Miracle-dz/UniFAR-FTUs huggingface dataset:
Double-quantum filter for rotating solids

A pulse sequence is demonstrated which effectively excites double-quantum coherences in solid samples rotating at the magic angle. A double-quantum filter using this sequence is presented and applied to /sup 13/C-labeled zinc acetate to demonstrate its effectiveness. The /sup 13/C-/sup 13/C scalar coupling between the methyl and carboxyl resonances was observed as well as a shift of 31 Hz in the center of both resonances arising from the homonuclear dipole Hamiltonian. It was also noted that when the spinning sideband patterns of the two coupled resonances overlap, carbon flip-flop transitions are facilitated, resulting in significant broadening of the spectral lines.
A Reliability-Based Adaptive IR-HARQ Scheme for Polar Coded Systems

In this paper, we propose a reliability-based adaptive incremental redundancy hybrid automatic repeat request (IR-HARQ) scheme for rate-compatible polar codes, where the size of retransmissions is determined by the current decoding reliability. Simulation results indicate that the proposed scheme can perform better than the existing schemes based on turbo codes, low-density parity-check (LDPC) codes and raptor codes in terms of both the throughput and latency. Furthermore, with the proposed scheme, rate-compatible polar codes can achieve the performance of about 0.5 dB away from the channel capacity with the information block length of 1024 bits.
Trajectory-based Badminton Shots Detection

Shot-by-shot match video segmentation is essential in video-based microscopic data annotation and collection for strategic analysis. With the help of deep learning vision technology, the shuttlecock trajectory can be depicted from broadcast video with accuracy around 78%. In this work, to develop automatic badminton match video labeling, we applied Artificial Neural Networks (ANNs) in the contest strategy data collection to speed up the labeling procedure. The proposed ANN was trained to detect badminton shot events based on shuttlecock trajectories in the contest video. Badminton shot events include serving, hitting, and dead ball. With the help of these shot events, the strategy analyst could annotate strategy information more efficiently and reduce labor costs significantly.
Design for Accelerated Testing of DC-Link Capacitors in Photovoltaic Inverters Based on Mission Profiles

The dc-link capacitor is considered as a weak component in photovoltaic (PV) inverter systems and its reliability needs to be evaluated and tested during the product development. Conventional reliability testing methods for capacitors are typically carried out under constant loading conditions, which do not reflect the real operating conditions (e.g., mission profile) of the dc-link capacitor in PV inverters. To address this issue, a new reliability testing concept for the dc-link capacitor in PV inverters is proposed in this article. In contrast to the conventional method, the proposed reliability testing method designs the test profile through the modification of the original mission profile (e.g., solar irradiance and ambient temperature) in order to maintain the test condition as close to the real application as possible. A certain acceleration factor is applied during the mission profile modification based on the lifetime model of the capacitor, in order to increase the thermal stress of the dc-link capacitor during test, and thereby effectively reduce the testing time.
Rotation Matrix Oriented Visual Inertial Online Initialization

In the monocular vision inertial system, an accurate real-time initialization state estimator is the prerequisite for the system to maintain steady operation. However, in the initialization of most current monocular visual inertial systems, the impact of rotation matrix on the other external parameters, such as gyroscope bias, accelerometer bias and velocity, is often ignored. Moreover, in global optimization, the keyframe constraints in the sliding window are not fully utilized, resulting in the continuous accumulation of estimated path errors. In order to solve the above problems, we propose our monocular visual inertial system based on the initialization state estimator with online external calibration and the relative pose based marginalization. In this system, the external parameters of the camera and IMU are calibrated during the initialization phase, the estimations of speed, gyroscope bias, accelerometer bias, scale factor and gravity are performed, and the relative pose optimization method is adopted to retain the constraint relation of keyframes to be marginalized in the optimization window and then input into the global optimization. Experiments show that our system can give real-time and accurate estimation in initialization, and can realize more stable and accurate pose estimations, even with continuous error accumulation.
Dual-durometer combination of vacuum cup for aerial grasping

In the application of a conventional vacuum cup, a large compression force is required to eliminate the gap between the end of the cup and the object surface. However, the large interaction force will be a challenge for micro flying robots to grasp the object in the air using the suction cup as a gripper. To address the problem, a dual-durometer combined vacuum cup for aerial grasping is proposed. The combined cup is composed of one outer hard cup and one inner soft cup. The soft cup is longer than the hard one so the initial contact phase is implemented by the soft cup. The required compression force to engage the soft cup is dramatically decreased. The hard cup will be guided and pressed by the adhesion force induced by the soft one. Therefore, a larger adhesion force can be provided by the outer hard cup. The comparative experiments of static error-tolerance and aerial grasping have been conducted. The results demonstrate that the required contact pressure is significantly reduced and the object can be grasped robustly.
MMC based SRM Drives for Hybrid EV with Decentralized BESS

Owing to the growing demand for pollution free energy in metropolitan transport, HEV’s (Hybrid electric vehicles) and EVs (Electric Vehicle) are gaining ample consideration due to their fuel efficient performance and no ecological damage due to the absence of harmful emissions. Thus countries all over the world are now paying increasing attention on the development of FV and HEV technology. HEVs (Hybrid Electric Vehicles) driven by SRM (switched reluctance motor) is supported by MMC (modular multilevel converter) has been proved to be a capable system by considering a hybrid vehicle system with decentralized battery energy storage system (BES S). In this drive, a SM (sub-module) is comprised of cell of battery and half-bridge converter and numerous such Sub-modules together form MMC. Adjustable discharging and charging functionality for every sub-module are acquired by scheming SMs switches. This topology is unmatched to the conventional SRM drives and is also very beneficial by offering several advantages. The functioning of the drive is effectively simulated in MATLAB and the performance is evaluated in the Generator Control Unit (GCU) with only driving case and GCU-Battery hybrid case. The battery cells are also investigated for analyzing fault tolerant ability in battery driving mode and GCU-battery hybrid mode.
Modeling a residential microgrid for energy management

This article proposes a discrete time model for the energy management of a residential microgrid. It consists on array of solar panels, a storage system (made up of a lithiumion battery bank) and a load profile in line to the one of a typical residence. It is considered that it can operate connected to the main electrical network. The way to determine the loads is indicated, as well as the way to obtain the power profile generated through simulation models and the modeling of batteries that make up the storage system. The goal of the work is to obtain a suitable model to implement Model Predictive Control (MPC) strategies in the management of microgrid energy resource.
An Integrated Design for Classification and Localization of Diabetic Foot Ulcer Based on CNN and YOLOv2-DFU Models

Diabetes is a chronic disease, if not treated in time may lead to many complications including diabetic foot ulcers (DFU). DFU is a dangerous disease, it needs regular treatment otherwise it may lead towards foot amputation. The DFU is classified into two categories such as infection (bacteria) and the ischaemia (inadequate supply of the blood). The DFU detection at an initial phase is a tough procedure. Therefore in this research work 16 layers convolutional neural network (CNN) for example 01 input, 03 convolutional, 03 batch-normalization, 01 average pooling, 01 skips convolutional, 03 ReLU, 01 add (element-wise addition of two inputs), fully connected, softmax and classification output layers for classification and YOLOv2-DFU for localization of infection/ischaemia models are proposed. In the classification phase, deep features are extracted and supplied to the number of classifiers such as KNN, DT, Ensemble, softmax, and NB to analyze the classification results for the selection of best classifiers. After the experimentation, we observed that DT and softmax achieved consistent results for the detection of ischaemia/infection in all performance metrics such as sensitivity, specificity, and accuracy as compared with other classifiers. In addition, after the classification, the Gradient-weighted class activation mapping (Grad-Cam) model is used to visualize the high-level features of the infected region for better understanding. The classified images are passed to the YOLOv2-DFU network for infected region localization. The Shuffle network is utilized as a mainstay of the YOLOv2 model in which bottleneck extracted features through ReLU node-199 layer and passed to the YOLOv2 model. The proposed method is validated on the newly developed DFU-Part (B) dataset and the results are compared with the latest published work using the same dataset.
A night-time outdoor data set for low-light enhancement

Low light Enhancement has been a hot topic in recent years, and many deep neural network (DNN)-based methods have achieved remarkable performance. However, the rapid development of DNNs also raises the urgent requirement of high-quality training sets, especially supervised night-time data sets. In this paper, we establish a night-time outdoor data set (NOD 1) that contains 1214 groups of images. We also generate appropriate and high-quality reference images for each group based on multi-exposure fusion strategy, which not only focuses on dark areas but also provides details for over-exposed areas in low light images. Furthermore, a simple but efficient network is presented as the baseline of NOD. Experimental results on NOD and other data sets show the generalizability and effectiveness of the proposed data set and baseline model.
PSO premised Power Tracking Driver under the Sectional Shielding Effect

Renewable energy sources play a vital role to satisfy the emerging power demand. Among all the renewable energy sources, PV will become much popular as it has its own advantages when compared to other sources. The major aspect in which all the researchers are focusing in PV is extracting maximum power from a PV module. There are various MPPT techniques proposed by the researchers. This paper has proposed the tracking on maximum power of a PV module with the help of particle swarm optimization technique. The proposed system was simulated using MATLAB/SIMULINK software and compared the results of PSO MPPT with other existing MPPT techniques.
A Design and Analysis Method for Estimator-based Multiple Model Adaptive Control

This paper aims to present a fault-tolerant control architecture based on the Multiple Model Adaptive Control (MMAC). The approach decouples the development of a bank of local controllers together with the estimation process consisting of a bank of estimators from the switching logic. A major contribution is the presentation of a promising framework based on µ-analysis for the stability analysis of the global control law. This analysis technique provides under certain assumptions, a domain of candidate controller combinations where stability robustness is guaranteed. Afterwards, the paper focuses on supervisory unit tuning. A key proposal is to use Luenberger observers and link their designs using Linear Matrix Inequality (LMI) with an online validity computation following a Bayesian law. A systematic strategy involving stability analysis through characteristic criterion to be minimized is described. The viability of the design methodology is illustrated for the inverted pendulum on a cart subject to parametric variations. Results demonstrate the successful stability and performance of the proposed Estimator-based MMAC.
Bidirectional Soft Switched LCLC based Solid State Transformer for Smart Grid Infrastructure

Since the last decade day by day, there is a revolution in an existing power grid network due to an increase in demand for the convenient electric power supply required for the smart component in the power grid network. So this is a time for the power grid to become smart in accordance with all components which are actively participated in his network which is nothing but Smart Grid network. A different component in a Smart grid network required variable AC/DC power supply at the different amplitudes and different frequencies. Power electronics converters are required to meet all these requirements. Solid State Transformer (SST) is one of the key components in a smart grid network. This paper presents the Bidirectional Soft Switched Solid-State Transformer for a Smart Grid Network. A proposed circuit presents in this paper used a LCLC type resonant network to achieved soft switching action. An analysis of the resonant network and it's effect on the performance indices are presents in this paper. A front end bidirectional boost rectifier is used in a proposed circuit to achieved bidirectional power flow feature and desired voltage gain at the output. A proposed circuit is designed for power level 10KW and is simulated in a PSIM/Simulink co-environment. The results were obtained at an input voltage of Three-phase 440V, 50 Hz, and provide the output of 800V, 12.5A and is verified for desired features in the proposed converter.
Transmission Contingency Analysis Based on Data-Driven Equivalencing of Radial Distribution Networks Considering Uncertainties

A transmission contingency analysis (TCA) method based on data-driven equivalencing of radial DN distribution networks is proposed. First, an offline-online-combined data-driven model training method is proposed. The historical data are exploited during offline model training considering the uncertainties of loads and distributed generations to achieve partially prepared root nodal power injection functions, where root nodal voltage magnitudes are taken as arguments. After that, the real-time data of loads and DGs are used to determine all coefficients in these functions. In the proposed TCA, DNs will be equivalent to simplified models by distribution system operators (DSOs) with the data-driven method and the models will be sent to the transmission system operator (TSO). Then, TSO can complete the TCA independently. Numerical experiments show that the proposed TCA approach has similar accuracy and higher efficiency compared with the traditional global-power-flow-based TCA approach. It not only significantly reduces communication time between TSO and DSOs, but also saves calculation times, which may benefit real practice in the coordination operation of transmission-distribution-coupled systems in the future.
Music Genre Classification using Machine Learning

The music industry has undergone major changes from its conventional existence and also in the form of music created in last few years. The ever-growing customer base has also increased the market for different music styles. Music not only bring the individuals together, but also provides insight for various cultures. Therefore, it is essential to classify the music according to the genres to satisfy the needs of the people categorically. The manual ranking of music is a repetitive, lengthy task and the duty lies with the listener. The proposed research work has compared few classification models and established a new model for CNN, which is better than previously proposed models. This research work has trained and compared the proposed models on GTZAN dataset, where most of the models were audio file trains, while a few of the models were trained on the spectrogram.
A Review on Techniques, Characteristics and approaches of an intelligent tutoring Chatbot system

Today is the age of Advancement in the various types of machines or devices. And due to this advance. Attribute the difference between human and machine decrease. And the technology, of the computer-based learning system, are integrating more towards Advance tools and technologies like (A.I, N.L.P.)”, such a system is known as an intelligent-tutoring system or conversation system, like a chatbot. It is necessary that with rapidly changing technology, teaching and learning methods of the education system should adapt to increase and enhance the capabilities of teachers and abilities of the students. Therefore, the intelligent tutoring system (Chatbot) using natural language which allows humans to interact with a computer is one of the best alternatives of these requirements where the human tutor is replacing with a virtual tutor. Nowadays, an intelligent tutoring system(Chatbots) are in high demands in our various fields like teaching and learning in education, business, e-comm, online trading, public health care, and sports etc. Different techniques and processes are involved in designing and developing chatbots. This review paper aims to take a survey of the critical and necessary methods used in this communication and conversational agents.
Random Number Generator Based on Metastabilities of Ring Oscillators and Irregular Sampling

This paper presents a new random number generator based on irregular sampling of regular waveform method. Wakeup uncertainty and shut-down metastabilities are used as additional entropy source to the timing jitter of the ring oscillators. To be able to change the stability of the rings, multiplexer and inverter are added to each ring. 34 ring oscillators are combined with an XOR gate to generate an irregular waveform that is used as the sampling clock. The select input of the multiplexers are controlled with a control unit. A signal that causes stability change with 2MHz frequency is routed from controller to the select inputs of the multiplexers and the XORed signal is masked with 2MHz, and 10ns width pulses. To reduce the speed of sampling, the irregular signal is downsampled that results in a final throughput of 13kbps. All of the circuits mentioned in this paper is implemented in Xilinx Zynq7000 FPGA. Furthermore, NIST 800–22 test suite is used to test the designed RNGs. Data sets of one million bits are collected with a UART module. All the NIST 800–22 test suite requirements are satisfied with the new proposed RNG.
Design of a Flexible End-Effector for Logistics Sorting Robot

In this paper we propose an end-effector for goods sorting robot in the e-commerce logistics warehouse, which is used to enable the sorting robot to flexibly grasp a variety of logistics goods. Taking advantages of the multi-finger gripper and the suction tool, this paper provides a scheme combining a vacuum suction tool and a three-finger gripper. In order to measure the utility of the flexible end-effector, a picking experiment is performed. The result proves that the end-effector meets its requirements.
A 0.8-6GHz Wideband Mixer with Noise Cancellation for Low Power Applications

This paper proposes an active mixer based on a common-gate noise canceling architecture implemented in 28nm CMOS technology. The design combines some circuit techniques and explores sub-threshold bias operation to improve the power efficiency. Dedicated to wideband applications, the mixer achieves a 800MHz to 6GHz bandwidth. In nominal mode the circuit consumes 4mW from 1.2V. It achieves a conversion gain of 15dB and a single side band noise figure (NFssb) of 6dB. In power saving mode the power consumption is reduced to 2mW, the conversion gain is 11dB and the NFssb is 7.2dB. For the two modes of operation, the IIP3 is respectively −1dBm and +0.5dBm.
The Morphological Characteristics and Physical Properties of Porous Corn Starch Hydrolyzed by Mixture of α-Amylase and Glucoamylase

This research was conducted to evaluate the effect of $\alpha$-amylase and glucoamylase mixture at various hydrolysis time (0,4,6,8 and 12 hours) to morphological characteristics and physical properties of porous corn starch granules. Scanning Electron Microscopy (SEM) showed the number and size of holes on the surface of starch granules increased with hydrolysis time; moreover, starch granule structure was broken at 8 and 12 hours. The hydrolyzed level of starch granules was increased gradually with reaction time; the highest hydrolysis value was 63.534% in 12 hours of reaction. The results indicated that porous starch had higher adsorption capacity than native starch which was showed in higher oil and water holding ability. Additionally, swelling and solubility of porous corn starch were increased with the prolonging of hydrolysis time and higher than that of native starch samples. Color value tended to increase with increasing hydrolysis time; however, the difference was very small. The results also indicated that viscosity and transmittance of the hydrolyzed starch sample decreased as the hydrolysis time increased as well as lower than native starch. Thus, porous corn starch could be a potential ingredient in the food field.
Global chemistry-climate modeling and evaluation

IGAC/SPARC Workshop on Global Chemistry-Climate Modeling and Evaluation; Davos, Switzerland, 21–24 May 2012 The study of chemistry-climate interactions represents an important focus of global change research. The interacting factors relating atmospheric chemistry and climate strongly couple the emerging issues of climate, stratospheric ozone depletion, and air quality, from both scientific and policy perspectives. Understanding how the chemistry and composition of the atmosphere may change over the 21st century is essential in preparing adaptive responses or establishing mitigation strategies. A changing atmosphere not only drives climate change but also directly impacts human health, agricultural productivity, and natural ecosystems. Provision of high-quality, policy-relevant information on the current state of climate and its possible future states, as well as options for mitigation, control, change, and adaptation, are thus strongly dependent on progress in modeling.
Effect of Temperature on Creeping Flashover Voltage of Nanofilled Oil-Pressboard Insulation

Creeping flashover (surface discharge) at oil-pressboard interface is considered a serious failure of the insulation system in power transformers. In this paper, creeping flashover voltage at oil-pressboard interface using the concept of nanofluids is experimentally evaluated. The creeping flashover test is carried out using a needle-plate electrode configuration considering 5 mm gap spacing. Three different types of nanofiller (CuO, MgO and ZnO) with different concentration levels (0.05, 0.1, 0.2 g/l) are used for enhancing oil-pressboard creeping flashover voltage. The effect of temperature on creeping flashover voltage is experimentally studied for all nanofiller types at the adopted concentration levels. The considered temperatures are room temperature (30°C), 50 °C, 80 °C and 120 °C. These temperatures are considered to study their effect on creeping flashover voltage taking into consideration a wide range of transformer loading. Average of ten creeping flashover voltages is investigated for all studied conditions. Also, Weibull distribution is used for analyzing the creeping flashover voltages at 10% and 50% probabilities for the studied conditions. Finally, interpretations of the obtained results are presented through a proposed mechanism.