An Experimental Study of IEEE 802.11n Devices for Vehicular Networks with Various Propagation Loss Models

Rani, Preeti; Sharma, Rohit

doi:10.1007/978-981-99-1312-1_11

Cited by 14 publications

(5 citation statements)

References 16 publications

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“…The MPEG-1, MPEG-2, MPEG-3, MPEG-4, and H-264 coding standards are generally used to compress digital videos for improved storage and transmission [6]. Video forgeries are detected using compression artifacts or coding clues acquired during compression.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Detection of Object-Based Video Forgery Using Various Groups of Pictures (GOP)

Khang,

Neyara Radwan

2023

WJCMS

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In recent years, there has been a lot of interest in detecting object-based video forgeries. There has been a lack of satisfactory performance with object-based forgery detectors until recently since a majority of them are still based on handcrafted features. There has been a great deal of interest in passive video forensics in recent years. Forgery of video encoded with advanced codec frameworks remains one of the biggest challenges in object-based forgery research. An object-based forgery detection approach is presented in this paper. To evaluate the proposed method, a derived test dataset of variable video lengths and frame sizes is also used in addition to the SYSU-OBJFORG dataset. This process's efficacy is verified by comparing its results with other methods. When tested on datasets with degraded-quality videos, the proposed framework performed better in real-life scenarios.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Detection of Object-Based Video Forgery Using Various Groups of Pictures (GOP)

Khang,

Neyara Radwan

2023

WJCMS

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“…Time-space complexity issues that pertain to multiple hierarchical segmentations such that each segment belongs to a different group with an individualistic set of features are solved by Multi-scale combinatorial grouping [29], which adopts edge boxing techniques replacing time-consuming visual boxing methods. Some sister techniques of edge boxing include DeepBox [28] and SharpMask [23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Faster R-CNN [22] Its accuracy with real-time results makes it the right choice for real-time object detection techniques-0.12 per image The extensive computation abilities require parallel computing-making it unsuitable for actual time application R-FCN [23] Requires less time than R-CNN when processing test set…”

Section: Limitationsmentioning

confidence: 99%

“…To handle this issue of process optimization with annotated images, [22] presented an algorithm for selection automation that reduces time and effort spent on model training, testing and feature extraction, known as an effective online algorithm (OHEM). Building on the same, the author [23] emulates the concept of selected deep convolutional frameworks for region-specific classification with networks like ResNet [27] and GoogLeNets.…”

Section: Limitationsmentioning

confidence: 99%

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An improved deep learning-based optimal object detection system from images

Yadav,

Jindal,

Rani

et al. 2023

Multimed Tools Appl

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Computer vision technology for detecting objects in a complex environment often includes other key technologies, including pattern recognition, artificial intelligence, and digital image processing. It has been shown that Fast Convolutional Neural Networks (CNNs) with You Only Look Once (YOLO) is optimal for differentiating similar objects, constant motion, and low image quality. The proposed study aims to resolve these issues by implementing three different object detection algorithms—You Only Look Once (YOLO), Single Stage Detector (SSD), and Faster Region-Based Convolutional Neural Networks (R-CNN). This paper compares three different deep-learning object detection methods to find the best possible combination of feature and accuracy. The R-CNN object detection techniques are performed better than single-stage detectors like Yolo (You Only Look Once) and Single Shot Detector (SSD) in term of accuracy, recall, precision and loss.

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“…Calculating the clamour reduction of advanced images can be difficult, and no singular tool is available. In other words, the factual amount measures used to determine how much commotion is reduced in images are the signal-to-noise ratio (PSNR) and the Root Mean Square Error (RMSE) [10]. This channel contrasts with a Wiener separating system when executing images spoiled by various clamour levels.…”

Section: Introductionmentioning

confidence: 99%

Optimized the Performance of Super Resolution Images by Salt and pepper Noise Removal based on a Modified Trimmed Median Filter

Muthmainnah,

Luís Miguel Cardoso

2023

WJCMS

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Image processing is an interesting area where noisy images can be restored from salt and pepper noise. Various filtering algorithms can be used in the restoration process. Pixels in the original image should not be affected by the restoration process. Despite changes in dimensions and image format, the problem of the existing work persists. A hybrid technique used Ant colony Optimization to remove high-density salt and pepper noise from images. This hybrid technique would remove salt and pepper noise in corrupted images. Ant Colony Optimization (ACO) identifies and selects noisy pixels from corrupted images. It eliminates salt and pepper noise (SP Noise), which causes black and white spots in the original image. All the processes are explained to prove the theory, and the simulation results are presented.

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An Experimental Study of IEEE 802.11n Devices for Vehicular Networks with Various Propagation Loss Models

Cited by 14 publications

References 16 publications

Automatic Detection of Object-Based Video Forgery Using Various Groups of Pictures (GOP)

Automatic Detection of Object-Based Video Forgery Using Various Groups of Pictures (GOP)

An improved deep learning-based optimal object detection system from images

Optimized the Performance of Super Resolution Images by Salt and pepper Noise Removal based on a Modified Trimmed Median Filter

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