Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial Neural Network based maximum power point tracker for photovoltaic system

Most of the existing MPPT algorithms suffer from the drawback of being slow or wrong tracking. Due to this the utilization efficiency is reduced. MPPT is used to maximize the output from the PVarray by tracking continuously the maximum power point. Among all MPPT methods, Perturb and Observe (P&O) method is used for its simplicity. In this paper Perturb and Observe method is used. But it moves the operating point far from the maximum point due to rapidly changing condition. In order to bring the operating point to the maximum, Artificial Neural Network (ANN) is used to improve the efficiency by detecting the atmospheric condition.
A new procedure for lifetime prediction of n-channel MOS-transistors using the charge pumping technique

A new procedure is proposed for a reliable lifetime prediction of MOS-transistors under hot-carrier stressing. This procedure is based on the measurement of the charge pumping current increase during the degradation. Unlike procedures based on threshold voltage (or transconductance) shifts, the new method is shown to be a better monitor for the hot-carrier degradation and is applicable for a wider range of stress conditions. It is also demonstrated that the procedure provides a more reliable lifetime prediction, especially under alternating stress conditions or under low-frequency dynamic stress conditions. <<ETX>>
Construction and Simulation of Athlete’s Wrong Action Recognition Model in Sports Training Based on Embedded Wireless Communication and Computer Vision

Embedded networking has a broad prospect. Because of the Internet and the rapid development of PC skills, computer vision technology has a wide range of applications in many fields, especially the importance of identifying wrong movements in sports training. To study the computer vision technology to identify the wrong movement of athletes in sports training, in this paper, a hidden Markov model based on computer vision technology is constructed to collect video and identify the landing and take-off movements and badminton serving movements of a team of athletes under the condition of sports training, Bayesian classification algorithm to analyze the acquired sports training action data, obtain the error frequency, and the number of errors of the landing jump action, and the three characteristic data of the displacement, velocity, and acceleration of the body’s center of gravity of the athlete in the two cases of successful and incorrect badminton serve actions and compared and analyzed the accuracy of the action recognition method used in this article, the action recognition method based on deep learning and the action recognition method based on EMG signal under 30 experiments. The training process of deep learning is specifically split into two stages: 1st, a monolayer neuron is built layer by layer so that the network is trained one layer at a time; when all layers are fully trained, a tuning is performed using a wake-sleep operation. The final result shows that the frequency of the wrong actions of the athletes on the landing jump is concentrated in the knee valgus, the total frequency of error has reached 58%, and the frequency of personal error has reached 45%; the problem of the landing distance of the two feet of the team athletes also appeared more frequently, the total frequency reached 50%, and the personal frequency reached 30%. Therefore, athletes should pay more attention to the problems of knee valgus and the distance between feet when performing landing jumps; the difference in the displacement, speed, and acceleration of the body’s center of gravity during the badminton serve will affect the error of the action. And the action recognition method used in this study has certain advantages compared with the other two action recognition methods, and the accuracy of action recognition is higher.
Seismic response of segmental buildings

This paper proposes an aseismic design concept in which the superstructure of a base-isolated building is divided into several segments. Each segment may comprise a few storeys and is interconnected by additional vibrational isolation systems. The dynamic characteristics of the segmental buildings are investigated. The optimum parameters of the vibration isolation systems are determined by minimizing the mean square acceleration response. The seismic response of a typical segmental building subjected to the N-S component of the 1940 El Centro earthquake input is evaluated and compared with the responses of the corresponding fixed-base and conventional base-isolated buildings. The comparisons show that, when the superstructure is segmented, while the acceleration response in the superstructure remains as small as that in the conventional base-isolated building, the displacement across the base isolation system at foundation level is substantially reduced.
Dynamic Simulation of the Maglev Vehicle/Guideway System

A numerical model has been developed to investigate the dynamic characteristics of the Maglev system. Modeling of the guideway with the surface irregularity and modeling of the Maglev vehicle are described. Numerical simulations are performed in Simulink to solve the coupled problem. Based on this model a study focused on the dynamic characteristics of the guideway and the vehicle/guideway interaction has been made. The effects of various approaches to model the Maglev vehicle at different levels of accuracy are also investigated. The numerical results are compared with the corresponding values given by MSB-AG-Fahrweg. The conclusions derived by the research are presented in the end of this article.
Complex phase/amplitude spatial light modulator advances and use in a multispectral optical correlator

Optical processing systems often require compact high frame rate spatial light modulators (SLMs), usually with application specific modulation requirements in the complex plane. In this paper we discuss several advances at Boulder Nonlinear Systems toward this goal, including our liquid crystal (LC) on VLSI 128 X 128 analog SLM, and our multispectral hybrid incoherent to coherent converter.We also present the analysis of optical modulation possibilities when utilizing zero twist nematic and planar aligned chiral smectic LC on a reflective backplane. Finally we present the design of a multispectral optical correlator for machine vision applications such as food inspection, security, or manufacturing inspection.
Frequency shift of piezoelectric microcantilever humidity sensors

Recent researches on dynamic behavior of micro cantilevers indicate that the flexural resonance frequencies of piezoelectric microcantilever sensors (PEMS) could be influenced by air in which it immersed as a result of viscous damping effect, which reduces the accuracy of the PEMS. A detailed theoretical analysis of the frequency response of a PEM immersed in air and excited by an arbitrary driving force is presented in this paper, in which the couple stress theory (Cosserat theory) is introduced to the dynamic deflection function of a PEM to explain the size effect. Numerical results have shown a good agreement with the experiments. Methods for prediction of dynamic characteristics of long beam-like micro components could be easily derived based on the presented theory, which is of value to users and designers of micro-electro-mechanical systems (MEMS).
Estimation of soil water holding capacity with Random Forests for drought monitoring

Abstract. Soil moisture is a key variable for drought monitoring but soil moisture measurements networks are very scarce. Land-surface models can provide a valuable alternative to simulate soil moisture dynamics, but only a few countries have such modelling schemes implemented for monitoring soil moisture at high spatial resolution. In this study, a soil moisture accounting model (SMA) was regionalized over the Iberian Peninsula, taking as a reference the soil moisture simulated by a high-resolution land surface model. To estimate soil water holding capacity, the parameter required to run the SMA model, two approaches were compared: the direct estimation from European soil maps using pedotransfer functions, or an indirect estimation by a Machine Learning approach, Random Forests, using as predictors altitude, temperature, precipitation, evapotranspiration and land use. Results showed that the Random Forest model estimates are more robust, especially for estimating low soil moisture levels. Consequently, the proposed approach can provide an efficient way to simulate daily soil moisture and therefore monitor soil moisture droughts, in contexts where high-resolution soil maps are not available, as it relies on a set of covariates that can be reliably estimated from global databases.
Personal-Zscore: Eliminating Individual Difference for EEG-Based Cross-Subject Emotion Recognition

It was observed that accuracy of the Subject-Dependent emotion recognition model was much higher than that of the Subject-Independent modelÂ in the field of electroencephalogram (EEG) based affective computing. This phenomenon is mainly caused by the individual difference of EEG, which is the key issue to be solved for the application of emotion recognition. In this work, 14 subjects from the SEED were selected for individual difference analysis. Through individual aggregation features evaluation, sample space visualization, and correlation analysis, we proposed four quantification indicators to analyze individual difference phenomenon. Finally, we presented the Personal-Zscore (PZ) feature processing method, and it was found that the data set processed with PZ method could represent emotion better than the original data set, and the conventional model with the PZ method was more robust. The accuracies of emotion recognition models trained with PZ processing have been improved to some extent, which showed that the PZ method could effectively eliminate the individual aggregation of feature space and improve the emotional representation ability of data sets. Hence, our findings may provide a new insight into the foundation for universal implementation of EEG-based application, and the Personal-Zscore feature processing method is of great significance for the development of effective emotion recognition system.
Gravity Gradiometer Aided Inertial Navigation Within Non-GNSS Environments

Title of dissertation: GRAVITY GRADIOMETER AIDED INERTIAL NAVIGATION WITHIN NON-GNSS ENVIRONMENTS Justin A. Richeson Doctor of Philosophy, 2008 Dissertation directed by: Professor Darryll J. Pines Department of Aerospace Engineering Gravity gradiometer aiding of a strapdown inertial navigation system (INS) in the event of Global Navigation Satellite System (GNSS) signal loss, or as a complement to an INS/GNSS system, is proposed. Gravity gradiometry is ideal for covert military applications where a self contained, passive, spoof-free aid is desirable, and for space navigation near planetary bodies and moons where GNSS is unavailable. This dissertation provides the first comprehensive discussion on gravity gradiometry fundamentals, map modeling, and regional and altitude effects on the gravitational gradient signal for use as a navigation aid. A thorough methodology to implement strapdown and stabilized gravity gradiometer instruments (GGIs) into an autonomous extended Kalman filter is also presented in the open literature for the first time. Lastly, a brief discussion on extraterrestrial navigation using gravity gradiometry is given. To quantify the potential performance for future gravity gradiometer instruments as an INS aid, extensive Monte Carlo simulations of a hypersonic scramjet cruise missile were performed. The results for the 1000 km range mission indicate that GGI updates significantly improve the navigation accuracy of the autonomous INS. The sensitivities of the system to variations in inertial measurement unit (IMU) quality, gravity field variation, GGI noise, update rate, and type are also investigated along with a baseline INS/Global Positioning System (GPS). Given emerging technologies that have the potential to drastically decrease gradiometer noise levels, a hypothetical future grade gravity gradiometer aided INS is shown to bound rootmean-square (RMS) position errors at 0.336 m, velocity errors at 0.0069 m/s, and attitude errors at 0.00977◦, which is comparable to the nominal INS/GPS system with 10 sec updates. The performance of two subsonic cases is also investigated and produced impressive passive navigation accuracy. A commercial aircraft simulation using a future grade GGI provided RMS errors of 0.288 m in position, 0.0050 m/s in velocity, and 0.0135◦ in attitude. A low altitude and velocity gravity gradiometer based survey simulation similarly showed sub-meter RMS position errors of 0.539 m, velocity errors of 0.0094 m/s, and attitude errors of 0.0198◦. GRAVITY GRADIOMETER AIDED INERTIAL NAVIGATION WITHIN NON-GNSS ENVIRONMENTS
Clinicopathologic Characteristics and Outcomes of Simultaneous Multiple Primary Lung Cancer

Background Simultaneous multiple primary lung cancer has been detected increasingly nowadays with the development of image technology. However, the clinicopathologic characteristics and outcomes are not clear. Methods All consecutive patients diagnosed as simultaneous multiple primary lung cancer according to Martini–Melamed and American College of Chest Physicians criteria from June 2010 to June 2019 in our center were enrolled. The clinicopathologic characteristics and outcomes were compared between patients with the same histological type and different histological types. Results A total of 336 patients were enrolled, consisting of 297 (88.4%) patients with the same histological type and 39 (11.6%) patients with different histological types. Compared to patients with the same histological type, patients with different histological types were more commonly males (87.2% vs. 34.0%; p < 0.001) with an older age (65 [62–69] vs. 59 [52–65] yrs; p < 0.001) at diagnosis. Also, patients with different histological types showed worse respiratory function and more advanced stage according to TNM staging. The 1-, 2-, and 3-year overall survival of overall patients was 97.7%, 96.1%, and 92.2%, and the 1-, 2-, and 3-year recurrence-free survival of overall patients was 96.8%, 92.9% and 85.7%, respectively. Importantly, patients with different histological types showed worse overall survival (p < 0.001) and recurrence-free survival (p=0.002) than patients with same histological type. The multivariable Cox proportional hazard model revealed that presence of different histological types was significant predictor for worse overall survival (adjusted hazard ratio: 10.00; 95% confidence interval: 2.92–34.48; p < 0.001) and recurrence-free survival (adjusted hazard ratio: 2.59; 95% confidence interval: 1.14–5.88; p=0.023). Conclusions Although relatively less common in simultaneous multiple primary lung cancer, patients with different histological types showed worse clinical characteristics and outcomes.
Correlated STORM-homoFRET Imaging: Applications to the Study of Membrane Receptor Self-Association and Clustering

Mapping the self-organization and spatial distribution of membrane proteins is key to understanding their function. We report here on a correlated STORM/homoFRET imaging approach for resolving the nanoscale distribution and oligomeric state of membrane proteins. Live cell homoFRET imaging of CEACAM1, a cell-surface receptor known to exist in a complex equilibrium between monomer and dimer/oligomer states, revealed highly heterogenous diffraction-limited structures on the surface of HeLa cells. Correlated super-resolved STORM imaging revealed that these structures comprised a complex mixture and spatial distribution of self-associated CEACAM1 molecules. This correlated approach provides a compelling strategy for addressing challenging questions about the interplay between membrane protein concentration, distribution, interaction, clustering, and function.
Polymer-based flexible capacitive sensor for three-axial force measurements

We have developed a flexible-substrate-based three-axial force sensor, composed of finger-shaped electrode capacitors, whose operation is based on the measurement of a capacitance change induced upon applying a three-axial load. The electrode design supports high sensitivity to shear forces. An overall flexibility of the sensor and elasticity of the capacitor's dielectric is obtained by integrating three polymers in the sensor's technology process, namely polyimide, parylene-C, and polydimethylsiloxane, combined with standard metallization processes. We have theoretically modeled the sensor's capacitance and its three-axial force sensitivity. The unit capacitors have static capacitances in the range of 20 pF. The electro-mechanical characterization of the capacitors reveals in the normal direction a sensitivity Sz = 0.024 kPa−1 for pressures <10 kPa, whereas for higher pressures the measured sensitivity Sz = 6.6 × 10−4 kPa−1. Typical measured shear force sensitivity Sx = 2.8 × 10−4 kPa−1. These values give our transducer high potential for use in skin-like sensing applications.
Comparação de três métodos de estimativa da evapotranspiração de referência para a região de Araraquara - SP

To verify the efficiency of the evapotranspiration reference methods (ETo), it is necessary to contrast with the standard method. This work has the purpose to compare three methods of ETo estimation: Solar Radiation (RS), Makkink (MAK) and a Class A tank (TCA) method with Penman-Monteith method (PM), in two phases of citrus growth, working with biweekly medium data for the winter-spring and summer-autumn periods. The research was developed in a citrus farm, in Araraquara - SP, Brazil, where it was installed an automated meteorological station and a Class A tank. Through a system of data acquisition, measures of the global solar radiation, radiation balance, temperature of the air, relative humidity of the air and speed of the wind, were obtained. The linear regression analysis shows that the TCA method can be fitted by, y = bx, where y represents EToPM and x represents EToTCA. For the others analyzed methods we found the model y = bx + a. The results of this study indicate that the TCA method overestimated ETo 26% in the summer-autumn period and 24% in the winter-spring period. The MAK method, underestimated ETo in the two analyzed periods, while the RS method overestimated ETo.
A History of the Development of Radiofrequency Neurotomy

Abstract The technique of lumbar medial branch radiofrequency neurotomy for facet joint pain has an intriguing history involving a diverse timeline of medical specialists. This paper aims to chart the pathway that led to its invention and the series of modifications and refinements that have led to modern practice. The story begins with the treatment of World War I soldiers by Nesfield, who used scalpels to cut “trapped” nerves. Inspired by Nesfield’s treatment, Rees developed the “percutaneous rhizolysis” technique in 1960. Shealy was the first to use radiofrequency electrodes for denervation of the facet joints, introducing his technique in 1971. Several radiofrequency electrode developments came about from collaborations with Cosman medical device entrepreneurs during the 1970s, including the Shealy Rhizolysis Kit, the Ray Rhizotomy Electrode, and the Sluijter-Mehta Kit. Subsequent dissections of Rees’ technique and modification of Shealy’s procedure by Bogduk saw the development of “percutaneous lumbar medial branch neurotomy” in 1980 by Bogduk and Long. Bogduk continued to contribute significantly to validation, refinement and acceptance of the technique. In 1998, the technique of pulsed radiofrequency was invented by Sluijter, Cosman, Rittman and van Kleef. Subsequent innovations have consisted of cooled radiofrequency neurotomy, multi-tined cannulae, endoscopic systems, and alternative denervation targets, such as the facet joint capsule. As we pass the first 100 years of the story, we believe there are more chapters to be written on this fascinating subject.
Effect of Laser Cladding Processing Parameters on Nitinol’s Clad Dimensions, Microstructure, and Hardness

Nickel Titanium alloy (Nitinol) is characterized by its good mechanical properties, good damping properties in addition to its distinctive shape-memory effect and superelasticity effect besides its great bio-mechanical compatibility and corrosion resistance. These properties have empowered its applications, particularly within the bio-medical and aerospace industry. Despite these exceptional properties, the manufacturing of Nitinol by conventional methods is exceptionally troublesome and costly and consequently must be inspected. Therefore, additive manufacturing specifically laser-based ones were used recently. In this research, the effect of processing parameters of laser cladding/ laser direct deposition on Nitinol’s Microstructure, Hardness and Clad Dimensions was evaluated. Systematic characterization of Nitinol samples was done utilizing Optical Microscopy and Vickers hardness tester. Samples of Nitinol were synthesized with different processing parameters using laser cladding and its properties were investigated and compared to one another to get the optimum processing parameters to synthesize a near net shape, fully dense Nitinol component with reliable properties. The results showed that there’s a processing parameter window at which the alloy possesses its best mechanical and functional properties which were of Laser power of value 1.25 Kw, Scan speed of 1.5 m/min and powder deposition rate of 1.5/1.5 RPM, these conditions resulted in the formation of martensite phase which is responsible for its functional properties with 40% volume fraction and a hardness value of 598 HV.
On predictability in South Asian stock markets

In this research I investigate persistence in monthly excess stock returns over risk free rates in two South Asian stock markets i.e. S&P CNX 500 and KSE 100 stock price indexes using non-Gaussian state space or unobservable component model with stable distributions and volatility persistence. Results from non-Gaussian state space models show that both markets encompass volatility persistence. KSE 100 has a stable characteristic exponent of 1.748, but for S&P CNX 500 index the value for the characteristic exponent is 1.999 which shows normal behavior in this market. Both markets encompass persistent signal in returns at 10% level of significance. The efficiently estimated excess returns for S&P CNX 500 are 0.01% per month (0.12% per annum), and 0.015% per month (0.18% per annum) for KSE 100 index. Key phrases: stock return predictability; unobserved components; fat tails; stable distributions JEL Codes: C22, C53, G14 Word Count Main Text: 2434