Image Transmission Research Articles

Energy-Efficient Wireless Multimedia Sensor Nodes for Plant Proximal Monitoring

The paper presents a double-radio wireless multimedia sensor node (WMSN) with a camera on board, designed for plant proximal monitoring. Camera sensor nodes represent an effective solution to monitor the crop at the leaf or fruit scale, with details that cannot be retrieved with the same precision through satellites or unnamed aerial vehicles (UAVs). From the technological point of view, WMSNs are characterized by very different requirements, compared to standard wireless sensor nodes; in particular, the network data rate results in higher energy consumption and incompatibility with the usage of battery-powered devices. Avoiding energy harvesters allows for device miniaturization and, consequently, application flexibility, even for small plants. To do this, the proposed node has been implemented with two radios, with different roles. A GPRS modem has been exclusively implemented for image transmission, while all other tasks, including node monitoring and camera control, are performed by a LoRaWAN class A end-node that connects every 10 min. Via the LoRaWAN downlink, it is possible to efficiently control the camera settings; the shooting times and periodicity, according to weather conditions; the eventual farming operations; the crop growth stages and the season. The node energy consumption has been verified in the laboratory and in the field, showing that it is possible to acquire one picture per day for more than eight months without any energy harvester, opening up further possible implementations for disease detection and production optimization.

An efficient optimized encryption and compression techniques to improve medical image security and transmission in the cloud

As digital technology for illness diagnosis and analysis has advanced, medical images are sent over the Internet. Cloud computing plays a major role for low-cost data storage and data sharing. In the healthcare industry, data security and privacy are key issues with cloud computing. It is imperative that healthcare professionals make sure that patient data is safe against hackers, unauthorized access, and thrift. To secure the confidential data and store the huge amount of data, encryption and compression techniques are used. This paper provides an enhanced optimized encryption and efficient hybrid compression strategies based on cloud environment. The proposed model involves various operations such as generate optimal key, encryption, compression, decompression and decryption. In order to transfer the data using high speed cloud data retrieval, we initially propose the Huffman Fano Hybrid Entropy approach. In the next step, the proposed model performs Elliptic Curve Coding based encryption technique to secure compressed medical image transmission. Here the shared secret keys are generated optimally by dynamic group based cooperative optimization algorithm, which makes use of the encryption quality measures and is called DGBCO–ECC model. On the receiving end, the image is decrypted and decompressed. The proposed model performance is validated by exploiting various parameters namely Mean Square Error, Standard Deviation of the Mean Error, Universal image quality index, Structural Similarity Index, Entropy, Peak Signal to Noise Ratio, Compression Ratio, Data Rate Saving and compression time. When the experimental outcome is contrasted with existing methods, it is found to perform better.

Enhancing Multispectral Breast Imaging Quality Through Frame Accumulation and Hybrid GA-CPSO Registration

Multispectral transmission imaging has emerged as a promising technique for imaging breast tissue with high resolution. However, the method encounters challenges such as low grayscale, noisy transmission images with weak signals, primarily due to the strong absorption and scattering of light in breast tissue. A common approach to improve the signal-to-noise ratio (SNR) and overall image quality is frame accumulation. However, factors such as camera jitter and respiratory motion during image acquisition can cause frame misalignment, degrading the quality of the accumulated image. To address these issues, this study proposes a novel image registration method. A hybrid approach combining a genetic algorithm (GA) and a constriction factor-based particle swarm optimization (CPSO), referred to as GA-CPSO, is applied for image registration before frame accumulation. The efficiency of this hybrid method is enhanced by incorporating a squared constriction factor (SCF), which speeds up the registration process and improves convergence towards optimal solutions. The GA identifies potential solutions, which are then refined by CPSO to expedite convergence. This methodology was validated on the sequence of breast frames taken at 600 nm, 620 nm, 670 nm, and 760 nm wavelength of light and proved the enhancement of accuracy by various mathematical assessments. It demonstrated high accuracy (99.93%) and reduced registration time. As a result, the GA-CPSO approach significantly improves the effectiveness of frame accumulation and enhances overall image quality. This study explored the groundwork for precise multispectral transmission image segmentation and classification.

Automatic Leaf Health Monitoring with an IoT Camera System based on Computer Vision and Segmentation for Disease Detection

Manual identification of diseases in crops is costly and subjective, driving the need for automated systems for accurate detection in the field. This requires the use of technologies based on the integration of IoT and deep learning models to improve the assessment capacity of crop health and leaf disease, with continuous monitoring. The literature review highlights technological solutions that include weed and disease detection using artificial intelligence and autonomous systems, as well as semantic segmentation algorithms to locate diseases in field images whose processes can be improved with systems based on microcontrollers and sensors. This research implements a leaf health monitoring system using IoT and AI technologies, with the development of an IoT device with a camera, the configuration of an MQTT broker in NODE-Red, and the implementation of a script in Python for leaf instance segmentation and image display. As a result, it is highlighted that image analysis, with the Python tool, allowed obtaining valuable information for precision agriculture, while the visualization or messaging interface allows health monitoring and management of crops. In conclusion, the System adequately performs image capture, processing, and transmission, being a contributes to precision agriculture solutions, considering that this can be improved with the integration of more complex deep learning algorithms to increase precision.

Design of a Non-Destructive Seed Counting Instrument for Rapeseed Pods Based on Transmission Imaging

Pod counting of rapeseed is a critical step in breeding, cultivation, and agricultural machinery research. Currently, this process relies entirely on manual labor, which is both labor-intensive and inefficient. This study aims to develop a semi-automatic counting instrument based on transmission image processing and proposes a new algorithm for processing transmission images of pods to achieve non-destructive, accurate, and rapid determination of the seed count per pod. Initially, the U-NET network was used to segment and remove the stem and beak from the pod image; subsequently, adaptive contrast enhancement was applied to adjust the contrast of the G-channel image of the pod to an appropriate range, effectively eliminating the influence of different varieties and maturity levels on the translucency of the pod skin. After enhancing the contrast, the Sauvola algorithm was employed for threshold segmentation to remove the pod skin, followed by thinning and dilation of the binary image to extract and remove the central ridge lines, detecting the number and area of connected domains. Finally, the seed count was determined based on the ratio of each connected domain’s area to the mean area of all connected domains. A transmission imaging device that mimics the human eye’s method of counting seeds was designed, incorporating an LED transmission light source, photoelectric switch-triggered imaging slot, an industrial camera, and an integrated packaging frame. Human–machine interaction software based on PyQt5 was developed, integrating functions such as communication between upper and lower machines, image acquisition, storage, and processing. Operators simply need to place the pod in an upright position into the imaging device, where its transmission image will be automatically captured and processed. The results are displayed on a touchscreen and stored in Excel spreadsheets. The experimental results show that the instrument is accurate, user-friendly, and significantly reduces labor intensity. For various varieties of rapeseed pods, the seed counting accuracy reached 97.2% with a throughput of 372 pods/h, both of which are significantly better than manual counting and have considerable potential for practical applications.

Пейзажные образы традиционной живописи гохуа в фортепианной музыке китайских композиторов XX – начала XXI в.

The purpose of the study is to identify musical means in the piano music of China of the 20th – early 21st century in the transmission of images of nature found in traditional Chinese Guohua painting in the genres of "mountains and waters" and "flowers and birds". The article points to the origins of the formation of piano art in China, the influence of Russian and Western European piano traditions on its formation. The specificity of Chinese piano art is noted. Chinese composers, having mastered the Western European experience, performing techniques, and piano genres, brought unique cultural codes of ancient Chinese traditions. Piano works demonstrate syncretic thinking, where ontological archetypes of nature images act as symbols of life meanings. The scientific novelty of the research lies in the identification of artistic analogies between the painting of Guohua and the piano music of China in the 20th – early 21st century. As a result of the research, the main musical means have been established that make it possible to create an atmospheric landscape of Gohua in piano pieces by means of musical language through intonation, harmony, texture, rhythm, as well as onomatopoeia of timbres and the manner of performance on traditional Chinese instruments.

New Image Crypto-Compression Scheme Based on Ecc and Chaos Theory for High-Speed and Reliable Transmission of Medical Images in the IOMT

Abstract The rapid advancement of IoT has significantly transformed the healthcare sector, leading to the emergence of the Internet of Medical Things (IoMT). Ensuring the security and privacy of medical data is crucial when integrating with smart and intelligent sensor devices within the hospital environment. In this context, we propose a lightweight crypto-compression scheme based on Elliptic Curve Cryptography (ECC) and Chaos theory to secure the medical images in IoMT Applications. The primary innovation in this method involves generating dynamic S-box and keys using the ECC mechanism and PieceWise Linear Chaotic Map (PWLCM). The Wavelet Transform Technology is employed in compression, and the compressed images are secured within an IoT framework. The proposed methodology has been performed in the experiments on various medical images. The findings and Security analysis reveal that the proposed method is more powerful and useful for secure medical image transmission in the IoT ecosystem.

Political Correctness, Social Image, and Information Transmission

A prominent argument in the political correctness debate is that people feel pressure to publicly espouse sociopolitical views they do not privately hold, and that such misrepresentations might render public discourse less vibrant and informative. This paper formalizes the argument in terms of social image and evaluates it experimentally in the context of college campuses. The results show that (i) social image concerns drive a wedge between the sensitive sociopolitical attitudes that college students report in private and in public; (ii) public utterances are indeed less informative than private utterances; and (iii) information loss is exacerbated by (partial) audience naïveté. (JEL D72, D83, D91, I23, Z13)

Pixel Distillation: Cost-Flexible Distillation Across Image Sizes and Heterogeneous Networks.

Previous knowledge distillation (KD) methods mostly focus on compressing network architectures, which is not thorough enough in deployment as some costs like transmission bandwidth and imaging equipment are related to the image size. Therefore, we propose Pixel Distillation that extends knowledge distillation into the input level while simultaneously breaking architecture constraints. Such a scheme can achieve flexible cost control for deployment, as it allows the system to adjust both network architecture and image quality according to the overall requirement of resources. Specifically, we first propose an input spatial representation distillation (ISRD) mechanism to transfer spatial knowledge from large images to student's input module, which can facilitate stable knowledge transfer between CNN and ViT. Then, a Teacher-Assistant-Student (TAS) framework is further established to disentangle pixel distillation into the model compression stage and input compression stage, which significantly reduces the overall complexity of pixel distillation and the difficulty of distilling intermediate knowledge. Finally, we adapt pixel distillation to object detection via an aligned feature for preservation (AFP) strategy for TAS, which aligns output dimensions of detectors at each stage by manipulating features and anchors of the assistant. Comprehensive experiments on image classification and object detection demonstrate the effectiveness of our method.

Content-aware robust semantic transmission of images over wireless channels with GANs

Content-aware robust semantic transmission of images over wireless channels with GANs

Mapping Paleolacustrine Deposits with a UAV-borne Multispectral Camera: Implications for Future Drone Mapping on Mars

NASA’s Ingenuity Mars Helicopter has marked a new era in planetary exploration by employing unmanned aerial vehicles (UAVs) to enhance our understanding of planetary surfaces. This study evaluates the potential of UAVs for mapping Martian environments, with Lake Natron, Tanzania, serving as an analog for Martian paleolakes. During two field seasons (2023 January and July), we used a Phantom 4 Pro drone equipped with a MicaSense RedEdge-M multispectral camera, supplemented by in situ analysis using a TerraSpec Halo VNIR-SWIR spectrometer, to capture high-resolution imagery and spectral data. Almost all image processing and analysis, except for image mosaic and digital elevation model (DEM) generation, was performed using Python scripting. We benchmarked the onboard image processing capabilities using a Raspberry Pi 5 single-board computer. Processing steps include digital number (DN)-to-radiance conversion, assessment of the best radiance-to-reflectance conversion method, image mosaic creation, DEM generation, calculation of optimal band indices, and selection of the best classification technique. The research underscores Lake Natron’s diverse lithologies as a suitable analog site and demonstrates significant improvements in classification when normalized elevation data are incorporated with spectral index maps through unsupervised classification methods. The study also addresses challenges related to high-resolution image transmission and processing, advocating for advanced techniques such as image compression and low-power computational models. Additionally, it highlights computational and power limitations as key obstacles, suggesting that emerging technologies such as photonic computing and hybrid controllers could provide viable solutions. These findings emphasize the transformative potential of UAVs in planetary exploration while outlining key areas for future research and technological development.

Diagnostic value of 4D pelvic floor ultrasound for postpartum mild to moderate pelvic organ prolapse in primiparas based on medical thermal imaging monitoring

Traditional diagnostic methods often rely on the doctor’s experience, which can lead to missed diagnosis or misdiagnosis. With the development of wireless network medical technology, it provides a new possibility for the diagnosis of pelvic floor ultrasound. The purpose of this study was to evaluate the diagnostic value of 4D pelvic floor ultrasound based on wireless network medical technology in puerperal mild to moderate pelvic organ prolapse, and to explore its feasibility in clinical application. In this study, puerperal women were selected for 4D pelvic floor ultrasound examination after delivery, and image data was transmitted in real time to remote experts for analysis combined with wireless network technology. The sensitivity, specificity and application effect of the technique were evaluated by comparing the ultrasound results with the clinical diagnosis results. The results show that compared with traditional methods, wireless network medical technology can significantly improve the efficiency of image transmission and diagnosis accuracy. Therefore 4D pelvic floor ultrasound based on wireless network medical technology can effectively diagnose the postpartum mild and moderate pelvic organ prolapse of first-time partrients, which has high clinical application value, not only improves the diagnostic efficiency, but also provides a new development direction for telemedicine, and is worthy of promotion and application in clinical practice.

Rate adaptive compressed sampling based on region division for wireless sensor networks

Image acquisition and transmission in wireless sensor networks (WSN) are core issues for some resource-deficient multimedia sensing applications. Reducing sampling rates and data transmission lowers sensor node costs and energy, addressing communication bottlenecks. Block compressed sampling (BCS) can meet the above requirements. For BCS, the sparsity or smoothness of the block signal is a crucial parameter, which determines the setting range of the sampling rate. For the sampling side of the sensor node, we cannot directly obtain the complete digital signal. Therefore, it becomes difficult to perform adaptive rate compressed sampling. In this paper, a novel adaptive sampling rate allocation scheme based on region division is proposed. First, we use a simple auxiliary vector to determine the complex and smooth regions of the current image. For the smooth region, we use a mean vector to divide each block into a residual block and a mean value block. Then the proposed prior probability sparsity estimation model is used to estimate the sparsity order of each residual block, while each mean value block requires only one measurement to restore losslessly. For the complex region, we first set a higher baseline sampling rate for it, and then adaptively allocate the remaining supplementary sampling rate based on the statistical characteristics of each block itself. Experiment results show that the proposed scheme can allocate an appropriate sampling rate to each block, reduce the total sampling rate, and significantly improve the signal reconstruction quality simultaneously.

Deep joint source-channel coding empowered two-way relay networks for wireless image transmission

Compared with the traditional uni-directional relaying, two-way relay networks provide important enhancements and optimizations to modern communication systems. However, with the increasing requirements of artificial intelligence applications for image data transmission, relay-assisted communication technologies are reaching the theoretical limit in terms of bandwidth, which hinders the further development of AI applications. To address this issue, we propose a deep joint source-channel coding empowered two-way relay network (DeepJSCC-TWRN) to help image transmission. Specifically, in the DeepJSCC-TWRN, a DeepJSCC is employed to improve image transmission quality of the TWRN from the perspective of visual semantic information, and each source can achieve optimal performance by being trained in a uniform deep learning framework. For measuring the performance of the proposed DeepJSCC-TWRN, we employ the peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) as performance metrics. Simulation results show that DeepJSCC-TWRN outperforms the baseline method, demonstrating the ability to preserve visual semantic information.

High-order direct modulation terahertz communications with a wideband time-coding metachip modulator.

Terahertz communication technology based on on-off keying (OOK) direct modulation is vital for sixth-generation communication systems, especially in short-distance and high-rate applications. However, low-order OOK modulation often leads to suboptimal anti-interference capabilities and a heightened demodulation threshold. Here, we propose a high-order direct modulation terahertz communication framework using a wideband time-coding metachip modulator. The modulator leverages the electromagnetic resonance properties within the metaunit structure, with control enabled by gallium arsenide Schottky diodes. By manipulating the timing of voltage pulses applied to these diodes, the equivalent electromagnetic resonance distributions can be precisely regulated in the time domain. This enables independent and accurate control over the amplitude and phase of terahertz harmonics. Leveraging this technique, three high-order modulation schemes-quadrature phase-shift keying, 16-phase-shift keying, and 16-quadrature amplitude modulation- are achieved in a direct modulation and direct detection system, demonstrating the real-time image transmission. The proposed method offers an important way to develop integrated and low-complexity terahertz wireless communication systems.

Advanced analytical methods for multi-spectral transmission imaging optimization: enhancing breast tissue heterogeneity detection and tumor screening with hybrid image processing and deep learning.

Light sources exhibit significant absorption and scattering effects during the transmission through biological tissues, posing challenges in identifying heterogeneities in multi-spectral images. This paper introduces a fusion of techniques encompassing the spatial pyramid matching model (SPM), modulation and demodulation (M_D), and frame accumulation (FA). These techniques not only elevate image quality but also augment the precision of heterogeneous classification in multi-spectral transmission images (MTI) within deep learning network models (DLNM). Initially, experiments are designed to capture MTI of phantoms. Subsequently, the images are preprocessed separately through a combination of different techniques such as SPM, M_D and FA. Ultimately, multi-spectral fusion pseudo-color images derived from U-Net semantic segmentation are fed into VGG16/19 and ResNet50/101 networks for heterogeneous classification. Among them, different combinations of SPM, M_D and FA significantly enhance the quality of images, facilitating the extraction of heterogeneous feature information from multi-spectral images. In comparison to the classification accuracy achieved in the original image VGG and ResNet network models, all images after preprocessing effectively improved the classification accuracy of heterogeneities. Following scatter correction, images processed with 3.5 Hz modulation-demodulation combined with frame accumulation (M_D-FA) attain the highest classification accuracy for heterogeneities in the VGG19 and ResNet101 models, achieving accuracies of 95.47% and 98.47%, respectively. In conclusion, this paper utilizes different combinations of SPM, M_D and FA techniques to not only enhance the quality of images but also further improve the accuracy of DLNM in heterogeneous classification, which will promote the clinical application of MTI technique in breast tumor screening.

Differentiable design of compact imaging systems with curved Fresnel optics

Phase elements can improve the performance and reduce the size of imaging systems, thanks to the additional degrees of freedom that are offered by the independent phase gradient on top of a refractive/reflective surface. Possible implementations include diffractive elements or metasurfaces, but these suffer from diffractive dispersion. Similar optical functionality however can be provided by thin, curved Fresnel optics, which solely rely on refraction. In this study, a differentiable raytracing framework is presented that offers precise and rapid optimization of curved Fresnel surfaces, by modeling them as a combination of a distinct geometrical and refractive surface, both differentiable with respect to the imaging merit function. The method is demonstrated by designing a compact imaging and projection lens, both with high numerical aperture. The paper analyzes the impact of Fresnelizing the optimized "theoretical" surfaces on both the imaging performance and transmission efficiency. It furthermore shows how the system performance can be enhanced through dedicated post-processing steps, emphasizing the practical relevance of compact Fresnel optics.

Topological one-way edge states in locally resonant metamaterials

The quantum anomalous Hall effect (QAHE) enables dissipation-free motion within its edge states, offering considerable potential for developments in the fields of electronics, phononics, and elastic waves. In this study, we introduce an elastic metamaterial designed to realize the QAHE at low frequencies. Our approach involves deploying a two-dimensional honeycomb spring-mass system on a rotating platform to break time-reversal symmetry via the Coriolis Force and incorporating local resonators to achieve low-frequency topological edge states. We begin by deriving the system’s equations of motion through analytical rigid body dynamics. Subsequently, we apply Bloch wave solutions to establish the dispersion relations and identify the topological edge states. We then conduct numerical simulations to confirm the one-way transmission characteristics of these states. The addition of local resonators not only extends the frequency range of the topological edge states but also lowers the operational frequency of the system. This robust waveguide, capable of one-way transmission, has potential applications in directional transmission, vibration damping, noise reduction, and acoustic imaging.

A novel chirp-based 2D hyperchaotic map for enhanced image encryption

Abstract This paper presents a novel image encryption algorithm based on a newly proposed two-dimensional hyperchaotic map derived from the chirp signal. Performance evaluations of the proposed map include bifurcation analysis, phase portrait visualization, sensitivity to initial conditions, Lyapunov exponent calculations, entropy measurements, and NIST tests. These evaluations confirm the map’s strong randomness and broad chaotic behavior. The proposed encryption algorithm utilizes the high sensitivity to initial conditions and wide chaotic range of the hyperchaotic map to enhance security. The algorithm achieves a high degree of confusion and diffusion through bit-level manipulation, chaotic permutation, and randomized row-column diffusion processes. As a result, it can effectively encrypt images of any size, whether color or grayscale. Comprehensive security evaluations, such as key analysis, histogram analysis, Shannon entropy analysis, correlation analysis, differential analysis, and robustness analysis, confirm the algorithm’s resilience against a wide range of cryptographic attacks. Thus, the proposed algorithm offers a promising solution for secure image transmission.

Visually meaningful image encryption for secure and authenticated data transmission using chaotic maps

Image ciphering techniques usually transform a given plain image data into a cipher image data resembling noise, serving as an indicator of the presence of secret image data. However, the transmission of such noise-like images could draw attention, thereby attracting the attackers and may face several possible attacks. This paper presents an approach for generating a visually meaningful image encryption (VMIE) scheme that combines three layers of security protection: encryption, digital signature, and steganography. The present scheme is dedicated to achieving a balanced performance in robustness, security and operational efficiency. First, the original image is partially encrypted by using the RSA cryptosystem and modified Hénon map (MHM). In the second stage, a digital signature is generated for the partially encrypted image by employing a hash function and the RSA cryptosystem. The obtained digital signature is appended to the partially encrypted image produced after implementing the zigzag confusion in the above partially encrypted image. Further, to achieve better confusion and diffusion, the partially encrypted image containing a digital signature undergoes through the application of 3D Arnold cat map (ARno times), to produce the secret encrypted image (Sr5). To ensure the security and robustness of the proposed technique against various classical attacks, the hash value obtained from the SHA-256 hash function and carrier images is utilized to generate the initial conditions Mh10 and Mh20 for modified Hénon map, and initial position Zip=(zrow,zcol) for zigzag confusion. In the proposed algorithm, the digital signature is utilized for both purposes to verify the sender’s authenticity and to enhance the encryption quality. The carrier image undergoes lifting wavelet transformation, and its high-frequency components are utilized in the embedding process through a permuted pattern of MHM, resulting in a visually meaningful encrypted image. The proposed scheme achieves efficient visual encryption with minimal distortion and ensures lossless image quality upon decryption (infinite PSNR), balancing high level of security along with a good computational efficiency.