Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It i...
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Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by i...
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Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.
The development of smart mobile devices brings convenience to people's lives, but also provides a breeding ground for Android malware. The sharp increasing malware poses a disastrous threat to personal privacy in ...
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The development of smart mobile devices brings convenience to people's lives, but also provides a breeding ground for Android malware. The sharp increasing malware poses a disastrous threat to personal privacy in the information age. Based on the fact that malware heavily resorts to system application programming interfaces(APIs) to perform its malicious actions,there has been a variety of API-based detection *** of them do not consider the relationship between APIs. We contribute a new approach based on the enhanced API order for Android malware detection, named EAODroid, which learns the similarity of system APIs from a large number of API sequences and groups similar APIs into clusters. The extracted API clusters are further used to enhance the original API calls executed by an app to characterize behaviors and perform classification. We perform multi-dimensional experiments to evaluate EAODroid on three datasets with ground truth. We compare with many state-of-the-art works, showing that EAODroid achieves effective performance in Android malware detection.
The discriminative correlation filter (DCF) is commonly utilized in UAV tracking because of its high tracking accuracy and computing speed. However, in aerial tracking scenarios, challenges such as target occlusion an...
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The discriminative correlation filter (DCF) is commonly utilized in UAV tracking because of its high tracking accuracy and computing speed. However, in aerial tracking scenarios, challenges such as target occlusion and similar object interference are likely to cause the predicted object position to deviate from the correct motion trajectory. To alleviate this issue, this paper proposes a correlation filter algorithm based on trajectory correction and context interference suppression for real-time aerial tracking. First, a tracking quality evaluation metric is proposed to determine the confidence of the current tracking results. When the object is in a low confidence status, the state matrices of the object position and velocity are constructed, and the Kalman filter strategy is utilized to correct the tracking trajectory automatically. In addition, temporal context-response regularization is designed to fully exploit previous temporal information in order to suppress background interference. Extensive experimental results on four mainstream datasets demonstrate that the proposed algorithm has high tracking performance while achieving a real-time tracking speed of 32 fps on a single CPU. IEEE
In Internet of Things(loT),data sharing among different devices can improve manufacture efficiency and reduce workload,and yet make the network systems be more vulnerable to various intrusion *** has been realistic de...
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In Internet of Things(loT),data sharing among different devices can improve manufacture efficiency and reduce workload,and yet make the network systems be more vulnerable to various intrusion *** has been realistic demand to develop an efficient intrusion detection algorithm for connected *** of existing intrusion detection methods are trained in a centralized manner and are incapable to identify new unlabeled attack *** this paper,a distributed federated intrusion detection method is proposed,utilizing the information contained in the labeled data as the prior knowledge to discover new unlabeled attack ***,the blockchain technique is introduced in the federated learning process for the consensus of the entire *** results are provided to show that our approach can identify the malicious entities,while outperforming the existing methods in discovering new intrusion attack types.
Scattering medium in light path will cause distortion of the light field,resulting in poor signal-to-noise ratio(SNR)of ghost *** disturbance is usually eliminated by the method of *** deduce the intensity fluctuation...
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Scattering medium in light path will cause distortion of the light field,resulting in poor signal-to-noise ratio(SNR)of ghost *** disturbance is usually eliminated by the method of *** deduce the intensity fluctuation correlation function of the ghost imaging with the disturbance of the scattering medium,which proves that the ghost image consists of two correlated results:the image of scattering medium and the target *** effect of the scattering medium can be eliminated by subtracting the correlated result between the light field after the scattering medium and the reference light from ghost image,which verifies the theoretical *** research may provide a new idea of ghost imaging in harsh environment.
This paper studies asynchronous energy-to-peak control for 2D Roesser-type Markov jump systems (RTMJSs). Given the practical challenge of obtaining the system state, output-feedback is utilized for closing the control...
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In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task ...
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In current research on task offloading and resource scheduling in vehicular networks,vehicles are commonly assumed to maintain constant speed or relatively stationary states,and the impact of speed variations on task offloading is often *** is frequently assumed that vehicles can be accurately modeled during actual motion ***,in vehicular dynamic environments,both the tasks generated by the vehicles and the vehicles’surroundings are constantly changing,making it difficult to achieve real-time modeling for actual dynamic vehicular network *** into account the actual dynamic vehicular scenarios,this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios,attempting to solve the dynamic decision-making problem in task offloading *** optimization objective is to minimize the average task completion time,which is formulated as a multi-constrained non-linear programming *** to the mobility of vehicles,a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and ***,the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient(MADDPG)is applied to solve the optimal solution of the optimization *** results show that the algorithm proposed in this paper is able to achieve lower latency task computation ***,the average task completion time of the proposed algorithm in this paper can be improved by 7.6%compared to the performance of the MADDPG scheme and 51.1%compared to the performance of deep deterministic policy gradient(DDPG).
Visual localization and object detection both play important roles in various *** many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely ***,few researchers ...
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Visual localization and object detection both play important roles in various *** many indoor application scenarios where some detected objects have fixed positions,the two techniques work closely ***,few researchers consider these two tasks simultaneously,because of a lack of datasets and the little attention paid to such *** this paper,we explore multi-task network design and joint refinement of detection and *** address the dataset problem,we construct a medium indoor scene of an aviation exhibition hall through a semi-automatic *** dataset provides localization and detection information,and is publicly available at https://***/drive/folders/1U28zk0N4_I0db zkqyIAK1A15k9oUKOjI?usp=sharing for benchmarking localization and object detection *** this dataset,we have designed a multi-task network,JLDNet,based on YOLO v3,that outputs a target point cloud and object bounding *** dynamic environments,the detection branch also promotes the perception of *** includes image feature learning,point feature learning,feature fusion,detection construction,and point cloud ***,object-level bundle adjustment is used to further improve localization and detection *** test JLDNet and compare it to other methods,we have conducted experiments on 7 static scenes,our constructed dataset,and the dynamic TUM RGB-D and Bonn *** results show state-of-the-art accuracy for both tasks,and the benefit of jointly working on both tasks is demonstrated.
Signature verification plays a critical role in various industries, including finance and document authentication. However, traditional verification techniques have limitations, such as a lack of robustness and an ina...
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