are below 9% when the <inline-formula> <tex-math notation="LaTeX">$\text{V}_{\mathrm{ TH}}$ </tex-math></inline-formula> varies ± 0.3 V and VSS rises by 1 V. With respect to precise control of the gray level, the time shifts of current pulses are within 11.48 us over the whole grayscale. The improvement in the power consumption of the proposed circuit is more than 21% than that of a circuit that is driven by pulse amplitude modulation.
A Monolithic Amorphous-Selenium/CMOS Single-Photon-Counting X-Ray Detector

We report on the design and characterization of a monolithic single-photon-counting (SPC) X-ray detector integrating an amorphous semiconductor sensor. Our prototype combines amorphous selenium (a-Se), a well-known photoconductor, with a CMOS readout integrated circuit (ROIC) containing two <inline-formula> <tex-math notation="LaTeX">$26\times196$ </tex-math></inline-formula> arrays of single-energy-threshold pixels. We achieve high spatial resolution for capturing single 60-keV photons by implementing 11.4-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-pitch pixels and by integrating a 70-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-thick a-Se sensor on the ROIC. We present experimental SPC results using a mono-energetic 60-keV radioactive source. Our results also suggest that our prototype a-Se/CMOS detector leverages the small-pixel effect (SPE) due to the large ratio of a-Se thickness to pixel pitch. This effect could overcome the slow inherent transient response of a-Se for high count rate X-ray diagnostic applications like mammography.
Multiphase Direct AC Wireless Power Transfer System: Comparative Proposals Using Frequency and Amplitude Modulations

Using multiload wireless power transfer (WPT) systems to supply power to multiple receivers with a single transmitter is an interesting topic. However, previous studies are all based on a dc–dc WPT system. If the loads demand ac power, additional dc–ac inverters need to be implemented, which increases the system complexity and cost. This article addresses two direct ac WPT systems that can regulate the ac power of multiple phases or loads independently, using frequency modulation and amplitude modulation (AM), respectively. The proposed WPT systems can directly control the ac power of multiple loads through the transmitter side, without any power converter at the receiver side. A typical application of the proposed system is for powering a three-phase load in a rotational object, e.g., three-phase motors in articulated robots or in-wheel motors. To demonstrate the feasibility of the proposed WPT systems, simulation and experiment studies of a wireless three-phase power transfer are presented. The results suggest that AM is a more advantageous solution to a direct ac WPT system. A power-loss breakdown analysis is presented to suggest future steps of efficiency improvement.
Evaluation of Loss Function for Stable Policy Learning in Dobutsu Shogi

AlphaZero achieved superhuman performance in Go, chess, and shogi, supported by massive computational re-sources. This paper investigates policy learning in AlphaZero, aiming at improving its learning efficiency. AlphaZero trains policy and value networks that are similar to those in a standard reinforcement learning setting, but in a different way closely coupled with game tree search in self-play. Inspired by recent studies that investigate policy optimization in tree search, we apply PPO, which is a state-of-the method for learning policy in reinforcement learning, into AlphaZero’s style learning. Then, we show its effectiveness in a small but interesting game where the database made by retrograde analysis is available. The results support that our method improves the sample efficiency in the learning.
A Wide Speed Operable Multiport Solar-assisted SRM EV Drive with Integrated Driving/Charging Capabilities

This work presents a multiport drive capable of integrating battery, on-board PV power, SRM and utility grid for integrated driving/charging functions and offering a wider speed range operation (WSO). The system supplies propulsion power from on-board battery independently as well as with PV assistance during light to high load conditions for a variable speed profile. On-board charger (OBC) exhibits unity power factor (UPF) charging from utility grid as well as dc charging with the on-board PV power at maximum power point offering self-charging feature during stand-still conditions. Also, trikle charging on-the-go is achieved while driving with PV power under light load conditions. Closed-loop operation of the complete system is carried out and verified on a 1 hp, 100V, 8/6 SRM, 1.78 Nm SRM using simulations results.
A Probabilistic Next Best View Planner for Depth Cameras Based on Deep Learning

In this work we present a novel method to plan the next best view of a depth camera by leveraging on a Convolutional Neural Network (CNN), and on a probabilistic occupancy map of the environment for ray casting operations. In particular, a hybrid approach is introduced that exploits the convolutional encoder-decoder to perform object completion, and an algorithm based on ray casting to evaluate the information gain of possible sensor view poses. Automatic object completion consists of inferring the occupancy probability of the regions of space that have not been observed. A comparison against several methods, including City-CNN, was carried out in 2D and 3D environments on publicly available datasets. In particular, to enable comparison, the original City-CNN algorithm was modified to work with depth cameras. Experiments indicate that the proposed method achieves the best results in terms of exploration accuracy. Results on a real robot are also presented.
Forecasting People’s Action via Social Media Data

In this paper, we explored predicting human activity based on users’ content from social media. The activities we do are related to our interests, personalities, political preferences and our future decisions. The data set we collect contains examples of social media users related to many daily business activities. Then, we use the latest tailored sentence embedding framework to recognize the semantics of human activities and automatically cluster these activities. We proposed a neural network architecture to predict all activities performed by a specific user based on previous posts and self-describing text. Besides, we explore how adding inferred user characteristics into our model contributes to this forecasting task.
A Workflow-Based Methodological Framework for Hybrid Human-AI Enabled Scientometrics

With cutting edge scientific breakthroughs, human-centred algorithmic approaches have proliferated in recent years and information technology (IT) has begun to redesign socio-technical systems in the context of human-AI collaboration. As a result, distinct forms of interaction have emerged in tandem with the proliferation of infrastructures aiding interdisciplinary work practices and research teams. Concomitantly, large volumes of heterogeneous datasets are produced and consumed at a rapid pace across many scientific domains. This results in difficulties in the reliable analysis of scientific production since current tools and algorithms are not necessarily able to provide acceptable levels of accuracy when analyzing the content and impact of publication records from large continuous scientific data streams. On the other hand, humans cannot consider all the information available and may be adversely influenced by extraneous factors. Using this rationale, we propose an initial design of a human-AI enabled pipeline for performing scientometric analyses that exploits the intersection between human behavior and machine intelligence. The contribution is a model for incorporating central principles of human-machine symbiosis (HMS) into scientometric workflows, demonstrating how hybrid intelligence systems can drive and encapsulate the future of research evaluation.
High Breakdown Voltage and Low-Current Dispersion in AlGaN/GaN HEMTs With High-Quality AlN Buffer Layer

We have successfully grown AlGaN/GaN high electron mobility transistor (HEMT) structure on the high-quality undoped thick AlN buffer layer with large band offset to replace the conventional high-resistivity GaN buffer layer. The AlGaN/GaN HEMT fabricated on this AlN buffer layer exhibits low OFF-state leakage current with high <inline-formula> <tex-math notation="LaTeX">${I}_{\mathrm{\scriptscriptstyle ON}}/{I}_{\mathrm{\scriptscriptstyle OFF}}$ </tex-math></inline-formula> of <inline-formula> <tex-math notation="LaTeX">$\sim 10^{{6}}$ </tex-math></inline-formula> due to enhanced confinement of the electrons in the 2-D electron gas (2-DEG) channel. The undoped AlN buffer layer is responsible for suppressing the trapping effects to greatly reduce the current dispersion in pulsed <inline-formula> <tex-math notation="LaTeX">${I}_{\text {D}}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}_{\text {D}}$ </tex-math></inline-formula> characteristics, which is hardly avoided in conventional deep acceptor-doped GaN buffer layer. The device also demonstrates high breakdown voltage of 2154 V with very high figure of merit (FOM) of ~1.8 GV<inline-formula> <tex-math notation="LaTeX">$^{2-1}$ </tex-math></inline-formula>cm<inline-formula> <tex-math notation="LaTeX">$^{-{2}}$ </tex-math></inline-formula>, one of the highest ever reported, suggesting that the AlGaN/GaN-based Hemts WITH AlN buffer layer are promising for high-performance RF and power applications.
Development of Pressure Measurement Equipment Fabricated by Robot Packaging Method

This manuscript describes a pressure sensor equipment for underwater robot external attachment. It is difficult to add sensors to many underwater robots. Most pressure sensors have a diaphragm plate, and the pressure sensor must be exposed to the environment to deform the diaphragm plate. Therefore, to add a pressure sensor to an underwater robot, it is generally necessary to redesign both the outer shell and the robot’s inner structure. To solve this problem, we propose encapsulating a pressure sensor in a plastic film bag. Vacuum packaging technology is applied to fabricate the sensor equipment. This technique is called the robot packaging method, and it is possible to fabricate the sensor equipment rapidly and inexpensively. This manuscript shows experimental results that it is necessary to encapsulate a sufficient amount of insulating fluid in the plastic film bag to fabricate the pressure sensor equipment by the robot packaging method.
Synthesis and Design of Highly Selective Multi-Mode Dual-Band Bandstop Filter

This paper presents the synthesis and design of the multi-mode dual-band bandstop filter (MM-DBBSF). A highly selective multi-mode dual-band bandstop response is obtained using a quarter wavelength coupled line structure. It has been shown that by increasing the coupled line’s order, the selectivity and the transmission zeros are increased in the desired stopband. Moreover, a step impedance resonator (SIR) is used between two coupled lines to achieve more transmission poles for better out of band selectivity. The paper show a detailed theoretical synthesis of the coupled line and SIR structure. In order to validate the theoretical model, ideal and microstrip topologies are designed and simulated. Furthermore, a high-frequency substrate is used to fabricate four prototypes. The simulated and measured results show good concurrence.
Privacy-Preserving Implicit Authentication Protocol Using Cosine Similarity for Internet of Things

Internet of Things provides complicated value-added services to mobile intelligent terminal users. Different sensors collect various data from the users and transmit the data to the mobile intelligent terminal for storage. Consequently, a great amount of personal and sensitive information related to these rich and colorful applications is stored in the mobile intelligent terminal. Mobile intelligent terminals have become the prominent target of network attackers. Security breach and privacy leakage severely thread the application development of the Internet of Things. We present a privacy-preserving implicit authentication framework using users’ behavior features sensed by the mobile intelligent terminal based on the artificial intelligence methodology. More precisely, we first summarize the security and privacy requirements for the security authentication of the mobile intelligent terminal. Then, we present a privacy-preserving implicit authentication framework using the cosine similarity and partial homomorphic public-key encryption scheme. Finally, a performance evaluation of the proposed protocol is conducted. The result shows that the communication and computation efficiency of our protocol is more efficient than other related protocols.
Proposal of Ferroelectric Based Electrostatic Doping for Nanoscale Devices

A ferroelectric based electrostatic doping (Fe-ED) technique is proposed, as the alternative to chemical doping, providing non-volatile and programmable free electrons and holes for nanoscale devices. We show that Fe-ED achieves non-volatility and reconfigurability via the ferroelectric film inserted into the polarity gate, producing the reconfigurable nanosheet FETs (NSFETs) without the requirement of a constant bias. Thanks to the naturally formed lightly doped drain structures and the extremely high doping concentration over $1\times 10^{21}$ cm−3 in source/drain (S/D) regions, Fe-ED NSFETs exhibit the promising potential benefits for device scaling including the improved subthreshold swing, the suppressed drain-induced barrier lowering, and the ultralow S/D region resistance. Our study suggests a promising doping strategy of Fe-ED for versatile reconfigurable nanoscale transistors and highly integrated circuits.
News Headline Grouping as a Challenging NLU Task

Recent progress in Natural Language Understanding (NLU) has seen the latest models outperform human performance on many standard tasks. These impressive results have led the community to introspect on dataset limitations, and iterate on more nuanced challenges. In this paper, we introduce the task of HeadLine Grouping (HLG) and a corresponding dataset (HLGD) consisting of 20,056 pairs of news headlines, each labeled with a binary judgement as to whether the pair belongs within the same group. On HLGD, human annotators achieve high performance of around 0.9 F-1, while current state-of-the art Transformer models only reach 0.75 F-1, opening the path for further improvements. We further propose a novel unsupervised Headline Generator Swap model for the task of HeadLine Grouping that achieves within 3 F-1 of the best supervised model. Finally, we analyze high-performing models with consistency tests, and find that models are not consistent in their predictions, revealing modeling limits of current architectures.
Design and Basic Experiment of Online Feedback Training System for Golf Putting

The purpose of this study is to develop an online feedback training system for golf putting that can improve the repeatability without affecting the swing feeling. By using the proposed system providing auditory feedback in real time, the repeatability of the swing can be improved in the same swing feeling as a game. To realize this system, an IMU (inertial measurement unit) is attached on the grip end of the putter and measures the angle of putter head. This mounting position of the IMU was examined and designed. In addition, the authors experimentally determined the maximum weight of the mountable IMU without affecting swing feeling. As a results, it turned out that up to 10g IMU can be attached on the club. Moreover this system gives auditory feedback corresponding to the loudness and the angle of the putter, therefore, users can know the angle of the putter head in real time. In particular, this paper reports on the proposal and evaluation experiments of this system.
Rearranging Tasks in Daily-life Environments Using a Monte Carlo Tree Search and a Feasibility Database

In this paper, we address the task of rearranging items with a robot. A rearranging task is challenging because one should solve the following issues: to determine how to pick the items and plan how and where to place the items. In this study, we focus on how to obtain a sequence of actions that the robot could execute reducing the failures when the motion planner creates the trajectory to move the robot, such as not finding a solution. To confirm the sequence of instructions before executing them with the robot, we combine a motion planner with a symbolic planner. For that purpose, we propose a Motion Feasibility Checker (MFC), which quickly decides if a given set of pick-and-place poses can be executed with respect to the robot’s kinematics. The MFC uses a database of possible pick and place poses of the target robot; given the initial and target pose of the item, the MFC finds a set of pick-and-place poses to execute that action with the robot. We use the Monte Carlo Tree Search (MCTS) to achieve a high performance of the symbolic planning. In the proposed method, the MCTS searches for the goal while it collaborates with the MFC. We tested the proposed method in a simulation environment doing a sandwich rearranging task in a convenience store setup.
Overconfidence and anomalies in Chinese stock markets: a literature review

There are numerous anomalies in the financial markets, which is difficult to be explained by the traditional efficient market theory. Overconfidence theory, motivated by the psychological evidence, increasingly becomes popular for better understanding the stock markets. This study aims to comprehensively and thoroughly review the relevant literature which looks at the economic consequences of investor overconfidence, and analyze how the overconfidence theory consequently raises some financial markets anomalies.
ReoFS: A Read-Efficient and Write-Optimized File System for Persistent Memory

In this paper, we present ReoFS, a read-efficient and write-optimized PM-aware (Persistent Memory-Aware) file system that provides strong consistency guarantee. Different from traditional journaling techniques, ReoFS optimizes write requests on the write I/O path and allows concurrent reads to be served opportunistically. To reduce the write overhead for metadata-related operations, we devise a backup buffering mechanism, which keeps a replica of metadata in the backup buffer and supports fast in-place update. To minimize the write overhead for file data modifications, we design a fine-grained short logging mechanism to migrate writes to persistent log blocks and make new writes visible immediately after the log is persisted. With these two designs, ReoFS removes the double write overhead off the critical path of request execution for both metadata and data operations. We conduct extensive experiments on Intel Optane DC PM platform and the results show that ReoFS outperforms existing PM -aware file systems by 1.3x-4x.
Consideration about Structure of Gait Motion Assist Device for Trunk Using McKibben Type Pneumatic Artificial Muscle

Stability and efficiency of walking can be obtained according to trunk motions such as side flexion and rotation. In this study, spine type wearable device which can correct a posture and assist these trunk motions was developed to support exercise therapy for people declined in ability of walking. Proposed device has advantage of high affinity with human body owing to adjustable stiffness mechanism. In this paper, the mechanism of device, design method of artificial muscle based on rubber property and assisting method are described. The mechanism of device imitates spine structure to realize adjustable stiffness characteristics. We applied an artificial muscle model to simplify of mechanism and control method. Finally, the effectiveness of proposed mechanism and assisting method was confirmed from experiment result.
Auto-Tuned Event-Based Perception Scheme for Intrusion Monitoring With UAS

This paper presents an asynchronous event-based scheme for automatic intrusion monitoring using Unmanned Aerial Systems (UAS). Event cameras are neuromorphic sensors that capture the illumination changes in the camera pixels with high temporal resolution and dynamic range. In contrast to conventional frame-based cameras, they are naturally robust against motion blur and lighting conditions, which make them ideal for outdoor aerial robot applications. The presented scheme includes two main perception components. First, an asynchronous event-based processing system efficiently detects intrusions by combining several asynchronous event-based algorithms that exploit the advantages of the sequential nature of the event stream. The second is an off-line training mechanism that adjusts the parameters of the event-based algorithms to a particular surveillance scenario and mission. The proposed perception system was implemented in ROS for on-line execution on board UAS, integrated in an autonomous aerial robot architecture, and extensively validated in challenging scenarios with a wide variety of lighting conditions, including day and night experiments in pitch dark conditions.
Digital Innovation and Disruptive Technologies in the "Intellectual Capital (IC) and Knowledge Management Systems (KMS) Disclosure": a Bibliometric Analysis

This study investigates the literary corpus on the impact of disruptive technologies on intangible factors; in particular, it analyzes trends in management accounting systems that involve aspects of the intellectual capital employed in innovation-oriented performance. The study discusses whether Integrated Reporting (IR) and Integrated Thinking (IT) contribute to disseminating IC, verifying how knowledge management systems can guide business processes towards innovation and the use of disruptive technologies. A bibliometric analysis of a database containing 115 articles in English language publications was conducted between 2006 and 2020, which offers a map of the knowledge produced and circulated by past studies. It gives insights into publication activities, prominent themes, citation trends, and state of collaborations among the contributors of past studies and aggregate contributions to the area of disruptive technologies studies. The study offers a retrospective analysis of the content published in disruptive technologies. The results indicate that field research has focused mainly on intellectual capital, integrated reporting, integrated thinking. The study outlines the importance of implementing IC diffusion models through KMS in organizations oriented to digital innovation.