Miracle-dz/UniFAR-FTUs huggingface dataset:
Underwater Acoustic Sparse Channel Estimation Based on DW-SACoSaMP Reconstruction Algorithm

The compressive sampling matching pursuit (CoSaMP) algorithm is widely used in orthogonal frequency division multiplexing (OFDM) underwater acoustic (UWA) sparse channel estimation with good estimation accuracy. However, the algorithm requires channel sparsity a priori information as well as a large number of columns in the measurement matrix because of the large delay of the UWA channel during channel estimation, which greatly increases the complexity of CoSaMP. Therefore, a sparsity adaptive CoSaMP based on a dynamic threshold and weak selection of atoms (DW-SACoSaMP) is proposed in this letter. The algorithm can quickly estimate the channel impulse response (CIR) without channel sparsity when used for UWA sparse channel estimation. Simulation results show that, compared with other reconstruction algorithms, the estimation accuracy of this algorithm is higher and the complexity is lower; when the signal to noise ratio (SNR) is greater than 8dB, the advantage of estimation accuracy is more obvious.
Haptic Shared-Control Methods for Robotic Cutting under Nonholonomic Constraints

Robot-assisted cutting is considered an important task in several fields, such as robotic surgery, nuclear decommissioning, waste management, and manufacturing. Despite the complex dexterity requirements of cutting tasks, very simple mechanically-linked master-slave manipulators still dominate many of the above fields (e.g., nuclear robotics). Moreover, even when more dexterous manipulators are available (e.g., in robot-assisted surgery), the employed systems show little or no autonomy, delegating all control to the experience of the human operator. To ameliorate this situation, we present two haptic shared-control approaches for robotic cutting. They are designed to assist the human operator by enforcing different nonholonomic-like constraints representative of the cutting kinematics. To validate our approach, we carried out a human-subject experiment in a real cutting scenario. We compared our shared-control techniques with each other and with a standard haptic teleoperation scheme. Results show the usefulness of assisted control schemes in complex applications such as cutting. However, they also show a discrepancy between objective and subjective metrics.
Energy‐efficient system‐on‐chip reconfigurable architecture design for sum of absolute difference computation in motion estimation process of H.265/HEVC video encoding

Motion estimation is the important and computationally intensive part of any video encoding. The objective of this paper is to design and analyze the coarse and fine reconfiguration of processing element‐based hardware design for block matching–based motion estimation in H.265/HEVC video processing. Sum of absolute difference (SAD) is the commonly used criteria for block matching in the motion estimation process. User input is taken as the parameter for coarse reconfiguration, and the threshold value in SAD is considered for the fine reconfiguration. Processing elements are the hardware units designed for performing SAD calculation. In this work, the hardware for block‐based motion estimation for H.265/HEVC encoder is designed using multiple processing elements, which will calculate the SAD values. A system‐on‐chip architecture is implemented and verified the optimization in terms of power and area utilization by reconfiguring the architecture dynamically based on the input video quality requirement.
PENGEMBANGAN VIDEO PEMBELAJARAN BERBANTU CAMTASIA STUDIO PADA MATERI PERKEMBANGAN TEKNOLOGI KELAS IV SD

ABSTRACT The purpose of this research is to know the feasibility of developing learning media device by using camtasia studio assisted learning video on developmental material of class IV SD. This research is research development or Research and Development, procedure in this research using steps include potential and problem, data collection, product design, design validation, design revision, product trial, product revision. Techniques of data collection using the results of observation, interviews, tests, and documentation. Data analysis technique used is der c riptif qualitative obtained from product quality questionnaire (material expert validation, media expert) in the form of quantitative data, then continued with quantitative descriptive technique using statistical test with test. The conclusions in the results of this study are the effectiveness of learning in the classroom by using camtasia studio-assisted learning videos and products worthy of use. Keywords : Media, R & D, Video
Deep Learning for In Situ and Real-Time Quality Monitoring in Additive Manufacturing Using Acoustic Emission

Additive manufacturing (AM) is considered as a revolution in manufacturing. However, the high expectations face technical difficulties that prevent further penetration into wider industries. The main reason is the lack of process reproducibility and the absence of a reliable and cost-effective process monitoring. This paper is a supplement to existing studies in this field and proposes a unique combination of highly sensitive acoustic sensor and machine learning for process monitoring. The acoustic signals from a real powder-bed fusion AM process were collected using a fiber Bragg grating. The process parameters are intentionally tuned to achieve three levels of quality categories, which are related to the porosity contents inside the workpiece. The quality categories are defined as high, medium, and poor quality and their corresponding porosity contents are 0.07%, 0.30%, and 1.42%, respectively. Wavelet spectrograms of the signals and their encoded label representations, obtained from spectral clustering, are taken as features. A deep convolutional neural network is used to classify the features from each category and the classification accuracy ranges between 78% and 91%. Hence, the proposed method has significant industrial potentials for in situ and real-time quality monitoring of AM processes since it requires minimum modifications of commercially available industrial machines.
Visualization and Machine Learning Techniques for NASA’s EM-1 Big Data Problem

In this paper, we help NASA solve three Exploration Mission-1 (EM-1) challenges: data storage, computation time, and visualization of complex data. NASA is studying one year of trajectory data to determine available launch opportunities (about 90TBs of data). We improve data storage by introducing a cloud-based solution that provides elasticity and server upgrades. This migration will save $120k in infrastructure costs every four years, and potentially avoid schedule slips. Additionally, it increases computational efficiency by 125%. We further enhance computation via machine learning techniques that use the classic orbital elements to predict valid trajectories. Our machine learning model decreases trajectory creation from hours/days to minutes/seconds with an overall accuracy of 98%. Finally, we create an interactive, calendar-based Tableau visualization for EM-1 that summarizes trajectory data and considers multiple constraints on mission availability. The use of Tableau allows for sharing of visualization dashboards and would eventually be automatically updated upon generation of a new set of trajectory data. Therefore, we conclude that cloud technologies, machine learning, and big data visualiz ation will benefit NASA’s engineering team. Successful implementation will further ensure mission success for the Exploration Program with a team of 20 people accomplishing what Apollo did with a team of 1000.
Design of Three Phase Solid State Transformer Deployed within Multi-Stage Power Switching Converters

This paper presents a symmetrical topology for the design of solid-state transformer; made up of power switching converters; to replace conventional bulky transformers. The proposed circuitry not only reduces the overall size but also provides power flow control with the ability to be interfaced with renewable energy resources (RESs) to fulfill the future grid requirements at consumer end. The proposed solid-state transformer provides bidirectional power flow with variable voltage and frequency operation and has the ability to maintain unity power factor; and total harmonic distortion (THD) of current for any type of load within defined limits of Institute of Electrical and Electronics Engineers (IEEE) standard. Solid state transformer offers much smaller size compared to the conventional iron core transformer. MATLAB/Simulink platform is adopted to test the validity of the proposed circuit for different scenarios by providing the simulation results evaluated at 25 kHz switching frequency.
Epilepsy analysis using open source EDF tools for information science and data analytics

Electroencephalogram (EEG) is the signals that measure the electrical variances of brain using metal electrodes. We observe the EEG signals by using European Data Format (EDF) BROWSER and EEG STUDIO. By using EDF BROWSER, we can get the mean and frequency from the filtered output signal using band‐pass filter. Using EDF BROWSER, we can also perform Root Mean Square (RMS) and signal samples. Using EEG STUDIO, we can analyze the average frequency and standard deviation. Epileptic seizure prediction and detection are done by spike detection, frequency domain analysis, and nonlinear methods. EEG signal contains different artifacts like electrooculography (EOG), EKG, and electrocardiogram (ECG). ECG signals are produced by heart. EOG signals are produced by eyes. EMG signals are produced by muscle coordination.
Patient comfort level prediction during transport using artificial neural network

Since patient comfort during transport is a matter of paramount importance, this paper aims to determine the possibilities of applying neural networks for its prediction and monitoring. Specific objectives of the research include monitoring and predicting patient transport comfort, with subjective assessment of comfort by medical personnel. An original Android application that collects signals from an accelerometer and a GPS sensor was used with the aim of achieving the research goals. The collected signals were processed and a total of twelve parameters were calculated. A multilayer perceptron was created in the proposed research. The evaluation results indicate acceptable accuracy and give the possibility to apply the same model to the next patient transport. The root mean square error was 0.0215 and the overall confusion matrix prediction accuracy was 90.07%. Moreover, the results were validated in real usage. The limitations and future work are highlighted.
MO PT : Optimize Mutation Scheduling for Fuzzers

Mutation-based fuzzing is one of the most popular vulnerability discovery solutions. Its performance of generating interesting test cases highly depends on the mutation scheduling strategies. However, existing fuzzers usually follow a speciﬁc (e.g., uniform) distribution to select mutation operators, which is inefﬁcient in ﬁnding vulnerabilities on general programs. Thus, in this paper, we present a novel mutation scheduling scheme MO PT , which enables mutation-based fuzzers to discover vulnerabilities more efﬁ-ciently. MO PT utilizes a customized Particle Swarm Optimization (PSO) algorithm to ﬁnd the optimal selection probability distribution of operators with respect to fuzzing effectiveness, and provides a pacemaker fuzzing mode to accelerate the convergence speed of PSO. We applied MO PT to the state-of-the-art fuzzers AFL, AFLFast and Vuzzer, and implemented MO PT -AFL, -AFLFast and -VUzzer respectively, and then evaluated them on 13 real world open-source programs. The results showed that, MO PT -AFL could ﬁnd 170% more security vulnerabilities and 350% more crashes than AFL. MO PT -AFLFast and MO PT -VUzzer also outperform their counterparts. Furthermore, the extensive evaluation also showed that MO PT provides a good rationality, compatibility
Analysis of malware propagation behavior in Social Internet of Things

Social Internet of Things (SIoT) is an evolution of the Internet of Things, where objects interact socially with each other in the sense that they can independently establish new relationships, offer, or discover services, in order to accomplish their tasks with minimum involvement of the user. This additional convenience comes at the expense of higher risk of speeding up malware propagation through the dynamically created relationships. Because of the undesirable effects of malware (eg, disruption of device operation), it is essential to understand their spreading behavior in order to minimize their negative impacts. In this paper, we analyze malware propagation behavior in SIoT and investigate different parameters that influence spreading of malware. Toward that end, a simulator has been developed to simulate the spreading process of malware in SIoT. Many propagation scenarios were analyzed using the proposed simulator. Simulation results show that adding more relationships in the SIoT or increasing the number of owned objects per user has increased malware spreading rate. For example, the time to infect all objects is faster by 45% when objects communicate through four relationships compared with the case when objects communicate through only two relationships in SIoT. We also investigated ways to restrict the malware spreading. Results show that preventing objects from establishing dynamic social relationship slows down the infection by 40% compared with the next best scenario (ie, blocking co‐location relationships), which means more time for vendors to patch up their products.
Quantum Game Analysis on Extrinsic Incentive Mechanisms for P2P Services

Peer-to-peer (P2P) services such as mobile P2P transmissions and resource sharing, provide efficient methods to deliver data without the deployment of any central server. Nevertheless, the <italic>free-riding</italic> phenomenon inherit in such services presses a need for incentive mechanisms to stimulate contributions of data transmissions or sharing. As a result, it is imperative to answer the following questions: <italic>whether</italic>, and if so <italic>to what extent</italic>, an incentive mechanism can invoke such contributions? To answer these questions, we employ an <inline-formula><tex-math notation="LaTeX">$n$</tex-math><alternatives><mml:math><mml:mi>n</mml:mi></mml:math><inline-graphic xlink:href="wang-ieq1-2933416.gif"/></alternatives></inline-formula>-player continuous quantum game model to analyze the general extrinsic incentive mechanisms as well as the reputation-based incentive mechanisms, a typical class of extrinsic incentive mechanisms. We focus on studying the extrinsic incentive mechanisms in this paper due to their wide scope of applications stemming from the fact that they promote cooperative behaviors by offering rewards rather than depending on the internal bounds (e.g., social ties) among peers, which may not always exist between any pair of peers. To the best of our knowledge, we are the first to analyze the extrinsic incentive mechanisms for P2P services from a quantum game perspective. Such a perspective is adopted because the extended strategy space in the quantum game broadens the range for searching optimal strategies and the introduction of <italic>entanglement</italic> makes the proposed analytical frameworks more practical due to the consideration of the peers’ relationships imposed by the rewards in extrinsic incentive mechanisms. Our quantum game-based analytical framework is generic because it is compatible with classic game-based schemes. The analytical results can provide a straightforward insight on evaluating the potential of the extrinsic incentive mechanisms and can serve as important references for designing new extrinsic incentive mechanisms.
Privacy implications of switching ON a light bulb in the IoT world

The number of connected devices is increasing every day, creating smart homes and shaping the era of the Internet of Things (IoT), and most of the time, end-users are unaware of their impacts on privacy. In this work, we analyze the ecosystem around a Philips Hue smart white bulb in order to assess the privacy risks associated to the use of different devices (smart speaker or button) and smartphone applications to control it. We show that using different techniques to switch ON or OFF this bulb has significant consequences regarding the actors involved (who mechanically gather information on the user's home) and the volume of data sent to the Internet (we measured differences up to a factor 100, depending on the control technique we used). Even when the user is at home, these data flows often leave the user's country, creating a situation that is neither privacy friendly (and the user is most of the time ignorant of the situation), nor sovereign (the user depends on foreign actors), nor sustainable (the extra energetic consumption is far from negligible). We therefore advocate a complete change of approach, that favors local communications whenever sufficient.
An analytical model to assess DC characteristics of independent gate Si FinFETs

In this paper, Poisson's equation is utilized to find the potential distribution inside the channel of an independent gate FinFET device by adding the effect of channel height. The channel height of the device plays an important role in surface potential calculation when top gate voltage is applied. Using surface potential, an I - V model is developed, which can find the device current both in linear and saturation regions of operation. The model is tested on devices of different dimensions and good agreement between modeled and simulated results is observed, which validates the authenticity of the proposed model.
Caching mechanism for mobile edge computing in V2I networks

The caching‐enabled mobile edge computing (MEC) system becomes increasingly crucial to the future long‐term evolution vehicle‐to‐infrastructure (LTE‐V2I) network, especially for fast delivery of popular content of delay‐sensitive applications in the backhaul capacity–limited vehicular networks. Most related studies mainly focus on computation offloading strategy design and coding caching, while the deployment strategy design of caching capacities for high‐speed vehicles has been overlooked. In this paper, we investigate the deployment problem in LTE‐V2I networks with particular consideration on high‐speed vehicles. First, in a one‐way scenario, a joint optimization framework is formulated to minimize the cache size of the MEC system, meanwhile maximizing the average downloading percentage, where the MEC system's backhaul capacities, vehicle speeds, content popularity distributions, and sides of the highway are taken into account. The impacts of LTE‐V2I system parameters, eg, vehicle speed, on the objective function are revealed through the one‐way optimization. Then, the two‐way optimization is formulated and solved by developing an iterative optimization approach based on the aforementioned one‐way scenario. Particularly, the optimal caching allocation between two ways is derived. Simulation results validate the effectiveness of the proposed approaches even with low backhaul capacities and high vehicle speed.
A simulator for the CLARA-A2 cloud climate data record and its application to assess EC-Earth polar cloudiness

<p>One of the primary purposes of satellite simulators is to emulate the inability of retrievals, based on visible and infrared sensors, to detect subvisible clouds from space by removing them from the model. The current simulators in the COSP rely on a single visible cloud optical depth (&#964;)-threshold (&#964;=0.3) applied globally to delineate cloudy and cloud-free conditions. However, in reality, the cloud sensitivity of a retrieval varies regionally. </p><p>This presentation describes the satellite simulator for the CLARA-A2 climate data record (CDR). The CLARA simulator takes into account the variable<br>skill in cloud detection of the CLARA-A2 CDR using long/lat-gridded values separated by daytime and nighttime, which enable it to filter out clouds from<br>climate models that would be undetectable by observations. We introduce two methods of cloud mask simulation, one that depends on a spatially variable<br>&#964;-threshold and one that uses the cloud probability of detection (POD) as a function of the model &#964; and long/lat. The gridded POD values are from the<br>CLARA-A2 validation study by Karlsson and Hakansson (2018). </p><p>Both methods replicate the relative ease or difficulty for cloud retrievals, depending on the region and illumination. They increase the cloud sensitivity where the cloud retrievals are relatively straightforward, such as over mid-latitude oceans, and they decrease the sensitivity where cloud retrievals are<br>notoriously tricky, such as where thick clouds may be inseparable from cold, snow-covered surfaces, as well as in areas with an abundance of broken and<br>small-scale cumulus clouds such as the atmospheric subsidence regions over the ocean. </p><p>The CLARA simulator, together with the International Satellite Cloud Climatology Project (ISCCP) simulator of the COSP, is used to assess Arctic clouds in the EC-Earth climate model compared to the CLARA-A2 and ISCCP H-Series CDRs. Compared to CLARA-A2, EC-Earth generally underestimates cloudiness in the Arctic. However, compared to ISCCP and its simulator, the opposite conclusion is reached. Based on EC-Earth, this paper shows that the simulated cloud mask of CLARA-A2 is more representative of the CDR than using a global optical depth threshold, such as used by the ISCCP simulator. <br>The simulator substantially improves the simulation of the CLARA-A2-detected clouds compared to a global optical depth threshold, especially in the polar regions, by accounting for the variable cloud detection skill over the year. </p>
An Energy-Efficient Routing Protocol for ZigBee Networks

ZigBee networks have known battery-life problems, so, in this paper, we propose an energy-efficient routing protocol for them named LCF-ZBR (Low Formation of cluster ZigBee Routing). If the energy of one particular cluster head node is not sufficient, an alternative cluster head is used to preserve the residual energy of the original node, thus avoiding cluster head node failure due to excessive use. This will help to prolong the service life of the entire network. LCF-ZBR and ZBR energy consumption problems were simulated using NS2, and the residual energy and the number of dead nodes were compared. The results showed that the proposed energy-efficient routing protocol was more effective.
Multiscale Convolutions for an Artificial Neural Network

The study investigates the possibility of using convolutional neural networks across input of different sampling rates, focusing on one-dimensional convolutions. This is an idea that has not been adequately studied although it may produce useful results that expand the usefulness of convolutional neural networks. The problem was approached from the perspective of algebraic multigrid. Three interpolation methods were tested on audio classification neural networks trained for input of different sampling rates: nearest neighbor, linear interpolation and inverse distance weighting. The approach was extended to pooling and fully connected layers. In the case of using a neural network trained for high sampling rate input with input of low resolution, the method of linear interpolation gave promising results. Moreover, the results hint that pooling layers should not be changed in the process of multiscaling. In the case of training for low sampling rate and testing with input of high sampling rate, there is no unique solution to the system of weight equations. In dealing with this problem, the approach of directly prolonging the convolution kernels was tried using the three interpolation methods that were explained above as well as the method of kernel dilation. The last method, kernel dilation, appeared to be considerably effective in upscaling.