Miracle-dz/UniFAR-FTUs huggingface dataset:
LSTM and Filter Based Comparison Analysis for Indoor Global Localization in UAVs

Deep learning (DL) based localization and Simultaneous Localization and Mapping (SLAM) has recently gained considerable attention demonstrating remarkable results. Instead of constructing hand-crafted algorithms through geometric theories, DL based solutions provide a data-driven solution to the problem. Taking advantage of large amounts of training data and computing capacity, these approaches are increasingly developing into a new field that offers accurate and robust localization systems. In this work, the problem of global localization for unmanned aerial vehicles (UAVs) is analyzed by proposing a sequential, end-to-end, and multimodal deep neural network based monocular visual-inertial localization framework. More specifically, the proposed neural network architecture is three-fold; a visual feature extractor convNet network, a small IMU integrator bi-directional long short-term memory (LSTM), and a global pose regressor bi-directional LSTM network for pose estimation. In addition, by fusing the traditional IMU filtering methods instead of LSTM with the convNet, a more time-efficient deep pose estimation framework is presented. It is worth pointing out that the focus in this study is to evaluate the precision and efficiency of visual-inertial (VI) based localization approaches concerning indoor scenarios. The proposed deep global localization is compared with the various state-of-the-art algorithms on indoor UAV datasets, simulation environments and real-world drone experiments in terms of accuracy and time-efficiency. In addition, the comparison of IMU-LSTM and IMU-Filter based pose estimators is also provided by a detailed analysis. Experimental results show that the proposed filter-based approach combined with a DL approach has promising performance in terms of accuracy and time efficiency in indoor localization of UAVs.
Kannada Alphabets Recognition using Decision Tree and Random Forest Models

Character recognition system is the most emerging technology for digitizing handwritten documents from any type of script. The classification has a variety of approaches and the techniques of classification are preferred as they utilize a combination of different methods of classification to give a more accurate machine-learning model. One such ensemble classification technique is Random Forest, which aims to classify data with the help of several decision trees. This article, describes the whole process of alphabet recognition from preprocessing of the datasets in creating a model for binary and multiclass classification using MATLAB and is aimed at highlighting the advantages of random forest classification on a single decision tree for prediction of handwritten letters.
On the Challenges of Open World Recognition Under Shifting Visual Domains

Robotic visual systems operating in the wild must act in unconstrained scenarios, under different environmental conditions while facing a variety of semantic concepts, including unknown ones. To this end, recent works tried to empower visual object recognition methods with the capability to i) detect unseen concepts and ii) extended their knowledge over time, as images of new semantic classes arrive. This setting, called Open World Recognition (OWR), has the goal to produce systems capable of breaking the semantic limits present in the initial training set. However, this training set imposes to the system not only its own semantic limits, but also environmental ones, due to its bias toward certain acquisition conditions that do not necessarily reflect the high variability of the real-world. This discrepancy between training and test distribution is called domain-shift. This letter investigates whether OWR algorithms are effective under domain-shift, presenting the first benchmark setup for assessing fairly the performances of OWR algorithms, with and without domain-shift. We then use this benchmark to conduct analyses in various scenarios, showing how existing OWR algorithms indeed suffer a severe performance degradation when train and test distributions differ. Our analysis shows that this degradation is only slightly mitigated by coupling OWR with domain generalization techniques, indicating that the mere plug-and-play of existing algorithms is not enough to recognize new and unknown categories in unseen domains. Our results clearly point toward open issues and future research directions, that need to be investigated for building robot visual systems able to function reliably under these challenging yet very real conditions.
High hydrogen storage capacity in calcium‐decorated silicene nanostructures

We report our first‐principle's calculations on the possibility of Ca‐decorated silicene sheet and zigzag silicene nanoribbons (ZSiNRs) as hydrogen storage medium. We predict that Ca atoms prefer to disperse on the silicene or at the edges of ZSiNRs without clustering, due to the strong binding between Ca and Si atoms. By adsorbing Ca atoms on the both sides of silicene, the hydrogen storage capacity can reach to 6.17 wt% (gravimetric density) with an average adsorption energy of 0.265 eV H2−1, which are quite optimial for reversible hydrogen adsorption and desorption at ambient conditions. The hydrogen storage capacity can be further improved to 8.43 wt% with the average adsorption energy in the range of 0.182–0.269 eV H2−1 in the Ca‐decorated ZSiNRs. The adsorption of H2 on Ca‐decorated Si nanostructures is mainly dominated by polarization and the orbital hybridizations. These findings indicate that the Ca‐decorated silicene and ZSiNRs have potential application in hydrogen storage.
Granger Causality-Based Pain Classification Using EEG Evoked by Electrical Stimulation Targeting Nociceptive Aδ and C Fibers

Classification of pain levels from evoked electroencephalography (eEEG) has been achieved for the purpose of objective assessment of pain. However, differentiation of acute and chronic pain resulting from the activation of nociceptive nerves, the <inline-formula> <tex-math notation="LaTeX">$\text{A}\delta $ </tex-math></inline-formula> and C fibers, respectively, has remained unsolved, mainly due to the lack of effective features. Granger causality (GC) was applied to show the role of specific brain areas in the differentiation between pain and tactile sensation but yet was used to differentiate nociceptive fiber activations. The purpose of this study is two-fold: first, to develop pain perception classification systems using GC as features, and second, to identify causal connectivity for effective differentiation of nociceptive fiber activations. The eEEG responding to three stimulation waveforms, 1 Hz square waves, 5 Hz sine waves, both targeting C fibers, and 250 Hz sine waves, targeting <inline-formula> <tex-math notation="LaTeX">$\text{A}\delta $ </tex-math></inline-formula>, were recorded. Three GC categories based on channel areas, frequency bands, and stimulation waveforms were computed from the eEEG and used as features to classify nociceptive fiber activations and pain levels. It was found that GCs in the alpha band produced the highest average classification accuracy of 99.71%, followed by the gamma band, with 98.66%. Meanwhile, the GC feature contributing most to differentiating <inline-formula> <tex-math notation="LaTeX">$\text{A}\delta $ </tex-math></inline-formula> and C was found in the alpha band. The GC flow computed from the results of 5 Hz and 250 Hz stimulation had an important role in the prediction of mild pain and maximum pain, respectively. These results demonstrated the high potential of GC as features of pain-related information.
Multi-modal Sequence-to-sequence Model for Continuous Affect Prediction in the Wild Using Deep 3D Features

Continuous affect prediction in the wild is a very interesting problem and is challenging as continuous prediction involves heavy computation. This paper presents the methodologies and techniques used in our contribution to predict continuous emotion dimensions i.e., valence and arousal in ABAW competition on Aff-Wild2 database. Aff-Wild2 database consists of videos in the wild labelled for valence and arousal at frame level. Our proposed methodology uses fusion of both audio and video features (multi-modal) extracted using state-of-the-art methods. These audio-video features are used to train a sequence-to-sequence model that is based on Gated Recurrent Units (GRU). We show promising results on validation data with simple architecture. The overall valence and arousal of the proposed approach is 0.22 and 0.34, which is better than the competition baseline of 0.14 and 0.24 respectively.
An Enhanced Invasive Weed Optimization in Resource-Constrained Project Scheduling Problem

In this research, an enhanced invasive weed optimization (EIWO) has been proposed to solve resource-constrained project scheduling problem (RCPSP) which subjects to the makespan minimization. Firstly, a hybrid population initialization method is illustrated to improve the quality of initial solutions. Secondly, to enhance the local exploitation ability, a local search approach is embedded in the spatial dispersal process. Thirdly, an improved competitive exclusion based on acceptance probability is proposed. At the end of this article, EIWO is tested and verified by standard benchmark problems from PSPLIB. Compared with the existing algorithms through computer numerical experiments, the new EIWO algorithm is more effective and efficient in solving RCPSP.
Improving Visual- Inertial Odometry with Robust Outlier Rejection and Loop Closure

In order to obtain more accurate pose estimation, the visual-inertial odometry (VIO) system with outlier rejection and loop closure is proposed in this paper. Considering that feature matching is an important part in the front-end of the VIO system, its accuracy will affect the performance of the entire system. So we introduce an outlier rejection method of the grid-based motion statistics (GMS) algorithm to the VIO system. And for more robust feature correspondence and better camera pose estimation, we propose an improved GMS method to eliminate the mismatched points. Besides, we adopt the loop closure strategy to correct the cumulative error of the VIO system. Finally, we estimate the camera pose, velocity and IMU bias simultaneously by minimizing the loss function which contains reprojection error and IMU error. A large number of experiments on EuRoC demonstrate that the proposed method outperforms the advanced VIO system ROVIO and is comparable to the state-of-the-art VIO system OKVIS.
Prospects for the advancement of the TikTok in the age of 5G communication

The rise of TikTok has recently lifted a new wave of UGC(User Generated Content) creation, showing a remarkable spread effect at home and abroad. As a 15-second music short video community Targeting younger audiences, TikTok's fragmented content is continually creating commercial value. With the advent of 5G network, the short video will come profound changes. Thus, it is essential to understand what technologies people will accept to help to improve the design, evaluation, and predict user responses to new technologies. This study empirically analyzes how the introduction of technical characteristics, as a diffusion strategy, affects consumers' intention to use. A survey was conducted with 295 people in China, and a revised TAM strategy was used to analyze the acceptability of technology. User adoption behaviours were analyzed rigorously, adding perceived enjoyment, social interactions, perceived risk as parameters of the basic TAM. This study indicates that perceived interactivity is the most important factor determining the user's attitude. Perceived risk has a negative influence on perceived interactivity. Perceived ease of use positively affects perceived usefulness. Perceived enjoyment has a direct impact on the perceived ease of use. On this basis, our result showcases the potential paths of development of social media companies like TikTok in the 5G era. Immersive content creation and realtime interaction will turn into a new development pattern in the TikTok future, such as social interaction in augmented reality. In the meantime, TikTok should also pay attention to protecting users' privacy to reduce users' concerns about the risk of new technology.
28 FDSOI RF Figures of Merit down to 4.2 K

This work presents a detailed RF characterization of 28 FDSOI nMOSFETs at cryogenic temperatures down to 4.2 K. Two main RF Figures of Merit (FoMs), i.e. current gain cutoff frequency (fT) and maximum oscillation frequency (fmax), as well as parasitic elements of the small-signal equivalent circuit are extracted from the measured S-parameters. The observed behavior of RF FoMs versus temperature is discussed in terms of small-signal equivalent circuit elements, both intrinsic and extrinsic (parasitics). This study suggests 28 FDSOI nMOSFETs as a good candidate for future cryogenic applications down to 4.2 K and clarifies the origin and limitations of the performance.
An Efficient Bidirectional broadcasting using Signal Initiation and Data Aggregation for WSN

Wireless Sensor networks are the need of the day due to the ease of installation and use. Moreover, with the current advancements of technology, it’s quite difficult to rely on wired networks due to overhead like the cable expenses, connection and repairing cost, limitation in movement and so on. In this Research work implements a broadcast network where Band based bi-directional algorithm is used. Using this algorithm, sensor data can be broadcasted towards the mobile sink with less expense and reduces data redundancy. Simulations are used to illustrate the efficiency of the transferability of data and energy spent by the system.
A Comprehensive Study on EEG Signals for Seizure Detection Techniques

Convulsions is a medical condition that affect the nervous system of human body. In medical language it is called as seizures. It is not a disease but it is a disorder, it usually occurs in the human brain of all the age groups irrespective of their age, starting from child age group to adults. It is usually caused by sudden change in the brain or a sudden electrical activity that occurs in the brain. Based on the duration, Convulsions are classified into different types, they are local convulsions, focal convulsion, generalized tonic clonic convulsions and partial convulsions. Convulsions will usually occur in the trauma patients [Road traffic accidents] and Post-operative i.e., brain surgery patients, alcoholic [drunker], stress life style conditions and Hypocalcaemia patients [Low calcium levels]. This detection is possible by analyzing the electroencephalogram (EEG) signals. Confluent advantages of all product functions (PFs) are once again transmitted in the high-performance genetic algorithm (GA)-technique to the same division model. Average classification accuracy of the GA-SVM technique is approximately more than 89.01% in all attempts.
A Fast Gridless Method for High Order Motion Parameter Estimation Based on PUMA

This paper considers the efficient estimation of the high order motion parameters of the maneuvering targets. A fast gridless long-time coherent integration algorithm based on maximum likelihood (ML) estimation is proposed. After some matrix manipulations, we transform the ML estimation to a weighted least square (WLS) problem and solve it by Principal-singular-vector Utilization for Modal Analysis (PUMA). The proposed method avoids the grid effect with low computational complexity because the motion parameters are estimated by solving a polynomial equation instead of multi-dimensional searching. Simulations are conducted for validating the effectiveness and illustrating the high performance of the proposed method.
A Short-Term Optimal Scheduling Model for Wind-Solar-Hydro Hybrid Generation System With Cascade Hydropower Considering Regulation Reserve and Spinning Reserve Requirements

In order to meet the challenges brought by the high penetration of intermittent and fluctuating wind and solar power, a short-term optimal scheduling model for wind-solar-hydro hybrid generation system with cascade hydropower is established with the objective of minimizing the amount of abandoned wind, solar and hydro power and maximizing the stored energy of hydro stations. Cascade hydropower is considered to provide spinning reserve and regulation reserve to ensure the security of system. Mixed Integer Linear Programming (MILP) method is used for the short-term optimal schedule of wind-solar-hydro hybrid generation system. The case studies show that spinning reserve and regulation reserve are beneficial to the hybrid generation system, and verify the practical applicability of the proposed model.
Achievable NOMA Gain for Downlink Ultra-Dense Small Cell Networks

In this paper, we analyze the downlink performance of non-orthogonal multiple access (NOMA) in ultra-dense networks (UDNs) incorporated into line-of-sight (LOS)/non-line-of- sight (NLOS) propagation.000 Nakagami fading is also considered for the small-scale fading. The expressions for coverage probability (CP) and network throughput are obtained. The numerical results show that the performance of NOMA first increases with the growth of base station (BS) density, and then decreases as the UDNs becomes denser. Moreover, the NOMA still show the superiority with network densification.
Kinematics modeling and analysis of deep-sea hydraulic manipulator

In order to verify the rationality of a self-designed 6DOF deep-sea hydraulic manipulator, this paper uses the D-H method to design the kinematics model, analyzes the forward kinematics and inverse kinematics of the manipulator, and verifies the accuracy of the kinematics model of the manipulator by using MATLAB. In addition, the working space of manipulator is analyzed based on Monte Carlo method, and cloud point diagram of manipulator workspace is obtained. The results show that the parameters of the self-designed manipulator are reasonable and the workspace is intuitive and accurate. This paper provides a reference and theoretical basis for the research of manipulator control algorithm and trajectory planning.
ESinGAN: Enhanced Single-Image GAN Using Pixel Attention Mechanism for Image Super-Resolution

Recently, the SinGAN model emerged, and it was famous for generating from a single image. The SinGAN model achieves superior performance to other advanced models for image super-resolution task (trained on a dataset or a single image). However, SinGAN does not consider the importance of feature pixels on the feature map. In this paper, we propose an Enhanced SinGAN model (ESinGAN), an unconditional generative model that can improve the defects of SinGAN using the Pixel Attention mechanism. To evaluate the proposed ESinGAN model’s performance, we carry out the experiments on a benchmark dataset. Through the experiments, we have achieved better performance than SinGAN and proved the effectiveness of the proposed method. Not only is the image significantly improved visually, but the PSNR and SSIM values of the model are also considerably increased. Besides, ESinGAN runs as fast as SinGAN under the same experimental environment.
Motion Guided Feature-Augmented Network for Action Recognition

Motion information is a crucial factor to identify human action recognition in videos. Existing state-of-the-art methods use traditional optical flow features representing the short-term motion information as the supplementary to appearance features, achieving performance improvements. However, conventional optical flow needs high computation cost and can’t be optimized in an end-to-end fashion. To reduce the computation complexity and make the computation fully-differential, we utilize a representation flow layer to mimic the process of iterative flow optimization. Besides, to attach high importance to the most critical elements, motion information is used to locate motion salient objects. Toward this end, we propose a motion-augmented module to adaptively aggregate multiple optical flow features, deriving motion saliency maps that can alleviate the unreliable motion problem. Then, these motion saliency maps attend appearance features, yielding motion-augmented features. Further, joining the motion-augmented module and flow layer, we propose a Motion Guided Feature-Augmented Network to learn motion-enhanced features. Experiments on two challenging action recognition benchmarks verify that the motion-augmented module can locate salient objects that are relevant to actions, and the proposed network can achieve the competitive performance with state-of-the-art methods.
Evaluation for ESD (Education for Sustainable Development) to achieve SDGs at University

Goal 4: Quality Education is one of goals in SDGs and it says the necessity for promoting ESD (education for sustainable development) as one of targets. For Humanitarian Technology, ESD should be also one of key concepts. In universities, ESD has become the core subject. Since its founding in 1928, the Chiba University of Commerce (CUC) has accepted many young people to study under the educational philosophy of “practical scholarship with high morality” as it started as a school for accounting. Recently, CUC is promoting “RE (Renewable Energy) 100” university to consider ethical consumption and generation as well. Therefore, ESD matches the philosophy of CUC. As part of ESD, CUC is organizing the special lecture for SDGs to provide students opportunities to think about what we can do for developing sustainable societies. In this paper, we evaluate the ESD at CUC using information technologies such as morphological analysis, Bag of Words, and word2vec model. Using information technologies, the evaluation for ESD can be done effectively.
Mood Versus Identity: Studying the Influence of Affective States on Mobile Biometrics

Mobile device usage data such as mobile app use and acceleration measurements fluctuate often as individuals carry out their daily tasks. As these data have emerged in recent years as promising biometric identifiers, it is important to understand the many causes of these variations such that these systems can adapt without degradation in performance. In this paper, we seek to understand the impact of changes in a person’s mood on the performance of a mobile biometric system using a publicly available dataset of 27 subjects. We explore the verification and identification tasks, along with mood prediction from smartphone data. We achieved an equal error rate of 3% and a d-prime value of 5.05 for the verification task, wherein experiments showed that verification is minimally influenced by an individual’s mood, although negative arousal slightly degraded performance. We created a multi-class problem to study the identification task, achieving an average 83% F 1-score. Here, we observed that subjects with lower identification accuracy (<70%) experienced fewer mood changes compared to the average (13.48), while all but one subject with high identification accuracy (>95%) experienced more mood changes compared to the average. Contrasting previous claims, our findings suggest that frequent changes in mood may have little negative impact performance. Finally, positive arousal and negative valence yielded the highest area under the curve (0.67) for mood prediction. This was also the class associated with the highest average genuine and lowest average imposter scores for verification experiments, suggesting a correspondence between recognition and mood prediction tasks that applications such as sensor-enhanced mHealth apps could leverage.