Image Data Transfer Research Articles

(Invited) Advances in Wide Bandgap Thermal Resonant Infrared Detectors

Exploring the surface of distant planets, notably Venus, requires robust technologies capable of withstanding extreme environmental conditions while providing valuable scientific insights. This paper delves into the advancements made in designing an instrument tailored for capturing infrared (IR) images in the harsh Venusian environment, where temperatures soar to 500°C. The presented IR detector harnesses the power of a sparse array of resonant-based micromechanical devices, strategically addressing the limitations of existing technologies that can only withstand extreme conditions for limited durations.At the core of this technology lies the utilization of high-temperature-tolerant InAlN/GaN as the primary resonating element. GaN's resilience and high temperature tolerance make it a crucial player in challenging space environments. Its wide bandgap and chemical stability are advantageous for enduring harsh conditions during space missions. In tandem with GaN's intrinsic properties, high-temperature-tolerant meta-absorbers strategically positioned on the surface of each pixel play a pivotal role in the functionality of the IR detector. These meta-absorbers exhibit a remarkable capacity to absorb incoming infrared (IR) radiation, promptly transforming it into heat. The resonant frequency of these devices exhibits a linear shift with temperature variations, and a high-temperature-tolerant read-out electronic adeptly senses this frequency shift, enabling the seamless transfer of imaging data. Extraction of IR source power occurs through the resonance frequency shift in the exposed GaN resonator, compared to a reference resonator. The reference resonator, lacking the IR-absorbing layer, establishes a baseline for minimal resonant shifts.The sparse array configuration strategically reduces imaging elements to optimize power consumption and cost-effectiveness. Inspired by techniques employed in medical imaging instruments, this approach allows the construction of images from a low-power 2D array, striking a balance between imaging capabilities and processing efficiency. This sparse array configuration serves a dual purpose, streamlining signal routing for integrated read-out electronics and simplifying the overall integration and packaging of the IR imaging instrument. Monolithic integration of the read-out electronic with the sensor platform on the same chip overcomes challenges related to electrical and mechanical stability at high temperatures.The resonant effect delivers a remarkable enhancement in the signal-to-noise ratio, surpassing existing thermal detector technologies by a factor of 70×. Furthermore, the strategic implementation of sparse arraying results in approximately a 4× reduction in power consumption, presenting an efficient and resilient solution for imaging in extreme environments. Diverging from traditional IR detectors limited in performance at high temperatures, these detectors rely on the temperature coefficient of elasticity (TCE), offering a wide dynamic range of operational temperatures.Finally, this paper includes recent results measured at 500°C, demonstrating the instrument's capability to operate effectively in Venus's challenging conditions. These advancements pave the way for future planetary exploration missions, promising a deeper understanding of Venus's geological and atmospheric processes.

Data-volume reduction of optical vortex laser absorption spectroscopy by coarse-graining using a quadrant photodiode

We developed optical vortex laser absorption spectroscopy (OVLAS) to measure the transverse flow velocity relative to the probe beam. In our previous OVLAS setups, a camera is used in the measurement system. This approach constrained the time resolution by image data transfer time and required large computational resources due to the considerable amount of image data. In this paper, we discuss the coarse-graining of OVLAS as a method to improve time resolution and reduce computational resources achieved by replacing the camera with a quadrant photodiode (QPD). Measurement image data obtained from a camera was numerically coarse-grained to evaluate the feasibility of measurements using a QPD. Subsequently, the transverse flow velocity was measured by QPD. Measured transverse flow velocity had a mean absolute percentage error of 18%, confirming the practical validity of coarse-graining OVLAS. The data volume required to determine the flow velocity was reduced to 1/120 from the original OVLAS.

Mathematical analysis of wavelet-based multi-image compression in medical diagnostics

Accurate diagnosis and well-informed treatment choices are only possible with the help of sophisticated medical imaging technology. The introduction of digital imaging methods has led to a dramatic rise in the volume of data associated with medical images, which in turn has led to difficulties in their storage, transmission, and administration. It is crucial to utilize effective image compression techniques to address these challenges without compromising the diagnostic integrity of the pictures. The wavelet transform has matured into a potent method for striking a good compromise between picture quality and file size reduction while compressing. The safe and efficient transfer of medical image data is a major concern in today’s healthcare settings. In this academic investigation, we investigate how wavelet transform-based techniques may be used to enhance medical picture compression. The proposed techniques optimize compression ratios while maintaining diagnostic picture quality by making use of the wavelet transform’s multi-resolution and frequency localization features. To address the unique challenges given by medical image collections, various iterations of the wavelet transform and compression techniques are investigated. Through a series of detailed tests involving several medical picture modalities, the effectiveness of these technologies is thoroughly evaluated, demonstrating their effectiveness in achieving significant data reduction without sacrificing clinical information.

Novel Artificial Intelligence Tool for Real-time Patient Identification to Prevent Misidentification in Health Care.

Errors in the identification of true patients in a health-care facility may result in the wrong dose or dosage being given to the wrong patient at the wrong site during radiotherapy sessions, radiopharmaceutical administration, radiological scans, etc. The aim of this article is to reduce the error in the identification of correct patients by implementation of the Python deep learning-based real-time patient identification program. The authors utilized and installed Anaconda Prompt (miniconda 3), Python (version 3.9.12), and Visual Studio Code (version 1.71.0) for the design of the patient identification program. In the field of view, the area of interest is merely face detection. The overall performance of the developed program is accomplished over three steps, namely image data collection, data transfer, and data analysis, respectively. The patient identification tool was developed using the OpenCV library for face recognition. This program provides real-time patient identification information, together with the other preset parameters such as disease site, with a precision of 0.92%, recall rate of 0.80%, and specificity of 0.90%. Furthermore, the accuracy of the program was found to be 0.84%. The output of the in-house developed program as "Unknown" is provided if a patient's relative or an unknown person is found in restricted region. This Python-based program is beneficial for confirming the patient's identity, without manual interventions, just before therapy, administering medications, and starting other medical procedures, among other things, to prevent unintended medical and health-related complications that may arise as a result of misidentification.

Federated Learning of Generative Image Priors for MRI Reconstruction.

Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has recently been introduced to address privacy concerns by enabling distributed training without transfer of imaging data. Existing FL methods employ conditional reconstruction models to map from undersampled to fully-sampled acquisitions via explicit knowledge of the accelerated imaging operator. Since conditional models generalize poorly across different acceleration rates or sampling densities, imaging operators must be fixed between training and testing, and they are typically matched across sites. To improve patient privacy, performance and flexibility in multi-site collaborations, here we introduce Federated learning of Generative IMage Priors (FedGIMP) for MRI reconstruction. FedGIMP leverages a two-stage approach: cross-site learning of a generative MRI prior, and prior adaptation following injection of the imaging operator. The global MRI prior is learned via an unconditional adversarial model that synthesizes high-quality MR images based on latent variables. A novel mapper subnetwork produces site-specific latents to maintain specificity in the prior. During inference, the prior is first combined with subject-specific imaging operators to enable reconstruction, and it is then adapted to individual cross-sections by minimizing a data-consistency loss. Comprehensive experiments on multi-institutional datasets clearly demonstrate enhanced performance of FedGIMP against both centralized and FL methods based on conditional models.

Multitarget Intelligent Recognition of Petrographic Thin Section Images Based on Faster RCNN

The optical features of mineral composition and texture in petrographic thin sections are an important basis for rock identification and rock evolution analysis. However, the efficiency and accuracy of human visual interpretation of petrographic thin section images have depended on the experience of experts for a long time. The application of image-based computer vision and deep-learning algorithms to the intelligent analysis of the optical properties of mineral composition and texture in petrographic thin section images (in plane polarizing light) has the potential to significantly improve the efficiency and accuracy of rock identification and classification. This study completed the transition from simple petrographic thin image classification to multitarget detection, to address more complex research tasks and more refined research scales that contain more abundant information, such as spatial, quantitative and category target information. Oolitic texture is an important paleoenvironmental indicator that widely exists in sedimentary records and is related to shallow water hydraulic conditions. We used transfer learning and image data augmentation in this paper to identify the oolitic texture of petrographic thin section images based on the faster region-based convolutional neural network (Faster RCNN) method. In this study, we evaluated the performance of Faster RCNN, a two-stage object detection algorithm, using VGG16 and ResNet50 as backbones for image feature extraction. Our findings indicate that ResNet50 outperformed VGG16 in this regard. Specifically, the Faster RCNN model with ResNet50 as the backbone achieved an average precision (AP) of 92.25% for the ooids test set, demonstrating the accuracy and reliability of this approach for detecting ooids. The experimental results also showed that the uneven distribution of training sample images and the complexity of images both significantly affect detection performance; however, the uneven distribution of training sample images has a greater impact. Our work is preliminary for intelligent recognition of multiple mineral texture targets in petrographic thin section images. We hope that it will inspire further research in this field.

Numerical simulations of an integrated radio-frequency/wireless coil design for simultaneous acquisition and wireless transfer of magnetic resonance imaging data

Objective. A novel magnetic resonance imaging (MRI) radio-frequency (RF) coil design, termed an integrated RF/wireless (iRFW) coil design, can simultaneously perform MRI signal reception and far-field wireless data transfer with the same coil conductors between the coil in the scanner bore and an access point (AP) on the scanner room wall. The objective of this work is to optimize the design inside the scanner bore to provide a link budget between the coil and the AP for the wireless transmission of MRI data. Approach. Electromagnetic simulations were performed at the Larmor frequency of a 3T scanner and in a WiFi wireless communication band to optimize the radius and position of an iRFW coil located near the head of a human model inside the scanner bore, which were validated by performing both imaging and wireless experiments. Main Results. The simulated iRFW coil with a 40 mm radius positioned near the model forehead provided: a signal-to-noise ratio (SNR) comparable to that of a traditional RF coil with the same radius and position, a power absorbed by the human model within regulatory limits, and a gain pattern in the scanner bore resulting in a link budget of 51.1 dB between the coil and an AP located behind the scanner 3 m from the isocenter, which would be sufficient to wirelessly transfer MRI data acquired with a 16-channel coil array. The SNR, gain pattern, and link budget for initial simulations were validated by experimental measurements in an MRI scanner and anechoic chamber to provide confidence in this methodology. These results show that the iRFW coil design must be optimized within the scanner bore for the wireless transfer of MRI data. Significance. The MRI RF coil array coaxial cable assembly connected to the scanner increases patient setup time, can present a serious burn risk to patients and is an obstacle to the development of the next generation of lightweight, flexible or wearable coil arrays that provide an improved coil sensitivity for imaging. Significantly, the RF coaxial cables and corresponding receive chain electronics can be removed from within the scanner by integrating the iRFW coil design into an array for the wireless transmission of MRI data outside of the bore.

Secured Transfer and Storage Image Data for Cloud Communications

In cloud computing, resources are used to communicate instead of local servers or individual devices. However, sharing resources among several users is a difficult issue in cloud communication. Cryptography and steganography techniques are used for cloud storage to address data security challenges. This paper presents a novel method for securely encrypting image data for transmission and link exchange with a cloud storage service. There are two phases to accomplish the encryption process, the first phase encrypts the image file by XORing it with a random key that is generated by a new hybrid of the chaotic map. The second phase converts the encrypted image format to audio format to add another layer of security and improve secure image data transfer. The random key is generated using a hybrid chaotic map and has the benefit of having more than 10256 key spaces and the necessary level of security. Based on a statistical analysis of the encryption, the quality of the image is evaluated using several criteria, and the results demonstrate the algorithm's ability to accomplish resist encryption

Style Transfer Generator for Dataset Testing Classification

The development of the Generative Adversarial Network is currently very fast. First introduced by Ian Goodfellow in 2014, its development has accelerated since 2018. Currently, the need for datasets is sometimes still lacking, while public datasets are sometimes still lacking in number. This study tries to add an image dataset for supervised learning purposes. However, the dataset that will be studied is a unique dataset, not a dataset from the camera. But the image dataset by doing the augmented process by generating from the existing image. By adding a few changes to the augmentation process. So that the image datasets become diverse, not only datasets from camera photos but datasets that are carried out with an augmented process. Camera photos added with painting images will become still images with a newer style. There are many studies on Style transfer to produce images in drawing art, but it is possible to generate images for the needs of image datasets. The resulting force transfer image data set was used as the test data set for the Convolutional Neural Network classification. Classification can also be used to detect specific objects or images. The image dataset resulting from the style transfer is used for the classification of goods transporting vehicles or trucks. Detection trucks are very useful in the transportation system, where currently many trucks are modified to avoid road fees

Advanced pest detection strategy using hybrid optimization tuned deep convolutional neural network

PurposeAgriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.Design/methodology/approachThe paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.FindingsThe analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.Originality/valueThe proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

Development of an Artificial Intelligence Model to Recognise Construction Waste by Applying Image Data Augmentation and Transfer Learning

The demand for categorising technology that requires minimum manpower and equipment is increasing because a large amount of waste is produced during the demolition and remodelling of a structure. Considering the latest trend, applying an artificial intelligence (AI) model for automatic categorisation is the most efficient method. However, it is difficult to apply this technology because research has only focused on general domestic waste. Thus, in this study, we delineate the process for developing an AI model that differentiates between various types of construction waste. Particularly, solutions for solving difficulties in collecting learning data, which is common in AI research in special fields, were also considered. To quantitatively increase the amount of learning data, the Fréchet Inception Distance method was used to increase the amount of learning data by two to three times through augmentation to an appropriate level, thus checking the improvement in the performance of the AI model.

Information Hiding in the DICOM Message Service and Upper Layer Service with Entropy-Based Detection.

The DICOM (Digital Imaging and COmmunication in Medicine) standard provides a framework for a diagnostically-accurate representation, processing, transfer, storage and display of medical imaging data. Information hiding in DICOM is currently limited to the application of digital media steganography and watermarking techniques on the media parts of DICOM files, as well as text steganographic techniques for embedding information in metadata of DICOM files. To improve the overall security of the DICOM standard, we investigate its susceptibility to network steganographic techniques. To this aim, we develop several network covert channels that can be created by using a specific transport mechanism – the DICOM Message Service and Upper Layer Service. The bandwidth, undetectability and robustness of the proposed covert channels are evaluated, and potential countermeasures are suggested. Moreover, a detection mechanism leveraging entropy-based metrics is introduced and its performance has been assessed.

Transfer, Collection and Organisation of Electrophysiological and Imaging Data for Multicentre Studies.

Multicentre studies are of utmost importance to confirm hypotheses. The lack of established standards and the ensuing complexity of their data management often hamper their implementation. The Brain Imaging Data Structure (BIDS) is an initiative for organizing and describing neuroimaging and electrophysiological data. Building on BIDS, we have developed two software programs: BIDS Manager and BIDS Uploader. The former has been designed to collect, organise and manage the data and the latter has been conceived to handle their transfer and anonymisation from the partner centres. These two programs aim at facilitating the implementation of multicentre study by providing a standardised framework.

Foveated Encoding for Large High-Resolution Displays

Collaborative exploration of scientific data sets across large high-resolution displays requires both high visual detail as well as low-latency transfer of image data (oftentimes inducing the need to trade one for the other). In this work, we present a system that dynamically adapts the encoding quality in such systems in a way that reduces the required bandwidth without impacting the details perceived by one or more observers. Humans perceive sharp, colourful details, in the small foveal region around the centre of the field of view, while information in the periphery is perceived blurred and colourless. We account for this by tracking the gaze of observers, and respectively adapting the quality parameter of each macroblock used by the H.264 encoder, considering the so-called visual acuity fall-off. This allows to substantially reduce the required bandwidth with barely noticeable changes in visual quality, which is crucial for collaborative analysis across display walls at different locations. We demonstrate the reduced overall required bandwidth and the high quality inside the foveated regions using particle rendering and parallel coordinates.

Design, optimisation and implementation of a DCT/IDCT-based image processing system on FPGA

In this paper, a Discrete Cosine Transform (DCT) and its inverse transform IDCT are designed and optimised for FPGA using the Xilinx VIVADO High-Level Synthesis (HLS) tool. The DCT and IDCT algorithms along with a filter logic written by C/C++ are simulated for functional verification and optimised through HLS and packaged as custom IPs. The IPs are incorporated into a VIVADO project to form an image processing system for hardware validation. The VIVADO design along with a Xilinx SDK application written by C language is implemented on a Zynq FPGA development board, ZedBoard. A C# GUI is developed to transfer image data to/from the FPGA and display the original and processed images. Experimental results are presented with discussion. The FPGA development method including the DCT/IDCT IP design, optimisation and implementation via HLS as well as the VIVADO project integration can be extended to a wider range of FPGA applications.

Implementation and Evaluation of Vision-Based Sensor Image Compression for Close-Range Photogrammetry and Structural Health Monitoring.

Much research is still underway to achieve long-term and real-time monitoring using data from vision-based sensors. A major challenge is handling and processing enormous amount of data and images for either image storage, data transfer, or image analysis. To help address this challenge, this study explores and proposes image compression techniques using non-adaptive linear interpolation and wavelet transform algorithms. The effect and implication of image compression are investigated in the close-range photogrammetry as well as in realistic structural health monitoring applications. For this purpose, images and results from three different laboratory experiments and three different structures are utilized. The first experiment uses optical targets attached to a sliding bar that is displaced by a standard one-inch steel block. The effect of image compression in the photogrammetry is discussed and the monitoring accuracy is assessed by comparing the one-inch value with the measurement from the optical targets. The second application is a continuous static test of a small-scale rigid structure, and the last application is from a seismic shake table test of a full-scale 3-story building tested at E-Defense in Japan. These tests aimed at assessing the static and dynamic response measurement accuracy of vision-based sensors when images are highly compressed. The results show successful and promising application of image compression for photogrammetry and structural health monitoring. The study also identifies best methods and algorithms where effective compression ratios up to 20 times, with respect to original data size, can be applied and still maintain displacement measurement accuracy.

Wireless Communication Through Networks and Its Applications

Communication started with Telegraphy in the 1840s developing with Telephony some decade later and radio at the beginning of the century. The modern Telecommunication age is here. We live in a world of cordless phones, cells, Mobiles, Satellite TV. This is the information age. New Weather sports are made available to us almost instantly via Modern digital networks. Telephones, radio, television have become integral parts of modernizing society. My motto is to present cellular and mobile communication. Cellular communication listed some 200 million subscribers with an average of 1,50,000 new ones joining each day. The demand for basic voice and data services has stretched the limits of wireless communication in meeting the needs of an increasingly mobile market. The market is stretching the technology calling for the addition of videoconferencing image capability and data transfer through high-speed networks. The contents is explained in the preceding papers are ‘The Cellular Concept’, which includes Introduction to mobile telephony and the basic elements of the Cellular concept with the frequency allocations. The future content explains the operation of the Cellular system and the Cellular principle. The future scope of mobile and Cellular communication has also been explained.

Content Based Image Retrieval on Natural and Artificial Images

Data or information now is not only presented in text, but can also be presented in other forms for example in the form of images. Still image data and moving picture require storage which is much bigger than the text data. As a consequence, we need a method that can speed up the process of indexing and retrieving digital images as well as to reduce storage needed. So increasing data in the form of digital images does not reduce the speed and performance of computers and image data transfer in the internet. In this paper, we try to explore the JPEG image which is currently considered as an international standard for images in WWW. Most of the images currently stored in a media that we use frequently or on the internet are in JPEG format. This JPEG format has many advantages compared to other formats one of the advantage of JPEG is the size smaller compared to other such as PNG, and TIF. Therefore, JPEG has a very important role in saving storage without reducing quality of image itself. The research was carried out to determine the effectiveness and speed of retrieving natural images and artificial images using the CBIR method, research using no less than 5,000 images, and applying queries from 50 to 80. The results showed than in terms of effectiveness, images artificially better than natural images while natural speed is better. On the hand, speed of retrieving of natural images much better compared to the artificial images.

Application and Analysis of Approximate Bus and Shift-Invert Encoding Technique for Image Data Transfer over Serial Bus

Application and Analysis of Approximate Bus and Shift-Invert Encoding Technique for Image Data Transfer over Serial Bus

Convolutional Neural Networks and Transfer Learning for Quality Inspection of Different Sugarcane Varieties

In this article, we employed computer vision and deep learning techniques to select and plant healthy billets, which increased plant population and the yield per hectare of sugarcane planting. We employed well-known convolutional neural network architectures to process large image data sets and transfer learning techniques to expand the results to different sugarcane varieties. It would be very time consuming to collect and label large data sets for each sugarcane variety, for which quality inspection is needed, prior to planting. We used a two-step transfer learning process to extend the trained architecture to new varieties. We compared results obtained during transfer learning using AlexNet, VGG-16, GoogLeNet, and ResNet101 architectures to classical computer vision methods. Our goal was to determine the best approach to detect damaged and good billets in the shortest processing time. Best results in both time and accuracy were obtained with AlexNet. For AlexNet, we compared the permutations of three sugarcane varieties in order to find the best model to identify the healthy sugarcane billets. We then reduced the number of images employed to retrain the model to determine the tradeoff between time and performance. Ultimately, one needs only a few dozen billets of the new variety to retrain the network. Our approach led to meaningful increments in the yield per hectare ranging from 33 to 80% depending on sugarcane variety.