Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Data Sample for SAR Ship Detection

As a kind of microwave remote sensing imaging radar, Synthetic Aperture Radar (SAR) has been widely used in military and civil fields because of its advantages of all-weather and all-time imaging. Data is the "crude oil" to promote the development of deep learning. Aiming at the problem of how to use SAR data efficiently, this paper studies the data samples from different data source, different target sizes and different training sample sizes. Firstly, based on the SAR ship dataset, the expansion of specific samples is completed to increase the diversity of samples used in the experiment. Secondly, through the cross experiment of two data source, Sentinel-1 data has strong universality for different data source models. Further research on GF-3 data verifies that low resolution and small-scale target detection has great challenges, which provides some reference for researchers.
Long-Term Localization With Time Series Map Prediction for Mobile Robots in Dynamic Environments

In many applications of mobile robot, the environment is constantly changing. How to use historical information to analysis environmental changes and generate a map corresponding with current environment is important to achieve high-precision localization. Inspired by predictive mechanism of brain, this paper presents a long-term localization approach named ArmMPU (ARMA-based Map Prediction and Update) based on time series modeling and prediction. Autoregressive moving average model (ARMA), a kind of time series modeling method, is employed for environmental map modeling and prediction, then predicted map and filtered observation are fused to fix the prediction error. The simulation and experiment results show that the proposed method improves long-term localization performance in dynamic environments.
3D Printed Capacitive Pressure Sensing Sole for Anthropomorphic Robots

This paper presents a newly designed, 3D printed pressure sensitive sole to obtain the tactile- sensing data from humanoid foot during standing or walking. The device was tested for high amounts of pressure that are typical during walking of an anthropomorphic robot. The capacitive pressure sensor embedded in the sole was obtained by 3D printing of the conductive flexible electrodes and a two-part rubber as the dielectric with microbubbles to increase the sensitivity. The 3D printed sensor was fabricated simultaneously with its packaging to increase its durability. The sensor was tested both for static and dynamic conditions and the average sensitivity was found to be 0.0004 KPa-1. The proof-of-concept device showed good response when it was stepped upon –thus proving its applicability for applications with large load or pressures.
Preliminary results of OLSR based MANET routing algorithms: OLSRd2-Qx reinforcement learning agents and ODRb

In MANETs, congestion typically occurs on the interconnecting nodes between two or more groups of nodes. Routing to avoid the congested nodes via alternate, perhaps longer paths, allows more throughput, e.g., 50% more in the canonical 9-node 2-ring scenario. OLSR-Q is based on the routing protocol OLSR and a reinforcement learning (RL) agent to learn the most appropriate link states or "Directional Air Time" metric to avoid the congested nodes. The challenges for the RL agent are (1) to avoid congestion before packets are dropped and (2) to minimize the number of real valued or discrete observations or states. In this paper, three simplified OLSRd2-Qx versions are presented and compared to OLSRd2 and a centralized ODRb, Omniscient Dijkstra Routing-balanced, algorithm. The proposed OLSRd2-Qload algorithm provides the expected 50% increase in throughput on the 9-node 2-ring scenario with a specific test traffic scenario. On the NATO IST-124 Anglova scenario, and using an acknowledged message application, the Q-learning agents remain to be improved. The superior results of the centralized load balancing approach taken in ODRb will be investigated to train multi-agents systems including OLSR-Q.
Convergence Time Analysis of Border Gateway Protocol Using GNS3

Border Gateway Protocol is a path vector protocol. BGP is commonly used by Internet Service Providers. This protocol is considered as exterior protocol. BGP connects different Autonomous System via the internet. As the global internet traffic is rising with the increase in web applications. Most of these applications impact by delay. One of the delay sources over the internet which caused by the BGP routing protocol. BGP delay comes from the convergence time. This paper demonstrates an analysis of BGP convergence time and BGP timers or intervals. This paper analysis BGP convergence based on keepalive and hold timers. In addition, the paper considers the most important interval of route advertisement and update delay. Moreover, the paper takes into account link events such as failover and link failure mechanisms. The simulation result shows that the keepalive should be greater than 10 seconds. Furthermore, the optimum value of route advertisement should be between 30 to 60 seconds. Finally, paper results show that the delay of link failure detection based on the keepalive of BGP increases the convergence delay of the BGP system.
Noise pollution monitoring using mobile crowd sensing and SAP analytics

Safe administration of the cities from environment point of view is part of the focus of smart cities related to improving the quality of life of inhabitants. This paper presents a solution to the noise pollution problem in our communities. The noise parameter monitoring and analysis is based on the concept of Mobile Crowd Sensing (MCS) using smartphones. The first part of the paper presents the technologies used together with their advantages, followed by a description of implemented applications: the phone application, the web application, as well as the integration with SAP that acts as analytics and decision support platform. The implemented smartphone application has the role of data acquisition for sound values, recording and transmitting the real time parameters as well as a location and temporal stamp. The web application acts as a visualization dashboard for recorded data that can be further analyzed and filtered in various forms.
Technology readiness for IoT adoption in Colombian SMEs

The adoption of technological trends such as the Internet of Things is a challenge for Small and Medium Enterprises (SMEs), considering their financial and technological limitations. Based on the literature review, IoT readiness in SMEs is related to two factors: availability of ICT infrastructure and organizational culture focused on adopting digital technologies. Likewise, the CIO is the person in charge of developing the digital transformation strategy and leading the IoT projects' execution. In particular, addressing the needs of Colombian SMEs, this article presents five guidelines to promote SMEs' IoT readiness, considering the Technology-Organization-Environment Model (TOE): The CIO as leader of the digital transformation process in SMEs, Training programs focused on promoting IoT skills in employees, Transition to high-speed broadband, Acquisition of low-cost IoT solutions suitable for SMEs, and Participation in digital transformation programs. Additionally, the document highlights the need for public policies focused on the massification of IoT.
An application of 8-APSK modulation for the uplink using SVD-SCMA

In recent years SCMA has been proposed as a strong candidate to perform as access technique for fifth-generation mobile communications systems; usually for the uplink, bit pairs are mapped directly to code words and MPA is used in the receiver. In this work, we proposed to encode three bits to increase the capacity of data transmission in the uplink using singular value decomposition of the initial mother constellation to generate the codewords. In the receiver, the detection and decoding of data is more complex than its predecessors with two bits, since the symbols in the IQ constellation are closer to each other, so a receiver based on neural networks is proposed to perform these tasks. The experimental performance of this proposal was evaluated by implementing a software defined radio platform. The results show that, even under unfavorable SNR conditions, it is possible to use 8-APSK as a modulation technique with low error rate in the detection and decoding of SCMA signals.
Mapping Convolutional Neural Networks onto Neuromorphic Chip for Spike-Based Computation

Recent years, spike-based neuron computing on scalable and event-based neuromorphic hardware has demonstrated impressive energy efficiency. In this paper, we propose a novel spiking scheme for 1-bit and 8-bit convolutional neural networks and a systematic mapping algorithm for their deployments on a digital neuromorphic ASIC, with which we can automatically partition input and output feature maps for a 1152*1024 crossbar computing element for a excellent resource efficiency. Experimental results on MNIST dataset show that we can achieve about 98.5% and 99.4% test accuracy for these two kinds of bitwidth networks respectively, while the chip can achieve nearly 863 and 174 images/sec real-time inference speed at 0.9 V, 252 MHz.
A More Accurate Power MOSFET Current Mirror Sensing Scheme in Synchronous Buck Converters

This paper presents a current-mirror sensing scheme for high-current trench MOSFETs in synchronous buck converters and implements in a three-die PQFN package capable of delivering 70-A current per phase. Current signal accuracy is critical in high-current multiphase converters that power CPUs, GPUs, DDR memories, and FPGAs in high-performance computers and artificial intelligence systems, since the current information is used not only in optimization of computer system performance and adaptive voltage positioning but also for current sharing between multiple phases and converter over-current protection. Inductor DCR and MOSFET RDS(on) current sensing methods have been used in synchronous buck converters for more than a decade, but the current sensing accuracy is limited to +/-10% and +/-5% respectively and becomes even worse when application conditions vary in wider ranges. Higher current reporting accuracy of the MOSFET current mirror sensing is experimentally verified using multiple synchronous buck converter boards, which are built to measure current sensing error of each power stage and to determine distribution of system current reporting tolerance. Smaller than +/-3%, 3-sigma current error is achieved over wide application ranges of ambient temperature and MOSFET gate drive voltage.
Design of Switching Current Sensor for Three-Phase SiC Inverter

This paper proposes an offset compensation method of the switching current sensor for the current control of the three-phase SiC inverter. Switching current sensors with Rogowski coils, which have analog signal processing circuits implemented with the operational amplifier (op amp), are integrated into the four-layer PCB gate drive board; in the end, the phase currents are reconstructed with the op amp subtractor. To eliminate the op amp offset voltage and bias current effect on the sensor output, an offset compensation S/W algorithm is proposed. A three-phase SiC inverter prototype was built and tested with the three-phase load to verify the feasibility of the method.
Deformationsdichteverteilung in Salzen komplexer Anionen I: X–N-Dichte in α- Calciumformiat bei 100 Κ und 296 Κ

Measurements of the deformation density in the formate group of a-Ca(HCOO)2 by the X N-technique at 100 Κ and 296 Κ are presented. In addition to the usual corrections (profile analysis for background, absorption, extinction) a correction for thermal diffuse scattering was applied. This correction lowered the deformation density by about 5 %. The oxygen lone pairs are observed at angles slightly less than 120° from the C—O bond axis indicating a sp-hybridization of the oxygen. The experimental results agree fairly well with theoretical static and dynamic electron density calculations for the bonds. Some differences are observed, however, for the lone pair region.
Soft Gel-free ECG electrodes based on Biocompatible Coconut-Oil and Carbon Black

Recent developments in telemedicine have caused significant interest in the prolonged monitoring of bioelectric signals. This drives the search for easy-to-use, biocompatible, and environmentally friendly alternatives to conventional resistive wet electrodes. Here we demonstrate the use of Coconut-Oil and Carbon Black based stretchable dry electrodes to monitor electrophysiological signals without the need for conductive gels. The developed material is embedded into an elastomer matrix, exhibits a specific resistance ρ of 33.2 12.3 Ω m, high conformability, and a stretchability up to 1500±%. The realised epidermal electrodes were used to record Electrocardiographic (ECG) signals in a 3-lead configuration and compared to commercial wet electrodes. Even after being elongated by 100 % for 100 stretch/release cycles, a reliable recording of the QRS-complex is demonstrated without the need for any contact enhancing or skin irritating substances, proving its potential use in long term ECG monitoring applications.
Location characteristic of LoS for the Over-Rooftop path in Urban Low Rise Environment

This paper describes the propagation prediction of location variation for the over-rooftop path in urban low-rise environment. Location variation prediction is based on the measurement of movement to the analysis area. In addition, the probability density distribution is presented to estimate the spreading characteristics of a broadband signal of five hundreds MHz due to multipath signals. Measurements were performed in a small town covered with low-rise commercial restaurants or houses. In practice, it is necessary to statistically model the location characteristics in accordance with changes in the building environment, which are reflected in propagation channel characteristics analysis.
Crystal structure of (2E,6E)-2,6-bis[2-(trifluoromethyl)benzylidene]cyclohexanone, C22H16F6O

Abstract C22H16F6O, monoclinic, P21/n (no. 14), a = 8.426(2) Å, b = 15.205(4) Å, c = 14.900(4) Å, β = 93.031(6)°, V = 1906.4 Å3, Z = 4, Rgt(F) = 0.0632, wRref(F2) = 0.1826, T = 293 K.
Proactive Supply Noise Mitigation and Design Methodology for Robust VLSI Power Distribution

To robustly provide the low-noise supply voltage through a VLSI power distribution network (PDN) system, we have proposed a proactive noise mitigation system and improved the low-noise PDN design methodology. For the proactive noise mitigation, we present a lightweight current prediction solution, which predicts the near future noise, and a major-minor voltage regulator (MMVR) structure to provide the fast and wide-range voltage scaling capability. For the low-noise PDN design methodology, we introduce a chip load model to fill the gap between off-chip PDN design and on-chip timing information.
Research on The Overall Framework of Intelligent Water Conservancy Construction

Water conservancy in China has gone through a long stage from farmland to the rapid development of digital, and now it has officially entered into a new stage named intelligent water conservancy. As one of the main tasks of the water conservancy industry, intelligent water conservancy plays a vital role in water conservancy projects in terms of safe and stable operation as well as comprehensive benefits. In this paper, a set of feasible overall framework of intelligent water conservancy construction based on the current electronic information technology has been put forward.
Fully Constrained Least Squares Linear Spectral Unmixing of The Scream (Verso, 1893)

Taking insights from the remote sensing field, in the recent decade, the advantages of hyperspectral imaging technology has been exploited for painting conservation. The estimation of pigment proportion in painting is a challenge that is due to the highly mixed nature of the paint layers, usually requiring to take into account the law of mixing colorants in turbid media. Having knowledge of some pigments and paint layers of the reverse side of The Scream (1893), in this study, we are exploring the feasibility of using a more simple linear model for its unmixing. The obtained results are promising, despite the simplicity of the mathematical model.
Technological Evolution in Grocery Retail: A Systematic Literature Review

Since the application of scanner and barcode technologies in the retail industry in the mid-1970s, grocery stores have been following and adhering to emerging technologies in this sector. Using a literature review of 90 per-reviewed articles, this paper offers a comprehensive view of the technological evolution in this sector. The results evidenced the application of a great diversity of technological tools in the productive processes on the grocery retail, namely regarding the continuous improvement in the supply chains and innovative resources that foster consumer loyalty by assessing their specific behaviors and needs in the purchase process.
Modified Resonant Controllers with Time Delay Compensation

The resonant (R) controllers are widely applied to reduce ripple current/voltage in three-phase systems with power electronic converters. The digital signal processor (DSP) implementation of R-control implies a time delay due to online computation and discretization effects. The time delay produces an associated phase lag and causes deterioration of the transient operating performance as well as of the system stability. To overcome this drawback, the two modified schemes are considered in this paper for an R-control component, and one of them have never been presented before. The comparative analysis of proposed R-controllers is performed by using Bode diagrams in a frequency domain. As can be seen from the simulation results, while operating frequencies are less than the resonant frequency, a transfer function with the first order numerator is more suitable choice. In contrast, at operating frequencies exceed the resonant frequency, the transfer function with the second order numerator should be used to achieve appropriate transient performance because its frequency response is closer to the conventional R-controller. For this reason, it is more preferable to employ the R-controller scheme for digital implementation that combines simultaneously two transfer functions with time delay compensation.
Machine Learning based Sentiment Analysis of Coronavirus Disease Related Twitter Data

Newly discovered coronavirus has resulted in an infectious disease named Coronavirus disease (COVID-19). The outbreak of Coronavirus has caused many nations to exercise lockdowns to curb the spread of this virus. In this situation, there has been an upsurge in the usage of the internet and social media platforms. Therefore, amidst this pandemic, another crisis has also evolved; caused by the spread of incomplete and often inaccurate information which keeps leading to mass fear and anxiety. Hence it is high time to address this informational crisis and find the polarity of people’s sentiments towards COVID-19 situation so that authorized bodies can take suitable actions to cope with the situation and also review the prevalent systems. Currently, most of the sentiment analysis focus on texts, therefore we extracted sentiment from real-time tweets as well as from individual input image/video. By making use of PySpark and the libraries within it, we enabled scalable and fault-tolerant stream processing of live data stream. This study uses classification algorithms such as logistic regression, decision tree, random forest, Naive Bayes. After evaluation of all the models, logistic regression model was seen to have the highest values for accuracy and F_score. Hence this model has been used for the final sentiment analysis.
Beacon: Automated Test Generation for Stack-Trace Reproduction using Genetic Algorithms

Software crashes are a problem all developers face eventually. Manually reproducing crashes can be very expensive and require a lot of effort. Recent studies have proposed techniques to automatically generate tests to detect and reproduce errors. But even if this topic has been widely studied, there has been little to no progress done for dynamically typed languages. This becomes important because current approaches take advantage of the type information inherent to statically typed languages to generate the sequence of instructions needed to reproduce a crash, thus making it unclear to judge if type information is necessary to reproduce errors. The lack of explicit type declarations in dynamic languages difficults the task of generating the instructions to replicate an error because the type checking can only be done during runtime, making algorithms less knowledgeable about the program and, therefore, making it more difficult to use search-based approaches because the algorithms have less information to work with. This paper presents a Genetic Algorithm approach to produce crash reproductions on Python based only on the information contained in the error's stack-trace. An empirical study analysing three different experiments were evaluated giving mostly positive results, achieving a high precision while reproducing the desired crashes (over 70%). The study shows that the presented approach is independent of the kind of typing of the language, and provides a solid base to further develop the topic.
Control of a DC-DC Dual Active Bridge Converter in DC Microgrids Applications

In this paper, a control strategy for an isolated DCDC dual active bridge converter to adapt different voltage levels in a DC microgrid is proposed. The control strategy allows regulating the output voltage while maintaining in zero the mean value of the current in the high frequency transformer. The system is represented with a linearized version of its generalized average model and the controller is designed using classical techniques. The performance of the proposal is validated with simulations and experimental results.
XGBoost Trained on Synthetic Data to Extract Material Parameters of Organic Semiconductors

The optimization of organic semiconductor devices relies on the determination of material and device parameters. However, these parameters are often not directly measurable or accessible and may change depending on the neighboring materials in the layered stack. Once the parameters are known, devices can be optimized in order to maximize a certain target, e.g. the brightness of a LED. Here, we combine the use of machine learning and a semiconductor device modelling tool to extract the material parameters from measurements. Therefore, we train our machine learning model with synthetic training data originating from a semiconductor simulator. In a second step, the machine learning model is applied to a measured data set and determines the underlying material parameters. This novel and reliable method for the determination of material parameters paves the way to further device performance optimization.