Mobile/multi-access edge computing (MEC) is developed to support the upcoming AI-aware mobile services, which require low latency and intensive computation resources at the edge of the network. One of the most challen...
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Endoscopic images usually have many overexposed regions due to strong and focused light sources and, consequently, physicians need to change the camera angle for a clear view from time to time. This work targets remov...
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Traditional systems for crop disease detection often rely on manual inspection methods, which are time-consuming, labor-intensive, and prone to human error. In this context, the necessity of using transfer learning be...
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In digital forensics, file fragment classification is an important step toward completing file carving process. There exist several techniques to identify the type of file fragments without relying on meta-data, such ...
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Image steganography conceals secret data within a cover image to generate a new image (stego image) in a manner that makes the secret data undetectable. The main problem in image steganography is to find the right bal...
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Image steganography conceals secret data within a cover image to generate a new image (stego image) in a manner that makes the secret data undetectable. The main problem in image steganography is to find the right balance between the maximum amount of secret data that can be transmitted or stored in the cover image and the quality of the stego image to make the secret data un-noticeable to human senses or other detection mechanisms. In this research, a new framework is proposed that integrates the edge detection strategy (using edge detectors) with the deep learning methods, such as a convolutional neural network (CNN), for making secret data embedding and extraction processes efficient. Firstly, the edges in the cover image are identified using a suitable edge detection method (i.e., using the canny or sobel algorithm), and then the secret data is embedded inside the edge-detected cover image using a deep learning approach, and finally, the created stego image is sent to the receiver. On the receiver side, the secret data is extracted from the stego image using the same deep learning model in a reverse manner. In this article, we considered three datasets, such as the Ting ImageNet, Bossbase, and USC-SIPI datasets, to make edge-detected cover images and then consider them to build a deep learning model. We then evaluated the performance of our proposed deep learning model based embedding and extraction approach using various metrics related to imperceptibility, capacity, and robustness. Experimental results show high imperceptibility with PSNR (Peak Signal-to-Noise Ratio) reaching up to 39.85 dB and SSIM (Structural Similarity Index) up to 0.997 when using sobel, and PSNR up to 33.08 dB and SSIM up to 0.995 when using canny. The developed model also provides a higher capacity of 8 bits per pixel (BPP) and have ensured the robustness of the system against common image processing attacks (e.g., JPEG compression, Gaussian noise, salt-and-pepper, speckle, and filtering
Wi-Fi signals are commonly used for conventional communication, yet they can also realize low-cost and non-invasive human sensing. However, Wi-Fi sensing in Multi-person scenarios is still a challenging problem. In th...
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ISBN:
(数字)9798350383508
ISBN:
(纸本)9798350383515
Wi-Fi signals are commonly used for conventional communication, yet they can also realize low-cost and non-invasive human sensing. However, Wi-Fi sensing in Multi-person scenarios is still a challenging problem. In this paper, we propose M
2
-Fi to achieve multi-person respiration monitoring using a handheld device. M
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-Fi leverages Wi-Fi BFI (beamforming feedback information) performs respiration monitoring. As a compressed version of the uplink CSI (channel state information), BFI transmission is unencrypted, easily obtained using frame capture, and does not require specific firmware to obtain. M
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-Fi is based on an interesting experiment phenomenon that when a Wi-Fi device is very close to a subject, near-field channel changes caused by the subject significantly cancel out changes from other subjects. We employed VMD (Variational Mode Decomposition) to eliminate the interference caused by hand movement in the BFI time series. Subsequently, we devised a deep learning architecture based on GAN (Generative Adversarial Networks) to recover fine-grained respiration waveforms from the respiration patterns extracted from the BFI time series. Our experiments on collected 50-hour data from 8 subjects show that M
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-Fi can accurately recover the respiration waveforms of multiple persons with handheld devices.
Software testing is very important in software development to ensure its quality and reliability. As software systems have become more complex, the number of test cases has increased, which presents the challenge of e...
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3D object detection using point clouds has been widely used for self-driving vehicles, roadside vehicle detection, and tracking. However, the detection accuracy using single-LiDAR-scanned point clouds suffers from occ...
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Goal-Oriented Requirements Engineering (GORE) plays a crucial role in facilitating effective communication between stakeholders in system development. Through the use of goal models, GORE provides a structured approac...
Goal-Oriented Requirements Engineering (GORE) plays a crucial role in facilitating effective communication between stakeholders in system development. Through the use of goal models, GORE provides a structured approach for eliciting, analyzing, and managing requirements from the perspective of stakeholders' goals and intentions. However, goal models are susceptible to poor practices, called also bad smells, that may hinder the effective communication and understanding among stakeholders, potentially leading to misinterpretations and inconsistencies in requirements. In particular, goal models are prone to linguistic bad smells encompassing a spectrum of anoma-lies such as unclear or ambiguous goal statements, conflicting or contradictory requirements, and instances of misspellings. Therefore, identifying and addressing linguistic bad smells in goal models is crucial for ensuring the quality and accuracy of goal models. In this paper, we define seventeen linguistic bad smells in goal models, classified into four categories: Syntax, Semantics, Pragmatics, and Complexity. Furthermore, we provide Natural Language Processing (NLP) based detection methods for twelve specific bad smells, which we have automated to target Textual GRL (TGRL) models. The proposed approach and tool are evaluated using two TGRL models achieving an F2-Score of 0.8.
Virus detection software is widely used for servers, systems, and devices that seek to maintain security and reliability. Although these programs provide an excellent safety level, the traditional defense methods fail...
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