Vision language models (VLMs) such as LLaVA, ChatGPT-4, and Gemini have recently emerged and gained the spotlight for their ability to comprehend the dual modality of image and textual data showing impressive performa...
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This research presents a navigation robotic system designed for the concurrent tasks of line following and obstacle avoidance in partially-known environments with presence of obstacles. By applying a strategically pos...
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This paper presents measurements of the reflection and transmission coefficient of electromagnetic waves through concrete and two concrete-based composites: concrete with steel fibers and concrete with carbon fibers w...
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Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into ...
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3D symmetric tensor fields have a wide range of applications in science and engineering. The topology of such fields can provide critical insight into not only the structures in tensor fields but also their respective...
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作者：
Alfardus, AsmaRawat, Danda B.
Department of Electrical Engineering and Computer Science WashingtonDC20059 United States
The complex distributed systems installed in vehicles represent the cutting edge of the automotive industry. Electronic control units communicate with each other by sending and receiving messages over a well-known pro...
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ISBN:
(纸本)9798331510633
The complex distributed systems installed in vehicles represent the cutting edge of the automotive industry. Electronic control units communicate with each other by sending and receiving messages over a well-known protocol called the Controller Area Network (CAN) bus system. Car Broadcast is a new, but insecure, way to communicate between external electronic devices. However, today's vehicles are on the brink of security because the CAN network lacks a secure authentication and authorization mechanism. The rise in cyber attacks such as spoofing, spoofing and most commonly denial of service attacks is the result of uncertain measures in the CAN bus network. Although many intrusion detection systems have been developed to provide more secure communication in the vehicle, CAN is still far from being the most secure communication protocol. Since cyber attacks can come from a little-known or completely unknown source, it is essential to take a probabilistic approach based on previous observations from previous attacks. Therefore, we propose a new intrusion detection system that uses binary logistic regression (BLR) to detect and mitigate attacks on a CAN bus network. Binary logistic regression is a very popular predictive model that is widely used in various fields. In binary logistic regression, data is first analyzed, then the probability of individual events is estimated by observing previous data, and then a binary classification model is created. An evaluation of the well-known Nsl-kdd and Kdd-99 datasets shows that our proposed method has a dominant overall performance. The final detection rate is 099.031% using Nsl-kdd with a positive rate as low as 00.073% and the rate of detection is 099.043% using kdd-99 with a positive false rate as low as 00.046%˙ Specifically, to detect denial of service (DoS) attacks, the proposed system achieved a detection rate of 099.061% and 099.098% in Nsl-kdd and kdd-99 dataset respectively. Comparative evaluation confirmed that BLR i
This study explores the role of teaching assistants (TAs) as assessors in a university’s computerscience program. It examines the challenges and implications of TAs in grading, with a focus on their expertise and gr...
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In this work, we propose a hybrid method to fast solve the optimization of array located on the PEC carrier inside the dielectric radome with parameters variation. By constructing a reusable low-rank reduced-order mod...
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Moving target detection is one of the most basic tasks in computer *** conventional wisdom,the problem is solved by iterative optimization under either Matrix Decomposition(MD)or Matrix Factorization(MF)*** utilizes f...
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Moving target detection is one of the most basic tasks in computer *** conventional wisdom,the problem is solved by iterative optimization under either Matrix Decomposition(MD)or Matrix Factorization(MF)*** utilizes foreground information to facilitate background *** uses noise-based weights to fine-tune the *** both noise and foreground information contribute to the recovery of the *** jointly exploit their advantages,inspired by two framework complementary characteristics,we propose to simultaneously exploit the advantages of these two optimizing approaches in a unified framework called Joint Matrix Decomposition and Factorization(JMDF).To improve background extraction,a fuzzy factorization is *** fuzzy membership of the background/foreground association is calculated during the factorization process to distinguish their contributions of both to background *** describe the spatio-temporal continuity of foreground more accurately,we propose to incorporate the first order temporal difference into the group sparsity constraint *** temporal constraint is adjusted *** foreground and the background are jointly estimated through an effective alternate optimization process,and the noise can be modeled with the specific probability *** experimental results of vast real videos illustrate the effectiveness of our *** with the current state-of-the-art technology,our method can usually form the clearer background and extract the more accurate ***-noise experiments show the noise robustness of our method.
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.
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