Search

To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle (USV) perception, this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection. Firstly, the visual recognition component employs an improved YOLOv7 algorithm based on a self-built dataset for the detection of water surface targets. This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure, addressing the problem of excessive redundant information during feature extraction in the original YOLOv7 network model. Simultaneously, this modification simplifies the computational burden of the detector, reduces inference time, and maintains accuracy. Secondly, to tackle the issue of sample imbalance in the self-built dataset, slide loss function is introduced. Finally, this paper replaces the original Complete Intersection over Union (CIoU) loss function with the Minimum Point Distance Intersection over Union (MPDIoU) loss function in the YOLOv7 algorithm, which accelerates model learning and enhances robustness. To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms, this paper further adopts the fusion of LiDAR and camera data, projecting the three-dimensional point-cloud data from LiDAR onto a two-dimensional pixel plane. This significantly reduces the rate of missed detections for water surface targets. [ABSTRACT FROM AUTHOR]

This paper proposes a predictive power control strategy for the three‐phase, six‐switch Vienna rectifier without grid voltage sensors to reduce the hardware cost and complexity of a high‐power PWM rectifier system. Firstly, an algorithm for calculating the AC‐side voltage in the αβ coordinate system is derived according to the operating principle of the Vienna rectifier, and a voltage observer is constructed by combining a second‐order low‐pass filter to estimate the grid voltage. Secondly, a soft start method is designed to solve the problem that the rectifier is prone to inrush current when it is started. Furthermore, the control method of grid voltage sensorless is combined with predictive power control with good dynamic characteristics and simple parameter settings to form the control strategy proposed in this paper. Finally, simulation analysis and experimental verification are carried out on the proposed control strategy. Simulation and experimental results show that the grid voltage estimation has high accuracy, a good surge current suppression effect, unit power factor operation, low input current harmonic content, and good dynamic and steady‐state performance. Therefore, the correctness and effectiveness of the strategy proposed in this paper are verified. [ABSTRACT FROM AUTHOR]

This paper proposes and evaluates two robust sheet tension estimation algorithms based on unwind and rewind variables, respectively. The proper sheet tension estimation is guaranteed by a continuous monitoring of the differences between the actual estimated values and the values predicted from the last calibration of the estimation parameters. The evaluation was performed with the aid of a developed winder emulator that allowed the offline testing in a real-time field environment, including the initial setting of the estimation constants, and evaluation of both four- and six-shipping-roll cycles. The sheet tension estimation results confirm the precise and stable operation of the proposed robust estimation algorithms, in tests conducted with signals from a winder model, and with tests involving actual field signals from an operational winder. [ABSTRACT FROM AUTHOR]

Fruit recognition and location are the premises of robot automatic picking. YOLOv3 has been used to detect different fruits in complex environment. However, for the object with definite features, the complex network structure will increase the computing time and may cause overfitting. Therefore, this paper has carried out a lightweight design for the YOLOv3. This paper proposed an improved T-Net to detect tomato images. Firstly, the T-Net reduces the residual network layers. This paper changed the number of cycles in each group of the residual unit to 1, 2, 2, 1, and 1. Second, two feature layers with different scales are selected according to the features of tomatoes. Meanwhile, the convolutional layer at the neck has been reduced by two layers. Finally, the location and approximate diameter of the ripe tomato are obtained by combining the node information of the Intel D435i camera and T-Net in the Robot Operation System. T-Net obtains mean average precision (mAP) of 99.2%, F1-score of 98.9%, precision of 99.0%, and recall of 98.8% at a detection rate of 104.2 FPS. The proposed T-Net has outperformed the YOLOv3 with 0.4%, 0.1%, and 0.2% increase in precision, mAP, and F1-score. The detection speed of T-Net is 1.8 times faster than YOLOv3. The mean errors of the center coordinates and diameter of the tomato are 8.5 mm and 2.5 mm, respectively. This model provides a method for efficient real-time detection and location of tomatoes. [ABSTRACT FROM AUTHOR]

Recently developed monitoring-systems show the need as also the possibilities for online- and offline-onsite-diagnostics. The limit in sensitivity for electrical pd-measurements according to IEC 60270 which is reached using filters for sinusoidal disturbances and compensation for corona impulses is still not satisfactory. The acoustic partial discharge measurement (pd-measurement) is also a well known and reliable method often used by transformer manufacturers in testing. In laboratory setups a gain in sensitivity of the acoustic in comparison to the electrical pd measurements could be established. This is reached by adapting the sensors to the propagation path, a modern but moderately priced acquisition hardware and the introduction of new signal processing algorithms. The developed sophisticated measuring, systems show remarkable results in online acoustic pd-measurements onsite. [ABSTRACT FROM AUTHOR]

For the year 1999 all Sea viewing Wide Field of view Sensor (SeaWiFS) scenes of the Danish waters from the North Sea to the Baltic Sea were browsed, and a total of 47 SeaWiFS scenes with reasonably low cloud cover and, therefore, potential in situ match-ups were found and processed. The in situ data used as match-ups were collected on routine monitoring cruises by Danish and Swedish environmental authorities. A few stations in the North Sea, Skagerak and the western Baltic Sea were sampled, while most stations were located in Kattegat and the inner Danish waters. A turbid water SeaWiFS atmospheric correction algorithm was applied, since the standard SeaWiFS algorithm for chlorophyll- a (CHL) has been shown to be fairly inaccurate in turbid coastal waters. This is due to both inaccurate atmospheric and to relatively high and variable abundance of yellow substance. The application of the turbid atmospheric correction substantially improved the SeaWiFS CHL estimates. Regressions between SeaWiFS estimates using the OC2 and OC4 algorithms used in the SeaDAS software (versions 3.3 and 4.0, respectively) and in situ CHL values were made as well, and regression with a number of other possible reflectance ratios with SeaWiFS channels. The best correlation was found to be R 2 =0.54 using a double-ratio algorithm using both R510/R555 and R443/R670, while the OC4v4 had the second best correlation of R 2 =0.39. Among other single ratios, the R510/R555 had the highest correlation with CHL, which was expected since this is also the ratio that OC4v4 most often switches to in the waters investigated here. The range of CHL concentrations in this study was rather limited (all but three points from 0.5-3 mg m -3 ) so there is a need for inclusion of more data to expand the concentration range. This should be possible using also data from 2000, 2001 and onwards and, hereafter, a more 'stable' empirical algorithm can be derived for the Danish waters. [ABSTRACT FROM AUTHOR]

The deep model is widely used and has been demonstrated to have more hidden security risks. An adversarial attack can bypass the traditional means of defense. By modifying the input data, the attack on the deep model is realized, and it is imperceptible to humans. The existing adversarial example generation methods mainly attack the whole image. The optimization iterative direction is easy to predict, and the attack flexibility is low. For more complex scenarios, this paper proposes an edge-restricted adversarial example generation algorithm (Re-AEG) based on semantic segmentation. The algorithm can attack one or more specific objects in the image so that the detector cannot detect the objects. First, the algorithm automatically locates the attack objects according to the application requirements. Through the semantic segmentation algorithm, the attacked object is separated and the mask matrix for the object is generated. The algorithm proposed in this paper can attack the object in the region, converge quickly and successfully deceive the deep detection model. The algorithm only hides some sensitive objects in the image, rather than completely invalidating the detection model and causing reported errors, so it has higher concealment than the previous adversarial example generation algorithms. In this paper, a comparative experiment is carried out on ImageNet and coco2017 datasets, and the attack success rate is higher than 92%. [ABSTRACT FROM AUTHOR]

A general and adaptable method is proposed to reliably extract quantitative information from smartphone images of microfluidic sensors. By analyzing and processing the color information of selected standard substances, the influence of light conditions, device differences, and human factors could be significantly reduced. Machine learning and multivariate fitting methods were proved to be effective for chroma correction, and a key element was the training of sample size and the fitting form, respectively. A custom APP was developed and validated using a high-sensitivity chromium ion quantification paper chip. The average chroma deviations under different conditions were reduced by more than 75% in RGB color space, and the concentration test error was reduced by more than half compared with the commonly used method. The proposed approach could be a beneficial supplement to existing and potential colorimetry-based detection methods. [ABSTRACT FROM AUTHOR]

A lightweight forest fire detection model based on YOLOv8 is proposed in this paper in response to the problems existing in traditional sensors for forest fire detection. The performance of traditional sensors is easily constrained by hardware computing power, and their adaptability in different environments needs improvement. To balance the accuracy and speed of fire detection, the GhostNetV2 lightweight network is adopted to replace the backbone network for feature extraction of YOLOv8. The Ghost module is utilized to replace traditional convolution operations, conducting feature extraction independently in different dimensional channels, significantly reducing the complexity of the model while maintaining excellent performance. Additionally, an improved CPDCA channel priority attention mechanism is proposed, which extracts spatial features through dilated convolution, thereby reducing computational overhead and enabling the model to focus more on fire targets, achieving more accurate detection. In response to the problem of small targets in fire detection, the Inner IoU loss function is introduced. By adjusting the size of the auxiliary bounding boxes, this function effectively enhances the convergence effect of small target detection, further reducing missed detections, and improving overall detection accuracy. Experimental results indicate that, compared with traditional methods, the algorithm proposed in this paper significantly improves the average precision and FPS of fire detection while maintaining a smaller model size. Through experimental analysis, compared with YOLOv3-tiny, the average precision increased by 5.9% and the frame rate reached 285.3 FPS when the model size was only 4.9 M; compared with Shufflenet, the average precision increased by 2.9%, and the inference speed tripled. Additionally, the algorithm effectively addresses false positives, such as cloud and reflective light, further enhancing the detection of small targets and reducing missed detections. [ABSTRACT FROM AUTHOR]

The zero-velocity update (ZUPT) algorithm is a pivotal advancement in pedestrian navigation accuracy, utilizing foot-mounted inertial sensors. Its key issue hinges on accurately identifying periods of zero-velocity during human movement. This paper introduces an innovative adaptive sliding window technique, leveraging the Fourier Transform to precisely isolate the pedestrian's gait frequency from spectral data. Building on this, the algorithm adaptively adjusts the zero-velocity detection threshold in accordance with the identified gait frequency. This adaptation significantly refines the accuracy in detecting zero-velocity intervals. Experimental evaluations reveal that this method outperforms traditional fixed-threshold approaches by enhancing precision and minimizing false positives. Experiments on single-step estimation show the adaptability of the algorithm to motion states such as slow, fast, and running. Additionally, the paper demonstrates pedestrian trajectory localization experiments under a variety of walking conditions. These tests confirm that the proposed method substantially improves the performance of the ZUPT algorithm, highlighting its potential for pedestrian navigation systems. [ABSTRACT FROM AUTHOR]

The detector array method has been widely used in the field of high-energy laser far-field spot parameter measurement due to its ability to directly measure the far-field spot of high-energy lasers, wide dynamic range of the detectors, high system sampling frequency, good real-time performance, and suitability for various testing environment requirements. However, during the measurement process, the irradiation of strong lasers or damage to other hardware systems can result in invalid points in the acquired spot images, thereby reducing the measurement accuracy of the system. In order to achieve accurate measurement of laser far-field spot parameters, this paper establishes an experimental model based on the analysis of the sampling spacing of the detector array target and proposes a laser far-field spot invalid point repair algorithm based on image correlation coefficient. Experimental results demonstrate that the algorithm proposed in this paper effectively reduces the impact of invalid points in the measurement system on the measurement accuracy, and achieves accurate measurement of high-energy laser measurement systems. [ABSTRACT FROM AUTHOR]

Suffering from various apple leaf diseases, timely preventive measures are necessary to take. Currently, manual disease discrimination has high workloads, while automated disease detection algorithms face the trade-off between detection accuracy and speed. Therefore, an accurate and lightweight model for apple leaf disease detection based on YOLO-V5s (ALAD-YOLO) is proposed in this paper. An apple leaf disease detection dataset is collected, containing 2,748 images of diseased apple leaves under a complex environment, such as from different shooting angles, during different spans of the day, and under different weather conditions. Moreover, various data augmentation algorithms are applied to improve the model generalization. The model size is compressed by introducing the Mobilenet-V3s basic block, which integrates the coordinate attention (CA) mechanism in the backbone network and replacing the ordinary convolution with group convolution in the Spatial Pyramid Pooling Cross Stage Partial Conv (SPPCSPC) module, depth-wise convolution, and Ghost module in the C3 module in the neck network, while maintaining a high detection accuracy. Experimental results show that ALAD-YOLO balances detection speed and accuracy well, achieving an accuracy of 90.2% (an improvement of 7.9% compared with yolov5s) on the test set and reducing the floating point of operations (FLOPs) to 6.1 G (a decrease of 9.7 G compared with yolov5s). In summary, this paper provides an accurate and efficient detection method for apple leaf disease detection and other related fields. [ABSTRACT FROM AUTHOR]

This review paper discusses the development trends of agricultural autonomous all-terrain vehicles (AATVs) from four cornerstones, such as (1) control strategy and algorithms, (2) sensors, (3) data communication tools and systems, and (4) controllers and actuators, based on 221 papers published in peer-reviewed journals for 1960–2023. The paper highlights a comparative analysis of commonly employed control methods and algorithms by highlighting their advantages and disadvantages. It gives comparative analyses of sensors, data communication tools, actuators, and hardware-embedded controllers. In recent years, many novel developments in AATVs have been made due to advancements in wireless and remote communication, high-speed data processors, sensors, computer vision, and broader applications of AI tools. Technical advancements in fully autonomous control of AATVs remain limited, requiring research into accurate estimation of terrain mechanics, identifying uncertainties, and making fast and accurate decisions, as well as utilizing wireless communication and edge cloud computing. Furthermore, most of the developments are at the research level and have many practical limitations due to terrain and weather conditions. [ABSTRACT FROM AUTHOR]

The Kalman filter algorithm is very important as a recursive algorithm method to optimize sensor output from physical parameter measurement systems, especially physics practicum demonstration systems. One of the distance parameter measurement demonstration systems used in Hooke’s law demonstration system is applied in physics practicum, the system has problems related to fluctuating or unstable sensor output. This research implements the Kalman filter algorithm on the Arduino IDE sketch to reduce noise that appears at the ultrasonic sensor output. The methodology used in this study includes the application of the Kalman filter algorithm to the Arduino IDE sketch with the variable value of the Kalman filter algorithm equation modified with a value of R=10, H=1, and Q=1, and returns the filtered Kalman out value. The Arduino output results are exported to Ms. Excel for further analysis and generate a filtered ultrasonic sensor output signal graph compared without using the Kalman filter. The ultrasonic sensor output noise filtration effectively reduces noise by showing a decrease in the mean squared error (MSE) value and obtaining the best performance of up to 89.23 %. The accuracy of Kalman filter filtration results can be seen from the calculation that the spring constant of filtered metal materials is smaller than the conventional measurement spring constant. Accurate and effective results with the implementation of the Kalman filter algorithm can be developed for the variation values of distance parameters and Kalman filter algorithm variables (R, Q, and H) with other value variations, especially variables that produce filtering curves close to straight lines. It was concluded that the Kalman filter algorithm was able to improve the performance of Hooke’s law prop system. [ABSTRACT FROM AUTHOR]

Over the past few years, more attention has been paid to jingle and jangle fallacies in psychological science. Jingle fallacies arise when two or more distinct psychological phenomena are erroneously labeled with the same term, while jangle fallacies occur when different terms are used to describe the same phenomenon. Jingle and jangle fallacies emerge due to the vague linkage between psychological theories and their practical implementation in empirical studies, compounded by variations in study designs, methodologies, and applying different statistical procedures' algorithms. Despite progress in organizing scientific findings via systematic reviews and meta-analyses, effective strategies to prevent these fallacies are still lacking. This paper explores the integration of several approaches with the potential to identify and mitigate jingle and jangle fallacies within psychological science. Essentially, organizing studies according to their specifications, which include theoretical background, methods, study designs, and results, alongside a combinatorial algorithm and flexible inclusion criteria, may indeed represent a feasible approach. A jinglefallacy detector arises when identical specifications lead to disparate outcomes, whereas jangle-fallacy indicators could operate on the premise that varying specifications consistently yield overrandomly similar results. We discuss the role of advanced computational technologies, such as Natural Language Processing (NLP), in identifying these fallacies. In conclusion, addressing jingle and jangle fallacies requires a comprehensive approach that considers all levels and phases of psychological science. [ABSTRACT FROM AUTHOR]

Three-dimensional point cloud registration is a critical task in 3D perception for sensors that aims to determine the optimal alignment between two point clouds by finding the best transformation. Existing methods like RANSAC and its variants often face challenges, such as sensitivity to low overlap rates, high computational costs, and susceptibility to outliers, leading to inaccurate results, especially in complex or noisy environments. In this paper, we introduce a novel 3D registration method, CL-PCR, inspired by the concept of maximal cliques and built upon the SC2-PCR framework. Our approach allows for the flexible use of smaller sampling subsets to extract more local consensus information, thereby generating accurate pose hypotheses even in scenarios with low overlap between point clouds. This method enhances robustness against low overlap and reduces the influence of outliers, addressing the limitations of traditional techniques. First, we construct a graph matrix to represent the compatibility relationships among the initial correspondences. Next, we build clique-likes subsets of various sizes within the graph matrix, each representing a consensus set. Then, we compute the transformation hypotheses for the subsets using the SVD algorithm and select the best hypothesis for registration based on evaluation metrics. Extensive experiments demonstrate the effectiveness of CL-PCR. In comparison experiments on the 3DMatch/3DLoMatch datasets using both FPFH and FCGF descriptors, our Fast-CL-PCRv1 outperforms state-of-the-art algorithms, achieving superior registration performance. Additionally, we validate the practicality and robustness of our method with real-world data. [ABSTRACT FROM AUTHOR]

The concept and data drift problems have received much attention in recent years. This aspect is crucial in many domains exhibiting non-stationary and cyclical patterns affecting their generative processes. Drift detection can be treated as a supervised task, with labeled data constantly used to validate the learned model. From a practical point of view, this is an impractical task because labeling is complex, costly, and time-consuming. On the other hand, unsupervised change detection techniques are cumbersome in applications because they generate many false alarms. The paper presents a new concept drift detection method based on feature analysis. Stream of data carries information about the distribution patterns that reflect different concepts that may be hidden in the data. The essential features are searched and ranked by LASSO. The rank of features and statistics are employed to feature drift detection. The proposed approach was experimentally checked based on synthetic and natural datasets. The results show that the proposed FBDD algorithm has an advantage over other solutions. [ABSTRACT FROM AUTHOR]

In recent years, the integration of deep learning into robotic grasping algorithms has led to significant advancements in this field. However, one of the challenges faced by many existing deep learning-based grasping algorithms is their reliance on extensive training data, which makes them less effective when encountering unknown objects not present in the training dataset. This paper presents a simple and effective grasping algorithm that addresses this challenge through the utilization of a deep learning-based object detector, focusing on oriented detection of key features shared among most objects, namely straight edges and corners. By integrating these features with information obtained through image segmentation, the proposed algorithm can logically deduce a grasping pose without being limited by the size of the training dataset. Experimental results on actual robotic grasping of unknown objects over 400 trials show that the proposed method can achieve a higher grasp success rate of 98.25% compared to existing methods. [ABSTRACT FROM AUTHOR]

Border surveillance and the monitoring of critical infrastructure are essential components of regional and industrial security. In this paper, our purpose is to study the intricate nature of surveillance methods used by hybrid monitoring systems utilizing Pan–Tilt–Zoom (PTZ) cameras, modeled as directional sensors, and UAVs. We aim to accomplish three occasionally conflicting goals. Firstly, at any given moment we want to detect as many intruders as possible with special attention to newly arriving trespassers. Secondly, we consider it equally important to observe the temporal movement and behavior of each intruder group as accurately as possible. Furthermore, in addition to these objectives, we also seek to minimize the cost of sensor usage associated with surveillance. During the research, we developed and analyzed several interrelated, increasingly complex algorithms. By leveraging RL methods we also gave the system the chance to find the optimal solution on its own. As a result we have gained valuable insights into how various components of these algorithms are interconnected and coordinate. Building upon these observations, we managed to develop an efficient algorithm that takes into account all three criteria mentioned above. [ABSTRACT FROM AUTHOR]

Sensors are the main components in Cyber-Physical Systems (CPS), which transmit large amounts of physical values and big data to computing platforms for processing. On the other hand, the embedded processors (as edge devices in fog computing) spend most of their time reading the sensor signals as compared with computing time. The impact of sensors on the performance of fog computing is very great, thus, the enhancement of the reading time of sensors will positively affect the performance of fog computing, and solves the CPS challenges such as delay, timed precision, temporal behavior, energy, and cost. In this paper, we propose an algorithm based on the 1st derivative of the sensor signal to generate an adaptive sampling frequency. The proposed algorithm uses an adaptive frequency to capture the sudden and rapid change in sensor signal in the steady state. Finally, we realize and tested it using the Ptolemy II Modeling Environment. [ABSTRACT FROM AUTHOR]

The knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors and perform an extensive inter-comparison and validation in order to estimate their absolute accuracy and long-term stability. We use the recently reprocessed data sets retrieved by the GOME-2 instruments aboard EUMETSAT's MetOp-A and MetOp-B satellites and generated by DLR in the framework of the O3M-SAF using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines H2O/O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. We evaluate the overall consistency between about 8 months measure ments from the newer GOME-2 instrument on the MetOp-B platform with the GOME- 2/MetOp-A data in the overlap period. Furthermore, we compare GOME-2 results with independent TCWV data from ECMWF and with SSMIS satellite measurements during the full period January 2007-August 2013 and we perform a validation against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE Glob Vapour project. We find global mean biases as small as ±0.03 gcm-2 between GOME-2A and all other data sets. The combined SSM/I-MERIS sample is typically drier than the GOME-2 retrievals (-0.005 gcm-2), while on average GOME-2 data overestimate the SSMIS measurements by only 0.028 gcm-2. However, the size of some of these biases are seasonally dependent. Monthly average differences can be as large as 0.1 gcm-2, based on the analysis against SSMIS measurements, but are not as evident in the validation with the ECMWF and the SSM/I+MERIS data. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three independent data sets, especially for land areas, although some discrepancies over ocean and over land areas with high humidity and a relatively large surface albedo are also present. [ABSTRACT FROM AUTHOR]

Objective. A significant challenge in surface electromyography (EMG) is the accurate identification of onset and offset of muscle activation while maintaining high real-time performance. Teagerâ€"Kaiser energy operator (TKEO) is widely used in muscle activity monitoring systems because of its computational simplicity and strong real-time performance. However, in contrast to TKEO ontology, few studies have examined how well the energy operator variants from multiple fields perform in conditioning EMG signals. This paper aims to investigate the role of the energy operator and its variants in EMG change point detection by a threshold detector. Approach. To compare the stability and accuracy of TKEO and its variants for EMG change point detection, the EMG data of extensor carpi radialis longus and flexor carpi radialis were acquired from twenty participants operating a controller under normal and disturbed conditions, and EMG change point detection was performed by four energy operators and their rectified versions. Main results. Based on the ‘standard’ change points collected by the controller, the detection results were evaluated by three evaluation indexes: detection rate, F 1 Score, and accuracy. The experimental results show that the multiresolution energy operator and the TKEO with rectified (abs-TKEO) are more suitable for EMG change point detection. Significance. This paper compared the effect of the energy operator and its variants on a threshold-based EMG change point detector. The experimental results in this paper can provide a reference for the selection of EMG signal conditioning methods to improve the detection performance of the EMG change point detector. [ABSTRACT FROM AUTHOR]

Pinch gestures for scaling an image were recognized successfully on a surface-capacitive touch screen that has only a single-touch function without increasing manufacturing cost. An algorithm for recognizing the gestures was developed, which calculates the distance between two touching points without detecting each position of the two points. [ABSTRACT FROM AUTHOR]

Traditional camera sensors rely on human eyes for observation. However, human eyes are prone to fatigue when observing objects of different sizes for a long time in complex scenes, and human cognition is limited, which often leads to judgment errors and greatly reduces efficiency. Object recognition technology is an important technology used to judge the object's category on a camera sensor. In order to solve this problem, a small-size object detection algorithm for special scenarios was proposed in this paper. The advantage of this algorithm is that it not only has higher precision for small-size object detection but also can ensure that the detection accuracy for each size is not lower than that of the existing algorithm. There are three main innovations in this paper, as follows: (1) A new downsampling method which could better preserve the context feature information is proposed. (2) The feature fusion network is improved to effectively combine shallow information and deep information. (3) A new network structure is proposed to effectively improve the detection accuracy of the model. From the point of view of detection accuracy, it is better than YOLOX, YOLOR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny, and YOLOv8. Three authoritative public datasets are used in these experiments: (a) In the Visdron dataset (small-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 2.5%, 1.9%, and 2.1% higher than those of YOLOv8s, respectively. (b) On the Tinyperson dataset (minimal-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 1%, 0.2%, and 1.2% higher than those of YOLOv8s, respectively. (c) On the PASCAL VOC2007 dataset (normal-size objects), the map, precision, and recall ratios of DC-YOLOv8 are 0.5%, 0.3%, and 0.4% higher than those of YOLOv8s, respectively. [ABSTRACT FROM AUTHOR]

In Wireless sensor networks (WSNs), clustering is an important and effective technique for raising the life span of the network, which directly led to an improved routing method. This technique includes combination of sensor nodes (SNs) to clusters and selecting the suitable CHs for every cluster. Actually, CHs accumulates the data from related SNs in the cluster and after that, it transmits those combined data to BS. The drawbacks of the collection of suitable cluster head (CH). Yet, a lot of analysis is still going on for resolving these issues depending upon specific parameters. Here this paper establishes a novel cluster based routing model via choosing the optimal CH. For optimal CHS, this work exploits a novel approach called Lion Mutated WOA (LM-WOA) that combined the theories of WOA and LA.Here, an innovative multi-objective function is described based upon diverse constraints like: Distance, Delay, Energy, Cluster Density, Traffic rate, Throughput and QoS. After electing the optimum CH, the data is transmitted along the chosen optimal path that is selected via LM-WOA model. The effectiveness of the newly implemented approach is ultimately reflected through analysis. [ABSTRACT FROM AUTHOR]

Vibration measurements can be used to evaluate the operation status of power equipment and are widely applied in equipment quality inspection and fault identification. Event-sensing technology can sense the change in surface light intensity caused by object vibration and provide a visual description of vibration behavior. Based on the analysis of the principle underlying the transformation of vibration behavior into event flow data by an event sensor, this paper proposes an algorithm to reconstruct event flow data into a relationship correlating vibration displacement and time to extract the amplitude-frequency characteristics of the vibration signal. A vibration measurement test platform is constructed, and feasibility and effectiveness tests are performed for the vibration motor and other power equipment. The results show that event-sensing technology can effectively perceive the surface vibration behavior of power and provide a wide dynamic range. Furthermore, the vibration measurement and visualization algorithm for power equipment constructed using this technology offers high measurement accuracy and efficiency. The results of this study provide a new noncontact and visual method for locating vibrations and performing amplitude-frequency analysis on power equipment. [ABSTRACT FROM AUTHOR]

Due to its large network scale, open communication environment, unstable wireless network, and other characteristics, it is extremely vulnerable to attacks and causes security problems, resulting in the collapse of the Internet of Vehicles system. The application of the Internet of Vehicles is becoming more and more extensive, but there are still problems such as information security and privacy leakage in the Internet of Vehicles. Through the analysis of the security threats and privacy protection requirements faced by the Internet of Vehicles system, this paper mainly studies information security, vehicle identity privacy, and location privacy in the process of Internet of Vehicles wireless communication. Therefore, it is urgent to conduct research on the information security and privacy protection issues of the Internet of Vehicles. This paper discusses the research on the security and privacy protection of the consensus algorithm for the Internet of Vehicles based on wireless sensors, compares and analyzes the wireless sensor data privacy protection protocols based on sharding technology, Tongtai encryption technology, and perturbation technology, and selects an optimized Kalman consensus filter. The algorithm is applied to the node information exchange of the sensor network, and two filters (low pass and band pass) are used to unify the observations and covariance of the network. Estimation of the sensor network state with and without data packet loss, the effect of system estimation error under different packet loss rates, data privacy protection algorithm performance, vehicle network data communication volume, and confusion factors on algorithm efficiency and the node energy consumption was compared and analyzed. Based on the application of wireless sensors, the estimation error and inconsistency estimation error of the algorithm in this paper finally converge to about 0.5, and both can maintain good stability and have good robustness. In addition, the communication volume of the algorithm in this paper is about 30% of the SCPDA algorithm. The Kalman consensus filtering algorithm reduces the amount of confusing data sent, improves privacy protection, and also achieves lower communication overhead. [ABSTRACT FROM AUTHOR]

Object detection algorithms play a crucial role in other vision tasks. This paper finds that the action recognition algorithm SlowFast's detection algorithm FasterRCNN (Region Convolutional Neural Network) has disadvantages in terms of both detection accuracy and speed and the traditional IOU (Intersection over Union) localization loss is difficult to make the detection model converge to the minimum stability point. To solve the above problems, the article uses YOLOv3 (You Only Look Once), YOLOX, and CascadeRCNN to improve the detection accuracy and speed of the SlowFast. This paper proposes a new localization loss function that adopts the Lance and Williams distance as a new penalty term. The new loss function is more sensitive when the distance difference is smaller, and this property is very suitable for the late convergence of the detection model. The experiments were conducted on the VOC (Visual Object Classes) dataset and the COCO dataset. In the final videos test, YOLOv3 improved the detection speed by 10.5 s. CascadeRCNN improved by 3.1%AP compared to FasterRCNN in the COCO dataset. YOLOX's performance on the COCO dataset is also mostly better than that of FasterRCNN. The new LIOU (Lance and Williams Distance Intersection over Union) localization loss function performs better than other loss functions in the VOC dataset. It can be seen that improving the detection algorithm of the SlowFast seems to be crucial and the proposed loss function is indeed effective. [ABSTRACT FROM AUTHOR]

An improved filtering algorithm, called the EttAF adaptive filtering, is proposed in this paper. Compared with the available algorithm, the equivalent observation error in the fusion centre from the external sensor is considered, which makes the observation noise matrix of the filter dynamically adjust according to the relative relationship between the fusion centre and the external sensor. The strong manoeuvring target is modelled based on the CS‐jerk model, and the error adaptive variation equation is derived for the external data. Simulation results show that compared to traditional algorithm the EttAF algorithm can effectively improve the accuracy of position and velocity by 10.8% and 22.3% respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of high cost and heavy preparation workload of indoor positioning technology, this paper proposes an ambient light-assisted smartphone-based pedestrian dead reckoning (PDR) indoor positioning technology. We propose a group weighted average algorithm to calculate the heading angle of pedestrians, which reduces the positioning error. During the PDR process, the smartphone continuously collects light intensity values, which are used to detect the light source location by using a light detection algorithm. Therefore, PDR has a self-correcting ability. The experimental results show that our method can effectively eliminate the cumulate error of traditional PDR and increase the operation distance. This paper has a certain contribution to the positioning of tunnels, underground shopping malls and other indoor places without sunlight interference. [ABSTRACT FROM AUTHOR]

In this paper we are going to deal with the surveillance and security system using raspberry pi. We are using VGG face algorithm to process and extract the features in the image that help for facial recognition. We also make use of different type of sensors for creating a security system that will send alerts to the email. [ABSTRACT FROM AUTHOR]

Objective. The aim of this work was to determine heterogeneous correction factors h Q clin , Q ref f clin , f ref det m , w to validate absorbed dose-to-medium D m , Q clin m , f clin calculation algorithms from detector readings. The impact of detector orientation perpendicular and parallel to the beam central axis on the correction factors was also investigated. Approach. The h Q clin , Q ref f clin , f ref det m , w factors were calculated for four types of detectors (PTW PinPoint T31016, PTW microDiamond T60019, PTW microSilicon T60023 and EBT3 film) placed in different media (cortical bone, lung, adipose tissue, Teflon and RW3) for the 6 MV energy beam with a 10 × 10 cm2 field size. These corrections were then applied to the detector measurements performed at different depths in heterogeneous phantoms. Main results. The h Q clin , Q ref f clin , f ref det m , w factors mainly depended on the media and slightly on the type of detector. Considering all detectors, the largest corrections were found in high-density media with values ranging from 0.911 to 0.934 in cortical bone. For comparison, the corrections in other media were closer to unity with values from 0.966 (lung and RW3) to 0.991 (adipose tissue). Except for the PinPoint T31016, detector orientation-dependence was observed especially in high-density media. A good agreement (≤1.5%) was found between D m , Q clin m , f clin calculations and the detector readings corrected with the h Q clin , Q ref f clin , f ref det m , w factor for all studied heterogeneous phantoms. Significance. This paper could serve as an initial guideline for medical physicists involved in the validation of the advanced type-b dose calculation algorithms reporting D m , Q clin m , f clin . To our knowledge, this is the first study to assess the impact of the orientation of different detectors in heterogeneous media. The orientation dependence of the detector response observed in water may not reflect what is observed in heterogeneous media, especially in high-density media. The knowledge of the h Q clin , Q ref f clin , f ref det m , w factors becomes mandatory for accurate interpretation of detector readings and comparisons with D m , Q clin m , f clin calculations. [ABSTRACT FROM AUTHOR]

This paper presents a practical contamination detection system for camera lenses using image analysis with deep learning. The proposed system can detect contamination in camera digital images through contamination learning utilizing deep learning, and it aims to prevent performance degradation of intelligent vision systems due to lens contamination in cameras. This system is based on the object detection algorithm YOLO (v5n, v5s, v5m, v5l, and v5x), which is trained with 4000 images captured under different lighting and background conditions. The trained models showed that the average precision improves as the algorithm size increases, especially for YOLOv5x, which showed excellent efficiency in detecting droplet contamination within 23 ms. They also achieved an average precision (mAP@0.5) of 87.46%, recall (mAP@0.5:0.95) of 51.90%, precision of 90.28%, recall of 81.47%, and F1 score of 85.64%. As a proof of concept, we demonstrated the identification and removal of contamination on camera lenses by integrating a contamination detection system and a transparent heater-based cleaning system. The proposed system is anticipated to be applied to autonomous driving systems, public safety surveillance cameras, environmental monitoring drones, etc., to increase operational safety and reliability. [ABSTRACT FROM AUTHOR]

The wrist is one of the most complex joints in our body, composed of eight bones. Therefore, measuring the angles of this intricate wrist movement can prove valuable in various fields such as sports analysis and rehabilitation. Textile stretch sensors can be easily produced by immersing an E-band in a SWCNT solution. The lightweight, cost-effective, and reproducible nature of textile stretch sensors makes them well suited for practical applications in clothing. In this paper, wrist angles were measured by attaching textile stretch sensors to an arm sleeve. Three sensors were utilized to measure all three axes of the wrist. Additionally, sensor precision was heightened through the utilization of the Multi-Layer Perceptron (MLP) technique, a subtype of deep learning. Rather than fixing the measurement values of each sensor to specific axes, we created an algorithm utilizing the coupling between sensors, allowing the measurement of wrist angles in three dimensions. Using this algorithm, the error angle of wrist angles measured with textile stretch sensors could be measured at less than 4.5°. This demonstrated higher accuracy compared to other soft sensors available for measuring wrist angles. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing line segment detectors will detect overdense meaningless textures, this paper proposes a fusing contour features optimization method for line segment detector, called CF-Lines. We define a new line segment attribute, called "line segment associate contour(LAC)" attribute, which includes the contour features, the length and the angle of line segment. After using existing algorithms to detect contours and line segments, CF-Lines calculates the LAC of all line segments. When the LAC is greater than the threshold and passes the quantitative verification, the line segment is removed as overdense meaningless texture. Using the YorkUrban and Wireframe datasets, the CF-Lines is tested and compared with original detectors. Experimental results show that CF-Lines performs better than the original detectors in average precision, F-score, average length of line segments and average number of line segments. [ABSTRACT FROM AUTHOR]

In this paper we present an open-source Time-of-Flight and radar dataset of a neonatal thorax simulator for the development of respiratory rate detection algorithms. As it is very difficult to gain recordings of (preterm) neonates and there is hardly any open-source data available, we built our own neonatal thorax simulator which simulates the movement of the thorax due to respiration. We recorded Time-of-Flight (ToF) and radar data at different respiratory rates in a range of 5 to 80 breaths per minute (BPM) and with varying upstroke heights. As gold standard a laser micrometer was used. The open-source data can be used to test new algorithms for non-contact respiratory rate detection. [ABSTRACT FROM AUTHOR]

With the ongoing integration of bionic technology and mechatronics, exoskeleton devices are finding applications in industry, healthcare, and logistics. This study is centered on enhancing the performance of heterogeneous sensor exoskeleton devices by presenting a six-degree-offreedom upper limb exoskeleton robot model based on the Denavit--Hartenberg (MDH) approach. The model's accuracy is verified using MATLAB. We construct a multi-objective optimization model that prioritizes workspace expansion. To realize this model, we propose an improved Harris Hawks algorithm (SCA-HHO) based on the sine cosine algorithm. The algorithm's effectiveness is compared with popular swarm intelligence methods (PSO, AOA, WOA) through cross-sectional simulations. SCA-HHO achieves average improvements of 6.30, 1.48, and 0.88% in objective function values compared with the swarm intelligence algorithms, respectively. This indicates SCA-HHO's superior suitability for solving the model proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient algorithm for edge-preserving filtering with multiple guidance images, so-called multilateral filtering. Multimodal signal processing for sensor fusion is increasingly important in image sensing. Edge-preserving filtering is available for various sensor fusion applications, such as estimating scene properties and refining inverse-rendered images. The main application is joint edge-preserving filtering, which can preferably reflect the edge information of a guidance image from an additional sensor. The drawback of edge-preserving filtering lies in its long computational time; thus, many acceleration methods have been proposed. However, most accelerated filtering cannot handle multiple guidance information well, although the multiple guidance information provides us with various benefits. Therefore, we extend the efficient edge-preserving filters so that they can use additional multiple guidance images. Our algorithm, named decomposes multilateral filtering (DMF), can extend the efficient filtering methods to the multilateral filtering method, which decomposes the filter into a set of constant-time filtering. Experimental results show that our algorithm performs efficiently and is sufficient for various applications. [ABSTRACT FROM AUTHOR]

This paper describes the optimal design of a miniature fiber-optic linear displacement sensor. It is characterized by its ability to measure displacements along a millimetric range with sub-micrometric resolution. The sensor consists of a triangular reflective grating and two fiber-optic probes. The measurement principle of the sensor is presented. The design of the sensor's triangular grating has been geometrically optimized by considering the step angle of the grating to enhance the sensor's resolution. The optimization method revealed a global optimum at which the highest resolution is obtained. [ABSTRACT FROM AUTHOR]

A foot placement of inertial sensors is commonly used for heel-strike (HS) and toe-off (TO) event detection. However, in clinical practice, such sensor placement may be difficult or even impossible due to the deformity of patients' feet. The first contribution of this paper is a new algorithm for HS and TO event detection for cases when the sensors are placed on the lateral malleolus. Such sensor placement allows gait analysis in patients with foot deformities. In addition, the placement of the sensor directly on the wide bone surface of the lateral malleolus ensures secure fixation of the sensor during walking. The proposed algorithm is based on deep neural networks, which can be easily adapted (by retraining the neural networks) for analysis of various pathological gait patterns. It is especially important in clinical practice when the number of possible pathological gait patterns is very large. The algorithm proposed in this paper was implemented in a new wearable system for the clinical gait analysis. The second contribution is a validation of this new wearable system. The performance of both proposed algorithm and gait analysis system was evaluated against a reference treadmill system where a capacitance–based pressure platform was used. A total of 117 healthy volunteers participated in the comparison (62 males and 55 females, age 24–55 years, height 162–183 cm). They were asked to perform 2 min walking trials with different speed. Mean accuracy ± precision was – 0.021 ± 0.091 s for gait cycle, 0.589 ± 1.144 steps/min for cadence, – 0.051 ± 0.544 % for stance phase, – 0.37 ± 0.649 % for single support, 0.296 ± 0.711 % for double support, 0.132 ± 0.561 % for load response, and 0.106 ± 0.661 % for preswing. Limitations of the proposed algorithm and its compassion with state-of-the-art algorithms were discussed. [ABSTRACT FROM AUTHOR]

Video splicing forgery is a common object-based intra-frame forgery operation. It refers to copying some regions, usually moving foreground objects, from one video to another. The splicing video usually contains two different modes of camera sensor pattern noise (SPN). Therefore, the SPN, which is called a camera fingerprint, can be used to detect video splicing operations. The paper proposes a video splicing detection and localization scheme based on SPN, which consists of detecting moving objects, estimating reference SPN, and calculating signed peak-to-correlation energy (SPCE). Firstly, foreground objects of the frame are extracted, and then, reference SPN are trained using frames without foreground objects. Finally, the SPCE is calculated at the block level to distinguish forged objects from normal objects. Experimental results demonstrate that the method can accurately locate the tampered area and has higher detection accuracy. In terms of accuracy and F1-score, our method achieves 0.914 and 0.912, respectively. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the geomagnetic sensor is vulnerable to external interference in the navigation process, this paper analyzes the frequency distribution range of geomagnetic signal and the noise characteristics in geomagnetic signal and proposes an improved morphological filtering and Hilbert–Huang transform (IMMF–IHHT) algorithm to extract and recognize the features of geomagnetic measurement signal. To avoid frequency aliasing and distortion caused by empirical mode decomposition, an improved morphological filtering algorithm based on mean constraint is used to preprocess the measured signal. The Hilbert spectrum of the decomposed signal is solved, the signal components are discriminated by the similarity criterion, and the signal components in line with the frequency range of the geomagnetic signal are extracted and processed to reconstruct the geomagnetic measurement signal. Simulation and experiments show that the signal-to-noise ratio and root-mean-square error of IMMF–IHHT combination algorithm are better than MF-HHT combination algorithm and IHHT algorithm. This algorithm has good signal feature extraction and recognition ability. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

This paper proposes a novel algorithm to determine the optimal orientation of sensing axes of redundant inertial sensors such as accelerometers and gyroscopes (gyros) for increasing the sensing accuracy. In this paper, we have proposed a novel iterative algorithm to find the optimal sensor configuration. The proposed algorithm utilizes the majorization-minimization (MM) algorithm and the duality principle to find the optimal configuration. Unlike the state-of-the-art approaches which are mainly geometrical in nature and restricted to sensors’ noise being uncorrelated, the proposed algorithm gives the exact orientations of the sensors and can easily deal with the cases of correlated noise. The proposed algorithm has been implemented and tested via numerical simulation in the MATLAB. The simulation results show that the algorithm converges to the optimal configurations and shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Aiming at the problems of inaccurate interaction point position, interaction point drift, and interaction feedback delay in the process of LiDAR sensor signal processing interactive system, a target tracking algorithm is proposed by combining LiDAR depth image information with color images. The algorithm first fuses the gesture detection results of the LiDAR and the visual image and uses the color information fusion algorithm of the Camshift algorithm to realize the tracking of the moving target. The experimental results show that the multi-information fusion tracking algorithm based on this paper has achieved higher recognition rate and better stability and robustness than the traditional fusion tracking algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of intermittently connected sensor networks where sensors are sleep most of time and become active randomly and rarely. In the networks, packet collision probability and power consumption can be reduced. Meanwhile, the delivery delay increases with the use of the low activation values. In this paper, we design the energy‐efficient intermittently connected sensor networks, considering this trade‐off of activation control. We first provide the algorithm that computes the optimal packet forwarding rule achieving the minimum delivery delay, which can be calculated using the activation rates. We then propose the activation rate control methods, which control the activation rates of the sensors based on the density of the sensors and/or the number of wake‐ups of sensors having relay packets. By doing this, we can improve the delay performance, while suppressing the increase in the packet collision probability and the power consumption of sensors. Through simulation experiments, we demonstrate the effectiveness of our activation control methods in the intermittently connected sensor networks. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]