The utilisation of median filtering (MF), a nonlinear signal processing technique, offers distinct advantages within picture anti-forensics. Consequently, there has been an increased focus on the forensic investigation of MF. However, due to lossy compression, identifying MF in the compressed domain is challenging. Towards this, research presents a novel approach for forensic analysis of MF in compressed images based on utilising deep noise residuals. In this framework, median filtering residuals (MFR) are employed to preprocess the images by passing through two streams. After that, the MFR output is extended to encompass two parallel blocks with different dilation rates to form a fusion feature vector. Further, the MFeRNet framework incorporates convolution, specifically developed to enhance information integration from several streams compared to conventional techniques. The proposed method, MFeRNet, aims to effectively integrate the three-level information of an image and comprehensively extract forensic clues in a compressed scenario. In addition, the experimental results demonstrate that the proposed methodology exhibits superior performance and reduced training time compared to the early reported techniques with equivalent convolution depth.

Unmanned aerial vehicles (UAVs) have been widely used in many facets of contemporary society over the past ten years due to their accessibility and affordability. The rise in drone usage brings up privacy and security issues. It is essential to be vigilant for unauthorized UAVs in restricted areas. In this work, a hybrid Convolutional Neural Network-Shuffled Frog Leap (CNN-SFL) Algorithm is proposed for classifying various flying objects, such as drones, helicopters, and artificial birds based on Micro-Doppler Signature (MDS) collected from HB100 radar mounted on UAV. Various array positioning and configuration, such as Uniform Linear Array (ULA), Uniform Rectangular Array (URA), and Uniform Circular Array (UCA), are taken into account when analyzing the accuracy for avoiding performance loss due to a significant Angle of Arrival (AoA) of the received signal. Further the activities of drones are also classified, and accuracy is assessed in comparison to existing algorithms. The results demonstrate that the proposed technique outperforms in all cases. In the end-fire direction, URA performs better as compared to the other configurations and in other directions, ULA performs better.

Intelligent vehicles detection (IVD) provides information to manage traffic efficiently, drive autonomous vehicles and feed data to intelligent traffic management systems (ITMS). IVD is a challenging task for the close proximity vehicles in lane-less traffic and heterogeneous systems. Most vehicle detection models are complex and limited to multi-scale feature extraction due to the involvement of existing feature extraction backbones. Also, they do not include heterogeneous traffic vehicles, usually present in developing countries. Therefore, this paper proposes a multi-class vehicle detection (MCVD) model to detect vehicles in heterogeneous traffic using a realistic traffic dataset from a developing country. MCVD is a deep learning (DL) model that consists of a convolutional neural network backbone called VDnet, a light fusion bi-directional feature pyramid network (LFBFPN) and a modified vehicle detection head (MVDH). VDnet extracts multi-scale features from the traffic input images using feature reuse methods. LFBFPN combines these features bi-directionally and provides robust feature maps. Finally, MVDH is applied to detect multi-class vehicles and classify them into respective categories. The proposed model achieves 91.45 mean average precision (mAP) on the heterogeneous traffic labeled dataset (HTLD). The proposed MCVD is tested over Nvidia Jetson TX2 edge computing boards to verify the real-time performance. It achieves 17 frames per second (FPS) on TX2. The performance evaluation results indicate that the proposed MCVD model is fast, accurate and better than the existing works. © 2000-2011 IEEE.

Due to the advancement of photo editing techniques, it has become easier to create fake photos that look incredibly realistic and are edited in a way that leaves no visible signs of manipulation, making them ideal for synthesis. However, Instagram, WeChat, and TikTok are some of the popular social media platforms where the images have been lossy compressed before uploading them. As a result, learning to spot forged images in their compressed form is crucial. As part of this, some forensic detection techniques have made great strides in uncompressed scenarios, but there is still much to learn about the forensics of lossy compressed images. Therefore, this research proposes a hybrid deep learning framework by dissecting compressed and manipulated images at the preprocessing and feature extraction levels. The suggested noise stream progressively prunes the texture information to prevent the model from fitting the compression noise. Hence, a noise stream is employed to extract temporal correlation characteristics to address the potential problem of ignoring temporal consistency in lossy compressed images. Further, residuals from two streams are fed to custom ResNet blocks to enhance the clues of manipulation and pooled to concatenate the enhanced fingerprints. Finally, the proposed method outperforms state-of-the-art techniques in identifying manipulation in lossy compressed images.

Free space optics (FSO) communication operates over the unlicensed spectrum providing high throughput, high security, and ease of installation. FSO can solve the last mile problem of connectivity with wide applications, i.e., disaster recovery, campus connectivity, backhaul connection, etc. However, the deployment scenario is constrained due to the severe atmospheric conditions and a precise line of sight requirements. This research work analyzes the performance of FSO communication under the combined effect of pointing errors (PEs) and atmospheric turbulence. The effects of jitter and boresight for PEs are modelled with generalized Nakagami-m distribution. A closed-form average bit error rate (BER) is derived for the generalized phenomenon and the theoretical results are compared with simulation and experimental results. Also, an experimental testbed is designed and implemented under a controlled indoor environment to analyze the atmospheric turbulence affects on FSO communication.

Visible light communication (VLC) is secured and high-speed communication for a specified area location. This provides a green-fuzzy technology for optical wireless communication system design. The VLC channel included different parameters, where the variation of the parameter can be analyzed from different channel estimation techniques. The VLC channel is affected by both static and mobile environments. Previously most of the research is on static VLC channels but recently mobile VLC communication is in trend and has more applications in the real world. Thermal noise, ambient noise, shot noise, the distance of the transceiver, and the position of the receiver are some parameters, which has caused a major effect on the VLC Channel estimation system. So many estimation techniques for VLC channels are available to estimate the performance of communication links. In this paper, a detailed comparative analysis is given for both DC-biased optical orthogonal frequency division multiplexing (DCO OFDM) and asymmetrically clipped orthogonal frequency division multiplexing (ACO OFDM). Here, a novel VLC channel estimation technique for multiple-input-multiple-output (MIMO) ACO OFDM is designed, where the performance of linear minimum mean square error (LMMSE) channel estimation technique can be concluded with the bit error rate (BER) response. The paper considers least square (LS) and LMMSE channel estimation techniques to compare the proposed work with the verified work.

In the last few decades, optical technology has become much advance to transmit higher traffic over a long distance. In spite of this, physical layer impairments (PLIs) poses a great concern over the optical communication to meet the demand of next generation technology. Hence, PLI aware algorithm is a major factor to improve the quality of an optical network. This paper discuss the quality estimation based on optical signal-to-noise ratio (OSNR). This approach includes noise due to amplifier spontaneous emission (ASE) in optical fiber, multiplexer, demultiplexer, and coherent crosstalk in optical switches to present OSNR in a wavelength division multiplexing/dense wavelength division multiplexing (WDM/DWDM) networks. In this work, estimation of ASE noise power for different values of wavelength and bandwidth has been discussed. This paper also presents a routing and wavelength assignment (RWA) algorithm based on OSNR to improve the quality of transmission.

Internet routing is based on a distributed system composed of many routers, grouped into management domains called autonomous systems (ASes). Routing information is exchanged between ASes using a border gateway protocol. When BGP is used to distribute a particular route, it can be also be used to distribute an MPLS label which is mapped to that route. In this paper the comparison of conventional BGP and MPLS based BGP has been done for different network topology with and without the application of route reflectors (RR).