Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapidly Measuring Charge Carrier Mobility of Organic Semiconductor Films Upon a Point-Contact Four-Probes Method

Field-effect mobility (<inline-formula> <tex-math notation="LaTeX">${\mu }_{\mathrm{ FET}}$ </tex-math></inline-formula>) of organic semiconductor films plays a key role in the performance of field-effect transistors (FETs). Numerical extraction of <inline-formula> <tex-math notation="LaTeX">${\mu }_{\mathrm{ FET}}$ </tex-math></inline-formula> from organic FET characteristics is not only time-consuming due to patterning of source/drain electrodes, but also frequently unreliable because of the contact resistances (<inline-formula> <tex-math notation="LaTeX">${R} _{\mathrm{ C}}$ </tex-math></inline-formula>) between source/drain electrodes and semiconductors. Here, we propose an approach to rapidly evaluate <inline-formula> <tex-math notation="LaTeX">${\mu }$ </tex-math></inline-formula> by a point-contact four-probes method (<inline-formula> <tex-math notation="LaTeX">$\mu _{\mathrm{ PFP}}$ </tex-math></inline-formula>) for organic semiconductor films. Four tip-like probes quickly contact the semiconductor film surface directly, without deposition of the conventional source/drain electrodes, to simultaneously inject current and measure the electric potential. The charge density and thus conductance of the film is manipulated upon scanning gate voltage, from which the extraction of <inline-formula> <tex-math notation="LaTeX">${\mu } _{\mathrm{ PFP}}$ </tex-math></inline-formula>, in good agreement with <inline-formula> <tex-math notation="LaTeX">${\mu }_{\mathrm{ FET}}$ </tex-math></inline-formula>, could be realized in a few seconds. This method, with easily accessible setup and numerical model, substantially accelerates the evaluation of <inline-formula> <tex-math notation="LaTeX">${\mu }_{\mathrm{ PFP}}$ </tex-math></inline-formula>, and thus could help screen materials and optimize film morphology for organic FETs applications.
Common-Mode Noise Modeling and Reduction for 1-MHz eGaN Multioutput DC–DC Converters

Enhancement-mode gallium nitride (eGaN) high-electron-mobility transistors (HEMTs) are promising to increase the switching frequency into multimegahertz for the high power density. However, it is a serious challenge to identify the equivalent lumped capacitances of megahertz multiwinding planar transformers for common-mode (CM) noise analysis to optimize the electromagnetic interference filters. The couple turn with its one-capacitor model is proposed to quantify the total CM noise current via the high-frequency transformer. With the modeled total CM noise current, the equivalent lumped interwinding capacitance model of the transformer can be derived conveniently and accurately. A complete CM noise path model is built for a GaN-based active clamp forward with a self-driven synchronous rectification multioutput converter. Then, a CM noise cancelation capacitance is applied and optimized to generate a reverse extraction current to cancel the CM noise current. Compared with other methods, the proposed modeling technique can build the equivalent lumped interwinding capacitance model of the multiwinding planar transformer in the printed circuit board layout stage before it is fabricated physically. The proposed model is verified by the experimental results with a 1-MHz GaN prototype with 100-V input, 5-V/6-A, and ±12-V/ 0.83-A outputs. The CM noise spectrum is well predicted. The CM noise is reduced by around 15 dB (a reduction of 12.6%) below 9 MHz.
Estimating Audience Engagement to Predict Movie Ratings

While watching movies, audience members exhibit both subtle and coarse gestures (e.g., smiles, head-pose change, fidgeting, stretching) which convey sentiment (i.e., engaged or disengaged) during feature length movies. Noticing these behaviors using computer vision systems is a very challenging problem—especially in a movie theatre environment. The environment is dark and contains views of people at different scales and viewpoints. Feature length movies typically run 80-120 minutes, and tracking people uninterrupted for this duration is still an unsolved problem. Facial expressions of audience members are subtle, short, and sparse; making it difficult to detect and recognize activities. Finally, annotating audience sentiment at the frame-level is prohibitively time consuming. To circumvent these issues, we use an infrared illuminated test-bed to obtain a visually uniform input of audiences watching feature length movies. We present a method which can automatically detect the change in behavior (key-gestures) using “key-frames”, which can convey audience sentiment. As the number of key-frames are many orders of magnitudes lower than the number of frames, the annotation problem is reduced to assigning a sentiment label for each key-frame. Using these discovered key-gestures, we create a movie rating classifier from crowd-sourced ratings and demonstrate its predictive capability. Our dataset consists of over 50 hours of audience behavior collected across 237 subjects.
Indoor Localization on Smartphones Using Built-In Sensors and Map Constraints

Smartphones have become an indispensable tool in the work and life of most people. Given the many kinds of sensors built into smartphones, solving indoor navigation problems using these phones has become feasible. This paper proposes such a solution, using a particle-filter-based indoor positioning method. This method improves the positioning accuracy and helps improve the user experience. The major features of the method are that it leverages and fuses the advantages of two different indoor positioning methods [pedestrian dead reckoning (PDR) and received-signal-strength-indicator-based indoor positioning], and considers the influence on positioning of the indoor spatial map. The smartphone built-in sensors are used as data sources and constitute a feasible method for PDR. The iBeacon devices are also used—because of the many advantages, they present in indoor environments, such as low energy consumption, convenient deployment, and popular applications—and their received signal strength indicator values are used as observations in the particle filter. The devices used in this paper are conventional, without any special equipment, and the experimental sites are common work environments. Therefore, we believe that the proposed method has a wide application potential for indoor navigation applications (e.g., museum guides), and is especially suitable for working with indoor navigation maps.
Integrated Thermal Silica Micro-Resonator Waveguide System With Ultra-Low Fluorescence

Thermally grown silica is one of the most desirable materials for the realization of optical waveguides and whispering-gallery mode micro-resonators due to the ultra-low propagation loss and broad transparency window from UV to mid-IR. Here, we present the design, fabrication, and characterization of a high-Q ring-resonator device with a monolithically integrated evanescent rib waveguide coupler made solely from silica thermally grown on a Si substrate. The device delivered an intrinsic Q-factor of $3.7\,\times \, 10^{6}$ and operated close to the critical coupling regime for both TE and TM polarizations. The achieved Q-factors were more than three orders of magnitude higher than those of comparable silica resonator devices. Furthermore, we showcase the ultra-low parasitic fluorescence unique to thermally grown silica and compare it to a device fabricated in Si3N4.
Analyze and Predicting the Student Academic Performance Using Data Mining Tools

In order to get reliable students' data, there are various methods being followed at present in the educational field. One among them is Data Mining Technique. The current situation of Data mining techniques is popularly practiced in many institutions to locate the hidden data of the student. KDD (Knowledge Discovery in Databases) is a method to identify the functional information from huge data. This technique is largely practiced by various institutions to find various patterns of stored data. It can also be found and extract the information. This type of method can largely believed, explain different problems happened in various institutions like education, economic, business, statics, medicine and, sport. The current work intention is to move towards the student achievement in secondary education applying BI/DM techniques. At present, the student's grades, demography, social and school-related information i.e. real-world data, is gathered through reports and questionnaire given by school reports. For this purpose, the two core groups were taken under Binary/ five level categorization and deterioration tasks. The present paper focuses on scheming various methods that will useful to the teachers and administrators of the institution to locate the weak student and develop their cognitive levels and atmosphere in which the student learns. It can also be observed in the paper that the dissimilar categorization methods based on the characteristics are used, to individual group or class and examine them from the existing data to classify the learner based on their presentation in academics. The ultimate objective of this paper is to find the failure of the students to avoid them from tumbling out and develop their scholastic performance.
Design of Secured Block Ciphers PRESENT and HIGHT Algorithms and its FPGA Implementation

Light weight cryptography is a technology for providing the security solutions for the hardware systems where there is no computational resource or limited resource. PRESENT and HIGHT are a class of lightweight block ciphers which uses two different ways of computational structure for cipher generation. The paper presents the design and FPGA implementation of the two cryptographic structures PRESENT and HIGHT. A new hardware architecture design has been developed here for both PRESENT and HIGHT. A comparative analysis of the presented designs PRESENT and HIGHT have been done with the existing designs. A significant design optimization has been achieved for HIGHT and a significant throughput has been achieved for PRESENT.
Plant Recognition System Based on Leaf Image

Nature has huge members of plants identifying them and classifying them is important task for botanists. Usually plants are recognized by leaf and its characteristics like shape, texture, vein structure, color etc. A system is developed which recognizes plants automatically based on leaf structure using image processing. Moreover evolutionary changes are also taking place in plants and it has impact on identification and classification. Image data base and related information is stored on cloud. Shape measure include area, perimeter, ratio of MajorAxisLength and MinorAxisLength, vein structure indicating angle of sub veins with major vein, texture are the major feature used. Botanists visit remote places, jungles where the plants need to be identified and classified. So an attempt is made to develop an automatic identification system where in an image of leaf is captured by any smart phone, uploaded on cloud, where image available and complete data base is trained. Captured image is processed features will be extracted and gives to classifies the plant classification result will be transmitted back to smart phone and related information.
Improved Local Maximal Occurrence Feature Representation for Person Re-Identification

Person re-identification is a technique to automatically search the same people in different camera views. It is a challenging task because of the huge variations caused by different illuminations and views in images. There is an effective method tackling the problem, which learns a useful feature representation for the pedestrian images. In recent research, a feature representation named Local Maximal Occurrence (LOMO)achieves outstanding performance, but LOMO has a disadvantage that its robustness to illumination is not excellent. In this paper, we propose an improved LOMO representation to further reduce the impact of light. The improved LOMO feature combines Color Name feature with LOMO, because Color Name feature has strong robustness to illumination. However, Color Narne feature has the weakness that it loses graphical details. Therefore, the feature is unsuitable for traditional pooling method. To overcome the disadvantage, a specific pooling method called global Gaussian pooling for Color Name feature is designed, which is based on Gaussian distribution and can weigh the different regions of an image. The important regions gain more attention in matching images. The experimental results on two public available datasets demonstrate the effectiveness of our proposed approach.
Block-Chain-Based Personal Data Hosting

The increase of data leakages in some of the world's largest companies is leading to a fundamental reassessment about the way data is collected, stocked and secured. The current model in which a third party manages individually data is starting to reach its limits. The block-chain model, popularized by the Bitcoin in the financial space, is growing and demonstrating that using a decentralized network of peers can lead to a secured and powerful computing. Present paper describes the implementation of a decentralized personal data management system. It ensures users own and control their data. We implement and test here a concrete application of the block-chain. It allows our system to avoid the usage of a traditional third-party role by managing an automated access-control system profitable to the user. Finally, we open a discussion on the possibility to set up an economic system based on the exchanges of tokens between the different entities of the solution.
The characteristics of human-robot coadaptation during human-in-the-loop optimization of exoskeleton control

Human-in-the-loop (HITL) optimization of exoskeleton control during assisted walking can improve human mobility and reduce the energy cost. This process involves human-robot coadaptation as suggested by prior studies. There was a drop in the same subjects metabolic cost under the same assisted walking condition before and after the optimization process. It means the subjects adapted to walking with the exoskeleton while the exoskeleton learned the optimal control parameters for the subjects. We analyzed the process of human bodies learning to walk with an ankle exoskeleton, aiming to quantify the characteristics of human-robot coadaptation during HITL optimization of exoskeleton control. Data of eleven participants from prior experiments were utilized in this study. We identified similar sample conditions for each participant and investigated the trend of metabolic cost along with the HITL exoskeleton control optimization process. Results showed that the relationship between human metabolic cost and the time past in the optimization cycle approximately followed exponential curves with widespread adaptation rates. For the optimization process of four parameters with each condition sampled for two minutes, the time constants were averaged at 238 ± 207 optimization sample conditions. Our results can provide guidance to the training process of robot assisted human motion.
External Force Estimation Method for Robotic Manipulator Based on Double Encoders of Joints

This paper proposes a novel external force estimation method for robotic manipulator based on double encoders of joints. The actuator structure with double encoders used in this method is presented and discussed in detail. The principle of utilizing the difference between the positions of double encoders to represent the joint actual torque is analyzed and discussed later. Then, the friction torque model is identified in consideration of velocity, temperature and load torque step by step to achieve the final comprehensive friction torque model. After the external torque of the joint is estimated by subtracting dynamic torque and friction torque from the output toque of motor based on the difference between the positions of double encoders, the external force of the manipulator is derived. Two validation experiments are carried out to measure the estimated external force under different conditions including static state and dynamic state. The results show that the method based on double encoders of joints is capable to estimate the external force of the robotic manipulator.
Research on Compliance Control for the single Joint of a Hydraulic Legged Robot

Compliance controller for the single joint of a hydraulic legged robot is presented in this paper. This method combines the active compliance controller of the impedance controller based on force control with the passive compliance of the Series Elastic Actuator (SEA) to achieve a compliant control function for a wide range of variable impedances and rapid response to load changes. The performance of this controller was assessed on a single joint hydraulic legged robot which has already been modelled. An experiment platform with double hydraulic drive units was designed for testing the force tracking performance of the hydraulic drive units. The experimental results have shown that the control method is reasonable and the system has a good performance of impact resistance and active compliance.
HPGA: A High-Performance Graph Analytics Framework on the GPU

In recent years, the rapidly growing use of graphs has sparked parallel graph analytics frameworks for leveraging the massive hardware resources, specifically graphics processing units (GPUs). However, the issues of the unpredictable control flows, memory divergence, and the complexity of programming have restricted high-level GPU graph libraries. In this work, we present HPGA, a high performance parallel graph analytics framework targeting the GPU. HPGA implements an abstraction which maps vertex programs to generalized sparse matrix operations on GPUs for delivering high performance. HPGA incorporates high-performance GPU computing primitives and optimization strategies with a high-level programming model. We evaluate the performance of HPGA for three graph primitives (BFS, SSSP, PageRank) with large-scale datasets. The experimental results show that HPGA matches or even exceeds the performance of MapGraph and nvGRAPH, two state-of-the-art GPU graph libraries.
A Robust Power Management Strategy With Multi-Mode Control Features for an Integrated PV and Energy Storage System to Take the Advantage of ToU Electricity Pricing

In this paper, a power management strategy (PMS) for an integrated residential solar photovoltaic (PV) and energy storage unit (ESU) is proposed for both grid-connected and islanded operations to take the advantage of time-of-use pricing. This is an effective solution to integrate storage and renewable energy sources, such as solar PV, with the conventional grid to improve the reliability and efficiency of the power system and to reduce the total electricity cost for the consumer. An automatic switching control strategy is proposed to realize a smooth switching among the various operation modes of the proposed energy management strategy. The integrated PV-storage system is composed of a 5-kW PV array, a 3.5-kW·h ESU formed by 12-V single lead acid batteries, and three power converters. The PMS ensures seamless switching of the ESU converter between the charging and the discharging mode and seamless switching between the inverting and the rectifying mode of the grid converter. It also ensures that the local loads are supplied when the grid fails. An experimental setup has been implemented in the laboratory using the real-time field-programmable gate array general-purpose inverter control card from National Instrument to verify the practical feasibility of the proposed PMS under the various scenarios of the PV irradiation, load changings, and the state of charge of the ESU. The results show that the proposed PMS is effective and robust against various scenarios.
Influence of Superconducting Fault Current Limiters on Traveling Wave Based Protection

Superconducting fault current limiters (SFCLs) have been used in electric power transmission systems to limit the current magnitudes when faults occur. Installation of SFCLs inevitably alters transmission line properties and thus may affect measurements in traveling wave based protection. In this paper, we investigate the impact of SFCLs on the performance of the traveling wave based protection schemes in high-voltage transmission systems. An SFCL and a 230 kV high-voltage transmission line are modeled into a simulation system in the electromagnetic transients program, version alternative transients program. The transmission line is represented by a frequency dependent model so that traveling wave characteristics can be effectively captured. We simulate faults on the transmission line, with the incoming and reflected traveling waves recorded and thoroughly analyzed. Also, the influence of SFCL stray capacitances on traveling wave shape is investigated. The results show that the traveling wave current magnitudes are attenuated by the SFCL and the shape of the traveling wave is slightly affected by SFCL stray capacitances. Possible consequences in protection performance are discussed and future research directions are suggested.
A Simple Model to Estimate the Percentage of Motor Plan Reuse From Hysteresis Effect Size

In sequential tasks, a partial reuse of former motor plans results in a persistence in the former posture (termed hysteresis). The cost-optimization hypothesis states that the percentage of reuse depends on the relative cognitive and mechanical cost of each movement. These costs should be constant across all drawers, yet previous studies found a larger hysteresis effect at the central drawers and declining effects toward the periphery. In the current study, we show that a simple mathematical model that assumes a sigmoid optimal grasp angle function and a fixed percentage of motor plan reuse explains the posture variance in a randomized and an ordered sequential drawer opening task. This finding indicates that (1) the optimal pro/supination angle is a sigmoid function of drawer height, (2) the percentage of motor plan reuse is constant across drawers, and (3) a constant percentage of reuse results in a larger hysteresis effect at the central drawers. Based on the model, the percentage of motor plan reuse in future studies can be estimated from the size of the motor hysteresis effect.
Modeling of Supercapacitor Behavior With an Improved Two-Branch Equivalent Circuit

In this paper, an improved two-branch equivalent circuit model with a controlled current source is proposed to describe the real-time charge/discharge and self-discharge operating characteristics of a supercapacitor (SC). First, the self-discharge mechanism of the SC is investigated based on the two-branch model and experimental analyses at the different charge and discharge stages. Furthermore, the controlled current source is introduced into the two-branch model to reflect the self-discharge effect of the SC. On this basis, the recursive least square method is adapted to identify the model parameters. To improve the accuracy of the proposed model, the iterative optimization algorithm is also adapted to optimize the dynamic parameters of the controlled current source. Finally, the improved two-branch equivalent circuit model with the controlled current source is simulated. The simulation results are basically in accordance with the experimental results. The relative error of the proposed model is less than 0.7%. Compared with the conventional two-branch model, the proposed model can not only reflect the SC dynamic behavior during the charge/discharge phase but also better reflect the self-discharge effect of the SC.