Miracle-dz/UniFAR-FTUs huggingface dataset:
Preparation and Characterization of a Thermal Insulating Carbon Xerogel-Epoxy Composite Adhesive for Electronics Applications

In some specific microelectronic applications, there is a demand for adhesive materials (glues/encapsulants) having very good thermal insulation properties. We have studied the properties obtained on epoxy-based samples loaded by several levels of xerogel filling. The thermal conductivity of these materials depends on the exact chemical composition and their manufacturing process. As a result, the thermal conductivity given in the manufacturer’s specifications or in the literature generally exhibits some dispersion. Therefore, we have developed a straightforward experimental rig, based on a vacuum chamber and miniature Pt100 sensors measurements, to precisely know the thermal conductivity of the various insulating materials used in the development and the assembly of select microsensors and microsystems. The results showed a decrease in the thermal conductivity of our xerogel-epoxy composites up to 35.8% compared with the value of the unfilled epoxy, corresponding to a thermal conductivity value of 107.9 mW m−1 K−1. In addition, cross-sectional images were observed by optical microscopy to characterize the specific microstructure of each sample to relate these observations to improvements in thermal conductivity.
Significance of Capacitor Voltage Balancing in Multilevel Inverter

In the recent trends, the use of multilevel inverter is rapidly increasing due to its good performance compared to conventional inverter. In this paper, the performance of Nested Neutral Point Clamped Multilevel inverter (NNPC) is presented. NNPC is a Hybrid type of Multilevel inerter which is nothing but the combination of two classical topologies that are Neutral Point Clamped(NPC) and Flying Capacitor(FC). As this Hybrid inverter involves Capacitor in its configuration therefore, their is an issue of Capacitor Voltage Balancing(CVB). Hence the necessary condition arises that is to maintain Vdc/3 voltage across Flying capacitors inorder to obtain perfect four level output[8]. Hence the performance of Nested Neutral Point Clamped Multilevel Inverter is observed in MATLAB Simulink by using simple pulse triggering without Capacitor Voltage Balance control and its simulation is also done with capacitor voltage balancing control, further its performance is also analysed by implementing it with level-shift Sinusoidal Pulse Width Modulation technique(Phase-Disposition) with capacitor voltage balancing control technique in MATLAB simulink.
A New Three Parameter Lifetime Model: The Complementary Poisson Generalized Half Logistic Distribution

We propose a new model with flexible failure rate called complementary Poisson generalized half logistic (CPGHL). Various properties of the model are explored and examine numerically such as the explicit expressions of the moments, mean deviations, Bonferroni and Lorenz curves, Shannon and Renyi entropy. The distribution of mixture of two CPGHL and some related models based on the log-transform of CPGHL are discussed. The asymptotic of moments of residual life and asymptotic distribution of order statistics are obtained. The characterization of Poisson half logistic (PHL) by truncated moments of a certain function of a random variable is discussed. Estimation of the model parameters was approached by maximum likelihood, least square, and percentile methods. Further, the estimation by maximum likelihood for right censored data of the model were considered. The proposed estimation techniques were assessed by simulation studies. Three data applications are provided one of them is a censored data to demonstrate how the new model outperforms some other existing distribution in practice.
Design and analysis of single switch dual output DC-DC SEPIC converter for PV applications

This paper deals with multiplier SEPIC converter with photovoltaic panel as the source. The multiplier SEPIC can achieve high voltage gain with devoid of extreme duty cycle and transformer. It experiences low voltage stress when compared with other buck boost converter. This is very important aspect for renewable energy sources. Further the voltage gain can be extended with the addition of diode and capacitor to the basic SEPIC structure. The basic SEPIC structure is known as 1x level. The converter works with 22V, 100W as input voltage and input power. The operating switching frequency is 20 kHz. This converter aims for regulating the output voltage to 48V by PI controller for different temperature and irradiation level. The multiplier SEPIC is simulated and the output is validate using MATLAB/SIMULINK.
Directional assistant for the visually impaired with data logger for anti-trafficking

Mobility is an important aspect of life, for day to day responsibilities, taking care of oneself, and for getting through life in general. While most people are blessed with the ability to do so, a few unfortunate ones face a lot of problems, and have to go through life while being assisted in some way or the other by people around them. The main inspiration behind this idea is to minimize the dependability of the visually impaired people, so as to help them move around a little bit easily than usual. This paper proposes a model and a system to navigate through unknown roads with safety and also provides an option of storing the user's location into a cloud for tracking purpose, which could also fight the problem of human trafficking.
Prediction of Total Phenolics and Flavonoids Contents in Chinese Wild Rice (Zizania latifolia) Using FT-NIR Spectroscopy

Total phenolics and flavonoids contents in Chinese wild rice were predicted using Near Infrared (NIR) spectroscopy as a rapid method. A Partial Least Square (PLS) algorithm was applied to perform the calibration. The models were calibrated by cross-validation and the chosen number of PLS factor was achieved according to the lowest Root Mean Square Error Cross-Validation (RMSECV) in calibration set. The correlation coefficient (R) and Root Mean Square of Error Prediction (RMSEP) in the test set were used as the evaluation parameters for the optimal model as follows: R = 0.985; RMSEP = 2.41 and the Residual Predictive Deviation (RPD) = 6.06 for total phenolics contents prediction by Multiplication Scatter Correction (MSC) model. For flavonoids contents prediction, R = 0.978, RMSEP = 1.23 and RPD = 4.81 by non preprocessing model. It could be concluded that NIR spectroscopy has significant potential in the nondestructive determination of total phenolics and flavonoids.
A Hybrid Lightweight Cryptographic Scheme For Securing Node Data Based On The Feistel Cipher And MD5 Hash Algorithm In A Local IoT Network

A cryptographic-based encryption algorithm that has low memory and minimal output space requirements as well as involving reduced computational overheads without compromising the strength and security of the encryption is desired for constrained devices in wireless sensor networks. Internet-of-Things (IoT) supports the automated collection and streaming of sensitive data across several other devices through the internet. The insecurity of the internet in supporting communication of critical data between nodes in IoT has been amplified due to the current data breaches and data privacy compromises on such networks in recent times. The paper addresses the challenge of data privacy and integrity breaches among IoT devices with the proposition of a methodology involving a thin cryptographic scheme that combines the Feistel cipher and the MD5 in providing improved security for IoT node data. The results showed an enhanced and fast encryption of data between IoT edge devices and the sink node.
An Analog Preconditioner for Solving Linear Systems

Over the past decade as Moore’s Law has slowed, the need for new forms of computation that can provide sustainable performance improvements has risen. A new method, called in situ computing, has shown great potential to accelerate matrix vector multiplication (MVM), an important kernel for a diverse range of applications from neural networks to scientific computing. Existing in situ accelerators for scientific computing, however, have a significant limitation: these accelerators provide no acceleration for preconditioning–a key bottleneck in linear solvers and in scientific computing workflows. This paper enables in situ acceleration for state-of-the-art linear solvers by demonstrating how to use a new in situ matrix inversion accelerator for analog preconditioning. As existing techniques that enable high precision and scalability for in situ MVM are inapplicable to in situ matrix inversion, new techniques to compensate for circuit non-idealities are proposed. Additionally, a new approach to bit slicing that enables splitting operands across multiple devices without external digital logic is proposed. For scalability, this paper demonstrates how in situ matrix inversion kernels can work in tandem with existing domain decomposition techniques to accelerate the solutions of arbitrarily large linear systems. The analog kernel can be directly integrated into existing preconditioning workflows, leveraging several well-optimized numerical linear algebra tools to improve the behavior of the circuit. The result is an analog preconditioner that is more effective (up to 50% fewer iterations) than the widely used incomplete LU factorization preconditioner, ILU(0), while also reducing the energy and execution time of each approximate solve operation by 1025x and 105x respectively.
An Optimal Dispatching Strategy for Charging and Discharging of Electric Vehicles Based on Cloud-Edge Collaboration

This paper proposes a decentralized scheduling method for electric vehicles charge and discharge management based on cloud-edge collaboration so as to protect users' privacy. Firstly, as a cloud computing center, distribution system operator solves an optimal power flow model based on second-order cone programming in order to minimize power costs. Secondly, as an edge computing unit, charging station solves an energy management model based on mixed-integer linear programming in order to track scheduling instructions of the distribution system operator. Finally, charging stations return benders cut constraints to distribution system operator to revise energy plan. And the scheduling instructions are updated iteratively to ensure the feasibility and optimality of the energy plan. The simulation is carried out in IEEE 33-bus test system. And the results show that the proposed cloud-edge collaborative strategy can reduce memory use, protect users' privacy as well as reducing power costs.
Distributed Charging Control of Electric Vehicles Considering Cooperative Factor in Photovoltaic Charging Station

This paper proposes a charging control of electric vehicles (EVs) considering an EV charging cooperative factor concept. This concept is included in the objective function of EV that aims to reduce its charging cost. This concept is defined as a factor on the basis of EV's parameters and requirements, namely capacity, state-of-charge, charging power rate, demanded energy and departure time. Each EV is assumed to have to two working states, i.e., non-cooperative and cooperative. The value of the charging cooperative factor is at the highest value when it is on the non-cooperative state. While the value of the charging cooperative factor is less that the maximum in the cooperative stage, and thus it will allow the related EV shifts charging time slots to others without deteriorating its own changing cost to support other in-need EVs to charge. This introduces the cooperative relationship between EVs, i.e., EVs may cooperatively help each other in reducing their charging costs. The EV charging problem is treated as a generalized Nash equilibrium problem and the solution is reached in a distributed way by utilizing the consensus network. The simulation results proof the workability and effectiveness of the proposed distributed charging control reducing the charging cost of EVs.
Improving Text Classification Using Knowledge in Labels

Various algorithms and models have been proposed to address text classification tasks; however, they rarely consider incorporating the additional knowledge hidden in class labels. We argue that hidden information in class labels leads to better classification accuracy. In this study, instead of encoding the labels into numerical values, we incorporated the knowledge in the labels into the original model without changing the model architecture. We combined the output of an original classification model with the relatedness calculated based on the embeddings of a sequence and a keyword set. A keyword set is a word set to represent knowledge in the labels. Usually, it is generated from the classes while it could also be customized by the users. The experimental results show that our proposed method achieved statistically significant improvements in text classification tasks. The source code and experimental details of this study can be found on Github11https://github.com/HeroadZ/KiL.
0.13 μm Low-Power CMOS Current Starved VCO for Vibration Energy Harvesters

This work presents a current starved voltage-controlled oscillator (VCO) based on the standard 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. Incorporated with the power management circuit (PMC), the current starved VCO generates an average periodic signal at a frequency of 84.81 kHz. In addition, to assure that the VCO has a stable periodic signal output regarding the varied control voltage, a voltage reference is realized. The proposed architecture makes full use of subthreshold and deep triode MOSFETs without any other components. A reference voltage of 258.35 mV is obtained in response to the supply voltage 1.0–3.2 V. The architecture exhibits a linear sensitivity of 0.49%/V at 27 °C. Power consumption of the proposed circuit is <inline-formula> <tex-math notation="LaTeX">$3.546~\mu \text{W}$ </tex-math></inline-formula> at 3 V of the voltage supply. The output of the architecture leads to a time-control local oscillator, which can be employed in a PMC for vibration energy harvesters.
Short-term Load Combination Forecasting Model Based on Causality Mining of Influencing Factors

With the continuous development of the reform of the power market system, the operation of the power system is becoming more flexible and uncertain, and the traditional load forecasting method is difficult to cope with more influencing factors and stronger randomness. To solve this problem, a short-term load combination prediction model based on causal relationship mining of influencing factors is proposed in this paper. Firstly, the historical load series is decomposed into three components by using Optimal Variational Mode Decomposition (OVMD). Then, the Granger causality algorithm is used to mine the influencing factors closely related to each wave type load. Finally, a short-term load combination prediction model based on causality mining is established. Simulation results show that the proposed short-term load forecasting method can significantly improve the accuracy of short-term load forecasting.
Grasping and Object Reorientation for Autonomous Construction of Stone Structures

Building large and stable structures from highly irregular stones is among the most challenging construction tasks with excavators. In this letter, we present a method for grasp planning and object manipulation that enables the world's first autonomous assembly of a large-scale stone wall with an unmanned hydraulic excavator system. Our method utilizes point clouds and mesh surface reconstruction of stones in order to plan grasp configurations with a 2-jaw gripper mounted on the excavator. Besides considering the geometry of the stone to sample force closure grasps, the grasp planner also takes into account collision constraints during object pick and place, informed by a LiDAR based point cloud map. Furthermore, we show an approach to reorient arbitrarily shaped objects that are not feasible to be directly placed at the desired location without violating collision constraints. Using a physics engine, we find a settled intermediate pose that allows direct placement and is reachable from the initial stone pose. The applicability of the proposed grasp planning method is demonstrated with the construction of a dry stone wall composed of over one hundred boulders using an autonomous excavator. We show a high primary grasp success rate (82.2 $\%$) and illustrate how the system recovers from slippage by relocating the object and re-planning the grasp correspondingly.
Design of Coaxial Fed Synchronous Microstrip Patch Antenna Array with Circular Polarization

The paper deals with a method for designing a patch antenna with circular polarization to be resonant at 9.5GHz (X-band) for RADAR applications. It aims at overcoming the drawbacks of a single patch antenna. The existing single patch antenna resulted in a gain of 6dB. To improve the gain further, the proposed model consists of a synchronous array of 4 linear elements designed to produce circular polarization at the desired frequency. The simulation of the array antenna resulted in a gain of 8dB with an axial ratio of 2.9dB. The return loss of the patch antenna was found to be −20dB at 9.5GHz.
Removing Artifacts and Optimal Features Extraction from EMG Envelope

In bioengineering, a biomedical signal features analysis often requires a calculation of the signal envelope to identify the human body behaviour and force effort. However, known methods of the envelope acquisition bring along some unwanted artifacts that complicate information processing and practical use in robotics and control systems. In this paper, we present the Kalman filter (KF), $H_{\infty}$ filter, and unbiased finite impulse response (UFIR) filtering algorithms modified to remove unwanted information from the electromyography (EMG) signal envelope. The modifications are provided assuming colored measurement noise and the algorithms. Extensive experimental investigations have been carried out for diverse EMG signal databases to show better envelope features as tested by several standard metrics.
A Reconfigurable Non-Uniform Power-Combining V-Band PA With +17.9 dBm Psat and 26.5% PAE in 16-nm FinFET CMOS

This article presents the design of a dual-mode V-band power amplifier (PA) that enhances the efficiency at power back-off (PBO) using load modulation. The PA utilizes a reconfigurable two-/four-way power combiner to enable two discrete modes of operation–full power and back-off power. The power combiner employs two techniques to further improve the PA efficiency at PBO: 1) usage of transformers with non-uniform turns ratios to reduce the difference in impedance presented to the PA cores between the two modes and 2) utilize a proposed switching scheme to eliminate the leakage inductance associated with the disabled path in back-off power mode (BPM). The two-stage PA achieves a peak gain of 21.4 dB with a fractional BW (fBW) of 22.6% (51–64 GHz). At 65 GHz, the PA has a Psat of +17.9 dBm with an OP1 dB of +13.5 dBm and a peak power added efficiency (PAE) of 26.5% in full-power mode. In BPM, the measured Psat, OP1 dB, and peak PAE are +13.8 dBm, +9.6 dBm, and 18.4%, respectively. The PAE is enhanced by 6% points at a 4.5-dB back-off. The PA is capable of amplifying a 6 Gb/s 16-QAM modulated signal with an EVMrms of −20.7 dB at an average Pout/PAE of +13 dBm/13.6%, respectively. This PA was implemented in 16-nm FinFET, occupies a core area of 0.107 mm2, and operates under a 0.95-V supply.
Multisensor Data Fusion for Improved Segmentation of Individual Tree Crowns in Dense Tropical Forests

Automatic tree crown segmentation from remote sensing data is especially challenging in dense, diverse, and multilayered tropical forest canopies, and tracking mortality by this approach is even more difficult. Here, we examine the potential for combining airborne laser scanning (ALS) with multispectral and hyperspectral data to improve the accuracy of tree crown segmentation at a study site in French Guiana. We combined an ALS point cloud clustering method with a spectral deep learning model to achieve 83% accuracy at recognizing manually segmented reference crowns (with congruence >0.5). This method outperformed a two-step process that involved clustering the ALS point cloud and then using the logistic regression of hyperspectral distances to correct oversegmentation. We used this approach to map tree mortality from repeat surveys and show that the number of crowns identified in the first that intersected with height loss clusters was a good estimator of the number of dead trees in these areas. Our results demonstrate that multisensor data fusion improves the automatic segmentation of individual tree crowns and presents a promising avenue to study forest demography with repeated remote sensing acquisitions.
Terahertz Sensor With Resonance Enhancement Based on Square Split-Ring Resonators

A novel design of a nearly perfect metamaterial absorber based on square split-ring resonators for terahertz sensing applications is proposed and analyzed. The design in this report is simulated and analyzed by using standard numerical simulation software. Magnetic and electric resonant field enhancement in the impedance matched absorber cavity enables stronger interaction with the dielectric analyte. The proposed structure is based on the simultaneous increase in the electromagnetic field and the surface current distribution at the resonance frequency. An absorptivity of 99% is achieved at 0.53 THz with a narrow resonance peak and a Q-factor of 44.17. At a fixed analyte thickness, the resonance frequency is sensitive to the refractive index of the surrounding medium. The influence of the thickness of the covering sample on the sensitivity and absorption coefficient of the absorber is comprehensively analyzed, and the reported design can be used as a refractive index sensor with a high sensitivity of 126.0 GHz/RIU and a figure of merit of 10.5 in the refractive index range from 1.0 to 2.0 at an analyte thickness of <inline-formula> <tex-math notation="LaTeX">$15.0~\mu \text{m}$ </tex-math></inline-formula>. The results show that the sensor has high sensitivity to the analyte covering it. The sensor not only exhibits good sensitivity to thin analytes but also shows high sensitivity to analytes more than <inline-formula> <tex-math notation="LaTeX">$10~\mu \text{m}$ </tex-math></inline-formula> thick in the terahertz low frequency band. Specifically, the sensitivity changes rapidly when the thickness of the sample changes in the range of 0-<inline-formula> <tex-math notation="LaTeX">$6~\mu \text{m}$ </tex-math></inline-formula>, but slowly in the range of 6-<inline-formula> <tex-math notation="LaTeX">$16~\mu \text{m}$ </tex-math></inline-formula>. In general, the response of the resonance frequency to changes in the refractive index of the sample becomes more sensitive as the thickness of the sample is increased from 0 to <inline-formula> <tex-math notation="LaTeX">$16~\mu \text{m}$ </tex-math></inline-formula>. The reported terahertz sensor of a metamaterial absorber has potential applications in biomedical sensing and trace detection of substances.