Miracle-dz/UniFAR-FTUs huggingface dataset:
An economics analysis of the Sheep Flock Effect in China's real estate market and stock market

Based on the comparative analysis of the Sheep Flock Effect in China's real estate market and stock market,this paper tries to explore the causes of psychology from the perspective of Behavioral Economics,and points out that public expectation is the fundamental factor that causes the Sheep Flock Effect in the two markets;at the same time,it makes a balanced analysis of the stability of the conditions with the aid of the differential equation model.According to the present situations of China's real estate market and stock market,policy suggestions are presented for making good use of public expectation and stabilizing the markets.
Running license plate recognition (LPR) algorithms on smart survillance cameras. A feasibility analysis

In many of cities, video surveillance is employed to prevent crime, having hundreds of cameras and sensors monitoring activities during the whole day. Due to the huge amount of video information generated in real time, these surveillance centers are requiring more technology and intelligence to support human operators in many complex situations. There are important analyses that could be realized with this video-data: from criminalistics event detection to particular object recognition. One important tool is License Plate Recognition(LPR) that helps detecting vehicles, that could have been robbed. Although corporative solutions exist, these techniques require a lot of processing power and special located cameras, that not always could be afford by the local government. In this context, the proposed project is based on applying open-source LPR algorithms that runs on already existent surveillance cameras. These cameras are observing a complete scene (not just a line as it is commonly used), so LPR algorithms are rather slow, processing only 1 image per second. For this reason, the objective is to improve the performance combining parallel LPR processing and object tracking algorithms. This work describes the ongoing implementation, the techniques currently used for object tracking and LPR implementation, and exposes results regarding the efficiency of the solution.
GPU-based empirical evaluation of activation functions in convolutional neural networks

Activation functions are important components of Convolutional Neural Networks (CNN) that introduces nonlinearity in the model to compute complex functions. There are different types of activation functions used with CNNs in different applications, however it turns out that an effective activation function yields better results and improves performance of the model. In this study four of the widely used activation functions are chosen to analyze and evaluate to figure out their efficiency in terms of the model's accuracy. Sigmoid, hyperbolic tangent, rectified linear unit (ReLU) and exponential linear unit (ELU) activation functions have been used with most of the successful models. A CNN model has been implemented on the MNIST dataset to perform the analysis task. The experiments have been performed on Nvidia GPU 940MX to accelerate the training and testing of the CNN model. It has been observed that ReLU the most popular activation function performs better than sigmoid and tanh and a recent activation function ELU performs better than ReLU.
Real time implementation for harmonics and reactive power compensation

Presence of harmonics in the power grid is a major power quality problem. Various techniques are available to compensate both current and voltage harmonics present in the supply. Conventional schemes are based on filtering technique where fundamental current component is separated from distorted load current. This paper deals with simulation and implementation of Synchronous Reference Frame theory based control scheme for shunt active power filter utilized to compensate for the harmonics drawn by a typical nonlinear load. Simulation is carried out in MATLAB/SIMULINK and hardware implementation is done using dSPACE. Results are presented and discussed to validate the performance of the algorithm in real time.
A containerized simulation platform for robot learning peg-in-hole task

In this paper, we build a simulation platform for robot learning peg-in-hole(PiH) task for to study the strategy of inserting the pegs with different geometry features in PiH task with tele-operation. PiH task as a typical assembly task in the industrial field has been widely researched. Though many researches proposed some general solution for PiH, most of them only rely on accurate force control can be achieved or the environment is structured. In the unstructured environment, it is still a huge challenge. And different sizes and shapes of pegs will significantly increase the difficulty of operation even human-in-loop method because of force and torque introduced from the contact environment and uncertainty from vision, many previous strategies cannot be adapted to these situations. Recently, machine learning method has been achieved many successful applications on robotics which can adapt on different situations with many uncertainties, but making robots learning in the real world still needs more setup, and it also may destroy the robots. Our simulation platform which based on state of art ROS and Gazebo and shipped with Docker and Weave virtual network provides a reproducible and easily deployable platform for robot learning the PiH task. And we also include a tele-operation method for the human operator to tele-operate the simulation robot with force feedback during the peg is approaching to the hole which will enable robot learning trajectory execution from human demonstrations.
Flexible Tri-Axis Tactile Sensor Using Spiral Inductor and Magnetorheological Elastomer

This paper describes a flexible and soft tactile sensor that measures the tri-axis force based on inductance measurement. The proposed sensor has four spiral inductors printed on a flexible circuit board and a mounted cylindrical elastomer (silicon rubber). A disk-shaped magnetorheological elastomer (ferromagnetic marker) is embedded in the cylindrical elastomer and its 3-D displacement is estimated by monitoring the inductance changes of the four inductors. In this paper, we investigated the relationship between the applied tri-axis force and inductance changes. Our results can be summarized as follows: 1) the inductance changes of the four inductors were monotonic and linear against the applied normal and shear force; 2) the applied tri-axis force could be estimated well with linear functions of the sum and difference of the measured inductances; and 3) the estimation error of the tri-axis force increased when a larger force was applied and/or faster contact speeds were used.
Torque Ripple Reduction in Five-Phase IPM Motors by Lowering Interactional MMF

This paper proposes a torque ripple reduction method for a five-phase interior permanent magnet (PM) motor by shifting asymmetrical rotor poles. The key of this method is to consider the PM torque ripple, reluctance torque ripple, and cogging torque simultaneously from two aspects. First, the main interactional harmonic, contributing to PM torque ripple, between stator magnetic motive force (MMF) and rotor MMF is developed. Then, the appropriate combination of unequal pole arcs is adopted to eliminate the main interactional harmonic. Second, the asymmetrical rotor pole-pair is selected as a repeating unit which produces cogging torque and reluctance torque with consistency in waveforms and phases. Then, three methods for shifting asymmetrical rotor pole-pair are proposed showing that the torque ripple can be reduced greatly and the lower-order harmonics of radial force is acceptable when the shifting angle of the repeating unit is 4.5 mechanical degrees. Through theoretical analysis and experimental test, it is verified that the proposed motor can offer lower torque ripple even under deep flux-weakening region.
An X-band class J GaN amplifier for radar remote sensing using >50 VDC FETs

A novel GaN technology for high power and high efficiency amplifiers operating in X-band is presented, which is ideally suited for radars used in remote sensing applications. Unlike state-of-the-art GaN-SiC technology operating at 28–40 V, the proposed devices operate at > 50 V bias to achieve high power density while still achieving relatively high efficiency. A prototype GaN-SiC device of 9.6 mm gate periphery and 3.2 pF output capacitance achieves > 80 W output power (> 8 W/mm or 25 W/pF) at 7.9 GHz with a radar pulse width of 35 μs and a duty cycle of 30%. Operating bias on the reported results ranges from 65 to 75 V. Higher voltage operation has the advantage of achieving higher power density and lower capacitance/Watt, but requires some form of circuit enhancing technique to maintain high efficiency. Several efficiency enhancing techniques have been successfully utilized at lower frequency in UHF, L and S-bands; these techniques operate the amplifier in class E, F or inverse F and require proper multi-harmonic tuning. At high frequency in upper C-band and X-band these approaches tend to become less effective. Therefore, in this paper efficiency enhancing techniques based on reactive load impedance at the fundamental as used in class J amplifiers is explored. The work is part of an SBIR Phase I award from Jet Propulsion Laboratory / NASA to Integra Technologies, Inc. with the objective of designing and building a 400 W power amplifier in X-band with 30% duty cycle and >40% power added efficiency (PAE) for space exploration.
Invariance With Dynamic Multipliers

This paper focusses on the robust or absolute stability analysis of feedback interconnections consisting of a linear time-invariant system and an uncertain linear or nonlinear component. Building on the framework of integral quadratic constraints, we establish a novel local stability result using general dynamic multipliers that enables the verification of hard time-domain state and output constraints. This is achieved by merging frequency-domain descriptions of uncertainties with time-domain dissipation arguments as they typically arise in Lyapunov theory. The general applicability of the results provided in this paper is illustrated by means of several examples that demonstrate the effectiveness of the presented approach, even if compared to techniques that are tailored to particular uncertainty classes.
pseudoEye — Mobility assistance for visually impaired using image recognition

Development in any sector throughout ages has been for the betterment of the human lives. While necessity has always leaded to wonderful creations, mankind has worked towards catering to the needs of people from various walks of life. Vision is one of the primary senses that assist greatly in carrying out everyday chores. Those with visual disabilities rely on their other senses greatly for performing various tasks. This paper presents the research carried out by us to develop a wearable device that will identify the texts in the surroundings of the wearer and convert it to voice message which will be given as feedback. We believe that it will reduce the dependence of blind people on others especially during commutation. The system consists of a small camera, a push button and a SoC(System on Chip) attached to a cap. The SoC is powered using a portable power bank. The camera captures the image, which is processed to extract the text present and converts it into voice signal that is transferred to the user. The setup is initiated on the press of a button, and helps the user make decisions regarding the directions to proceed and henceforth.
Orientation Dependence of Plasmonically Enhanced Spontaneous Emission

We computationally explore how the orientation of dipolar emitters placed near plasmonic nanostructures affects their radiative enhancement and spontaneous emission rate. We demonstrate that the expressions for these quantities show a subtle dependence on the molecular orientation, and this information is lost when typical calculations assume a random orientation and perform an average over all directions. This orientation dependence is strongly affected by the location of the emitter, the emission wavelength, and the symmetry of the system. While the plasmonic nanostructure can significantly modify the far-field from a molecule in its vicinity, this modification is heavily dependent on both the wavelength and the orientation of the emitter. We show that if a fluorescent molecule can be constrained to emit in a specific direction, we are able to obtain far superior control over its spontaneous emission and decay rate than otherwise and discuss implications for single molecule experiments.
Impact of power quality on voltage stability assessment in power systems using RTDS

The main objective of this work is to provide a voltage stability analysis using synchronized measurements in power transmission systems, considering the impact of power quality problems such as harmonic distortion and frequency variations on the determination of the maximum loading point. RTDS-supported hardware-in-the-loop simulations are conducted using a commercial PMU (Alstom/Reason) and a 6-bus test system is used for the computational tests.
Quality factors and their impact on the business performance in the ecuadorian manufacturing sector-Loja

This article analyzes the manufacturing industry based on quality factors such as: leadership, communication, satisfaction of needs, stimuli and sanctions, institutional horizon, continuous improvement and its relation with profitability in micro, small, medium enterprises (MSMEs) of Loja-Ecuador. Profits on assets (ROA) and profitability on assets (ROE) are used to measure profitability. The method used is descriptive and the Kruskal Wallis H test was applied. The sample consisted of 24 MSMEs from the manufacturing sector. The results show that there are no quality factors in the profitability of the MSMEs.
Fault detection for a switched battery system via constrained nonlinear state estimation

Accurate Health Usage and Monitoring System (HUMS) for military vehicle batteries is an important enabler for ensuring mission effectiveness and optimising vehicle Through Life Support (TLS). This paper reviews a switched battery system that is typical of some military vehicles and develops two approaches for constructing a diagnostic observer to evaluate battery health; a constrained Extended Kalman Filter (EKF) approach and a Moving Horizon Estimator (MHE) approach. These two approaches are implemented and evaluated against data from a test bench emulating a military vehicle battery system. Both approaches were successful in detecting faults in the dataset, with the performance of both the MHE and EKF near identical. The MHE was capable of having a tighter tolerance on the residual and therefore is more sensitive to faults; However, this was not evident in our results due to the severity of our degraded batteries not exploiting this difference in sensitivity. The MHE approach provides a more natural way to describe the Fault Detection and Isolation (FDI) system, but the EKF performs sufficiently and is both computationally simpler to implement and execute. It was also noted that faulty batteries could only be detected so long as there was an adequate stimulus applied to the system. As such the development of a probing signal to stimulate the system in periods of low stimulus would provide regular assessments of battery health.
Design of reconfigurable robot

Reconfigurable robot is solution for unknown environment with various obstacles. Also energy consumption is better, when suitable locomotion mode is selected. Proposed service robot is able to locomotes via using of wheels or legs. The design of such structure has several advantage as better ability to cross the various types of terrain. The prototype of the designed robot will be used as educational tool.
Combined two phase page ranking algorithm for sequencing the web pages

Many existing page ranking algorithms are used in web mining to display the result in search engine result page. But these existing algorithms are either based on the inlinks and outlinks of the page or content of the page. It never consider the user interest on the page to calculate the page rank of that particular page. This leads to a need of web page ranking algorithm concerning content and usage of the pages. Proposed algorithm focuses on title, Meta, H1 and paragraph tags to find the similarity of the page. Tag Analyzer Algorithm is used to analyze these tags' content. Besides, it considers how long the user stays in that page to compute user interest score. TPPR technique computes the score in two phases. Based on the output of TPPR algorithm, the URLs are sequenced and displayed to the user. Event Explore technique detects whether the user is idle or active on the page. The proposed algorithm produced better performance and displays the most relevant web pages in the top of the result. From the results, “Two Phase Page Ranking” (TPPR) algorithm is better than PR algorithm of taken data set. TPPR algorithm can be used in web mining to improve the ranking system of search engine.
Sentiment analysis to predict election results using Python

Sentiment analysis is an evaluation of the opinion of the speaker, writer or other subject with regard to some topic. In US presidential election 2016, Donald Trump, Hillary Clinton and Bernie Sanders were among the top election candidates. The opinion of the public for a candidate will impact the potential leader of the country. Twitter is used to acquire a large diverse data set representing the current public opinions of the candidates. The collected tweets are analyzed using lexicon based approach to determine the sentiments of public. In this paper, we determine the polarity and subjectivity measures for the collected tweets that help in understanding the user opinion for a particular candidate. Further, a comparison is made among the candidates over the type of sentiment. Also, a word cloud is plotted representing most frequently appearing words in the tweets.
Incremental 3 D Line Segments Extraction for Surface Reconstruction from Semi-dense SLAM

Semi-dense SLAM systems have become popular in the last few years. They can produce much denser point clouds than sparse SLAM while being computationally efficient (using only CPU). In previous works, the surface of the viewed scene was reconstructed in real-time by combining sparse SLAM system and incremental surface reconstruction method. However, it is challenging to utilize the large scale point clouds of semi-dense SLAM for real-time surface reconstruction. In this thesis, in order to obtain meaningful surfaces and reduce the number of points used in surface reconstruction, we propose to simplify the point clouds generated by semi-dense SLAM using 3D line segments. Specifically, we present a novel incremental approach for real-time 3D line segments extraction. This approach reduces a 3D line segment fitting problem into two 2D line segment fitting problems, which take advantage of both image edge segments and depth maps. We first detect edge segments from keyframes. Then we search 3D line segments along the detected edge pixel chains by minimizing the fitting error on both image plane and depth plane. By incrementally clustering the detected line segments, the resulting 3D representation for the scene achieves a good balance between compactness and completeness. Our experimental results show that the 3D line segments generated by our method are highly accurate compared to other methods. With the reconstructed surfaces, we demonstrate that using the extracted 3D line segments greatly improves the quality of 3D surface compared to using the 3D points directly from SLAM systems. ii
Computationally effective spectral MUSIC algorithm for monostatic MIMO radar using real polynomial rooting

A computationally effective real-valued variant of multiple signal classification (MUSIC) algorithm for monostatic multiple-input multiple-output (MIMO) radar is presented. Reduced-dimension transformation is utilized to reduce the dimension of the received data matrix at first, and then the unitary transformation is employed to transform the complex covariance matrix of the received data into a real-valued one. To further reduce the computational complexity, a real polynomial rooting technique is presented to determine the local maxima of the MUSIC spectrum that corresponding to the DOAs of the targets instead of the computationally-expensive spectrum search. Simulations results demonstrate that the presented algorithm can greatly reduce the computational complexity without sacrificing the estimation accuracy.
The Impact of Regulatory Heterogeneity on Agri‐Food Trade

We estimate the impact of regulatory heterogeneity on agri-food trade using a gravity analysis that relies on detailed data on non-tariff measures (NTMs) collected by the NTM-Impact project. The data cover a broad range of import requirements for agricultural and food products for the EU and nine of its major trade partners. We find that trade is significantly reduced when importing countries have stricter maximum residue limits (MRLs) for plant products than exporting countries. For most other measures, due to their qualitative nature, we were unable to infer whether the importer has stricter standards relative to the exporter, and we do not find a robust relationship between these measures and trade. Our findings suggest that, at least for some import standards, harmonising regulations will increase trade. We also conclude that tariff reductions remain an effective means to increase trade even when NTMs abound.
Pancreatic cancer: treatment approaches and trends

Pancreatic cancer is one of the most challenging diseases due to its often late diagnose which results in limited therapeutic options and poor prognosis. To date, the only curative treatment is complete tumor removal surgery but only a few patients are eligible to do it. The median survival period after surgery followed by chemotherapy adjuvant treatment is about 2 years. Since its approval by the FDA, Gemcitabine has become the first-line chemotherapy agent for treatment of advanced pancreatic cancer. The FOLFIRINOX regimen is also used as a treatment scheme for pancreatic cancer; however, this regimen has resulted in small improvements in overall patient’s survival. It is appropriated to clarify that the FOLFIRINOX regimen can only be administered in patients with good performance status. Due to the absence of outstanding result after patient’s treatment with diverse chemotherapeutic agents combinations or unsuccessful administration of single-agent drugs to treat pancreatic cancer, the immunotherapy has become a new hope. A more comprehensive understanding of cancer microenvironment and the chemical communication between cancer cells and immune cells can result in new therapeutic approaches that will improve the elimination of pancreatic cancer cells, enhancing life quality for these patients and increasing the overall survival.