Explore the vast range of titles available.

Two-way image communication in a wireless channel needs to be viable with channel properties such as transfer speed, energy-effective, time usage, and security because image capability consumes a huge space in the gadget and is quite effective. Is required in a manner. The figure goes through attacks. In addition, the quiesical issue for additional time of pressure is that the auxiliary interaction of pressure occurs through the dewar receiving extra time. To address these issues, compressed sensing emerges, which packs the image into hours of sensing, is generated in an expedient manner that reduces time usage and saves the use of data transfer capability, however Bomb in transmission. A variety of examinations cleared a way for dealing with security issues in compressive sensing (CS) through giving security as an alternative negotiation. In addition, univariate factors opted for CS as the issue of rearranging image quality is because of the aggregation of clutter. Along these lines related to the above issues, this paper proposed two-way image transmission to the Corvus Coron module, which presents an energy-effective with the CS model, as an inbuilt interaction in the CS transmission through the security framework. Receives what was designated as the pack-protected plot. Impeccable entertainment with the famous arbitrary network conjecture in CS. The result of the test is that the practical module presents energy-efficient and conserved transmission in the form of low error rate with low computational time.

Purpose The manufacturing of intelligent and secure visual data transmission over the wireless sensor network is key requirement nowadays to many applications. The two-way transmission of image under a wireless channel needed image must compatible along channel characteristics such as band width, energy-efficient, time consumption and security because the image adopts big space under the device of storage and need a long time that easily undergoes cipher attacks. Moreover, Quizzical the problem for the additional time under compression results that, the secondary process of the compression followed through the acquisition consumes more time. Design/methodology/approach Hence, for resolving these issues, compressive sensing (CS) has emerged, which compressed the image at the time of sensing emerges as a speedy manner that reduces the time consumption and saves bandwidth utilization but fails under secured transmission. Several kinds of research paved path to resolve the security problems under CS through providing security such as the secondary process. Findings Thus, concerning the above issues, this paper proposed the Corvus corone module two-way image transmission that provides energy efficiency along CS model, secured transmission through a matrix of security under CS such as inbuilt method, which was named as compressed secured matrix and faultless reconstruction along that of eminent random matrix counting under CS. Originality/value Experimental outputs shows intelligent module gives energy efficient, secured transmission along lower computational timing also decreased bit error rate.

A drastic change in communication is happening with digitization. Technological advancements will escalate its pace further. The human health care systems have improved with technology, remodeling the traditional way of treatments. There has been a peak increase in the rate of telehealth and e-health care services during the coronavirus disease 2019 (COVID-19) pandemic. These implications make reversible data hiding (RDH) a hot topic in research, especially for medical image transmission. Recovering the transmitted medical image (MI) at the receiver side is challenging, as an incorrect MI can lead to the wrong diagnosis. Hence, in this paper, we propose a MSB prediction error-based RDH scheme in an encrypted image with high embedding capacity, which recovers the original image with a peak signal-to-noise ratio (PSNR) of ∞ dB and structural similarity index (SSIM) value of 1. We scan the MI from the first pixel on the top left corner using the snake scan approach in dual modes: i) performing a rightward direction scan and ii) performing a downward direction scan to identify the best optimal embedding rate for an image. Banking upon the prediction error strategy, multiple MSBs are utilized for embedding the encrypted PHR data. The experimental studies on test images project a high embedding rate with more than 3 bpp for 16-bit high-quality DICOM images and more than 1 bpp for most natural images. The outcomes are much more promising compared to other similar state-of-the-art RDH methods.

Deep learning-based semantic communication has achieved remarkable progress with CNNs and Transformers. However, CNNs exhibit constrained performance in high-resolution image transmission, while Transformers incur high computational cost due to quadratic complexity. Recently, VMamba, a novel state space model with linear complexity and exceptional long-range dependency modeling capabilities, has shown great potential in computer vision tasks. Inspired by this, we propose MNTSCC, an efficient VMamba-based nonlinear joint source-channel coding (JSCC) model for wireless image transmission. Specifically, MNTSCC comprises a VMamba-based nonlinear transform module, an MCAM entropy model, and a JSCC module. In the encoding stage, the input image is first encoded into a latent representation via the nonlinear transformation module, which is then processed by the MCAM for source distribution modeling. The JSCC module then optimizes transmission efficiency by adaptively assigning transmission rate to the latent representation according to the estimated entropy values. The proposed MCAM enhances the channel-wise autoregressive entropy model with attention mechanisms, which enables the entropy model to effectively capture both global and local information within latent features, thereby enabling more accurate entropy estimation and improved rate-distortion performance. Additionally, to further enhance the robustness of the system under varying signal-to-noise ratio (SNR) conditions, we incorporate SNR adaptive net (SAnet) into the JSCC module, which dynamically adjusts the encoding strategy by integrating SNR information with latent features, thereby improving SNR adaptability. Experimental results across diverse resolution datasets demonstrate that the proposed method achieves superior image transmission performance compared to existing CNN- and Transformer-based semantic communication models, while maintaining competitive computational efficiency. In particular, under an Additive White Gaussian Noise (AWGN) channel with SNR = 10 dB and a channel bandwidth ratio (CBR) of 1/16, MNTSCC consistently outperforms NTSCC, achieving a 1.72 dB Peak Signal-to-Noise Ratio (PSNR) gain on the Kodak24 dataset, 0.79 dB on CLIC2022, and 2.54 dB on CIFAR-10, while reducing computational cost by 32.23%. The code is available at (accessed on 09 July 2025).

High-quality picture transmission over intelligent devices in Mobile Multimedia Sensor Networks (MWMSN) requires fast transmission rates, throughput, and a low Bit Error Rate (BER). Energy efficiency is always a top priority for battery-powered intelligent devices like smartphones and tablets. The Multiple Input and Multiple Output (MIMO) technology is extensively used in MWMSN with cooperative communication (CC). In order to increase the effectiveness of multimedia sensor networks, diverse network techniques are driven by cooperative communications because these systems are more likely to be built with traditional limited resources and scattered hardware. Since each node in the network is mobile, energy consumption and routing pose significant problems for cooperative mobile multimedia sensor networks. An optimisation model is developed to select the minimum number of multi-hops between the source and destination for the cooperative MWMSN network to address these challenges. This paper adopts a new Modified Cat Swarm Optimisation (MCSO) to solve a multi-objective function involving target throughput, mobility, energy consumption and outage probability. The simulation shows that the proposed approach has achieved better performance against state-of-art approaches regarding aggregate throughput, peak signal-to-noise ratio and structural similarity index.

Secure transmission of images over wireless communications systems can be done using RSA, the most known and efficient cryptographic algorithm, and OFDMA, the most preferred signal processing choice in wireless communications. This paper aims to investigate the performance of OFDMA system for wireless transmission of RSA-based encrypted images. In fact, the performance of OFDMA systems; based on different signal processing techniques, such as, discrete sine transforms (DST) and discrete cosine transforms (DCT), as well as the conventional discrete Fourier transforms (DFT) are tested for wireless transmission of gray-scale images with/without RSA encryption. The progress of transmitting the image is carried by firstly, encrypting the image with RSA algorithm. Then, the encrypted image is modulated with DFT-based, DCT-based, and DST-based OFDMA systems. After that, the modulated images are transmitted over a wireless multipath fading channel. The reverse operations will be carried at the receiver, in addition to the frequency domain equalization to overcome the channel effect. Exhaustive numbers of scenarios are performed for study and investigation of the performance of the different OFDMA systems in terms of PSNR and MSE, with different subcarriers mapping and modulation techniques, is done. Results indicate that the ability of different OFDMA systems for wireless secure transmission of images. However, the DCT-OFDMA system showed superiority over the DST-OFDMA and the conventional DFT-OFDMA systems.

Terahertz and millimeter waves penetrate various dielectric materials, including plastics, ceramics, crystals, and concrete, allowing terahertz transmission and reflection images to be considered as a new imaging tool complementary to X-Ray or Infrared. Terahertz imaging is a well-established technique in various laboratory and industrial applications. However, these images are often two-dimensional. Three-dimensional, transmission-mode imaging is limited to thin samples, due to the absorption of the sample accumulated in the propagation direction. A tomographic imaging procedure can be used to acquire and to render three-dimensional images in the terahertz frequency range, as in the optical, infrared or X-ray regions of the electromagnetic spectrum. In this paper, after a brief introduction to two dimensional millimeter waves and terahertz imaging we establish the principles of tomography for Terahertz Computed tomography (CT), tomosynthesis (TS), synthetic aperture radar (SAR) and time-of-flight (TOF) terahertz tomography. For each technique, we present advantages, drawbacks and limitations for imaging the internal structure of an object.

Visible Light Communication (VLC) systems commonly employ optical orthogonal frequency division multiplexing (O-OFDM) to achieve high data rates, benefiting from its robustness against multipath effects and intersymbol interference (ISI). However, a key limitation of asymmetrically clipped direct current biased optical–OFDM (ACO-OFDM) systems lies in their inherently high peak-to-average power ratio (PAPR), which significantly affects signal quality and system performance. This paper proposes a joint chaotic encryption and modified μ-non-linear logarithmic companding (μ-MLCT) scheme for ACO-OFDM–based VLC systems to simultaneously enhance security and reduce PAPR. First, image data is encrypted at the upper layer using a hybrid chaotic system (HCS) combined with Arnold’s cat map (ACM), mapped to quadrature amplitude modulation (QAM) symbols and further encrypted through chaos-based symbol scrambling to strengthen security. A μ-MLCT transformation is then applied to mitigate PAPR and enhance both peak signal-to-noise ratio (PSNR) and bit-error-ratio (BER) performance. A mathematical model of the proposed secured ACO-OFDM system is developed, and the corresponding BER expression is derived and validated through simulation. Simulation results and security analyses confirm the effectiveness of the proposed solution, showing gains of approximately 13 dB improvement in PSNR, 2 dB in BER performance, and a PAPR reduction of about 9.2 dB. The secured μ-MLCT-ACO-OFDM not only enhances transmission security but also effectively reduces PAPR without degrading PSNR and BER. As a result, it offers a robust and efficient solution for secure image transmission with low PAPR, making it well-suitable for emerging wireless networks such as cognitive and 5G/6G systems.

With increasingly more smart cameras deployed in infrastructure and commercial buildings, 3D reconstruction can quickly obtain cities’ information and improve the efficiency of government services. Images collected in outdoor hazy environments are prone to color distortion and low contrast; thus, the desired visual effect cannot be achieved and the difficulty of target detection is increased. Artificial intelligence (AI) solutions provide great help for dehazy images, which can automatically identify patterns or monitor the environment. Therefore, we propose a 3D reconstruction method of dehazed images for smart cities based on deep learning. First, we propose a fine transmission image deep convolutional regression network (FT-DCRN) dehazing algorithm that uses fine transmission image and atmospheric light value to compute dehazed image. The DCRN is used to obtain the coarse transmission image, which can not only expand the receptive field of the network but also retain the features to maintain the nonlinearity of the overall network. The fine transmission image is obtained by refining the coarse transmission image using a guided filter. The atmospheric light value is estimated according to the position and brightness of the pixels in the original hazy image. Second, we use the dehazed images generated by the FT-DCRN dehazing algorithm for 3D reconstruction. An advanced relaxed iterative fine matching based on the structure from motion (ARI-SFM) algorithm is proposed. The ARI-SFM algorithm, which obtains the fine matching corner pairs and reduces the number of iterations, establishes an accurate one-to-one matching corner relationship. The experimental results show that our FT-DCRN dehazing algorithm improves the accuracy compared to other representative algorithms. In addition, the ARI-SFM algorithm guarantees the precision and improves the efficiency.

Reliable and efficient image transmission is crucial for deep-space exploration. However, the extremely long distance and complex deep-space environment introduce severe design and implementation challenges. In this work, we propose a novel high-efficiency system, CS-LTP-Spinal, to address the challenges encountered in deep-space image transmission. CS-LTP-Spinal is designed to work over the Licklider transmission protocol (LTP) of the delay-tolerant network (DTN). By incorporating compressed sensing (CS) and the Spinal codes as the application and physical layer techniques, CS-LTP-Spinal can satisfy the constraints originating from resource asymmetry between the space vehicles and the ground station. To match the time-varying deep-space channels, two coarse-grained rate-adaptive transmission strategies are designed that employ different CS decompression mechanisms based on erasure-tolerant and error-tolerant decompression, respectively, to exploit the robustness of CS reconstruction to erasures and errors. Then, the rates of CS compression and Spinal coding are optimized over the application, transport, and physical layers. A semi-physical deep-space communication platform is built, and extensive simulations on the Mars-to-Earth scenario are conducted. The results demonstrate that the designed CS-LTP-Spinal system with cross-layer optimized rate-adaptive transmission strategies has significant performance advantages over its counterparts by achieving near-ideal image transmission efficiency.