Miracle-dz/UniFAR-FTUs huggingface dataset:
SUBSIDIZING THE NEWS? Organizational press releases’ influence on news media’s agenda and content

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Simultaneous and Sequential Triggering in Multi-Finger Floating-Base SCRs Depending on TLP Pulse Rise Time

Multi-finger floating-base silicon controlled rectifiers (SCRs) of discrete technologies are investigated by transmission line pulses (TLP) with short (300ps) and long (10ns) pulse rise times (RT). Transient interferometric mapping (TIM) is applied to study the finger triggering dynamics. The measurements are correlated with TCAD simulation. It is found that for short RT the fingers trigger simultaneously which is due to the positive influence of a voltage overshoot related to delayed conductivity modulation. For long RT the fingers trigger sequentially which is accompanied by the voltage drops near the holding voltage. The time delay between the triggering of neighboring fingers is in the 3–150ns range and depends on the actual current density in fingers. The sequential finger triggering is due to lateral carrier diffusion-limited processes and the coupling via substrate currents.
A Research Method of Vibration Stationarity Based on Correlation Coefficient

Due to the influence of various factors during the operation of mechanical equipment, the vibration appears the characteristics of non-stationarity. In order to analyze the correlation of other monitoring indicators that cause vibration non-stationarity, it is necessary to study the vibration stationarity. A research method of vibration stationarity based on correlation coefficient is proposed in this paper. Firstly, the correlation coefficient of vibration data and corresponding time is calculated by sliding to reflect the vibration trend. Secondly, the fluctuation threshold is determined by statistical characteristics. Finally, the vibration stationarity is tested. The test results of vibration data of nuclear main pump show that the method proposed in this paper can better test the vibration stationarity, which lays a foundation for the study of the factors related to the operating states of mechanical equipment.
Effect of skin tone and activity on the performance of wrist-worn optical beat-to-beat heart rate monitoring

Wrist-worn reflective photoplethysmography (PPG) has gained popularity as an unobtrusive method for heart rate monitoring, also known as optical heart rate monitoring (OHR). Recently beat-to-beat heart rate information measured with OHR technology has also started to be utilized in heart rate variability analysis as well as in detection of cardiac arrhythmias. However, as an optical method, the quality of the PPG signal and thus the performance of the technology is affected by the skin tone of the measurement subject. In addition, movement and superficial blood perfusion are other relevant factors affecting to OHR monitoring. We evaluated the performance of optical heart rate monitor manufactured by PulseOn company in controlled trial with 36 subjects of varying skin tones. The results show clear dependence between the skin tone and the coverage percentage of good quality beat-to-beat intervals (BBI) as well as improvement of the coverage and the BBI accuracy after physical activity, especially with people with darker skin tone.
A New Compound Fault Diagnosis Method for Gearbox Based on Convolutional Neural Network

This paper focus on the fault diagnosis problem for the compound faults of rotating machine, in which the rolling bearing and the sun gear faults simultaneously occurred are considered as the compound fault. Considering the traditional compound fault diagnosis methods usually utilize the manual fault features extraction, which are mainly dependent on engineering experience, we propose a compound fault diagnosis method named multi-sensor based convolutional neural network (MCNN). For vibration signals of compound faults, the different transmission paths and the positions of the sensors means one part of the embedded single faults may have higher energy. The vibration signals collected from three sensors at different positions can help guarantee the completeness of the characteristics of the compound fault. Then, the multi-sensor signals are combined together and fused by the convolutional operation of the convolutional neural network (CNN) model. The CNN model, which can automatically extract features from the vibration signals and achieve classification, is used for fault extraction and fault recognition. The experiments are presented on the physical platform of power transmission, and the proposed fault diagnosis method can be verified with the satisfied performance.
Wearable IoT Electronic Nose for Urinary Incontinence Detection

This paper presents the development of a low cost and low power wearable Electronic Nose with IoT connectivity for detecting synthetic urine. This system was designed with the ultimate aim to relieve the burdens associated with urinary incontinence for both sufferers and healthcare workers in hospitals and care homes. Commercially proven metal oxide (MOX) gas sensors for Volatile Organic Compound (VOC) detection such as the SGP30 and MiCS-5524 series of gas sensors were used, as well as the custom-made WO3, SnO2 and CuO MOX sensors developed at the Microsensors and Bioelectronics laboratory at the University of Warwick. A web-application was created to facilitate real-time monitoring over the MQTT protocol and indicate when urine has been detected. The capabilities of the IoT for this purpose allow for enhanced patient mobility and simultaneous patient monitoring. An artificial neural network was used to classify synthetic urine from other odours. The model was able to identify synthetic urine with 100% accuracy based on prior training and testing datasets.
Assessing Computer Anxiety in Vocational High-School Students

Computer skills for vocational high-school students in the era of the industrial revolution 4.0 are undoubtedly urgent. In fact, some students found it difficult to do tasks on computer network engineering subjects and computer systems. This research assesses computer anxiety in multimedia vocational high-school students in Jakarta. The research questions are: (1) what kind of computer anxiety experienced by the students? (2) how many students experienced a high level of computer anxiety, medium level, and low level? Participants are 351 vocational high-school students. Measurement using Wren & Benson's (2004) scale in the context of classroom learning, consisting of 25 items with a reliability of 0.92 which measures computer anxiety from three dimensions, namely ‘thoughts', ‘behavior in the classroom’ and ‘physical reactions’. Using confirmatory factor analysis (CFA) we confirmed the computer anxiety experienced by the vocational high-school students. The sum of the students who experience computer anxiety in the low category = 14%, moderate = 72.9%, and high = 13.1%.
Satellite Fusion Positioning Algorithm Based on GDOP

In passive satellite positioning, different positioning methods have different areas of high accuracy. In the TDOA/FDOA positioning method, there is a positioning blind area that runs through the satellites’ sub-satellite point, while in the AOA positioning method, the satellite's sub-satellite point is the region with the highest positioning accuracy. Aiming at the problem of positioning blind area in TDOA/FDOA positioning, this paper proposes a Satellite Fusion Positioning Algorithm Based on GDOP. By calculating the theoretical error of AOA positioning and TDOA/FDOA positioning, the average values of its random error is estimated as the weight of the fusion positioning. Then use the weight values to fuse the positioning results of the two methods. This algorithm can combine the positioning advantages of each of the two positioning algorithms to effectively improve the problem of positioning blind area in TDOA/FDOA positioning. Moreover, when the variance of the original positioning data is close, the algorithm can reduce the randomness of the positioning results and improve the positioning accuracy.
Blockchain-Based Transparent Disaster Relief Delivery Assurance

Blockchain technology presents benefits that change the way business partners interact. This new way of establishing democratic trust encourages business owners to think differently.Disaster relief and aid industries are built on the power of collaborating participants. A very high number of participants in different hierarchies, including donors, charities, disaster victims, insurance companies and government agencies interact under extraordinary circumstances of a disaster and hard times. Establishing a new way of trust brings forward a better disaster recovery.In this paper, we propose a blockchain-based ecosystem. The blockchain-based disaster recovery not only would enhance the basic processes around disaster relief, but also promote the willingness of help by transparency and potential fraud prevention. This new blockchain system introduces an opportunity to be more resilient, to react rapidly, to communicate transparently, and to include new contributors such as IoT.
Optimal Selection of Controlled Variables

This paper considers the selection of controlled variables for the unconstrained degrees of freedom, such that near-optimal operation is achieved with constant setpoints (“self-optimizing control”). From a second-order Taylor approximation around the optimal point, we derive an exact local method. This may be used to find the optimal linear combination of measurements to use as controlled variables. We also derive the approximate singular value rule, which is very useful for quick screening and elimination of poor candidate variables.
Neuro-Fuzzy Controller Based on Model Predictive Control for a Nonlinear Underactuated Mechanical System

Fuzzy Logic Control (FLC) has been viewed as an effective feedback approach that simplifies the mathematical burden when developing complex control systems. The rationale of FLC is the digital implementation of “human-like” control rules provided either by experience or by an expert agent. Unfortunately, for high-precision control applications, FLC has proved difficult to identify an appropriate set of such rules. This drawback was compensated by the emergence of Neuro-FLC techniques based on artificial neural networks. Neuro-FLC has therefore focused on self-learning inference methodologies in which the required rules (and fuzzy sets) have been determined automatically. Despite some attractive features, many self-learning approaches present significant challenges when the hardware implementation is resource-constrained. One such challenge relates to what has been called the rule explosion problem: this describes the fact that FLC inference methodologies tend to create very large rule sets for multivariable control systems. Therefore, this paper proposes a Neuro-FLC based on a sub-cluster rule reduction in order to implement the algorithm on a resource constrained embedded system (e.g., ARM Microntroller). The Neuro-FLC technique learns from a Model Predictive Algorithm (MPC) and it is implemented for controlling an underacuated nonlinear mechanical system. The algorithm proposed is capable of deploying the optimal energy to the system that guarantees stability while the performance related to the time-response can be safely chosen by the user.
Inter-Turn Stator Winding fault Diagnosis for Permanent Magnet Synchronous Motor based Power Spectral Density Estimators

The inter-turn Stator Winding fault diagnostic system for Permanent Magnet Synchronous Motor is a substantial concern because of its consequential effects on electrical powertrain performance. However, extracting the characteristic of the fault can be difficult at the presence of additional interferences. Under this constraint, the present article suggests a new technique for inter-turn stator winding fault diagnosis. The detection method is mainly based on calculating the stator current Power Spectral Density. Primary, the model of Permanent Magnet Synchronous Motor under faulty operating condition is established by considering some parameters assigned to fault severity and fault location. Afterward, two Power Spectral Density methods have been compared in terms of their frequency resolution, and their effect in fault spectral components determination. These methods are namely the Welch and Autoregressive model based Burg. The results obtained confirm the preponderance of the Welch method over the Burg estimator in fault characteristics extraction for big record data.
Service Function Chaining on Programmable Data Plane

Service Function Chaining (SFC) enables advanced in-network packet processing by passing through an ordered set of service functions. However, it is challenging to achieve high performance because chaining functions like service function forwarders generally run on commodity servers, which offer limited throughput and require additional round-trip times. In this paper, we present P4-SFC, a novel SFC architecture that leverages the flexibility of emerging programmable switches for high performance SFC packet processing. P4-SFC achieves low latency and high throughput by migrating the chaining functions to programmable switches. Our benchmark results show that P4-SFC outperforms existing the server-based solution in chain latency.
Deep subsoiling of a subsurface-compacted typical hapludult under citrus orchard

Soil management practices which increase the root depth penetration of citrus are important to the longevity and yield maintenance of this plant, especially in regions where long periods of drought are common, even in soil conventionally subsoiled to a depth of 30-40 cm, when the orchard was first established. The objective of this study was to evaluate the efficiency of subsoiling on the physical and hydric properties of a Typical Hapludult and fruit yield in a 14-year-old citrus orchard located in Piracicaba, SP. The treatments consisted of: no-subsoiling (with no tilling of the soil after the orchard was planted); subsoiling on one side of the plant lines (SUB. 1); and subsoiling on both sides of the plant lines (SUB. 2). The subsoiling treatments were carried out 1.5 m from the plant lines and to a depth of 0.8 m. Soil samples were taken 120 days after this operation, at four depths, in order to determine physical and hydric properties. Fruit yield was evaluated 150 days after subsoiling. Subsoiling between the plant lines of an old established citrus orchard alters the physical and hydric properties of the soil, which is reflected in increased soil macroporosity and unsaturated hydraulic conductivity, and reduced soil bulk density, critical degree-of-compactness and penetration resistance. The improvements in the physical and hydric properties of the soil were related to an increase in fruit number and orchard yield.
A Low Cost Tactile Sensor for Large Surfaces Based on Deformable Skin with Embedded IMU

This paper deals with the design, fabrication and preliminary experimental results of a novel soft tactile sensing system for large surfaces, aiming at detecting the location of the contact points on the surface of the sensor. The sensor is composed by soft material with an IMU embedded on a deformable silicon-based surface. Using the data provided from the IMU during morphological variations of the soft sensor, the aim of the sensing system is to recognize different locations of single contact points and linear regions of contact points resembling the contacts with soft linear objects. In order to achieve this behaviour, an artificial neural network has been used, and evaluated trough experiments. The reported results show that the sensing system is able to discriminate between a grid of single contact locations, and among different linear regions of contact points, with a mean accuracy superior than 80%, and peak accuracy of 97.97% (for the single single point contact locations) and 97.54% (for the linear regions of contact points.)
A Combined Training Algorithm for RBF Neural Network Based on Particle Swarm Optimization and Gradient Descent

Radial basis function neural network usually has an extremely complex surface of the error function for its strong non-linear mapping capability. This brings us to lot of challenges for the neural network training, especially when you can’t find a suitable training method. The convergence rate of radial basis function neural network will be slow when the gradient descent algorithm is used for the training of neural network. Meanwhile, it also has a great possibility to fall into the local minimum which means the network may performs badly in the real prediction. On the other hand, the training method based on particle swarm optimization algorithm shows the weakness on local searching ability, although it can get rid of the trouble of falling into the local minimum than the gradient descent algorithm to some extent. In order to solve the above problems, on the basis of the characteristics of the two algorithms, this paper proposes a combined training algorithm for radial basis function neural network, which can overcome the disadvantage of the above algorithm and take advantage of the two in the meanwhile. In the combined algorithm, the neural network is trained by the particle swarm optimization algorithm at first. As the training progresses, we replace particle swarm optimization algorithm with the gradient descent algorithm after the threshold of convergence rate is reached. Besides, some data sets in UCI are simulated with the proposed training method. The performance of proposed training algorithm is validated by experimental result and compared to other training algorithm.
Y-Source Two-Stage Matrix Converter and Its Modulation Strategy

Aiming at the defect of low voltage transfer ratio of two-stage matrix converter(TSMC), the proposed topology combines the Y-source network with the TSMC to form the Y-source two-stage matrix converter(YSTSMC) in this paper, and proposes its modulation strategy. The Y-source network is located in the dc-link of TSMC, and uses a tightly coupled inductor with three windings to transfer energy, which can increase voltage transfer ratio. Based on the mathematical model of the Y-source network, a small-signal model is established and dynamic analysis is performed. The analysis result of the impact of the output voltage of the rectifier stage on the output voltage of the system can be obtained. The space vector pulse width modulation (SVPWM) strategy is proposed to control YSTSMC, and according to the fluctuation of dc-link voltage, the modulation ratio of inverter stage is corrected in real time. By analyzing the voltage transfer ratio of YSTSMC, and the calculation expression of the maximum voltage transfer ratio is obtained. Finally, the experimental results are provided to verify the feasibility and validity of the proposed method.
Frequency Comb MIMO OFDM Radar With Nonequidistant Subcarrier Interleaving

To overcome the necessity of high sampling rates for common orthogonal frequency-division multiplexing (OFDM)-based radar waveforms with a good range resolution, a scheme called frequency comb OFDM radar has recently been proposed. It keeps the sampling rates at the transmitter and receiver constant while simultaneously increasing the signal bandwidth in the radar channel. This letter describes the extension of the scheme for multiple-input multiple-output (MIMO) applications where a nonequidistant subcarrier interleaving can be used to prevent a reduction of the unambiguous range that occurs in case of regular allocation to the transmit channels. The functionality is demonstrated for the first time with a $4 \times 4$ MIMO system in a measurement scenario with static and emulated moving targets.
Secure 4G SEPS-AKA Protocol for UMTS Networks

User authentication is one of the crucial challenges that has to be addressed in UMTS (Universal Mobile Telecommunications Service) in order to grant the access to the services during handovers. Therefore, to provide the authentication the proposed protocol makes use of the existing Extensible Authentication Protocol (EAP). To provide the authentication in the LTE networks, an authentication framework EAP is used. EAPAKA is one of the methods of EAP framework. EAP-AKA, uses the mechanism Authentication and Key Agreement (AKA). EAPAKA is used in UMTS networks to secure the communication channel. In LTE, this mechanism is called as Evolved Packet System Authentication and Key Agreement (EPS-AKA). Mobile users in LTE networks access the packet data network through Evolved UMTS Terrestrial Radio Access Network (EUTRAN). However, with the thorough literature survey, it is proved that EPS-AKA mechanism is susceptible to the security vulnerabilities such as disclosure of the user identity, Man in the Middle attack and replay attacks. To overcome these security attacks, a new Secure Evolved Packet System (SEPS) protocol has been proposed. The formal security verification of the proposed SEPS protocol using widely accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool is presented in this paper. In addition to this, the proposed protocol is compared with the other protocols in terms of performance. The proposed SEPS protocol is efficient and robust. This makes the proposed protocol suitable for the practical implementation.
Small-Scale Horizontal Axis Hydrokinetic Turbine as Alternative for Remote Community Electrification in Sarawak

This paper review work on the design, construction, and field test of a small-scale horizontal axis hydrokinetic turbine for off-grid remote communities in the equatorial region, such as in Sarawak, Malaysia. The turbine will work at the upper course of rivers or tributaries, which are typically shallow, have a higher current velocity, and occasionally swarmed with debris from the rainforest after a torrential downpour. Current small-scale commercial turbines for extracting kinetic energy from the shallow river are relatively expensive, making it beyond reach to the underprivileged off-grid remote communities. Besides, their turbine design might not be entirely suitable for mitigating the debris in equatorial rivers. With most of the components or parts being custom made for the turbine, this will also result in a higher operating cost, and the device may succumb to longer downtime. Consequently, this study has chosen the approach of constructing a small-scale hydrokinetic turbine that is suitable for shallow equatorial rivers from readily available off-the-shelf materials to reduce the capital and operating costs while minimizing possible longer downtime. During the field test, the prototype was able to generate 92.29 W or 2.21 kWh per day from a 1.26 m/s river current velocity.