Miracle-dz/UniFAR-FTUs huggingface dataset:
A robust indoor localization algorithm based on polynomial fitting and Gaussian mixed model

Wireless sensor network (WSN) positioning has a good effect on indoor positioning, so it has received extensive attention in the field of positioning. Non-line-of sight (NLOS) is a primary challenge in indoor complex environment. In this paper, a robust localization algorithm based on Gaussian mixture model and fitting polynomial is proposed to solve the problem of NLOS error. Firstly, fitting polynomials are used to predict the measured values. The residuals of predicted and measured values are clustered by Gaussian mixture model (GMM). The LOS probability and NLOS probability are calculated according to the clustering centers. The measured values are filtered by Kalman filter (KF), variable parameter unscented Kalman filter (VPUKF) and variable parameter particle filter (VPPF) in turn. The distance value processed by KF and VPUKF and the distance value processed by KF, VPUKF and VPPF are combined according to probability. Finally, the maximum likelihood method is used to calculate the position coordinate estimation. Through simulation comparison, the proposed algorithm has better positioning accuracy than several comparison algorithms in this paper. And it shows strong robustness in strong NLOS environment.
3D Mapping for a Large Crane Using Rotating 2D-Lidar and IMU Attached to the Crane Boom

This paper describes a novel method for large-scale 3D mapping for construction cranes with an arbitrary motion of the sensor system (2D lidar and IMU) attached to the crane boom. A heavy lidar with a slowly rotating base is needed to make a large-scale map both vertically and horizontally for cranes. This sensor configuration and mapping conditions entail handling each 2D scan separately, making it difficult to adopt existing rotating 2D lidar-based methods that construct a virtual 3D scan from a set of 2D scans. In the proposed method, we introduce a complementary filter with moving average filtering for lidar pose estimation, which is more robust to severe vibration than Kalman filter-based methods. As there are only a small amount of overlaps between 2D lidar scans, we propose a map correction method based on a pose graph optimization with planar environmental constraints. We evaluate the proposed method in a simulation and a small-sized real environment and compare it with one of the state-of-the-art methods. The evaluation results reveal that the proposed method can accurately estimate the sensor poses, thereby generating a high-quality, large-scale 3D point cloud map.
Radar Signatures based Classification under Strict System Limitations

Due to the wide availability of 5G mobile networks, joint communication and radar sensing (JCRS) receives increasing attention by research communities. Here, radar sensing can be done as a side product of communication without additional hardware costs. In contrast to dedicated radar systems, the maximum range as well as the range resolution of these systems are limited. In this paper, we have investigated the limitations of radar systems through a classification problem, recognizing 10 digit-shaped foil balloons. For this purpose, we have recorded a dataset using a 77-GHz frequency modulated continuous wave (FMCW) radar. Furthermore, we have created multiple datasets with different quality levels by reducing the range resolution and the snapshot rate of the recorded measurements. Finally, we have analyzed the behaviours of two machine learning (ML) approaches, random forests (RF) and multilayer perceptron (MLP) to understand the limitations of restricted systems.
Visualization of 3D Point Clouds for Vehicle Detection Based on LiDAR and Camera Fusion

Obstacle detection problem is the most studied problem in computer vision. Methodologies of object detection generally work in a single modality, such as vision, Light Detection and Ranging (LiDAR), or laser. Multiple modalities like LiDAR with Camera are mainly used in robotics and automation. LiDAR is the best way for creating point clouds which are crucial for Object Detection. Object Detection systems currently deploy various deep learning models. In this paper, the detection of vehicles is proposed using Camera and Lidar data. Preprocessing is done with the help of the open3d library. Random sample consensus (RANSAC), Density-based spatial clustering of applications with noise (DBSCAN) Algorithms are used for visualization and clustering of the LiDAR point cloud. The detection of potential vehicles as clusters is proposed in this paper. Major datasets for this purpose are KITTI, Waymo Open Dataset, and the Lyft Level 5 AV Dataset.
Aspect-based Sentiment Analysis in Tourism Industry for Tourism Recommender System

The improvement of some aspects in tourism industry needs further study through aspect-based sentiment analysis based on tourist experience. The aim of this study is presenting the empiric results of aspect-based sentiment analysis to extract some useful aspects of services for leveraging tourism industry development based on user's feedback. The study uses customer review data from TripAdvisor website for developing classification model using machine learning algorithms including Decision Tree (DT), Random Forest Classifier (RFC), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost) models. Each machine learning model under study is trained using K-Fold Cross Validation. The empiric results found that accuracy of RFC achieved the highest result (99.5%) followed by XGBoost (87.3%), DT (82.9%), and SVM (80.5%). The extracted meaningful aspects from customer feedback give many valuable insights for service quality improvement in tourism industry in the effort to strengthening the rising-up of sustainability economic growth through tourism industry.
StaVen: A Secure and Lightweight PWA for Traffic Facilitation

To resolve the difficulties with the existing transport system such as traffic congestion, infrastructural improvements could no longer be the only resolution. It demands the aid of IT. Information technology supports components in the road transport systems such as traffic lights, sign boards, toll machines to be smart with the advancement of sensors to communicate with each other via wireless technology. Vehicular Ad-hoc NETwork (VANET), is one such research area, where there is a wireless communication between vehicles. With the advancement and sophistication of VANET and the development of high-level applications, there will be a massive disruption in the field of wireless communication in terms of rapid handovers, network availability, security, and safety. There is also an urgent need for effective trust management, given the dire consequences of acting on false information sent out by malicious peers. In this article, we discuss the design of a Progressive Web Application (PWA) called StaVen (which is an anagram of VANET-S where ‘S’ stands for Secured) to facilitate the traffic for first responders to help them reach the site of the accident within the golden period, and also improve the safety standards.
Atomic fluorescence in a mode-hopping laser field

The authors apply the recently developed theory of multiplicative stochastic correlation functions with Markovian random jumps to the problem of resonance fluorescence from a two-level atom in a strong laser field. The finite bandwidth of a single-mode laser or the appearance of distinct lines in a multimode laser is considered to be brought about by a random mode-switching process. The two-time dipole correlation functions, which determine the spectral distribution of the fluorescence and the temporal correlations between the emitted photons are averaged exactly over the stochastics of the driving field. The authors' explicit expressions only involve single-time averages, which can be easily evaluated for a given probability distribution. These results generalise and modify earlier results. The authors obtain closed expressions, rather than implicit differential equations, which can handle the continuous case as well, and take into account the initial correlations, which always arise when the laser coherence time is finite. They illustrate the dependence on the stochastics of the laser model by comparing the multimode description with the phase-diffusion process and the Lorentz wave, which all give rise to the same laser profile, but to a different fluorescence spectrum.
Stress-Coping Tweets Acquisition: A Two-phase Bootstrapping Method on Patterns and Semantic Features

Stress is integral to biological survival. However, without an appropriate coping response, high stress levels and long-term stressful situations may lead to negative mental health outcomes. Since the COVID-19 pandemic, remote assessment of mental health has become imperative. The majority of past studies focused on detecting users' stress levels rather than coping responses using social media. Because of the diversity of human expression and because people do not usually express stress and the corresponding coping response simultaneously, it is challenging to extract users' tweets about their coping responses to stressful events from their daily tweets. Consequently, there are two goals being pursued in this study: to anchor users' stress statuses and to detect their stress responses based on the existing stressful conditions. In order to accomplish these goals, we propose a framework that consists of two phases: the construction of stress dataset and the extraction of coping responses. Since the stressed users' data are lacking, the first phase is to construct a stress dataset based on stress-related hashtags, personal pronouns, and emotion recognition. In addition, to ensure the collection of enough tweets to observe the coping responses of stressed users, we broadened the survey's scope by collecting all tweets from the same user. In the second phase, stress-coping tweets were extracted by utilizing bootstrapping-based patterns and semantic features. The bootstrapping method was used to enrich word patterns for text expression and the semantic feature to assess the meaning of sentences. The collected data included the tweets of the stressed users identified in Phase 1 and the various coping responses from Phase 2 can contribute to developing a tool for the remote assessment of mental health. The experimental results show that our two-phase method outperforms the baseline and can help improve the efficiency of extracting stress-coping tweets.
Achieving Linear Scaling in Provisioning End-to-End Network Slicing

Network slicing (NS) will be one of the key enablers for 6G systems. NS allows different types of networks to co-exist virtually on the same physical infrastructure in order to support various implementations and verticals. Each type of network slice has features that can support various infrastructure implementations in order to achieve the performance requirements of the different applications without the need for redundant and underutilized infrastructures. As the numbers of verticals and users are increasing very rapidly. automated and fast network slice provisioning becomes necessary. In this paper, the provisioning time for an end-to-end (E2E) network slicing framework is reduced significantly reaching linear scaling in number of slice requests. This is achieved by utilizing techniques from the field of complex networks, namely Dijkstra's and A *. The results show significant reduction in the processing time of the network slicing provisioning as a function of the number of nodes in both the physical infrastructure and the virtual network slices. The results are compared to other techniques.
Analysis on Speech-Emotion Recognition with Effective Feature Combination

Speech-Emotion Recognition, (SER) is the process of attempting to recognize the emotional aspects of speech and the affective states irrespective of the semantic contents of the speech. This is to make capital out of the fact that underlying emotions are often reflected in the voice of a person. While studying speech-emotion recognition, it is a pertinent issue to find the combination of the audio features that we can extract from the speech and see which combination falls into place perfectly with a suitable classification system. But having a well-defined database for speech analysis and research is as much important to SER study. Hence, we have used the RAVDESS dataset. In our study we have used acoustic features that can reflect well-defined and sharp changes in emotional expression; for this we have extracted features like Amplitude Envelope, RMS and more from the time-domain, Spectral Centroid, Spectral bandwidth and more from the frequency-domain and Mel-frequency cepstrum coefficients and more from the time-frequency domain. We have used the MLPClassifier for the classification of emotions. Our results show that a combination of MFCC, mel spectrogram and chroma is able to best explain the speech emotions through the MLPClassifier.
DQN Algorithm Based Acoustic Vibration Control of Tiny Objects on a Chladni Plate

Controlling tiny objects by acoustic vibration is a new non-invasive and non-contact control method. Its non-contact control of tiny objects has great advantages in many applications of biology, chemistry, engineering and physics. The current research is generally to observe the impact of acoustic vibration on tiny objects, but the method of controlling the movement of objects is still the traditional control method. This paper mainly uses reinforcement learning to control object movement, which can optimize the control effect. In this paper, a system for controlling the motion of tiny objects through acoustic vibration is designed to control the non-contact displacement of objects, and a tiny object movement control algorithm based on Deep Q Network(DQN) algorithm is proposed to optimize the control effect of particles. The effectiveness of the algorithm in acoustic manipulation is verified by experiments.
Partially-Calibrated Sparse Subarrays for Direction-of-Arrival Estimation with Increased Degrees of Freedom

This paper investigates the problem of direction-of-arrival (DOA) estimation using multiple partially-calibrated sparse subarrays. In particular, we present the Generalized Coarray Multiple Signal Classification (GCA-MUSIC) DOA estimation algorithm to scenarios with partially-calibrated sparse subarrays. The proposed GCA-MUSIC algorithm exploits the difference coarray for each subarray, followed by a specific pseudo-spectrum merging rule that is based on the intersection of the signal subspaces associated to each subarray. This rule assumes that there is no a priori knowledge about the cross-covariance between subarrays. In that way, only the second-order statistics of each subarray are used to estimate the directions with increased degrees of freedom, i.e., the estimation procedure preserves the coarray Multiple Signal Classification and sparse arrays properties to estimate more sources than the number of physical sensors in each subarray. Numerical simulations show that the proposed GCA-MUSIC has better performance than other similar strategies.
Enhancement of Vehicular Visible Light Communication Using Spherical Detector and Custom Lens Combinations

Vehicular Visible light communication (VLC) technology has recently attracted much interest from researchers and scientists. This technology enables connectivity between vehicles and infrastructures along the road by using vehicles’ headlights and taillights as wireless transmitters. The reliability of vehicle-to-vehicle (V2V) VLC systems is affected by several factors, such as car mobility, optics system design, and visibility conditions, where the first two have the most impact on the VLC system performance. This paper, therefore, focuses on the relative positions of the cars and the design of the optics, especially on the receiving end, which has been proposed with the use of a polar detector instead of the rectangular detectors commonly used in the literature. We investigate the achievable gain compared to the conventional detector for different vehicle locations, utilizing a professional optical system design and ray tracing approach. Then, to improve the performance, we introduce the utilization of an imaging receiver by integrating the polar detector with different optical commercial lens combinations, such as Fresnel and Aspherical lenses. To further improve the V2V system performance, we propose a novel optical lens combination design by integrating double-convex lens with half-Plano-concave lens, which allows the correction of more optical aberrations, such as chromatic and spherical aberration. Utilizing the non-sequential ray tracing tools, we designed these VLC systems and perform a realistic channel modeling study considering the typical 3D CAD models of vehicles and roads as well as the possibility of horizontal and vertical movement between the vehicles. Based on the channel impulse responses (CIRs) obtained from the ray tracing simulations, we analyzed the performance of V2V VLC systems with all lens combinations at different vehicle positions on the road. We further investigated the impact of different system parameters on the overall V2V system performance, such as receiver diameter and bandwidth. The obtained results demonstrated that with a carefully chosen system and lens parameters, the proposed system design of lens combination provides an enhancement of up to 7 dB in total received power compared to the case without a lens. Our results also revealed that the proposed system design outperforms the benchmark ones for all lateral displacements and longitudinal distances.
Switching Langevin Dynamics for Gene Regulatory Networks

We consider the problem of estimating a gene regulatory network under switching noise dynamics. Several stochastic models based on discretized Langevin dynamics are introduced to model different types of intrinsic and extrinsic noise in gene regulatory networks. We propose a Bayesian technique that chooses the appropriate dynamical system to describe the noise. The resulting Bayesian hierarchical model uses a categorical distribution to probabilistically select the dynamical system. We then exploit particle filtering to infer the noise type as well as the state trajectory. We demonstrate the advantage of our proposed method through simulations.
A Novel Framework for Network Based Secure Message Transmission Based on Fragmentation and Cryptography

Here, crypto message fragmentation technique is applied to guarantee the immutability, non-repudiation, reliability, integrity and synchronization of the message during transmission. Multipath routing protocols' are used to get over the single path routing protocols' drawbacks and achieving high performance. The message is encrypted using standard encryption algorithms that are chosen at random: this randomization makes cryptanalysis more difficult. Data availability, data integrity and data's secrecy are addressed in an ad hoc network environment using Secure Message Transmission (SMT). There is no requirement for link encryption because the SMT technique operates end-to-end and between the source and destination nodes, assuming security association (SA). Although it may be used to support end-to-end communication encryption, data integrity and origin authentication are guaranteed by this SA between the end nodes.
A Quantitative Risk Assessment Model for Distribution Cyber-Physical System Under Cyberattack

An accurate and comprehensive risk assessment in a distribution cyber-physical system (DCPS) is essential to ensure its smooth operation, effective control, and exposure of hidden dangers and security implications. Unfortunately, prior risk assessment methods are mostly limited in two aspects. First, the current studies do not respect the complex network topology and intertwined devicewise dependencies, thereby failing to delineate and model the risk propagation mechanism in DCPS accurately. Second, the prior work tends to focus on gauging the risks underlying physical systems independently, overlooking the quantification of risk impact on physical systems incurred by the cyberattack. To fill the gap, in this article, we propose a unified risk assessment model that gauges the comprehensive security implication of DCPS in a Bayesian regime, in which the three key components, namely, the prior probability, posterior probability, and minimum load-loss ratio, are calculated via the cumulative densities, epidemic model, and optimal load shedding, respectively. We benchmark our proposed model on a public testbed, IEEE 39-bus system, with extensive experiments. The results substantiate the viability and effectiveness of our model and suggest that the vulnerability of DCPS is positively correlated with the three components in our Bayesian modeling. We hope this finding can shed some light on future research by providing a plausible apparatus for measuring the security implications of physical systems in DCPS under cyberattacks.
An Intrinsic McAulay-Seidman Bound for Parameters Evolving on Matrix Lie Groups

Lower bounds on the mean square error (MSE) are of fundamental importance to know the ultimate achievable estimation performance of any unbiased estimator. Even if the Cramér-Rae bound (CRB) is the most popular one, mainly due to its simplicity of calculation, other bounds are of interest in several applications. In this communication, $w$e derive a new intrinsic McAulay-Seidman bound (IMSB) for the estimation of unknown deterministic parameters lying on Lie groups, which generalize known results on the intrinsic CRB. The validity of the proposed IMSB is shown for the Gaussian observation model with unknown deterministic parameters belonging to SO(3) by comparing the IMSB with the intrinsic MSE.
Prediction Analysis of Diabetes Mellitus Based on Machine Learning Algorithm

Diabetes Mellitus is a metabolic disease of several etiologies characterized by chronic hyperglycemia with impaired carbohydrate, fat, and protein metabolism due to defects in insulin secretion, insulin action, or both. Diabetes will make approximately 6.7 million deaths in 2021, so one person dies every 5 seconds. Diabetes accounts for at least USD 966 billion dollars in health spending and has seen a 316% increase over the last 15 years. In Indonesia, Diabetes Mellitus will reach 21 million people in 2030 and is increasing. In the long term, it will place a significant financial burden on the health care system as well as on individuals and society as a whole. Based on the description above, the purpose of this study is to predict people with Diabetes Mellitus using the K-Nearest Neighbor (K-NN) algorithm. The data is taken from Kaggle and data processing uses Python. The testing model in this study used a confusion matrix and the testing accuracy used Recall and precision. The results show that K-NN has an accuracy value of 84.41%, which means for every 77 test samples there are 17 people diagnosed as positive. This is indicated by the recall value of 92.85% and the precision value of 86%. This result shows the K-NN algorithm has good accuracy in the prediction problem.
GAUSS-newton-like methods for nonlinear least squares with equality constraints-local convergence and applications

Starting from the parameter estimation problem in implicit models this paper describes algorithms for the numerical solution of nonlinear least squares problems with nonlinear equality constraints, The algorithms are based on the GAUSS-NEWTON/NEWTON-method. The combination of both techniques results in two-stage iterative procedures. A local convergence analysis of the described algorithms is given. The numerical methods are used for solving a parameter estimation problem arising in chemical kinetics