Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a Basic Educational Kit for Robot Development Using Deep Neural Networks

In this paper, we propose a basic educational kit for robotic system development using deep neural networks (DNNs). To develop systems robust to changes in dynamic environments, much research is focusing on learning-based recognition and robotic manipulation systems. However, existing robotic techniques and DNNs are not systematically integrated and packages for beginners have not yet been developed. Therefore, we developed a robotic system using DNNs and a system manual to serve as a basic educational kit that can be easily used by anyone. Our goal was to educate beginners in both robotics and machine learning, especially when DNNs are used. Initially, we set the following requirements for the kit: (1) easy to understand; (2) experience-based; and (3) applicable in many areas. We analyzed the research and development of DNNs and divided the process into Date Collecting (DC), Machine Learning (ML), and Task Execution (TE) steps. Finally, we applied our hierarchical system architecture to a physical robotic grasping system. We implemented the DC and TE steps in the physical robotic system by using robotics middleware and in the ML step, we prepared a sample script.
Spatial Scheduling of Informative Meetings for Multi-Agent Persistent Coverage

In this work, we develop a novel decentralized coordination algorithm for a team of autonomous unmanned aerial vehicles (UAVs) to surveil an aggressive forest wildfire. For dangerous environmental processes that occur over very large areas, like forest wildfires, multi-agent systems cannot rely on long-range communication networks. Therefore, our framework is formulated for very restrictive communication constraints: UAVs are only able to communicate when they are physically close to each other. To accommodate this constraint, the UAVs schedule a time and place to meet in the future to guarantee that they will be able to meet up again and share their belief of the wildfire state. In contrast with prior work, we allow for a discrete time, discrete space Markov model with a large state space as well as restrictive communication constraints. We demonstrate the effectiveness of our approach using simulations of a wildfire model that has $10^{298}$ total states.
Speaker to Emotion: Domain Adaptation for Speech Emotion Recognition with Residual Adapters

Despite considerable recent progress in deep learning methods for speech emotion recognition (SER), performance is severely restricted by the lack of large-scale labeled speech emotion corpora. For instance, it is difficult to employ complex neural network architectures such as ResNet, which accompanied by large-sale corpora like VoxCeleb and NIST SRE, have proven to perform well for the related speaker verification (SV) task. In this paper, a novel domain adaptation method is proposed for the speech emotion recognition (SER) task, which aims to transfer related information from a speaker corpus to an emotion corpus. Specifically, a residual adapter architecture is designed for the SER task where ResNet acts as a universal model for general information extraction. An adapter module then trains limited additional parameters to focus on modeling deviation for the specific SER task. To evaluate the effectiveness of the proposed method, we conduct extensive evaluations on benchmark IEMOCAP and CHEAVD 2.0 corpora. Results show significant improvement, with overall results in each task outperforming or matching state-of-the-art methods.
Supervised Descent Method for Full-wave Microwave Imaging

This work studies the application of the supervised descent method (SDM) to two dimensional full-wave microwave imaging. In SDM-based inversion, the reconstructed models are updated based on the learned descent directions of the cost function and the residual between simulated and measured data. It provides a flexible method to incorporate prior information into the inversion that can improve both the quality of reconstruction and the efficiency of reconstruction. In this work, we studied both pixel- and model-based SDM inversion. An online restart scheme is applied to further reduce data misfit. The inversion algorithm is validated using both synthetic and experimental data. The results show good generalization ability of this machine-learning-based inversion scheme, and the reconstruction can be achieved in both high efficiency and accuracy.
Entrepreneurial Intentions Among University's Students In Malaysia

The issue of employability is rising year by year, and Malaysia is also taking it as a serious challenge. The Malaysian government has carried out various initiatives to address the issue of employability; conversely, one of them was successfully promoting the development of entrepreneurship by increasing entrepreneurial courses for instance. The intention was to increase the entrepreneurial intentions amongst university's students to create job opportunities for themselves which could lead to a boost for a developing nation's economy as well. However, the government's initiatives did not succeed as they were expected. The main reason for it ending as a flopped plan was due to failing to identify an individual's intentions. Therefore, the primary purpose of this research is to inspect the Malaysian universities student's intention of being an entrepreneur because, without intention, they might not even consider the option of initiating their own business. This research adapted and integrated the two most common models of examining the entrepreneurial intention, i.e, Theory of Planned Behaviour (‘TPB’) by Ajzen and Shapero and Sokol's model of an Entrepreneurial Event (‘SEE’). This is a quantitative study and a total of 160 students from different Malaysian universities were surveyed using non-probability sampling techniques and by a well-established instrument with the standardized questionnaire, to examine hypothesized relationships. SPSS was used for analyzing the demographic profile and SMART-PLS was utilized for testing the developed hypotheses. The results show that attitude, subjective norms, and perceived behavioural control positively affect the perceived desirability as well as perceived feasibility. Moreover, perceived desirability and perceived feasibility are also positively associated with an entrepreneurial intention. Furthermore, perceived desirability is found as a mediator for the relationship among predictors of entrepreneurial intention but perceived feasibility fails to mediate the association of subjective norm and entrepreneurial intention. By understanding the background of university student's intentions towards entrepreneurship, the Malaysian government and organizations can better present themselves in formulating better initiatives to stimulate and encourage youth to have entrepreneurial intentions in order to initiate their own business. This study also offers some useful managerial implications using the advanced technique of IPM to enhance the performance of critical factors that contribute towards entrepreneurial intentions.
Relocating Mining Microseismic Earthquakes in a 3-D Velocity Model Using a Windowed Cross-Correlation Technique

Microseismic source location is a fundamental research interest in real-time microseismic monitoring and hazard risk assessment, and it provides the basis for determining the fracture zones and calculating seismic source parameters of the microseismicity (e.g., the event magnitude, the focal mechanism). In the present work, we carefully relocate some microseismic earthquakes that occurred in the Yongshaba mine (China) using the shooting 3-D ray-tracing (3D-RT) method based on a high-resolution 3-D velocity model, which is determined by a large amount of seismic data. Furthermore, a semiautomatic waveform cut method based on the cross-correlation (CC) technique (WCC) is developed for a quick, robust and precise determination of the direct P-phase relative delay times. Compared with the full waveform cross-correlation (FWCC)-based method, the WCC-based method is free from the influence of complex later-phase waveform spectra. To verify the proposed method, we artificially generate the locations of 892 synthetic microseismic events, the P-phase travel times of which are calculated based on the given 3-D velocity model and the 3-D ray-tracing technique. The relocated hypocentres of these synthetic events obtained by the developed method are improved compared with those obtained by a homogeneous velocity model and the straight line-ray tracing based L1 norm time difference (TD) method (HV-SL-TD1), as well as those obtained by the 3-D velocity model and the straight line-ray tracing based L1 norm and L2 norm TD methods (3D-SL-TD1 and 3D-SL-TD2). Finally, we apply the proposed method to eight experimental blasts with pre-measured locations. The synthetic and application tests show that, despite some multi-path phenomena existing in the ray-tracing methods, the WCC-based method performs as well as the experienced manual picking method, and the results obtained by the 3D-RT location have smaller location errors (~30 m) than those of the homogeneous velocity model-based methods (>40 m). The TD-based method is slightly better than the trigger time (TT)-based method when using the same norm, and the location precision of the L1 norm-based methods have a better resilience to larger picking errors than that of the L2 norm-based methods. Further improvements can be obtained by improving the resolution of the 3-D velocity model and including spatial voids in the model (e.g., tunnels and cavities) for the inversion.
An Improved Target Detection Method Based on Sonar Image Processing

Autonomous underwater vehicle (AUV) shows great significance in the marine exploration and subsea pipeline inspection. In order to realize the submersible’s autonomous obstacles avoidance, we need to detect the target from the complex seabed reverberation based on sonar image processing at first. Traditional detection methods generally rely on simple clustering or threshold segmentation, however most of them cannot meet the requirements of the practical applications on account of their slow processing speed and low detection accuracy.In this paper, we propose an improved target detection method, in which the influence of high noise, fish and seabed reverberation in sonar images can be suppressed and the real-time processing speed can be improved. Firstly, the real-time data flow collected by multi-beam sonar in different scenes is expressed as series of images and pre-processed by the system. For the second step, according to the characteristics of sonar image, an improved OTSU algorithm is carried out to detect the target by recalculating the optimum threshold and limiting its search range, then the contour detection algorithm is combined to extract the foreground target from background successfully, and the morphological processing is used to further reduce the impact of small bright spots. The final step is to transplant the method into the image processing board, and debug the real-time system to analyze and accomplish further image pre-processing for the cooperation with obstacle avoidance part. Experimental results show that the proposed method is robust and works accurately and efficiently under complex circumstances. Compared with the traditional methods, it achieves significant performance improvement, with better real-time performance and higher accuracy rate.
Periodic Metallic Posts Loaded Compact TM01-TE11 Mode Converter

A novel and compact mode converter design based on metallic posts, to convert circular waveguide TM01 to TE11 mode, is presented in this paper. The metallic posts have square cross section and placed in lower semicircular waveguide to provide phase shift for mode conversion. The proposed mode converter is designed at 3 GHz and maximum efficiency of 98% is achieved at this frequency, based on the simulation results. Mode conversion is verified by radiation pattern plot, with and without mode converter. The mode conversion efficiency is more than 90% over a bandwidth of 100 MHz. Due to purely metallic design, high power capability is upto 100 MW peak power. Compact, lightweight, symmetric structure and aligned ports are advantages of the design and makes it suitable for portable and airborne High Power Microwave (HPM) systems.
An Optimal Fuzzy Control Method for Nonlinear Time-Delayed Batch Processes

An optimal fuzzy controller design scheme is proposed to address the influence of time delay and disturbance on the control performance of nonlinear batch processes. First, a two-dimensional (2D) equivalent Takagi-Suguno (T-S) fuzzy error model is formulated. By introducing a quadratic performance index function and adopting 2D Lyapunov–Krasovskii theory, the existence condition of the optimal fuzzy control law is given. Furthermore, its solvable condition, which depends on time-delay bounds, is constructed in terms of linear matrix inequalities, and its gain is obtained by using an optimization algorithm. This design has the advantages of faster tracking and better tracking performance. Finally, two different algorithms (with and without optimization) are used to control the water level of a triple-capacity water tank. The results show that the presented strategy is more effective and feasible.
Implementation of 5G Authentication and Key Agreement Protocol on Xbee Networks

While 5G systems are being deployed across the globe in a phased manner, trust models for communication between the User Equipment (UE) and the core network play a vital role in fulfilling the network goals promised by 5G. Towards that end, the 5G - Authentication and Key Agreement (5G-AKA) protocol has been standardised by the 3GPP consortium after multiple iterations and corrective responses. Despite this progress, there is a requirement to collate information from multiple ETSI technical specifications and several research contributions to implement a standardised security architectural model for 5G networks. Identifying the absence of single point repository containing 5G security protocols, this demonstration provides first hand collation and description of state-of-the-art details in simple and unified manner. In this demonstration, we implement the 3GPP compliant 5G-AKA protocol on a network of XBee S2C devices wherein the protocol involves a sequence of modules involving secure authentication, key exchange and payload transmission. To implement the security protocol, we extract the precise recommendations of 3GPP and also use open source algorithms wherever the implementation is left open as proprietary solution. Other than implementing standard modules from 3GPP specifications, we also showcase novel key exchange and provenance-recovery mechanisms that potentially fit the 5G standards in the framework of device-to-device (D2D) communication.
Assembly Challenge and Solution for GaN on Si substrate

Gallium nitride (GaN)-based high-electron-mobility transistor (HEMT) power devices have advantage like high voltage operation, high temperature application and high switch speed compared to the traditional silicon (Si)-based semiconductor devices. Due to GaN high cost, it had development combined on a variety of substrates, including sapphire, silicon carbide (SiC) and silicon (Si). Si substrates have become attractive because of the lower device cost and the ability to use standard semiconductor processing lines. The challenges of GaN on Si substrate are big lattice mismatch and hard characteristic of III–V material. Die sawing is a key process due to the GaN brittle characteristic that can cause chipping and mircocracks that cause bad thermal dissipation for devices.In paper, we optimize dicing capabilities in the Flip Chip Scale Package (FCCSP). Due to GaN layer has higher hardness and melting point than Si substrate, Laser Grooving is recommended to use before the blade saw GaN layer and reduce top chipping. On GaN layer result of Laser Grooving cut is very clean on the topside of the die with no chipping issues. Backside quality is a key monitor item of die sawing due to the excessive force required to break and separate the hard III–V material. This paper finds out a suitable die sawing process parameter to meet backside chipping spec and a competitive result compared to Si die sawing.The characterization analysis is including typical reliability testing (Temperature Cycle Test, un-bias HAST and High Temperature Storage Test) are used as verification monitor items for package. The result shows no delamination and SAT issues in the reliability stage.
A Multiple Classifiers System for Automatic Multimodal Brain Tumor Segmentation

We propose a system consisting of multiple classifiers to increase the accuracy of the automatic segmentation of multimodal brain tumor, by breaking the segmentation problem into sub-problems. We convert a multiclass classification problem into one-vs-all classification problem. The system involves five random forest classifiers. Instead of using a classifier to classify all classes, each classifier is trained in a two-class fashion to predict one tumor class. The dataset used to evaluate the classifiers is 3D MRI dataset from the BRATS challenge in MICCAI 2016. The results of the experiments show an improvement in the segmentation accuracy over using a classifier to classify all classes.
Chronic Fluoxetine Impairs the Effects of 5-HT1A and 5-HT2C Receptors Activation in the PAG and Amygdala on Antinociception Induced by Aversive Situation in Mice

Growing evidence suggests an important role of fluoxetine with serotonin 5-HT1A and 5-HT2C receptors in the modulation of emotion and nociception in brain areas such as the amygdala and periaqueductal gray (PAG). Acute fluoxetine impairs 5-HT2C (but not 5-HT1A) receptor activation in the amygdaloid complex. Given that fluoxetine produces its clinical therapeutic effects only when given chronically, this study investigated the effects of chronic treatment with fluoxetine on the effects produced by 5-HT1A or 5-HT2C receptors activation in the amygdala or PAG on fear-induced antinociception. We recorded the effects of chronic fluoxetine on serotonin and its metabolite 5-hydroxyindoleacetic acid (5-HIAA) levels as well as serotonin turnover; 5-HT1A and 5-HT2C receptor protein levels in the amygdala and PAG. Also, we evaluated the effects of chronic fluoxetine combined with intra-amygdala or intra-PAG injection of MK-212 (a 5-HT2C agonist; 0.63 nmol) or 8-OH-DPAT (a 5-HT1A agonist; 10 nmol) on the antinociceptive response in mice confined in the open arm of the elevated plus-maze (EPM). Nociception was assessed with the writhing test induced by intraperitoneal injection of 0.6% acetic acid. Results showed that fluoxetine (20 mg/kg, s.c.) enhanced the open-arm induced antinociception (OAA) and reduced the number of writhes in mice confined in the enclosed arm, featuring an analgesic effect. In addition, fluoxetine increased the expression of 5-HT2C receptors and 5-HT levels whereas reduced its turnover in the amygdala. While fluoxetine did not change 5-HT and 5-HIAA levels, and its turnover in the PAG, it up-regulated 5HT1A and 5-HT2C receptors in this midbrain area. Chronic fluoxetine (5.0 mg/Kg, an intrinsically inactive dose on nociception) antagonized the enhancement of OAA produced by intra-amygdala or intra-PAG injection of MK-212. Fluoxetine also impaired the attenuation of OAA induced by intra-amygdala injection of 8-OH-DPAT and totally prevented OAA in mice that received intra-PAG 8-OH-DPAT. These results suggest that (i) 5-HT may facilitate nociception and intensify OAA, acting at amygdala 5-HT1A and 5-HT2C receptors, respectively, and (ii) fluoxetine modulates the OAA through activation of 5-HT2C receptors within the PAG. These findings indicate that chronic fluoxetine impairs the effects of 5-HT1A and 5-HT2C receptors activation in the amygdala and PAG on fear-induced antinociception in mice.
Effect of Angle of View and Partial Sleep Deprivation on Distance Perception

The present study aimed to investigate the effects of intensive effort on egocentric distance perception according to different angles of view after sleep deprivation at the beginning (SDB) or at the end (SDE) of the night and after a normal sleep night (NNS). Ten male students soccer players (age 22.8 ± 1.3 years; body mass 72.0 ± 10.4 kg; body height 180.0 ± 3.0 cm) performed a repeated cycling (RS) exercise (10 × 6 s maximal cycling with 24 s in between) after SDB, SDE, and NNS. They were asked to estimate three distances (i.e. 15, 25, and 35 m) before and after RS from different angles of view [i.e. in front (0°) and in side (45° left and 45° right)]. For 35 m, distance estimation was better during NNS compared to SDB and SDE for the front and the two side angles either before or after RS (p < 0.05). Concerning 25 m, distance estimation was better after compared to before RS for the front angle during the NNS session (p < 0.05). For 15 m, distance estimation was better during NNS than SDB and SDE for the front and both side angles after RS (p < 0.05). We concluded that partial sleep deprivation negatively affected the estimation of the egocentric distance for the three angles of view either at rest or after RS exercise.
Beyond non-backtracking: non-cycling network centrality measures

Walks around a graph are studied in a wide range of fields, from graph theory and stochastic analysis to theoretical computer science and physics. In many cases it is of interest to focus on non-backtracking walks; those that do not immediately revisit their previous location. In the network science context, imposing a non-backtracking constraint on traditional walk-based node centrality measures is known to offer tangible benefits. Here, we use the Hashimoto matrix construction to characterize, generalize and study such non-backtracking centrality measures. We then devise a recursive extension that systematically removes triangles, squares and, generally, all cycles up to a given length. By characterizing the spectral radius of appropriate matrix power series, we explore how the universality results on the limiting behaviour of classical walk-based centrality measures extend to these non-cycling cases. We also demonstrate that the new recursive construction gives rise to practical centrality measures that can be applied to large-scale networks.
Ecological mechanisms explaining interactions within plant–hummingbird networks: morphological matching increases towards lower latitudes

Interactions between species are influenced by different ecological mechanisms, such as morphological matching, phenological overlap and species abundances. How these mechanisms explain interaction frequencies across environmental gradients remains poorly understood. Consequently, we also know little about the mechanisms that drive the geographical patterns in network structure, such as complementary specialization and modularity. Here, we use data on morphologies, phenologies and abundances to explain interaction frequencies between hummingbirds and plants at a large geographical scale. For 24 quantitative networks sampled throughout the Americas, we found that the tendency of species to interact with morphologically matching partners contributed to specialized and modular network structures. Morphological matching best explained interaction frequencies in networks found closer to the equator and in areas with low-temperature seasonality. When comparing the three ecological mechanisms within networks, we found that both morphological matching and phenological overlap generally outperformed abundances in the explanation of interaction frequencies. Together, these findings provide insights into the ecological mechanisms that underlie geographical patterns in resource specialization. Notably, our results highlight morphological constraints on interactions as a potential explanation for increasing resource specialization towards lower latitudes.