Search Results (15,677)

Action assessment and feedback can effectively assist fitness practitioners in improving exercise benefits. In this paper, we address key challenges in human action recognition and assessment by proposing innovative methods that enhance performance while reducing computational complexity. Firstly, we present Oct-MobileNet, a lightweight backbone network, to overcome the limitations of the traditional OpenPose algorithm’s VGG19 network, which exhibits a large parameter size and high device requirements. Oct-MobileNet employs octave convolution and attention mechanisms to improve the extraction of high-frequency features from the human body contour, resulting in enhanced accuracy with reduced model computational burden. Furthermore, we introduce a novel approach for action recognition that combines skeleton-based information and multiple feature fusion. By extracting spatial geometric and temporal characteristics from actions, we employ a sliding window algorithm to integrate these features. Experimental results show the effectiveness of our approach, demonstrating its ability to accurately recognize and classify various human actions. Additionally, we address the evaluation of traditional fitness exercises, specifically focusing on the BaDunJin movements. We propose a multimodal information-based assessment method that combines pose detection and keypoint analysis. Label sequences are obtained through a pose detector and each frame’s keypoint coordinates are represented as pose vectors. Leveraging multimodal information, including label sequences and pose vectors, we explore action similarity and perform quantitative evaluations to help exercisers assess the quality of their exercise performance. Full article

Hierarchical reinforcement learning (HRL) offers a hierarchical structure for organizing tasks, enabling agents to learn and make decisions autonomously in complex environments. However, traditional HRL approaches face limitations in effectively handling complex tasks. Reward machines, which specify high-level goals and associated rewards for sub-goals, have been introduced to address these limitations by facilitating the agent’s understanding and reasoning with respect to the task hierarchy. In this paper, we propose a novel approach to enhance HRL performance through topologically sorted potential calculation for reward machines. By leveraging the topological structure of the task hierarchy, our method efficiently determines potentials for different sub-goals. This topological sorting enables the agent to prioritize actions leading to the accomplishment of higher-level goals, enhancing the learning process. To assess the efficacy of our approach, we conducted experiments in the grid-world environment with OpenAI-Gym. The results showcase the superiority of our proposed method over traditional HRL techniques and reward machine-based reinforcement learning approaches in terms of learning efficiency and overall task performance. Full article

This short article is concerned with the floating high-side gate driver suitable for driving the high side MOSFET/IGBT switch in the series capacitor buck converter family or converters with similar topological features. Biasing the high-side driver in series capacitor buck converters presents an engineering challenge. To alleviate the problem, a modified high-side driver with a voltage lift circuit for driving a high-side switch is proposed. The suggested solution, while being simple and low cost, has several benefits over the earlier propositions. The primary advantage of the proposed circuit is that it relies on a regulated supply to recharge its boot capacitor so to properly bias the high-side driver. Moreover, the proposed driver can operate in a wide range of input voltages while self-adjusting the correct voltage lift according to the operating point of a particular phase of the series buck converter. Thus, any number of phases can be implemented, avoiding cross-coupling effects and providing the high-switch with gating pulses of same amplitude. Full article

The rapid advancement of renewable energy technologies necessitates innovative solutions for the efficient deployment and management of microgrid systems. This paper presents a detailed study on the implementation of edge–cloud collaboration-based plug and play (PnP) and topology identification for microgrids, focusing on the Jingshan AC/DC Microgrid Cluster System (JS-MCS) in Hubei, China. Firstly, the paper elucidates the structure of JS-MCS’s physical system and integral information system, comprising the cloud platform, communication network, edge unit, and terminal unit. The following sections introduce the methods of PnP, describing the overall procedure and specific implementations between cloud and edge and cloud and terminal. Then, a novel approach for topology identification is further explored based on correlation analysis. An exemplary analysis of the studied system, JS-MCS, accentuates practical insights into the application of PnP and topology identification methods. Full article

As urbanization accelerates, traffic congestion in cities has become a problem. Therefore, accurately identifying urban residents’ travel patterns is crucial for urban traffic planning and intelligent transportation systems. In this study, a convolutional neural network (CNN) approach based on multichannel feature extraction using mobile phone signaling data to identify user travel modes is proposed. Here, a trajectory generation method was designed for five types of travel modes. By designing a spatiotemporal threshold screening method, anomalies were identified and processed, combined with the feature analysis method, key points in the signaling extracted, the travel trajectory sliced, and travel sub-trajectory data generated. Next, in the travel mode identification stage, road network information was introduced to improve localization accuracy, and the method for calculating feature values improved. A user travel feature dataset was generated by calculating the feature values, and the travel modes represented by each class were classified and recognized based on the CNN method. Satisfactory results were achieved through experiments using mobile phone signaling and field research data in Kunming, China. The experimental results showed that analysis based on mobile phone signaling data could classify, identify, and obtain different travel category modes. This method’s accuracy was 84.7%. The method provided a feasible way of identifying travel patterns in the context of smart cities and big data, providing strong support for urban transport planning and management, and has the potential for wider application. Full article

Deep neural networks are widely used for voiceprint recognition, whilst voiceprint recognition models are vulnerable to attacks. Existing protection schemes for voiceprint recognition models are insufficient to withstand various robustness attacks and cannot prevent model theft. This paper proposes a black-box voiceprint recognition model protection framework that combines active and passive protection. It embeds key information into the Mel spectrogram to generate trigger samples that are difficult to detect and remove and injects them into the host model as watermark W, thereby enhancing the copyright protection performance of the voiceprint recognition model. To restrict the use of the model by unauthorized users, the index number corresponding to the model and the encrypted model information are stored on the blockchain, and then, an exclusive smart contract is designed to restrict access to the model. Experimental results show that this framework effectively protects voiceprint recognition model copyrights and restricts unauthorized access. Full article

Datasets play an important role in the field of object detection. However, the production of the dataset is influenced by objective environment and human subjectivity, resulting in class imbalanced or even long-tailed distribution in the datasets. At present, the optimization methods based on data augmentation still rely on subjective parameter adjustments, which is tedious. In this paper, we propose a multi-stage adaptive Copy-Paste augmentation (MSACP) algorithm. This algorithm divides model training into multiple training stages, each stage forming unique training preferences for that stage. Based on these training preferences, the class information of the training set is adaptively adjusted, which not only alleviates the problem of class imbalance in training, but also expands different sample sizes for categories with insufficient information at different training stages. Experimental verification of the traffic sign dataset Tsinghua–Tencent 100K (TT100K) was carried out and showed that the proposed method not only can improve the class imbalance in the dataset, but can also improve the detection performance of models. By using MSACP to transplant the trained optimal weights to an embedded platform, and combining YOLOv3-tiny, the model’s accuracy in detecting traffic signs in autonomous driving scenarios was improved, verifying the effectiveness of the MSACP algorithm in practical applications. Full article

Inertial kinetics and kinematics have substantial influences on human biomechanical function. A new algorithm for Inertial Measurement Unit (IMU)-based motion tracking is presented in this work. The primary aims of this paper are to combine recent developments in improved biosensor technology with mainstream motion-tracking hardware to measure the overall performance of human movement based on joint axis-angle representations of limb rotation. This work describes an alternative approach to representing three-dimensional rotations using a normalized vector around which an identified joint angle defines the overall rotation, rather than a traditional Euler angle approach. Furthermore, IMUs allow for the direct measurement of joint angular velocities, offering the opportunity to increase the accuracy of instantaneous axis of rotation estimations. Although the axis-angle representation requires vector quotient algebra (quaternions) to define rotation, this approach may be preferred for many graphics, vision, and virtual reality software applications. The analytical method was validated with laboratory data gathered from an infant dummy leg’s flexion and extension knee movements and applied to a living subject’s upper limb movement. The results showed that the novel approach could reasonably handle a simple case and provide a detailed analysis of axis-angle migration. The described algorithm could play a notable role in the biomechanical analysis of human joints and offers a harbinger of IMU-based biosensors that may detect pathological patterns of joint disease and injury. Full article

When light propagates in foggy weather, it is affected and scattered by suspended particles in the air. As a result, images taken in this environment often suffer from blurring, reduced contrast, loss of details, and other issues. The primary challenge in dehazing images is to estimate the transmission coefficient map in the atmospheric degradation model. In this paper, we propose a dehazing algorithm based on the optimization of the “haze-line” prior and non-local self-similarity prior. First, we divided the input haze image into small blocks and used the nearest neighbor classification algorithm to cluster the small patches, which were referred to as “patch-lines”. Based on the characteristics of these “patch-lines”, we could estimate the transmission coefficient map for the image. We then applied the transmission map to a weighted least squares filter to smooth it. Finally, we calculated the clear image using the haze degradation model. The experimental results demonstrate that our algorithm enhanced the image contrast and preserved the fine details, both qualitatively and quantitatively. Full article

The research focuses on the analysis of seismic data, specifically targeting the detection, edge segmentation, and classification of seismic images. These processes are fundamental in image processing and are crucial in understanding the stratigraphic structure and identifying oil and natural gas resources. However, there is a lack of sufficient resources in the field of seismic image detection, and interpreting 2D seismic image slices based on 3D seismic data sets can be challenging. In this research, image segmentation involves image preprocessing and the use of a U-net network. Preprocessing techniques, such as Gaussian filter and anisotropic diffusion, are employed to reduce blur and noise in seismic images. The U-net network, based on the Canny descriptor is used for segmentation. For image classification, the ResNet-50 and Inception-v3 models are applied to classify different types of seismic images. In image detection, Tchebichef invariants are computed using the Tchebichef polynomials’ recurrence relation. These invariants are then used in an optimized multi-class SVM network for detecting and classifying various types of seismic images. The promising results of the SVM model based on Tchebichef invariants suggest its potential to replace Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs) for seismic image detection. This approach offers a more efficient and dependable solution for seismic image analysis in the future. Full article

In this paper, we propose a power-efficient memory operation, selective match line precharge, based on an analysis of power consumption in ternary content-addressable memory (TCAM). A statistical study reveals that, on average, 58% of power dissipation related to the match line operation is saved by deactivating the unnecessary swing of match lines. The improvement has been simulated and proved using 180 nm CMOS technology. Full article

Self-protection deceptive interferences (SPDI) are widely used in electronic countermeasures. Smeared spectrum (SMSP) interference, as a typical SPDI, can form a large number of dense false targets at the receiver output to affect effective target detection. Therefore, the suppression of SMSP interference is a compelling issue. The existing SMSP interference suppression methods inevitably result in energy loss of the target due to signal processing. This paper proposes a novel interference suppression method based on echo preprocessing to address this problem. Firstly, the pulse compression (PC) and the coherent integration (CI) characteristics of SMSP interference in the pulse Doppler radar are obtained through the derivation of formulas. Then, echo preprocessing is introduced, and the steps of interference suppression are listed in detail. Finally, the SMSP interference is suppressed because the preprocessed interference forms a center-shifting and range-scaling in the distance dimension after PC, and CI gain cannot be further obtained. The proposed method does not lose the energy of the true target because it does not involve filtering and reconstruction processing. Simulations show that the target detection probability of the proposed method can reach 100% via peak search after the interference suppression when the signal-to-noise ratio is greater than −10 dB and the jamming-to-signal ratio (JSR) is less than 35 dB. Compared with three representative methods in the recent literature, the proposed method has better robustness and higher JSR tolerance. Full article

Cloud-based services are growing more rapidly than ever, and so does the management challenge on their providers’ side. Cloud-based datacenter networks are built with nodes of huge processing power, connected by high bandwidth capacities to carry their interior traffic requirements. However, such cloud networks still have limits that are imposed not necessarily by their physical components, but by the schemes of resource management being deployed. Traditionally, for an institute to provide services, it needs to have its own datacenter facility that interconnects its servers through a topology that matches its desired administrative policies and scaling objectives. With the theme of cloud-based IaaS, such datacenter topologies can be created virtually over the cloud. Nowadays, a significant part of those institutes who provide us with our daily services have their infrastructures hosted over cloud ones. Therefore, resources of such cloud networks need to be efficiently utilized, in order to keep their performance and hosting prices competitive. A typical datacenter network mainly consists of server nodes and network links. Besides the resources of the server nodes, the network bandwidth resources are considered a crucial key determinant for the whole datacenter performance. Indeed, a server without sufficient bandwidth capacities is almost useless. Proposals in the literature present schemes for resource utilization on either side of the problem at a time: the nodes or the links. Working in isolation can never deliver efficient mapping solutions. ABLA is an Application-Based, and Load balancing Approach for adaptive mapping proposal. ABLA’s methodology tackles both sides of the datacenter, the nodes and links. It starts by (1) breaking down the node’s resource requirement for the requested applications to be hosted over the virtual server machines besides (2) reading the topological connectivity and bandwidth requirements for each virtual node to all other nodes in the virtual datacenter topology. Compared to other models in the literature, the simulation results show that our proposed ABLA model provides for complete mapping services via load-balanced hosting networks. This allows for competitive hosting prices, with higher performance and service satisfaction rates. Full article

Unmanned aerial vehicles have found applications in fields such as environmental monitoring and the military. Although the collected data in some of these application domains are sensitive, public channels are deployed during the communication process. Therefore, many protocols have been presented to preserve the confidentiality and integrity of the exchanged messages. However, numerous security and performance challenges have been noted in the majority of these protocols. In this paper, an elliptic curve cryptography (ECC) and symmetric key-based protocol is presented. The choice of ECC was informed by its relatively shorter key sizes compared to other asymmetric encryption algorithms such as the Rivest–Shamir–Adleman (RSA) algorithm. Security analysis showed that this protocol provides mutual authentication, session key agreement, untraceability, anonymity, forward key secrecy, backward key secrecy, and biometric privacy. In addition, it is robust against smart card loss, password guessing, known secret session temporary information (KSSTI), privileged insider, side-channeling, impersonation, denial-of-service (DoS), and man-in-the-middle (MitM) attacks. The comparative performance evaluation showed that it has relatively low computation, storage, and communication complexities. Full article