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This paper proposes a data valuation algorithm for inertial measurement unit-based human activity recognition (IMU-based HAR) data based on meta reinforcement learning. Unlike previous studies that received feature-level input, the algorithm in this study added a feature extraction structure to the data valuation algorithm, and it can receive raw-level inputs and achieve excellent performance. As IMU-based HAR data are multivariate time-series data, the proposed algorithm incorporates an architecture capable of extracting both local and global features by inserting a transformer encoder after the one-dimensional convolutional neural network (1D-CNN) backbone in the data value estimator. In addition, the 1D-CNN-based stacking ensemble structure, which exhibits excellent efficiency and performance on IMU-based HAR data, is used as a predictor to supervise model training. The Berg balance scale (BBS) IMU-based HAR dataset and the public datasets, UCI-HAR, WISDM, and PAMAP2, are used for performance evaluation in this study. The valuation performance of the proposed algorithm is observed to be excellent on IMU-based HAR data. The rate of discovering corrupted data is higher than 96% on all datasets. In addition, classification performance is confirmed to be improved by the suppression of discovery of low-value data. [ABSTRACT FROM AUTHOR]

Indoor 3D positioning is useful in multistory buildings, such as shopping malls, libraries, and airports. This study focuses on indoor 3D positioning using wireless access points (AP) in an environment without adding additional hardware facilities in large-scale complex places. The integration of a deep learning algorithm into indoor 3D positioning is studied, and a 3D dynamic positioning model based on temporal fingerprints is proposed. In contrast to the traditional positioning models with a single input, the proposed method uses a sliding time window to build a temporal fingerprint chip as the input of the positioning model to provide abundant information for positioning. Temporal information can be used to distinguish locations with similar fingerprint vectors and to improve the accuracy and robustness of positioning. Moreover, deep learning has been applied for the automatic extraction of spatiotemporal features. A temporal convolutional network (TCN) feature extractor is proposed in this paper, which adopts a causal convolution mechanism, dilated convolution mechanism, and residual connection mechanism and is not limited by the size of the convolution kernel. It is capable of learning hidden information and spatiotemporal relationships from the input features and the extracted spatiotemporal features are connected with a deep neural network (DNN) regressor to fit the complex nonlinear mapping relationship between the features and position coordinates to estimate the 3D position coordinates of the target. Finally, an open-source public dataset was used to verify the performance of the localization algorithm. Experimental results demonstrated the effectiveness of the proposed positioning model and a comparison between the proposed model and existing models proved that the proposed model can provide more accurate three-dimensional position coordinates. [ABSTRACT FROM AUTHOR]

Most of the traditional image feature point extraction and matching methods are based on a series of light properties of images. These light properties easily conflict with the distinguishability of the image features. The traditional light imaging methods focus only on a fixed depth of the target scene, and subjects at other depths are often easily blurred. This makes the traditional image feature point extraction and matching methods suffer from a low accuracy and a poor robustness. Therefore, in this paper, a light field camera is used as a sensor to acquire image data and to generate a full-focus image with the help of the rich depth information inherent in the original image of the light field. The traditional ORB feature point extraction and matching algorithm is enhanced with the goal of improving the number and accuracy of the feature point extraction for the light field full-focus images. The results show that the improved ORB algorithm extracts not only most of the features in the target scene but also covers the edge part of the image to a greater extent and produces extracted feature points which are evenly distributed for the light field full-focus image. Moreover, the extracted feature points are not repeated in a large number in a certain part of the image, eliminating the aggregation phenomenon that exists in traditional ORB algorithms. [ABSTRACT FROM AUTHOR]

With the aging of the human body and the reduction in its physiological capacities, falls have become a huge threat to individuals' physical and mental health, leading to serious bodily damage to the elderly and financial pressure on their families. As a result, it is vital to design a fall detection algorithm that monitors the state of human activity. This work designs a human fall detection algorithm based on hierarchical decision making. First, this work proposes a dimensionality reduction approach based on feature importance analysis (FIA), which optimizes the feature space via feature importance. This procedure reduces the dimension of features greatly and reduces the time spent by the model in the training phase. Second, this work proposes a hierarchical decision-making algorithm with an XGBoost model. The algorithm is divided into three levels. The first level uses the threshold approach to make a preliminary assessment of the data and only transfers the fall type data to the next level. The second level is an XGBoost-based classification algorithm to analyze again the type of data which remained from the first level. The third level employs a comparison method to determine the direction of the falling. Finally, the fall detection algorithm proposed in this paper has an accuracy of 98.19%, a sensitivity of 97.50%, and a specificity of 98.63%. The classification accuracy of the fall direction reaches 93.44%, and the algorithm can efficiently determine the fall direction. [ABSTRACT FROM AUTHOR]

The human visual system (HVS) mechanism has been successfully introduced into the field of infrared small target detection. However, most of the current detection algorithms based on the mechanism of the human visual system ignore the continuous direction information and are easily disturbed by highlight noise and object edges. In this paper, a multi-scale strengthened directional difference (MSDD) algorithm is proposed. It is mainly divided into two parts: local directional intensity measure (LDIM) and local directional fluctuation measure (LDFM). In LDIM, an improved window is used to suppress most edge clutter, highlights, and holes and enhance true targets. In LDFM, the characteristics of the target area, the background area, and the connection between the target and the background are considered, which further highlights the true target signal and suppresses the corner clutter. Then, the MSDD saliency map is obtained by fusing the LDIM map and the LDFM map. Finally, an adaptive threshold segmentation method is employed to capture true targets. The experiments show that the proposed method achieves better detection performance in complex backgrounds than several classical and widely used methods. [ABSTRACT FROM AUTHOR]

In this paper, possibilities for network traffic protection in future hybrid passive optical networks are presented, and reasons for realizing and utilizing advanced network traffic protection schemes for various network traffic classes in these networks are analyzed. Next, principles of the Prediction-based Fair Wavelength and Bandwidth Allocation (PFWBA) algorithm are introduced in detail, focusing on the Prediction-based Fair Excessive Bandwidth Reallocation (PFEBR) algorithm with the Early Dynamic Bandwidth Allocation (E-DBA) mechanism and subsequent Dynamic Wavelength Allocation (DWA) scheme. For analyzing various wavelength allocation possibilities in Hybrid Passive Optical Networks (HPON) networks, a simulation program with the enhancement of the PFWBA algorithm is realized. Finally, a comparison of different methods of the wavelength allocation in conjunction with specific network traffic classes is executed for future HPON networks with considered protection schemes. Subsequently, three methods are presented from the viewpoint of HPON network traffic protection possibilities, including a new approach for the wavelength allocation based on network traffic protection assumptions. [ABSTRACT FROM AUTHOR]

Multi-UAV (multiple unmanned aerial vehicles) flying in three-dimensional (3D) mountain environments suffer from low stability, long-planned path, and low dynamic obstacle avoidance efficiency. Spurred by these constraints, this paper proposes a multi-UAV path planning algorithm that consists of a bioinspired neural network and improved Harris hawks optimization with a periodic energy decline regulation mechanism (BINN-HHO) to solve the multi-UAV path planning problem in a 3D space. Specifically, in the procession of global path planning, an energy cycle decline mechanism is introduced into HHO and embed it into the energy function, which balances the algorithm's multi-round dynamic iteration between global exploration and local search. Additionally, when the onboard sensors detect a dynamic obstacle during the flight, the improved BINN algorithm conducts a local path replanning for dynamic obstacle avoidance. Once the dynamic obstacles in the sensor detection area disappear, the local path planning is completed, and the UAV returns to the trajectory determined by the global planning. The simulation results show that the proposed Harris hawks algorithm has apparent superiorities in path planning and dynamic obstacle avoidance efficiency compared with the basic Harris hawks optimization, particle swarm optimization (PSO), and the sparrow search algorithm (SSA). [ABSTRACT FROM AUTHOR]

Monitoring a patient's vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data adhere to strict integrity and availability requirements. Reducing the volume of healthcare data and the frequency of transmission can improve a device's battery life via an inference algorithm. Furthermore, this approach creates issues for improving transmission metrics related to accuracy and efficiency, which are traded-off against each other, with increasing accuracy reducing efficiency. This paper demonstrates that machine learning (ML) can be used to overcome the trade-off problem. The damped least-squares algorithm (DLSA) is used to enhance both metrics by taking fewer samples for transmission whilst maintaining accuracy. The algorithm is tested with a standard heart rate dataset to compare the metrics. The results showed that the DLSA provides the best performance, with an efficiency of 3.33 times for reduced sample data size and an accuracy of 95.6%, with similar accuracies observed in seven different sampling cases adopted for testing that demonstrate improved efficiency. This proposed method significantly improve both metrics using ML without sacrificing one metric over the other compared to existing methods with high efficiency. [ABSTRACT FROM AUTHOR]

Text document clustering is one of the data mining techniques used in many real-world applications such as information retrieval from IoT Sensors data, duplicate content detection, and document organization. Swarm intelligence (SI) algorithms are suitable for solving complex text document clustering problems compared to traditional clustering algorithms. The previous studies show that in SI algorithms, particle swarm optimization (PSO) provides an effective solution to text document clustering problems. This PSO still needs to be improved to avoid the problems such as premature convergence to local optima. In this paper, an approach called dynamic sub-swarm of PSO (subswarm-PSO) is proposed to improve the results of PSO for text document clustering problems and avoid the local optimum by improving the global search capabilities of PSO. The results of this proposed approach were compared with the standard PSO algorithm and K-means algorithm. As for performance assurance, the evaluation metric purity is used with six benchmark data sets. The experimental results of this study show that our proposed subswarm-PSO algorithm performs best with high purity comparing the standard PSO and K-means traditional algorithms and also the execution time of subswarm-PSO comparatively takes a little less than the standard PSO algorithm. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid progress of unmanned aerial vehicle (UAV) technology, UAV-based systems have been widely used in both civilian and military applications. Researchers have proposed various network architectures and routing protocols to address the network connectivity problems associated with the high mobility of UAVs, and have achieved considerable results in a flying ad hoc network (FANET). Although scholars have noted various threats to UAVs in practical applications, such as local magnetic field variation, acoustic interference, and radio signal hijacking, few studies have taken into account the dynamic nature of these threat factors. Moreover, the UAVs' high mobility combined with dynamic threats makes it more challenging to ensure connectivity while adapting to ever-changing scenarios. In this context, this paper introduces the concept of threat probability density function (threat PDF) and proposes a particle swarm optimization (PSO)-based threat avoidance and reconnaissance FANET construction algorithm (TARFC), which enables UAVs to dynamically adapt to avoid high-risk areas while maintaining FANET connectivity. Inspired by the graph editing distance, the total edit distance (TED) is defined to describe the alterations of the FANET and threat factors over time. Based on TED, a dynamic threat avoidance and continuous reconnaissance FANET operation algorithm (TA&CRFO) is proposed to realize semi-distributed control of the network. Simulation results show that both TARFC and TA&CRFO are effective in maintaining network connectivity and avoiding threats in dynamic scenarios. The average threat value of UAVs using TARFC and TA&CRFO is reduced by 3.99~27.51% and 3.07~26.63%, respectively, compared with the PSO algorithm. In addition, with limited distributed moderation, the complexity of the TA&CRFO algorithm is only 20.08% of that of TARFC. [ABSTRACT FROM AUTHOR]

Part cleaning is very important for the assembly of precision machinery. After cleaning, the parts are randomly distributed in the collection area, which makes it difficult for a robot to collect them. Common robots can only collect parts located in relatively fixed positions, and it is difficult to adapt these robots to collect at randomly distributed positions. Therefore, a rapid part classification method based on a non-pooling YOLOv5 network for the recognition of randomly distributed multiple types of parts is proposed in this paper; this method classifies parts from their two-dimensional images obtained using industrial cameras. We compared the traditional and non-pooling YOLOv5 networks under different activation functions. Experimental results showed that the non-pooling YOLOv5 network improved part recognition precision by 8% and part recall rate by 3% within 100 epochs of training, which helped improve the part classification efficiency. The experiment showed that the non-pooling YOLOv5 network exhibited improved classification of industrial parts compared to the traditional YOLOv5 network. [ABSTRACT FROM AUTHOR]

Ultrasonic flow meters (UFMs) based on transducer arrays offer several advantages. With electronic beam steering, it is possible to tune the steering angle of the beam for optimal signal-tonoise ratio (SNR) upon reception. Moreover, multiple beams can be generated to propagate through different travel paths, covering a wider section of the flow profile. Furthermore, in a clamp-on configuration, UFMs based on transducer arrays can perform self-calibration. In this manner, userinput is minimized and measurement repeatability is increased. In practice, transducer array elements may break down. This could happen due to aging, exposure to rough environments, and/or rough mechanical contact. As a consequence of inactive array elements, the measured transit time difference contains two offsets. One offset originates from non-uniform spatial sampling of the generated wavefield. Another offset originates from the ill-defined beam propagating through a travel path different from the intended one. In this paper, an algorithm is proposed that corrects for both of these offsets. The algorithm also performs a filtering operation in the frequency-wavenumber domain of all spurious (i.e., flow-insensitive) wave modes. The advantage of implementing the proposed algorithm is demonstrated on simulations and measurements, showing improved accuracy and precision of the transit time differences compared to the values obtained when the algorithm is not applied. The proposed algorithm can be implemented in both in-line and clamp-on configuration of UFMs based on transducer arrays. [ABSTRACT FROM AUTHOR]

A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a smart phone by utilizing a novel feature termed multiresolution critical spectral verge (MCSV), based on frequency-derived information for epileptic seizure classification which was optimized using a flower pollination algorithm (FPA). A wireless sensor technology (WSN) was utilized to record the electroencephalography (EEG) signal of epileptic patients. Next, the EEG signal was pre-processed utilizing a multiresolution-based adaptive filtering (MRAF) method. Then, the maximal frequency point at which the power spectral density (PSD) of each EEG segment was greater than the average spectral power of the corresponding frequency band was computed. This point was further optimized to extract a point termed as critical spectral verge (CSV) to extract the exact high frequency oscillations representing the actual seizure activity present in the EEG signal. Next, a support vector machine (SVM) classifier was used for channel-wise classification of the seizure and non-seizure regions using CSV as a feature. This process of classification using the CSV feature extracted from the MRAF output is referred to as the MCSV approach. As a final step, cloud-based services were employed to analyze the EEG information from the subject's smart phone. An exhaustive analysis was undertaken to assess the performance of the MCSV approach for two datasets. The presented approach showed an improved performance with a 93.83% average sensitivity, a 97.94% average specificity, a 97.38% average accuracy with the SVM classifier, and a 95.89% average detection rate as compared with other state-of-the-art studies such as deep learning. The methods presented in the literature were unable to precisely localize the origination of the seizure activity in the brain region and reported a low seizure detection rate. This work introduced an optimized CSV feature which was effectively used for multichannel seizure classification and localization of seizure origination. The proposed MCSV approach will help diagnose epileptic behavior from multichannel EEG signals which will be extremely useful for neuro-experts to analyze seizure details from different regions of the brain. [ABSTRACT FROM AUTHOR]

This article proposes a queueing model of the operation of a wireless sensor network node, in which a threshold strategy for starting the node after a period of no transmission is used. In this model, transmission of packets is resumed when the number of packets in the accumulation buffer reaches a predefined level. In the literature, most of the results for models with limited access to the service station are obtained in equilibrium. In this paper, a formula for the Laplace transform of the transient queue-size distribution is obtained and written using the key input parameters of the system. The analytical apparatus uses the concept of the embedded Markov chain, the formula for total probability, renewal theory and some supporting algebraic results. Numerical examples are attached as well. [ABSTRACT FROM AUTHOR]

The complete coverage path planning is a process of finding a path which ensures that a mobile robot completely covers the entire environment while following the planned path. In this paper, we propose a complete coverage path planning algorithm that generates smooth complete coverage paths based on clothoids that allow a nonholonomic mobile robot to move in optimal time while following the path. This algorithm greatly reduces coverage time, the path length, and overlap area, and increases the coverage rate compared to the state-of-the-art complete coverage algorithms, which is verified by simulation. Furthermore, the proposed algorithm is suitable for real-time operation due to its computational simplicity and allows path replanning in case the robot encounters unknown obstacles. The efficiency of the proposed algorithm is validated by experimental results on the Pioneer 3DX mobile robot. [ABSTRACT FROM AUTHOR]

In a D2D (device-to-device) communication system, this paper proposes a relay selection strategy based on social perception. Firstly, the social threshold is introduced into the D2D relay network to screen and filter the potential relay users, thus effectively reducing the detection cost. Then, an auction algorithm is used to motivate the relay users to increase their transmission power. The simulation results show that the algorithm not only improves the throughput but also reduces the probability of a system outage. [ABSTRACT FROM AUTHOR]

Motion planning is one of the important research topics of robotics. As an improvement of Rapidly exploring Random Tree (RRT), the RRT* motion planning algorithm is widely used because of its asymptotic optimality. However, the running time of RRT* increases rapidly with the number of potential path vertices, resulting in slow convergence or even an inability to converge, which seriously reduces the performance and practical value of RRT*. To solve this issue, this paper proposes a two-phase motion planning algorithm named Metropolis RRT* (M-RRT*) based on the Metropolis acceptance criterion. First, to efficiently obtain the initial path and start the optimal path search phase earlier, an asymptotic vertex acceptance criterion is defined in the initial path estimation phase of M-RRT*. Second, to improve the convergence rate of the algorithm, a nonlinear dynamic vertex acceptance criterion is defined in the optimal path search phase, which preferentially accepts vertices that may improve the current path. The effectiveness of M-RRT* is verified by comparing it with existing algorithms through the simulation results in three test environments. [ABSTRACT FROM AUTHOR]

A decentralized PI/PID controller based on the frequency domain analysis for two input two output (TITO) coupled tank systems is exploited in this paper. The fundamentals of the gain margin and phase margin are used to design the proposed PI/PID controller. The basic objective is to keep the tank at the predetermined level. To satisfy the design specifications, the control algorithm is implemented for decoupled subsystems by employing a decoupler. First-order plus dead time (FOPDT) models are obtained for the decoupled subsystems using the model-reduction technique. In addition, the control law is realized by considering the frequency domain analysis. Further, the robustness of the controller is verified by considering multiplicative input and output uncertainties. The proposed method is briefly contrasted with existing techniques. It is envisaged that the proposed control algorithm exhibits better servo and regulatory responses compared to the existing techniques. [ABSTRACT FROM AUTHOR]

The exploration of unknown environments is an essential application of multi-robot systems, particularly in critical missions, such as hazard detection and search and rescue. These missions share the need to reach full coverage of the explorable space in the shortest time possible. To minimize the completion time, robots in the fleet must be able to reliably exchange information about the environment with one another. One of the main methods to expand coverage is by placing relays. Existing relay-placement algorithms tend to either require prior knowledge of the environment, or they rely on maintaining specific distances between the relays and the rest of the robots. These approaches lack flexibility and adaptability to the environment. This paper introduces the "Connectivity-Aware Relay Algorithm" (CARA), a dynamic context-aware relay-placement algorithm that does not require any prior knowledge of the environment. We compare CARA against a state-of-the-art distance-based relay-placement algorithm. Our results demonstrate that CARA outperformed the state-of-the-art algorithm in terms of the time to completion by a factor of 10 as it placed, on average, half the number of relays. [ABSTRACT FROM AUTHOR]

The robotic navigation task is to find a collision-free path among a mass of stationary or migratory obstacles. Various well-established algorithms have been applied to solve navigation tasks. It is necessary to test the performance of designed navigation algorithms in practice. However, it seems an extremely unwise choice to implement them in a real environment directly unless their performance is guaranteed to be acceptable. Otherwise, it takes time to test navigation algorithms because of a long training process, and imperfect performance may cause damage if the robot collides with obstacles. Hence, it is of key significance to develop a mobile robot analysis platform to simulate the real environment which has the ability to replicate the exact application scenario and be operated in a simple manner. This paper introduces a brand new analysis platform named robot navigation analysis platform (RoNAP), which is an open-source platform developed using the Python environment. A user-friendly interface supports its realization for the evaluation of various navigation algorithms. A variety of existing algorithms were able to achieve desired test results on this platform, indicating its feasibility and efficiency for navigation algorithm analysis. [ABSTRACT FROM AUTHOR]

Indoor localization problems are difficult due to that the information, such as WLAN and GPS, cannot achieve enough precision for indoor issues. This paper presents a novel indoor localization algorithm, GeoLoc, with uncertainty eliminate based on fusion of acceleration, angular rate, and magnetic field sensor data. The algorithm can be deployed in edge devices to overcome the problems of insufficient computing resources and long delay caused by high complexity of location calculation. Firstly, the magnetic map is built and magnetic values are matched. Secondly, orientation updating and position selection are iteratively executed using the fusion data, which gradually reduce uncertainty of orientation. Then, we filter the trajectory from a path set. By gradually reducing uncertainty, GeoLoc can bring a high positioning precision and a smooth trajectory. In addition, this method has an advantage in that it does not rely on any infrastructure such as base stations and beacons. It solves the common problems regarding the non-uniqueness of the geomagnetic fingerprint and the deviation of the sensor measurement. The experimental results show that our algorithm achieves an accuracy of less than 2.5 m in indoor environment, and the positioning results are relatively stable. It meets the basic requirements of indoor location-based services (LBSs). [ABSTRACT FROM AUTHOR]

In this paper, we propose a new path planning algorithm based on the probabilistic roadmaps method (PRM), in order to effectively solve the autonomous path planning of mobile robots in complex environments with multiple narrow channels. The improved PRM algorithm mainly improves the density and distribution of sampling points in the narrow channel, through a combination of the learning process of the PRM algorithm and the APF algorithm. We also shortened the required time and path length by optimizing the query process. The first key technology to improve the PRM algorithm involves optimizing the number and distribution of free points and collision-free lines in the free workspace. To ensure full visibility of the narrow channel, we extend the obstacles through the diagonal distance of the mobile robot while ignoring the safety distance. Considering the safety distance during movement, we re-classify the all sampling points obtained by the quasi-random sampling principle into three categories: free points, obstacle points, and adjacent points. Next, we transform obstacle points into the free points of the narrow channel by combining the APF algorithm and the characteristics of the narrow channel, increasing the density of sampling points in the narrow space. Then, we include potential energy judgment into the construction process of collision-free lines shortening the required time and reduce collisions with obstacles. Optimizing the query process of the PRM algorithm is the second key technology. To reduce the required time in the query process, we adapt the bidirectional A* algorithm to query these local paths and obtain an effective path to the target point. We also combine the path pruning technology with the potential energy function to obtain a short path without collisions. Finally, the experimental results demonstrate that the new PRM path planning technology can improve the density of free points in narrow spaces and achieve an optimized, collision-free path in complex environments with multiple narrow channels. [ABSTRACT FROM AUTHOR]

The accuracy of pose estimation from feature-based Visual Odometry (VO) algorithms is affected by several factors such as lighting conditions and outliers in the matched features. In this paper, a generic image processing pipeline is proposed to enhance the accuracy and robustness of feature-based VO algorithms. The pipeline consists of three stages, each addressing a problem that affects the performance of VO algorithms. The first stage tackles the lighting condition problem, where a filter called Contrast Limited Adaptive Histogram Equalization (CLAHE) is applied to the images to overcome changes in lighting in the environment. The second stage uses the Suppression via Square Covering (SSC) algorithm to ensure the features are distributed properly over the images. The last stage proposes a novel outliers rejection approach called the Angle-based Outlier Rejection (AOR) algorithm to remove the outliers generated in the feature matching process. The proposed pipeline is generic and modular and can be integrated with any type of feature-based VO (monocular, RGB-D, or stereo). The efficiency of the proposed pipeline is validated using sequences from KITTI (for stereo VO) and TUM (for RGB-D VO) datasets, as well as experimental sequences using an omnidirectional mobile robot (for monocular VO). The obtained results showed the performance gained by enhancing the accuracy and robustness of the VO algorithms without compromising on the computational cost using the proposed pipeline. The results are substantially better as opposed to not using the pipeline. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is the major solution for constructing or updating a map of an unknown environment while simultaneously keeping track of a mobile robot's location. Correlative Scan Matching (CSM) is a scan matching algorithm for obtaining the posterior distribution probability for the robot's pose in SLAM. This paper combines the non-linear optimization algorithm and CSM algorithm into an NLO-CSM (Non-linear Optimization CSM) algorithm for reducing the computation resources and the amount of computation while ensuring high calculation accuracy, and it presents an efficient hardware accelerator design of the NLO-CSM algorithm for the scan matching in 2D LiDAR SLAM. The proposed NLO-CSM hardware accelerator utilizes pipeline processing and module reusing techniques to achieve low hardware overhead, fast matching, and high energy efficiency. FPGA implementation results show that, at 100 MHz clock, the power consumption of the proposed hardware accelerator is as low as 0.79 W, while it performs a scan match at 8.98 ms and 7.15 mJ per frame. The proposed design outperforms the ARM-A9 dual-core CPU implementation with a 92.74% increase and 90.71% saving in computing speed and energy consumption, respectively. It has also achieved 80.3% LUTs, 84.13% FFs, and 20.83% DSPs saving, as well as an 8.17× increase in frame rate and 96.22% improvement in energy efficiency over a state-of-the-art hardware accelerator design in the literature. ASIC implementation in 65 nm can further reduce the computing time and energy consumption per scan to 5.94 ms and 0.06 mJ, respectively, which shows that the proposed NLO-CSM hardware accelerator design is suitable for resource-limited and energy-constrained mobile and micro robot applications. [ABSTRACT FROM AUTHOR]

In this paper, we propose a spectrum sensing algorithm based on the Jones vector covariance matrix (JCM) and AlexNet model, i.e., the JCM-AlexNet algorithm, by taking advantage of the different state characteristics of the signal and noise in the polarization domain. We use the AlexNet model, which is good at extracting matrix features, as the classification model and use the Jones vector, which characterizes the polarization state, to calculate its covariance matrix and convert it into an image and then use it as the input to the AlexNet model. Then, we calculate the likelihood ratio test statistic (AlexNet-LRT) based on the output of the model to achieve the classification of the signal and noise. The simulation analysis shows that the JCM-AlexNet algorithm performs better than the conventional polarization detection (PSD) algorithm and the other three (LeNet5, long short-term memory (LSTM), multilayer perceptron (MLP)) excellent deep-learning-based spectrum sensing algorithms for different signal-to-noise ratios and different false alarm probabilities. [ABSTRACT FROM AUTHOR]

Density peak clustering is the latest classic density-based clustering algorithm, which can directly find the cluster center without iteration. The algorithm needs to determine a unique parameter, so the selection of parameters is particularly important. However, for multi-density data, when one parameter cannot satisfy all data, clustering often cannot achieve good results. Moreover, the subjective selection of cluster centers through decision diagrams is often not very convincing, and there are also certain errors. In view of the above problems, in order to achieve better clustering of multi-density data, this paper improves the density peak clustering algorithm. Aiming at the selection of parameter dc, the K-nearest neighbor idea is used to sort the neighbor distance of each data, draw a line graph of the K-nearest neighbor distance, and find the global bifurcation point to divide the data with different densities. Aiming at the selection of cluster centers, the local density and distance of each data point in each data division is found, a γ map is drawn, the average value of the γ height difference is calculated, and through two screenings the largest discontinuity point is found to automatically determine the cluster center and the number of cluster centers. The divided datasets are clustered by the DPC algorithm, and then the clustering results are perfected and integrated by using the cluster fusion rules. Finally, a variety of experiments are designed from various perspectives on various artificial simulated datasets and UCI real datasets, which demonstrate the superiority of the F-DPC algorithm in terms of clustering effect, clustering quality, and number of samples. [ABSTRACT FROM AUTHOR]

Eye tracking is a technology aimed at understanding the direction of the human gaze. Event detection is a process of detecting and classifying eye movements that are divided into several types. Nowadays, event detection is almost exclusively done by applying a detection algorithm to the raw recorded eye-tracking data. However, due to the lack of a standard procedure for how to perform evaluations, evaluating and comparing various detection algorithms in eye-tracking signals is very challenging. In this paper, we used data from a high-speed eye-tracker SMI HiSpeed 1250 system and compared event detection performance. The evaluation focused on fixations, saccades and post-saccadic oscillation classification. It used sample-by-sample comparisons to compare the algorithms and inter-agreement between algorithms and human coders. The impact of varying threshold values on threshold-based algorithms was examined and the optimum threshold values were determined. This evaluation differed from previous evaluations by using the same dataset to evaluate the event detection algorithms and human coders. We evaluated and compared the different algorithms from threshold-based, machine learning-based and deep learning event detection algorithms. The evaluation results show that all methods perform well for fixation and saccade detection; however, there are substantial differences in classification results. Generally, CNN (Convolutional Neural Network) and RF (Random Forest) algorithms outperform threshold-based methods. [ABSTRACT FROM AUTHOR]

Current surveillance systems frequently use fixed-angle cameras and record a feed from those cameras. There are several disadvantages to such systems, including a low resolution for far away objects, a limited frame range and wasted disk space. This paper presents a novel algorithm for automatically detecting, tracking and zooming in on active targets. The object tracking system is connected to a camera that has a 360° horizontal and 90° vertical movement range. The combination of tracking, movement identification and zoom means that the system is able to effectively improve the resolution of small or distant objects. The object detection system allows for the disk space to be conserved as the system ceases recording when no valid targets are detected. Using an adaptive object segmentation algorithm, it is possible to detect the shape of moving objects efficiently. When processing multiple targets, each target is assigned a color and is treated separately. The tracking algorithm is able to adapt to targets moving at different speeds and is able to control the camera according to a predictive formula to prevent the loss of image quality due to camera trail. In the test environment, the zoom can sufficiently lock onto the head of a moving human; however, simultaneous tracking and zooming occasionally results in a failure to track. If this system is deployed with a facial recognition algorithm, the recognition accuracy can be effectively improved. [ABSTRACT FROM AUTHOR]

A mobile agent is a software application that moves naturally among hosts in a uniform and non-uniform environment; it starts with one host and then moves onto the next in order to divide data between clients. The mobile paradigm is utilized in a wide assortment of medical care applications such as the medical information of a patient, the recovery of clinical information, the incorporation of information pertaining to their wellbeing, dynamic help, telemedicine, obtaining clinical data, patient administration, and so on. The accompanying security issues have grown in tandem with the complexity and improvements in mobile agent technologies. As mobile agents work in an insecure environment, their security is a top priority when communicating and exchanging data and information. Data integrity, data confidentiality and authentication, on-repudiation, denial of service, and access control, are all key security concerns with mobile agent migration. This paper proposes a Verifiable, Secure Mobile Agent Migration model, based on two polynomials (t, n), and an edge secret imparting plan with Blowfish encryption, to enable secure information transmission in clinical medical care. [ABSTRACT FROM AUTHOR]

Object detection is a critical technology of environmental perception for autonomous driving vehicle. The Convolutional Neural Network has gradually become a powerful tool in the field of vehicle detection because of its powerful ability of feature extraction. In aiming to reach the balance between speed and accuracy of detection in complex traffic scenarios, this paper proposes an improved lightweight and high-performance vehicle–pedestrian detection algorithm based on the YOLOv4. Firstly, the backbone network CSPDarknet53 is replaced by MobileNetv2 to reduce the number of parameters and raise the capability of feature extraction. Secondly, the method of multi-scale feature fusion is used to realize the information interaction among different feature layers. Finally, a coordinate attention mechanism is added to focus on the region of interest in the image by way of weight adjustment. The experimental results show that this improved model has a great performance in vehicle–pedestrian detection in traffic scenarios. Experimental results on PASCAL VOC datasets show that the improved model's mAP is 85.79% and speed is 35FPS, which has an increase of 4.31% and 16.7% compared to YOLOv4. Furthermore, the improved YOLOv4 model maintains a great balance between detection accuracy and speed on different datasets, indicating that it can be applied to vehicle–pedestrian detection in traffic scenarios. [ABSTRACT FROM AUTHOR]

In long-term use, cracks will show up on the road, delivering monetary losses and security hazards. However, the road surface with a complex background has various disturbances, so it is challenging to segment the cracks accurately. Therefore, we propose a pavement cracks segmentation method based on a conditional generative adversarial network in this paper. U-net3+ with the attention module is used in the generator to generate segmented images for pavement cracks. The attention module highlights crack features and suppresses noise features from two dimensions of channel and space, then fuses the features generated by these two dimensions to obtain more complementary crack features. The original image is stitched with the manual annotation of cracks and the generated segmented image as the input of the discriminator. The PatchGAN method is used in the discriminator. Moreover, we propose a weighted hybrid loss function to improve the segmentation accuracy by exploiting the difference between the generated and annotated images. Through alternating gaming training of the generator and the discriminator, the segmentation image of cracks generated by the generator is very close to the actual segmentation image, thus achieving the effect of crack detection. Our experimental results using the Crack500 datasets show that the proposed method can eliminate various disturbances and achieve superior performance in pavement crack detection with complex backgrounds. [ABSTRACT FROM AUTHOR]

The basic properties of blockchain, such as decentralization, security, and immutability, show promising potential for IoT applications. The main feature—decentralization of blockchain technology—depends on the consensus. However, consensus algorithms are mostly designed to work in extensive computational and communication environments for network security and immutability, which is not desirable for resource-restricted IoT applications. Many solutions are proposed to address this issue with modified consensus algorithms based on the legacy consensus, such as the PoW, PoS, and BFT, and new non-linear data structures, such as DAG. A systematic classification and analysis of various techniques in the field will be beneficial for both researchers and industrial practitioners. Most existing relevant surveys provide classifications intuitively based on the domain knowledge, which are infeasible to reveal the intrinsic and complicated relationships among the relevant basic concepts and techniques. In this paper, a powerful tool of systematic knowledge classification and explanation is introduced to structure the survey on blockchain consensus algorithms for resource-constrained IoT systems. More specifically, an ontology was developed for a consensus algorithm apropos of IoT adaptability. The developed ontology is subdivided into two parts—CONB and CONIoT—representing the classification of generic consensus algorithms and the ones that are particularly proposed for IoT, respectively. Guided by this ontology, an in depth discussion and analysis are provided on the major consensus algorithms and their IoT compliance based on design and implementation targets. Open research challenges and future research directions are provided. [ABSTRACT FROM AUTHOR]

This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment's borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) autonomous navigation requires access to translational and rotational positions and velocities. Since there is no single sensor to measure all UAV states, it is necessary to fuse information from multiple sensors. This paper proposes a deterministic estimator to reconstruct the scale factor of the position determined by a simultaneous localization and mapping (SLAM) algorithm onboard a quadrotor UAV. The position scale factor is unknown when the SLAM algorithm relies on the information from a monocular camera. Only onboard sensor measurements can feed the estimator; thus, a deterministic observer is designed to rebuild the quadrotor translational velocity. The estimator and the observer are designed following the immersion and invariance method and use inertial and visual measurements. Lyapunov's arguments prove the asymptotic convergence of observer and estimator errors to zero. The proposed estimator's and observer's performance is validated through numerical simulations using a physics-based simulator. [ABSTRACT FROM AUTHOR]

Access Control Lists (ACL) are critical to protecting network and cyber-physical systems. Traditional firewalls mostly use reactive methods to enforce ACLs, so that new ACL updates cannot take effect immediately. In this paper, based on our previous work, we propose CPACK, an intelligent cyber-physical access control kit, which uses a smart algorithm to upgrade the ACL list. CPACK adopts a proactive way to enforce ACL and reacts to a new ACL update and network view update in real time. We implement CPACK on both Floodlight and ONOS controller. We then conduct a large number of experiments to compare CPACK with the Floodlight firewall application. The experimental results show that CPACK has a better performance than the existing Floodlight firewall application. CPACK is also integrated into the new version of Floodlight and ONOS controller. [ABSTRACT FROM AUTHOR]

The existing multi-manipulator sorting method for gangue that utilizes a multi-task allocation strategy is not satisfactory. The single manipulator working space is fixed, lowering the cooperation degree between the manipulators and leading to a low sorting rate. Therefore, this paper proposes a multi-manipulator cooperative sorting method that can work globally. First, a benefit function based on the sorting time and quality of the gangue is constructed by combining the gangue flow information and the manipulator state. The time parameter is obtained via the manipulator's dynamic target tracking trajectory planning algorithm based on PID control. Secondly, the benefits matrix is standardized and updated many times to improve the Hungarian algorithm to achieve task allocation, and the initial solution with priority is obtained. Finally, the solutions are analyzed and processed cooperatively in order of priority. The conflicts between multiple robotic arms are eliminated through task cooperation and trajectory cooperation until the sorting task that the robot arm can execute is obtained from the allocation results. Experiments involving different sorting methods were completed on a multi-arm coal and gangue sorting experimental robot platform. The experimental results show that the sorting efficiency of the proposed method is about 10% and 20% higher than that of the fixed space dynamic and designated space fixed points methods, respectively, under different belt speeds. This method can guarantee system benefits, effectively implements cooperative control of multi-manipulator operations in the whole area, and improves the efficiency of coal gangue sorting. [ABSTRACT FROM AUTHOR]

With the aim of ensuring secure transmission in high-mobility wireless scenarios, this paper proposes a 2D permutation-aided Orthogonal Time Frequency Space (OTFS) secure transmission scheme, which uses the Gosudarstvennyi Standard (GOST) algorithm to perform disturbance control on the OTFS modulation domain. Furthermore, we develop an improved SeLective Mapping (SLM) algorithm, which can significantly improve the Peak-to-Average Power Ratio (PAPR) problem with very low complexity. In addition, we carry out the security analysis, investigating the proposed scheme's resistance performance to a range of effective attacks. Finally, our numerical results show that our proposed transmission scheme can guarantee the underlying security property of OTFS. [ABSTRACT FROM AUTHOR]

This paper uses intelligent methods such as a time recurrent neural network to predict network traffic, mainly to solve the problems of resource imbalance and demand differentiation under the current 5G cloud-network collaborative architecture. An improved tree species optimization algorithm is proposed to optimize the initial network data, and the LSTM model is used to predict the data center traffic to obtain better network traffic prediction accuracy, take corresponding measures, and finally build a scheduling algorithm that integrates business cooperative caching and load balancing based on traffic prediction to reduce the peak pressure of the 5G data center network. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) play an increasingly essential role in the field of polar ocean exploration, and the Doppler velocity log (DVL)-aided strapdown inertial navigation system (SINS) is widely used for it. Due to the rapid convergence of the meridians, traditional inertial navigation mechanisms fail in the polar region. To tackle this problem, a transverse inertial navigation mechanism based on the earth ellipsoidal model is designed in this paper. Influenced by the harsh environment of the polar regions, unknown and time-varying outlier noise appears in the output of DVL, which makes the performance of the standard Kalman filter degrade. To address this issue, a robust Kalman filter algorithm based on Mahalanobis distance is used to adaptively estimate measurement noise covariance; thus, the Kalman filter gain can be modified to weight the measurement. A trial ship experiment and semi-physical simulation experiment were carried out to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively resist the influence of DVL outliers and improve positioning accuracy. [ABSTRACT FROM AUTHOR]

Wireless Sensors Networks are integrating human daily life at a fast rate. Applications cover a wide range of fields, including home security, agriculture, climate change, fire prevention, and so on and so forth. If WSN were initially flat networks, hierarchical, or cluster-based networks have been introduced in order to achieve a better performance in terms of energy efficiency, topology management, delay minimization, load balancing, routing techniques, etc. As cluster-based algorithms proved to be efficient in terms of energy balancing, security has been of less importance in the field. Data shared by nodes in a WSN can be very sensitive depending on the field of application. Therefore, it is important to ensure security at various levels of WSN. This paper proposes a formal modeling of the energy consumed to secure communications in a cluster-based WSN in general. The concept is implemented using the Proof of Authentication (POAh) paradigm of blockchain and applied to a Multi-hop Clustering Algorithm based on spectral classification. The studied metrics are residual energy in the network, the number of alive nodes, first and last dead node. [ABSTRACT FROM AUTHOR]

The main goals of the following paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements. Moreover, the authors aim to present an enhanced workflow for processing multitemporal image data using both commercial and open-source solutions. The research was provoked by the need for a relevant comparison between airborne and ground sensors for vegetation analysis and monitoring. The research team used an eBee fixed-wing platform and the multiSPEC 4c and Sequoia sensors. The authors carried out field measurements using a handheld spectrometer by Trimble—GreenSeeker. There were two flight campaigns which took place near the village of Tuhan in the Czech Republic. The results from the first campaign were discouraging, showing less possibility in the correlation between the aerial and field data. The second campaign resulted in a very high percentage of correlation between both types of data. The researchers present the image processing steps and their enhanced photogrammetric workflow for multitemporal data which helps experts and nonprofessionals to reduce their processing time. [ABSTRACT FROM AUTHOR]

The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE's efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations. [ABSTRACT FROM AUTHOR]

Conventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplification algorithm (MIWSA), is proposed in this paper. First, the point cloud is organized with a bounding box and kd-trees to find the neighborhood of each point, and the points are divided into small segments. Second, the feature index of each point is calculated to indicate the characteristics of the points. Third, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) are applied to weight these indexes to determine whether each point is a feature point. Fourth, non-feature points are judged as saved or abandoned according to their spatial relationship with the feature points. To verify the effect of the MIWSA, 3D model scanning datasets are calculated and analyzed, as well as field area scanning datasets. The accuracy for the 3D model scanning datasets is assessed by the surface area and patch numbers of the encapsulated surfaces, and that for field area scanning datasets is evaluated by the DEM error statistics. Compared with existing algorithms, the overall accuracy of the MIWSA is 5% to 15% better. Additionally, the running time is shorter than most. The experimental results illustrate that the MIWSA can simplify point clouds more precisely and uniformly. [ABSTRACT FROM AUTHOR]

Ship fire is one of the greatest dangers to ship navigation safety. Nevertheless, typical detection methods have limited detection effectiveness and accuracy due to distance restrictions and ship motion. Although the issue can be addressed by image recognition algorithms based on deep learning, the computational complexity and efficiency for ship detection are tough. This paper proposes a lightweight target identification technique based on the modified YOLOv4-tiny algorithm for the precise and efficient detection of ship fires, taking into account the distinctive characteristics of ship fires and the marine environment. Initially, a multi-scale detection technique is applied to broaden the detection range and integrate deep semantic information, thereby enhancing the feature information of small targets and obscured objects and improving the detection precision. Then, the proposed algorithm employs the SE attention mechanism for inter-channel feature fusion to improve the capability of feature extraction and the precision of ship fire detection. Last but not least, picture transformation and migration learning are added to the small ship fire dataset to accelerate the convergence pace, improve the convergence effect, and reduce dataset dependence. The simulation experiments reveal that the proposed I-YOLOv4-tiny + SE model outperforms the benchmark algorithm in terms of ship fire detection accuracy and detection efficiency and that it satisfies the real-time ship fire warning criteria in demanding maritime environments. [ABSTRACT FROM AUTHOR]

The traditional on-board centralized-distributed mission negotiation architecture has poor security and reliability. It can easily give rise to the collapse of the whole system when the master node is attacked by malicious nodes. To address this issue, the decentralized consistency algorithms commonly used in the internet world are referred to in this paper. Firstly, four typical consistency algorithms suitable for the Internet and which are named RAFT, PBFT, RIPPLE and DPOS are selected and modified for a multi-satellite autonomous mission negotiation. Additionally, based on the above modified consistency algorithms, a new double-layer decentralized consistency algorithm named DDPOS is proposed. It is well known that the above four common consistency algorithms cannot have both a low resource occupation and high security. The DDPOS algorithm can integrate the advantages of four common consistency algorithms due to its freedom of choice attribute, which can enable satellite clusters to flexibly adopt different appropriate consistency algorithms and the number of decentralized network layers. The DDPOS algorithm not only greatly improves the security and reliability of the whole satellite cluster, but also effectively reduces the computing and communication resources occupation of the satellite cluster. Without the presence of a malicious node attack, the resource occupation of the DDPOS algorithm is almost the same as that of the RAFT algorithm. However, in the case of a malicious node attack, compared with the RAFT algorithm, the total computation and total bandwidth occupation of the DDPOS algorithm have decreased by 67% and 75%, respectively. Moreover, it is surprising that although the DDPOS algorithm is more complex, its code size is only about 8% more than the RAFT algorithm. Finally, the effectiveness and feasibility of the DDPOS algorithm in the on-board practical application are analyzed and verified via simulation experiments. [ABSTRACT FROM AUTHOR]

Compressed ultrafast photography (CUP) is a type of two-dimensional (2D) imaging technique to observe ultrafast processes. Intelligence reconstruction methods that influence the imaging quality are an essential part of a CUP system. However, existing reconstruction algorithms mostly rely on image priors and complex parameter spaces. Therefore, it usually takes a lot of time to obtain acceptable reconstruction results, which limits the practical application of the CUP. In this paper, we proposed a novel reconstruction algorithm named PnP-FFDNet, which can provide a high quality and high efficiency compared to previous methods. First, we built a forward model of the CUP and three sub-optimization problems were obtained using the alternating direction multiplier method (ADMM), and the closed-form solution of the first sub-optimization problem was derived. Secondly, inspired by the PnP-ADMM framework, we used an advanced denoising algorithm based on a neural network named FFDNet to solve the second sub-optimization problem. On the real CUP data, PSNR and SSIM are improved by an average of 3 dB and 0.06, respectively, compared with traditional algorithms. Both on the benchmark dataset and on the real CUP data, the proposed method reduces the running time by an average of about 96% over state-of-the-art algorithms, and show comparable visual results, but in a much shorter running time. [ABSTRACT FROM AUTHOR]

This paper presents two novel swarm intelligence algorithms for gene selection, HHO-SVM and HHO-KNN. Both of these algorithms are based on Harris Hawks Optimization (HHO), one in conjunction with support vector machines (SVM) and the other in conjunction with k-nearest neighbors (k-NN). In both algorithms, the goal is to determine a small gene subset that can be used to classify samples with a high degree of accuracy. The proposed algorithms are divided into two phases. To obtain an accurate gene set and to deal with the challenge of high-dimensional data, the redundancy analysis and relevance calculation are conducted in the first phase. To solve the gene selection problem, the second phase applies SVM and k-NN with leave-one-out cross-validation. A performance evaluation was performed on six microarray data sets using the two proposed algorithms. A comparison of the two proposed algorithms with several known algorithms indicates that both of them perform quite well in terms of classification accuracy and the number of selected genes. [ABSTRACT FROM AUTHOR]

Among the numerous indoor localization methods, Light-Detection-and-Ranging (LiDAR)-based probabilistic algorithms have been extensively applied to indoor localization due to their real-time performance and high accuracy. Nevertheless, these methods are challenged in symmetrical environments when tackling global localization and the robot kidnapping problem. In this paper, a novel hybrid method that combines visual and probabilistic localization results is proposed. Augmented Monte Carlo Localization (AMCL) is improved for position tracking continually. LiDAR-based measurements' uncertainty is evaluated to incorporate discrete visual-based results; therefore, a better diversity of the particle can be maintained. The robot kidnapping problem can be detected and solved by preventing premature convergence of the particle filter. Extensive experiments were implemented to validate the robustness and accuracy performance. Meanwhile, the localization error was reduced from 30 mm to 9 mm during a 600 m tour. [ABSTRACT FROM AUTHOR]

With the development and integration of GNSS systems in the world, the positioning accuracy and reliability of GNSS navigation services are increasing in various fields. Because the current multisystem fusion leads to an increase in the ambiguity dimension and the ambiguity parameters have discrete characteristics, the current conventional search algorithm leads to low search efficiency when the ambiguity dimension is large. Therefore, this paper describes a new algorithm that searches the optimal lattice points by lattice theory through the breadth-first algorithm and reduces the search space of ambiguity by calculating and judging the Euclidean distance between each search variable and the target one so as to propose a new lattice ambiguity search algorithm based on the breadth-first algorithm. The experimental results show that this method can effectively improve the search efficiency of ambiguity in high-dimension situations. [ABSTRACT FROM AUTHOR]

Wi-Fi fingerprinting is the most popular indoor positioning method today, representing received signal strength (RSS) values as vector-type fingerprints. Passive fingerprinting, unlike the active fingerprinting method, has the advantage of being able to track location without user participation by utilizing the signals that are naturally emitted from the user's smartphone. However, since signals are generated depending on the user's network usage patterns, there is a problem in that data are irregularly collected according to the patterns. Therefore, this paper proposes an adaptive algorithm that shows stable tracking performances for fingerprints generated at irregular time intervals. The accuracy and stability of the proposed tracking method were verified by experiments conducted in three scenarios. Through the proposed method, it is expected that the stability of indoor positioning and the quality of location-based services will improve. [ABSTRACT FROM AUTHOR]