Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of two semantic segmentation databases for smoke detection

Researchers have found strong correlation between warm summer and the frequency and intensity of fires around the world. Climate models due to global warming tells us that average summer temperature will increase drastically in the next few decades entailing an increase of wildfire. Computer vision is a good tools to detect and locate an incipient fire and prevent a rapid spread of fire destroying huge forest areas as in Australia or Brazil. Smoke is the first clue of an incipient fire that can be detected by a camera to warn firemen to act as quickly as possible. Convolutional neural networks and semantic segmentation can achieve this task by giving location and scale of the fire to firemen. In order to efficiently train this type of network architectures, we need a database composed of many images and corresponding masks. The complexity of the smoke in terms of shape, texture, color and intensity is difficult to segment properly. The diversity of smoke types in the image database is crucial for generalizing prediction in real-world circumstances. Numerous research papers proposed new network architectures for segmenting smoke in visible images spectrum and tested the accuracy of the segmentation on their database. Database that, for the most of the time, was not available. This article deals with comparison of a network performances on two smoke databases and highlight the importance of a rich images database in terms of quality rather than quantity.
Impedance Matching Optimization for GPS Frequency Band based on CSRR Load Technique Neighboring Triangular Slot Antenna

Impedance matching optimization based on the CSRR load technique neighboring a triangular slot is present. This antenna comprises an asymmetrical triangular slot fed by a coplanar waveguide (CPW) with stair-step impedance and a complementary split-ring resonator (CSRR). The CSRR is applied to control the operating frequency of 1.575 GHz, while the triangular slot will control the frequency of 2.45 GHz and 5.5 GHz. In the simulation, the antenna can support the operating frequency of 1.575 GHz, 2.45 GHz, and 5.5 GHz for GPS and WLAN IEEE802.11 a/b/g application. Moreover, the antenna size is compact with a dimension of 51.3 mm x 44 mm. The 2-D radiation pattern is omnidirectional at the operating frequency of 1.575 GHz and 2.45 GHz and directional at the operating frequency of 5.5 GHz. However, the peak gain of the antenna is approximate 2 dBi.
Neural Network Based Drone Recognition Techniques With Non-Coherent S-Band Radar

Increasing drone accidents and abuse has led to the development of drone detection systems. In this study we proposed two classification approaches for the recognition of flying drones using non-coherent S-band radar. The radar data including drone, ship and bird targets is collected in various scenarios. While both of the proposed classification methods utilize neural networks, the first one is trained with the features extracted from track information. The second technique is based on radar images, meaning that video classification methods are employed. The results were investigated with respect to the correct classification performance and false alarm rate. Experimental results have shown that the image based method is better at recognizing not only drone but also birds and ships with a lower false alarm rate.
Automotive Synthetic Aperture Radar Imaging using TDM-MIMO

Using multiple-input multiple-output (MIMO) structure for synthetic aperture radar (SAR) image formation brings advantages such as azimuth or elevation beamforming with small aperture size. Due to the simplicity of time-division multiplexing (TDM), it became a widely used approach for 77-GHz automotive radar systems. However, this method brings some limitations for platform movement and represents particular drawbacks in the reconstructed images. This paper tries to eliminate restrictions and reduce artifacts using a random antenna activation pattern. The proposed idea leads to significant improvements for various simulation scenarios for our defined antenna structure.
Seven Level T-Type Switched Capacitor Inverter Topology for PV Applications

The conventional neutral point clamped (NPC) multilevel inverter topology needs voltage balancing circuits to balance the dc-link capacitor, the number of component count is high and output voltage is half of the input voltage which increase the size of the source side dc/dc converter in PV applications. In this paper, a new topology of seven level neutral point clamped inverter is developed. The proposed inverter has a self-voltage boosting capability using a floating capacitor to boost the output voltage one and half of the input voltage. In this proposed topology, no additional sensors are required for the floating capacitor voltage stability, which results in minimizing the complexity of designing the inverter. The direct link between the neutral point and the mid-point of the dc-link capacitors significantly reduces the leakage current and common mode voltage in this inverter. A thorough comparison between the developed topology and recent suggested topologies is carried out which set the benchmark for the proposed one due to its lower switch count and higher voltage gain. The proposed topology is verified in simulation and prototype hardware model and results are discussed with dynamic load variations. The efficiency of the inverter is 97.1% @200W and low as 88.1 % @ inductive load. The voltage THD is 17.01% for simulation and 19.3% I for experimental is results.
Brain Affective System Inspired Control Architecture: An Application to Nonlinear System

This paper presents a bio-inspired intelligent controller to enhance the performance of a nonlinear system. The controller is designed by capturing the emotional intelligence of mammalian brain mediated by the limbic system in which certain parts are responsible for generating emotions and these can be combined together as brain affective system inspired control architecture (BASIC). This controller has been used to cope with nonlinearities present in control applications. In this paper brain inspired control architecture is proposed while incorporating the sensory cortex explicitly in the architecture and the computational equations of various modules are suitably developed. The performance of the proposed controller is analyzed on a typical nonlinear system, namely, Permanent magnet synchronous motor for speed control, harmonics in stator phase currents and ripples produced in electromagnetic torque at different operating conditions. Its performance is compared with the existing control techniques in offline simulations as well as in real-time Hardware-in-loop environment. The results establish the computational efficiency, accuracy and robustness of the proposed controller.
Sequence-Frame Coupling Admittance Analysis and Stability of VSC Connected to Weak Grid

This paper develops small-signal admittance of grid-connected voltage source converter (VSC) in the positive-negative (pn) sequence-frame for ease of stability analysis. Based on the converter admittance in pn frame, the stability problems associated with the coupling admittance created by the phase-locked loop (PLL) and outer loop controllers are studied. To overcome the disadvantage of the traditional outer loop controller and improve system stability, an improved outer loop controller that can reduce the coupling admittance is proposed. VSC system connected to a weak grid is modelled in MATLAB-SIMULINK to validate the theoretical study and demonstrate the effectiveness of the improve outer controller.
Creating Inclusive Engineering and Computer Science Classes – the impact of COVID-19 on student experiences and perceptions of gender inclusivity

The global COVID-19 pandemic necessitated a rapid shift by universities to online and remote education. This paper contributes to understanding of gender inclusive learning in face to face and virtual educational environments, specifically in the context of teamwork within engineering and computer science classes. Students’ experiences and perceptions of gender inclusive teamwork before and during the COVID-19 pandemic were collected using an online survey (N = 217). A principal component analysis revealed five components in students’ reported experiences: Psychological Safety, Perceptions of the Facilitator, Confidence, Respect for Diversity, and Perceived Fairness. A multivariate analysis of covariance with gender and semester (before or during COVID-19) as the independent variables and age as the covariance found main effects only. Students’ perceived psychological safety and confidence were higher in the online teamwork during COVID-19 than in the previous semester of face-to-face teamwork. The result is reassuring, as it indicates, consistent with other literature, that inclusion in student teamwork is likely to be improved when online elements are introduced.
Walking Step Length Estimation Using Waist-Mounted Inertial Sensors With Known Total Walking Distance

This paper presents a new constrained optimization-based smoothing algorithm for walking step length estimation using waist-mounted inertial sensors, where the total walking distance is known. The walking trajectory is estimated by double integrating acceleration. Due to sensor noises, the walking step length estimation accuracy degrades as the walking distance becomes longer. To tackle this problem, we introduce a known distance straight-line walking trajectory constraint and a constant speed constraint to the smoothing algorithm. These constraints reduce the walking step estimation accuracy degradation even for long walking distance. Two experiments are conducted to evaluate the pedestrian trajectory and walking step length estimation accuracy. The accuracy of a 20 m walking trajectory estimation has been investigated in the first experiment. This experiment compares the estimated position and velocity with Lidar-based references. The second experiment is to demonstrate the usefulness of the proposed walking step length estimation method. The result shows that the average of mean relative errors is 0.6801% for three different walking speed levels. The proposed method can be applied to generate training data for walking step length estimation without requiring spatial infrastructure.
User Interface Redesigning Based on a Usability Design Framework

A usable user interface relies heavily on experience of designers. The resulted user interface helps ensure that important and critical information from senders reaches the intended recipients. Achievement of this particular goal is crucial, especially for mobile government application. Nowadays, mobile application becomes an essential tool to connect governments and citizens. So citizens can access government services as well as fulfill their responsibilities as good citizens. Unusable or unable to access applications affects how well citizens perform their duties and live their lives. In order to promote the use of mobile government applications, usability needs to be designed carefully. Several of the applications are tasked to delivery important or urgent messages to citizens. Such applications include Early Warning System (EWS), Earthquake Report, Weather Forecast, etc. Their main goals are to deliver critical information which could affect millions of lives. Incorporating usability to the user interface can facilitate effective dissemination of such critical information to prevent catastrophic loss. Thus, it is important that the intended citizens receive the right information, in the right place at the right time. Most usability models, which are used to guide engineering of usability, are not specific to mobile government applications. Many of them are too abstract. Thus, a more appropriate usability model and a comprehensive design guideline are needed by the stakeholders involved with the design of mobile government applications. This paper illustrates how such applications can be redesigned based on the framework proposed in this paper to achieve a higher level of usability.
Chipless RFID Sensing for Dielectric Property of Light Weight Concrete

This paper presents a passive chipless RFID tag (Electromagnetic sensor) based on the modified printed monopole antenna with a frequency filter technique. The proposed chipless RFID sensor is the wireless sensor technology with the nondestructive technique (NDT) testing. This proposed RFID tag is designed to obtain both determination and identification (ID) properties for a sensor application. To determine the dielectric constant of MUT under test, the circular monopole tag antenna is modified using adding the elliptical shape to achieve the wideband operating frequency. Moreover, this sensor is used the stripline frequency filter technique to obtain the 1-bit ID at the frequency of 2.77 GHz. The CST Microwave Studio simulator program uses to design and optimize the passive chipless RFID parameters. Simulated results show the proposed chipless RFID sensor can identify and determine the dielectric property of the LWC under test.
Multi-Style Unsupervised Image Synthesis Using Generative Adversarial Nets

Unsupervised cross-domain image-to-image translation is a very active topic in computer vision and graphics. This task has two challenges: 1) lack of paired training data and 2) numerous possible outputs from a single image. The existing methods rely on either paired data or perform one-to-one translation. A novel Multi-Style Unsupervised image synthesis model using Generative Adversarial Nets (MSU-GAN) is proposed in this paper to overcome these disadvantages. Firstly, the encoder-decoder structure is used to map the image to domain-shared content features space and domain-specific style features space. Secondly, to translate an image into another domain, the content code and the style code are combined to synthesize the resulting image. Finally, the bidirectional cycle-consistency loss is used for the unpaired training data; the inter-domain adversarial loss and the reconstruction loss are used to ensure the output image’s realism. Simultaneously, MSU-GAN is able to synthesize multi-style images due to disentangled representation. A Multi-Style Unsupervised Feature-Wise image synthesis model using Generative Adversarial Nets (MSU-FW-GAN) based on the MSU-GAN is proposed for the shape variation tasks. There are two different testing strategies, which include random style transfer and style guide transfer. For objective comparison, the proposed model performs well on all evaluation metrics. The random style transfer experiment results show that compared with CycleGAN on the photo2portraits dataset, MSU-FW-GAN FID, IS scores dropped by 12.77% and 8.06%. For the summer2winter dataset, MSU-GAN FID and IS scores increased by 24.51% and 3.64%. Qualitative results show that without paired training data, MSU-GAN and MSU-FW-GAN can synthesize multi-style and better realistic images on various tasks.
Time-to-Digital Converter IP-Core for FPGA at State of the Art

The Field Programmable Gate Array (FPGA) structure poses several constraints that make the implementation of complex asynchronous circuits such as Time–Mode (TM) circuits almost unfeasible. In particular, in Programmable Logic (PL) devices, such as FPGAs, the operation of the logic is usually synchronous with the system clock. However, it can happen that a very high–performance specifications demands to abandon this paradigm and to follow an asynchronous implementative solution. The main driver forcing the use of programmable logic solutions instead of tailored Application Specific Integrated Circuits (ASIC), best suiting an asynchronous design, is the request coming from the research community and industrial R&D of fast–prototyping at low Non Recursive Engineering (NRE) costs. For instance in the case of a high–resolved Time–to–Digital Converter (TDC), a signal clocked at some hundreds of MHz implemented in FPGA allows implementing a TDC with resolution at ns. If a higher resolution is required, the signal frequency cannot be increased further and one of the aces up the designer’s sleeve is the propagation delay of the logic in order to quantize the time intervals by means of a so-called Tapped Delay–Line (TDL). This implementation of TDL–based TDC in FPGAs requires special attention by the designer both in making the best use of all available resources and in foreseeing how signals propagate inside these devices. In this paper, we investigate the implementation of a high–performance TDL–TDC addressed to 28–nm 7–Series Xilinx FPGA, taking into account the comparison between different technological nodes from 65–nm to 20–nm. In this context, the term high–performance means extended dynamic–range (up to 10.3 s), high–resolution and single–shot precision (up to 366 fs and 12 ps r.m.s respectively), low differential and integral non–linearity (up to 250 fs and 2.5 ps respectively), and multi–channel capability (up to 16).
Wide Bandgap DC–DC Converter Topologies for Power Applications

Over the last decade, dc–dc power converters have attracted significant attention due to their increased use in a number of applications from aerospace to renewable energy. The interest in wide bandgap (WBG) power semiconductor devices stems from outstanding features of WBG materials, power device operation at higher temperatures, larger breakdown voltages, and the ability to sustain larger switching transients than silicon (Si) devices. As a result, recent progress and development of converter topologies, based on WBG power devices, are well-established for power conversion applications in which classical Si-based power devices show limited operation. Currently, Si carbide (SiC) and gallium nitride (GaN) are the most promising semiconductor materials that are being considered for the new generation of power devices. The use of new power semiconductor devices, such as GaN high electron mobility transistors (GaN HEMTs), leads to minimization of switching losses, allowing high switching frequencies (from kHz to MHz) for realizing compact power converters. Finally, design recommendations and future research trends are also presented.
Adaptive Feedforward Neural Network Control With an Optimized Hidden Node Distribution

Composite adaptive radial basis function neural network (RBFNN) control with a lattice distribution of hidden nodes has three inherent demerits: 1) the approximation domain of adaptive RBFNNs is difficult to be determined a priori; 2) only a partial persistence of excitation (PE) condition can be guaranteed; 3) in general, the required number of hidden nodes of RBFNNs is enormous. This article proposes an adaptive feedforward RBFNN controller with an optimized distribution of hidden nodes to suitably address the above demerits. The distribution of the hidden nodes calculated by a K-means algorithm is optimally distributed along the desired state trajectory. The adaptive RBFNN satisfies the PE condition for the periodic reference trajectory. The weights of all hidden nodes will converge to the optimal values. This proposed method considerably reduces the number of hidden nodes, while achieving a better approximation ability. The proposed control scheme shares a similar rationality to that of the classical PID control in two special cases, which can thus be seen as an enhanced PID scheme with a better approximation ability. For the controller implemented by digital devices, the proposed method, for a manipulator with unknown dynamics, potentially achieves better control performance than model-based schemes with accurate dynamics. Simulation results demonstrate the effectiveness of the proposed scheme. This result provides a deeper insight into the coordination of the adaptive neural network control and the deterministic learning theory. Impact Statement—Adaptive RBFNN control learns to control a robot manipulator when both the structures and parameters of the target robot are unknown in advance. Unfortunately, current adaptive RBFNN controllers need a large-scale neural network to approximate the dynamics of the robot manipulator, and the learning performance cannot be guaranteed to converge. The proposed method in this article not only reduces the scale of neural networks to substantially alleviate the computational burden but also evidently achieves better learning performance. Simulation examples show that this method increases the control accuracy by more than nine times and reduces the scale of neural networks by 35 times as compared to the traditional lattice scheme. Intuitively, people usually believe that a model-based controller with an accurate dynamic model may achieve the best control performance. However, compared with the model-based controller with an accurate dynamic model, the proposed control scheme with an unknown dynamic model even further increases the control accuracy by 1.5 times. This technology provides a more straightforward path for engineers, who may be not experts in complicated control system analysis methods, to design an adaptive robotic controller to achieve enhanced performance.
Classical and Quantum Improvements of Generic Decision Tree Constructing Algorithm for Classification Problem

In the work, we focus on the complexity of the generic of a decision tree classifier constructing algorithm. The decision tree is constructed in ( ( running time in the classical case, where is a class numbers, is the input data size, is an attributes number, is a tree height. We offer two options for improving the classical version of the generic algorithm, the running time of using these options are ( (general case) and ( (for independent attributes). After that we suggest a quantum improvement, which uses quantum subroutines like the amplitude amplification and the Dȕrr-Høyer minimum search algorithms. The running time of the quantum algorithms is ( √ ( ) that is better than the complexity of the classical algorithm in the general case.
Object Tracking with Histogram weighted Centroid augmented Siamese Region Proposal Network

In this paper, we propose an histogram weighted centroid based Siamese region proposal network for object tracking. The original Siamese region proposal network uses two identical artificial neural networks which take two different images as the inputs and decide whether the same object exist in both input images based on a similarity measure. However, as the Siamese network is pre-trained offline, it experiences many difficulties in the adaptation to various online environments. Therefore, in this paper we propose to incorporate the histogram weighted centroid feature into the Siamese network method to enhance the accuracy of the object tracking. The proposed method uses both the histogram information and the weighted centroid location of the top 10 color regions to decide which of the proposed region should become the next predicted object region.
SVM And k-NN Based Classification of Wheat Leaf Disease

: Agriculture is the primary source of livelihood in India for about 58 per cent of India’s population. Protection of this source is main concerns. Preventing plants from different type of disease is a great challenge now. Automatic detection of infected plant can reduce the manual effort and the same time decrease processing time. Image Processing and Machine learning are the two main weapon, by which we can achieve this detection of infected plant. In this paper we mainly focused on Wheat Leaf disease classification by two machine learning approaches SVM and K-nearest neighbor.
DECOMPOSITION OF THE KRONECKER SUMS OF MATRICES INTO A DIRECT SUM OF IRREDUCIBLE MATRICES

In this paper, we decompose (under unitary similarity) the Kronecker sum A A (= A⊗ I + I ⊗A) into a direct sum of irreducible matrices, when A is a 3×3 matrix. As a consequence we identify K(A A) as the direct sum of several full matrix algebras as predicted by Artin– Wedderburn theorem, where K(T ) is the unital algebra generated by T and T ∗.