Miracle-dz/UniFAR-FTUs huggingface dataset:
GPR Survey on an Iron Mining Area after the Collapse of the Tailings Dam I at the Córrego do Feijão Mine in Brumadinho-MG, Brazil

This article shows the interesting results of a pioneer effort by IAG/USP researchers to use ground-penetrating radar (GPR) for humanitarian purposes, guiding the rescue of victims in the tragedy of Brumadinho. The tailings Dam I at the Córrego do Feijão iron ore mine, located in the Brumadinho complex, Minas Gerais State, Brazil, collapsed on 25 January 2019. About 11.7 million m3 of mining mud was spilled from the dam, burying bodies, equipment, structural buildings, buses, and cars along a length of 8.5 km up to the Paraopeba River. Additionally, the contaminated mud traveled more than 300 km along the bed of the Paraopeba River toward the São Francisco River. This work shows the results of a geophysical investigation using the GPR method 17 days after the event. To carry out the geophysical survey, an excavator was used for soil compaction. The data acquisition was performed on the tracks left by the excavator chain using SIR-4000 equipment and antennas of 200 and 270 MHz (GSSI). The GPR studies aimed to map bodies, structural buildings, and equipment buried in the mud. The location of the profiles followed preferably the edge of the slope due to the higher probability of finding buried bodies and objects. The GPR results allowed the detection of subsoil structures, such as concentrations of iron ore and accumulations of sand from the dam filter. The GPR was effective because the iron ore sludge in the mixing process became porous and the pores were filled with air, which provided penetration and reflection of the GPR electromagnetic waves up to a depth of 3.5 m. The results were surprising. Although no bodies or underground equipment were found, the results of this research served to eliminate the studied areas from future excavations, thus redirecting the rescue teams and optimizing the search process. These important results can serve as an additional motivation for the use of GPR in future humanitarian work in areas of tragedies.
Influence of Antioxidants on Electrical Tree Properties in EPDM under Liquid Nitrogen

This paper focused on the effects of antioxidant on the resistance to electrical tree degradation in EPDM under liquid nitrogen. In the study, a needle-plane electrode system was used to investigate the electrical treeing characteristics under liquid nitrogen. In the test, the pulse amplitude was 25 kV with pulse frequency of 200 Hz. The results showed that the electrical tree was deaccelerated under liquid nitrogen in EPDM. Moreover, the antioxidant was served as the degradation inhibitor in EPDM, which restrained electrical treeing process in both room temperature and liquid nitrogen environment. This research has substantial commercial significance in high temperature superconducting insulation areas.
Laser-radar based mapping and navigation for an autonomous mobile robot

The environment mapping and navigation components of MOBOT-III are described. MOBOT-III is a laser-radar-based autonomous mobile robot designed to operate in an unknown indoor environment with moving obstacles. A simple environment representation that is continuously updated while MOBOT-III moves through the environment is used as the basis for the navigation method described. The environment representation is maintained by sensor data processing components, and a world model is built up successively as the system explores its environment. <<ETX>>
A Smile Detection Method Based on Improved LeNet-5 and Support Vector Machine

Conventional facial expression recognition methods usually deal with frontal face images via only one or several features, which are easy to loss useful information and sensitive to face poses, scales and noise. As an interesting application of facial expression, this paper proposes an effective smile detection method under unconstrained scenarios, employing convolution neural network to learn and automatically extract discriminative features from a large number of human face images. Specifically, our method firstly converts the original color images to grayscale images, and due to the important role of mouth in expression analysis, we then localizes the mouth region according to 5 key points on the face. After the brightness adjustment and size normalization, the mouth images are input as training images of an improved LeNet-5 model to learn and automatically extract the discriminative features of the mouth regions. Finally, a SVM classifier is trained to distinguish smiling or non-smiling. Experimental results of the public MTFL database and GENKI-4K database show that the accuracy rates of our method are up to 87.81% and 86.80%, respectively.
Kinematic analysis of 7 DOF anthropomorphic arms

A kinematic analysis of anthropomorphic 7 DOF serial link spatial manipulators with revolute joints is presented. To determine joint angles uniquely for a given end-effector position and orientation, the redundancy is parameterized by a scalar variable which corresponds to the angle between the arm plane and a reference plane. The forward kinematic mappings from joint-space to end-effector coordinates and arm angle and the augmented Jacobian matrix which gives end-effector and arm angle rates as functions of joint rates are given. Conditions under which the augmented Jacobian becomes singular are given and are shown to correspond to the arm being either at a kinematically singular configuration or at a nonsingular configuration for which the arm angle ceases to parameterize the redundancy. <<ETX>>
A Simple Steady-State Error Compensation Method of Deadbeat Predictive Control for Permanent Magnet Synchronous Machine

Deadbeat predictive control features fast dynamic responses, compared to the proportional integral (PI) and proportional resonant (PR) control, etc. However, it's quite sensitive to plant uncertainties, such as the system inductance, hence the steady state error between the command and the feedback is inevitable in a practical system. Based on the relationship between parameter mismatch and current tracking error, a new compensation algorithm for the deadbeat predictive control as used for the permanent magnet synchronous motor (PMSM) is proposed. The new method is derived for surface mounted permanent magnet synchronous motor(SMPMSM) first, and then extended to interior permanent magnet synchronous motor (IPMSM). The proposed method does not need a paralleled integral controller or parameter estimator to achieve the zero steady state error, and is simple and easy to implement in the micro-processor. IPMSM drive system simulation and experiment results verify the performance of the proposed new method.
Guaranteeing Uniform Ultimate Boundedness for Uncertain Systems Free of Matching Condition

A fuzzy-based optimal approach to robust control design is proposed for interconnected uncertain systems with mismatching conditions, which were previously unavailable. The interconnected system contains uncertainty, which may include initial conditions, unknown system parameters and input disturbance. The uncertainty bound lies within a prescribed fuzzy set. The system does not satisfy the matching condition. The robust control design in this paper consists of a control scheme design and control gain optimization. A new robust control scheme is first proposed, whose structure is deterministic and not if–then fuzzy rule based. The control gain design problem is then formulated as constrained optimization by fuzzy description of the uncertainty bound, which minimizes the fuzzy system performance and the control effort. It is shown that the global solution to this optimization problem always exists and is unique. The closed-form solution and closed-form minimum cost are presented. The resulting control is able to render the system performance in twofold. First, it guarantees uniform boundedness and uniform ultimate boundedness regardless of the actual value of uncertainty. Second, it minimizes a fuzzy-based performance index. The novelty of this research is a new and carefully orchestrated effort in blending several creative methods and tools; including simultaneous state transformation and control design, dual deterministic and fuzzy features of the performance index, and raised control order; into an integrated framework, resulting in a tractable design problem.
Hybrid Collaborative Filtering Based on Users Rating Behavior

Several collaborative filtering (CF) approaches have been developed in order to improve the quality of the recommendations. However, this improvement has always been measured as the average quality of the performed recommendations across all the users. It has not been analyzed for each individual user. In this paper, the existence of a more precise CF approach for each user is demonstrated. So, a novel hybrid method that merges recommendations provided by different CF approaches based on a multi-class classification algorithm is proposed. This classification is performed based on the user rating behavior. Experiments have been carried out on the MovieLens and Netflix datasets. The experimental results demonstrate an improvement on quality of both predictions and recommendations using the proposed hybrid CF approach. In addition, experiments have compared state-of-the-art baselines with the results obtained by the proposed approach.
An Optimization Strategy for Improving Security in Steganography

To improve the security performance of steganography, many adaptive steganographic algorithms have been presented recently. These algorithms enhance the security mainly by pixel-level's adaption while paying no attention to the image-level's adaption. Image-level's adaption means that different images have different characteristics and thus should be considered with different steganographic strategy. In this paper, we propose an optimization strategy to achieve image-level's adaption based on the texture. In the proposed method, we treat different images with individual steganography strategy according to their textures. For each cover image, a bank of Wavelet filters is assessed and the one to achieve the smallest change of the image, with respect of features used in steganalyzer, after embedding the message is adopted. The experiment result shows that the proposed method improves the security significantly compared with the state-of-arts adaptive steganographic algorithms.
T-Suite: A RESTful Distributed Development and Runtime Environment on Things Devices

Tens of billions of IoT devices are connecting to the Internet, entailing several essential issues such as high response latency of IoT applications, network congestion, and privacy. New computing paradigms are rising and trying to offload the computation from could to edge and things devices. Due to the extremely resource-constrained environment and distributed architecture, that developing and deploying applications on heterogeneous low-end IoT devices to afford the computing load is a significant challenge for developers. In this paper, we propose T-Suite, a lightweight script application development and runtime framework based on REST style for IoT devices. T-Suite includes a RESTful development and deployment environment and a mixed runtime environment which can improve the performance of script applications. A series of micro-benchmarks are conducted, and the results show that T-Suite enables scripting on MCU based IoT devices with a little increment of computing resource consumption.
Modular-Concatenated-Cell (MCC) Multilevel Converter: A Novel Circuit Topology and Innovative Logic-Equations-Based Control Technique

This paper introduces a new circuit topology for multilevel converters. The proposed multilevel converter circuitry is realized by the cascaded connection of a 2-level converter and modular-concatenated-cell (MCC) H-bridge cells. In the proposed MCC multilevel topology, the classic two-level converter functions as the main converter through generating the major voltage-steps, whereas auxiliary concatenated H-bridge cells produce the intermediate voltage-levels. The overall output voltage is formed by adding the intermediate voltage-levels to the major ones. The crucial distinction between the cascaded H-bridge (CHB) converters and proposed MCC multilevel converter topology is that the latter exploits flying-capacitors (FCs) instead of isolated dc-voltage sources, and uses per-phase redundant switching states to regulate FC voltages. The voltage rating of semiconductor power switches and FCs within the concatenated cells of the proposed MCC multilevel converter all are 1 p.u. that lead to a modular structure.
Thermal Monitoring of Power Electronic Modules Using Device Self-Sensing

This work proposes techniques for real-time monitoring of power module thermal response and state-of-health. It presents a thermal observer that dynamically estimates device losses and reliability-critical temperatures. These quantities are estimated instantaneously every switching period and averaged over one fundamental excitation period with nearly zero lag and an outstanding accuracy and insensitivity to modeling errors compared to prior technologies. The observer structure is combined with a loss injection algorithm that manipulates gate resistance and switching frequency for small signal device loss excitation, creating a device self-sensing unit that exhibits two innovative features: On the one hand, it enables the adaptive calibration of the device loss model. This improves the accuracy of thermal monitoring while avoiding time-consuming off-line calibrations. On the other hand, it can conduct an in-situ thermal impedance spectroscopy of the power module over multiple frequency decades without interruption of normal converter operation. The extracted magnitude and phase of the thermal impedance allow an effective state-of-health diagnosis as they reflect structural changes in the power module, e.g. solder delamination. Proposed techniques are evaluated in simulations and experiments for a state-of-the-art IGBT power module. Overall, the methodology promotes precise thermal management and predictive maintenance efforts, and aims to ensure more safe and reliable operation of future converter systems.
A Novel Transmitting Module Self-Monitoring Protection System Based On MEMS Sensors and Actuators

Design, fabrication, and measurements of a novel transmitting module self-monitoring protection system are presented in this letter. Such a system is based on microelectromechanical systems (MEMS) sensors and actuators for monitoring and protecting the microwave amplifier circuit. The transmitting module self-monitoring protection system consists of the RF MEMS switches, the directional coupler, and the MEMS power sensors. When the microwave amplifier circuit is at an abnormal state (“open,” “short,” or “overload”), the transmitting module self-monitoring protection system can isolate the microwave amplifier circuit out of order and reconfigure the transmitting module into impedance matching within 36 ms. When the microwave amplifier circuit is at the overload state, the error between the set switching power and the actual switching power is less than 9%. The measured results agree well with the theory.
Circuit Modeling and Performance Evaluation of Superconducting Wireless Power Transfer Systems

In the context of wireless power transfer (WPT) technology, low allowable current density and large resistivity of conventional copper conductors limit the increments of primary current and transfer efficiency. A new WPT scheme using high temperature superconducting (HTS) primary and secondary can be well expected to develop various high-power and high-efficiency WPT devices. This paper presents the circuit modeling and performance evaluation of superconducting wireless power transfer systems. The circuit simulation results lay some technical bases for developing practical HTS WPT devices.
Sparse Tensor Network System Identification for Nonlinear Circuit Macromodeling

Volterra-series nonlinear system identification has recently been transformed into a tensor decomposition and opti-mization problem. Such tensor network reformulation with small tensor cores overcomes the curse of dimensionality in deploying high order Volterra kernels, and allows the fast simulation of arbitrarily nonlinear systems. This paper further advances the tensor core identification by adding sparsity: pruning unimportant weights and retraining the remaining weights, while retaining most accuracy. The resultant sparse macromodels are hardware friendly and can facilitate low power signal processing.
A Normalized Quantitative Method for GaN HEMT Turn-ON Overvoltage Modeling and Suppressing

Gallium nitride high electron mobility transistor has been seen as a power semiconductor devices with lots of potential since it has been commercialized. However, the advantages of fast switching speed with low conduction resistance always have to compromise to the fast switching on evoked overvoltage. Though it has been realized that the overvoltage problem arises from the joint effect of fast switch speed and parasitics, there is a lack of ways for quantitative analysis. This paper proposes a reliable circuit model based on a quantitative way to analyze the cause of overvoltage and finds optimized parameters to suppress. A normalized method is adopted to make the analysis more general, and the voltage and current stress determined safe operation area during the switching on process under the constraint of maximum overvoltage is detailed. The analysis is verified by a double pulse test experiment, with the calculated and measured maximum overvoltage matching very well. The analysis can help us to choose reasonable turn-on speed and power loop parasitic inductance before the printed circuit board (PCB) manufacture with an acceptable maximum overvoltage guaranteed.
Anonymity of TOR Users Demystified

The Onion Routing (TOR) system is often seen as the best anonymity tool out there, and is used by nearly 2.5 million people daily. Many of these users tend to believe that TOR offers more privacy protection than what it is actually intended or able to provide. In this paper, we specifically focus on the TOR browser – one of the two key components of the TOR system. In particular, we demonstrate that, if used in its default settings, the TOR browser provides little if any protection against four most common forms of user tracking. Hence, to achieve true online anonymity, extra efforts and vigilance need to be exercised on the part of the TOR user.
Connectivity Probability Based Spray and Wait Routing Algorithm in Mobile Opportunistic Networks

The mobile opportunistic networks (MONs) aim to realize the data delivery in challenged network environment that is characterized by intermittent connectivity and dynamic network topology. To address the routing problems in MONs, a connectivity probability based spray and wait routing algorithm (CPSWR) is presented. First of all, in view of the data sending and receiving status of nodes in MONs, a three-dimensional connectivity matrix for network nodes is constructed. Then, CPSWR calculates the connectivity probability between nodes for the next time slot based on connectivity matrix. Finally, the spray process is guided by the connectivity probability. In CPSWR, the packet replication only takes place between nodes with high connectivity probability to improve the effectiveness of data forwarding. The simulation results demonstrate that compared with traditional spray and wait routing algorithm (SWR), CPSWR can reduce inefficient data forwarding and improve packet delivery performance.
Stochastic Analysis on Fog Computing Empowered Mobile Crowdsensing with D2D Communications

As a new cloud service provision paradigm, fog computing has revolutionized many mobile computing applications, including mobile crowdsensing (MCS). In this study, we focus on the fog computing empowered MCS with device-to-device (D2D) communications, where both the task dissemination and data collection are deployed as services in the fog nodes (e.g., base stations), and the mobile nodes opportunistically forward both the tasks and the sensing data via D2D communications by epidemic routing. One natural question arose in such scenario is on the affection to the quality-of-sensing (e.g., coverage) from the service deployment. To this end, we are motivated to conduct a stochastic analysis on the D2D-based MCS supported by fog computing. In particular, we use ordinary differential equations (ODEs) to describe the task dissemination phase and the data collection phase and derive the achievable coverage as a function of parameters such as the number of fog nodes exploited, the number of mobile nodes, the encounter rates. We also apply our analysis to find out the optimal time allocation for deadline-constrained MCS applications. Through extensive simulation-based evaluations, we verify the correctness of our analysis, with the average error less than 9.6%, and the optimality of our time allocation scheme.
Coupling- and Load-Independents Output Voltage and ZPA Operation in LCC-Series Compensated IPT System

The inevitable coils misalignment of the loosely coupled transformer (LCT) and load variations in a wireless EV (electric vehicle) charging system can dramatically affect the output and input characteristics. In this paper, a resonant method is investigated for a closed-loop LCC-series compensated inductive power transfer (IPT) system. With the proposed method, coupling- and load-independents voltage transfer characteristic and zero phase angle (ZPA) operation are achieved simultaneously. A laboratory prototype is implemented and the validity of proposed LCC-series compensated IPT system is verified.