Coding Method Research Articles

VLSI Implementation of Speech Steganography with Advanced Wavelet Transform

In the realm of secure communication systems, the integration of advanced wavelet transforms into speech steganography systems has emerged as a promising path. Secure transmission of sensitive information in military communications, confidential exchange of data in corporate environments, and protection of personal information in telecommunication networks. However, the current system employs conventional methods of speech steganography, relying on basic encryption techniques and simplistic embedding algorithms. While functional, these approaches lack robustness and fail to adequately conceal secret data within speech signals, leaving them vulnerable to detection and interception. So, the proposed system leverages advanced wavelet transforms to enhance the security and efficiency of speech steganography. By exploiting the multi-resolution analysis capabilities of wavelets, the system achieves improved embedding capacity while maintaining perceptual transparency. Additionally, the use of dynamic embedding algorithms based on wavelet coefficients ensures adaptability to varying signal characteristics, enhancing robustness against attacks and noise interference. The VLSI implementation of this system optimizes resource utilization and computational efficiency, making it suitable for real-time applications in communication systems.

Parallel BFS through pennant data structure with reducer hyper‐object based data hiding for 3D mesh images

AbstractData hiding refers to the technique employed to conceal sensitive data within various forms of multimedia, such as video, audio, text, 2D images, 3D images, and more, while also being able to successfully extract said information from the multimedia file. Although numerous data hiding techniques exist for 2D images, research on data hiding in 3D images is still in its early stages. Current 3D image steganography methods suffer from limitations in terms of embedding capacity and time complexity. To overcome these difficulties, we propose a novel 3D image steganographic technique using Parallel Breadth First Search (PBFS) with hyper‐object. Our approach utilizes the PBFS searching strategy, along with layer synchronization, to embed private information within the vertices of the 3D mesh images. To maximize the effectiveness of the process and minimize time, cost, complexity, and execution, we involved the data structure “bag” to parallelize the BFS. The implementation of bags is based on the pennant data structure. This methodology obtained an impressive Embedding Capacity (EC) of 9.00 bits per vertex (bpv) while maintaining superior visual quality and time complexity. Consequently, the proposed methodology holds great potential for widespread utilization across numerous sectors, including private and government organizations such as intelligence agencies, intellectual property rights management, cloud data security, defense, covert communication, medical imagery, and more.

Correction to: Image Steganography Method for Securing Multiple Images Using LSB–GA

Correction to: Image Steganography Method for Securing Multiple Images Using LSB–GA

LSB8 using DSF to Hide Secret Message

An efficient method of message steganography will be proposed, the method will use 8 bits from the speech sample binary value to hold one character from the secret message, thus the capacity hiding will equal the covering DSF size. Using lower LSBs of the speech sample binary value will not much affect the sample value, so the quality of the stego DSF will be excellent and the stego file will be closed to the covering file. The proposed method will add an extra security issues by using a private key, this key will be used to calculate the starting sample where to start characters hiding, it will determine the starting LSB to get the set of 8 LSBs, and it will be also used to generate eight element chaotic key, this key will be converted to 8 orders of LSBs to hide the bits of characters, these bit will be not consecutive. The PK will a long enough long provide a good key space capable to resist hacking attempt and the extracted message will be very sensitive to the selected values of the private key. The proposed method will provide enhancements in the quality, security, speed and sensitivity of message steganography, these enhancements will be proved based on the discussing of obtained experimental results.

Patient aggression experienced by professional nurses in acute psychiatric ward: South Africa.

Aggression of patients in hospital wards has become an endemic problem and professional nurses are particularly at high risk. This article presents the types of patient aggression experienced by professional nurses working in an acute psychiatric ward in Vhembe District, South Africa. Vhembe District, South Africa. A qualitative approach using exploratory, descriptive and contextual research design was used. Four hospitals were purposively selected and 10 professional nurses were conveniently sampled to participate in the study. Individual interviews were used to collect data, which were then analysed through Tesch Open Coding Method. Measures to ensure trustworthiness and ethical considerations were adhered to throughout the study. This study shed some light on the professional nurses' lived experiences regarding the types of aggression from patients in an acute psychiatric ward in Vhembe District. The types of aggression are physical aggression, destructive behaviour and verbal aggression. The findings show that the types of aggression to which professional nurses are exposed are overwhelming and the consequences are shocking. As a result, the health of professional nurses is compromised. Therefore, this study recommends further studies to determine the kind of support needed by professional nurses working in an acute psychiatric ward and to investigate the impact of aggression from patients in acute psychiatric ward with regard to the quality of care. This article contributes to the body of knowledge regarding patients' aggression in acute wards in Vhembe District, South Africa.

Tensor-Based Sparse Representation for Hyperspectral Image Reconstruction Using RGB Inputs

Hyperspectral image (HSI) reconstruction from RGB input has drawn much attention recently and plays a crucial role in further vision tasks. However, current sparse coding algorithms often take each single pixel as the basic processing unit during the reconstruction process, which ignores the strong similarity and relation between adjacent pixels within an image or scene, leading to an inadequate learning of spectral and spatial features in the target hyperspectral domain. In this paper, a novel tensor-based sparse coding method is proposed to integrate both spectral and spatial information represented in tensor forms, which is capable of taking all the neighboring pixels into account during the spectral super-resolution (SSR) process without breaking the semantic structures, thus improving the accuracy of the final results. Specifically, the proposed method recovers the unknown HSI signals using sparse coding on the learned dictionary pairs. Firstly, the spatial information of pixels is used to constrain the sparse reconstruction process, which effectively improves the spectral reconstruction accuracy of pixels. In addition, the traditional two-dimensional dictionary learning is further extended to the tensor domain, by which the structure of inputs can be processed in a more flexible way, thus enhancing the spatial contextual relations. To this end, a rudimentary HSI estimation acquired in the sparse reconstruction stage is further enhanced by introducing the regression method, aiming to eliminate the spectral distortion to some extent. Abundant experiments are conducted on two public datasets, indicating the considerable availability of the proposed framework.

High invisibility image steganography with wavelet transform and generative adversarial network

Recently, extensive research is showing that deep learning has enormous potential in image steganography. However, most steganography methods based on deep learning are not sufficiently invisible. In this paper, a novel end-to-end deep neural network for image steganography based on generative adversarial network (GAN) and discrete wavelet transformation (DWT) is proposed, which can greatly improve the invisibility of the method. Firstly, the cover image is transformed from the RGB domain to the DWT domain to embed information in frequency. Furthermore, a multi-scale attention convolution to fuse different scales of feature information is proposed, which can help the model to find a better location to embed the information. Finally, the fusion module is used to embed the information into the cover image at different modification intensities. In addition, a new Discriminator that compares cover images and stego images in a patch-to-patch way to improve the ability of the Discriminator. We analyze our algorithm from multiple aspects (e.g., security and invisibility) to verify the superior performance of the proposed method. To demonstrate generalization ability, experiments are conducted on three widely used datasets and show the superior performance of our method compared to other steganography methods based on deep learning.

Adaptively hybrid fractal image coding

AbstractAn adaptively hybrid method was proposed to improve the performance of fractal coding methods. First, it is found that the range blocks with large variances (RBLVs) play a crucial role in degrading decoded images, and the effect of the remaining range blocks with small variances (RBSVs) can be ignored. Then, an adaptive method was proposed to divide the range blocks into the above two categories: RBLVs and RBSVs. Second, RBLVs were designed to be encoded in an extended domain block pool (EDBP). Then, better block‐matching effect can be obtained, which will result in better decoded image quality. Further, the no‐search fractal encoding method is adopted for RBSVs to achieve faster encoding speed and fewer bits per pixel (bpp). Finally, four fractal coding methods were adopted to assess the performance of the proposed method. Experimental results show that compared with the previous methods, the PSNR quality of decoded images in the proposed method can be improved by about 0.15–0.4 dB, about 20%–35% of the total computations in encoding process can be saved, and about 0.2 bpp can be saved. Moreover, under the same decoding time, the proposed method can achieve comparable or smaller deviations regarding the decoded image.

Early Childhood Caries and Dental Care Utilization in Mangalore, India: Parents' Perceptions.

Parents often underuse dental care services for their children with early childhood caries (ECC), resulting in a high burden of untreated dental caries. To describe parental perceptions and challenges in dental care utilization for their children with ECC. A descriptive qualitative study was conducted with parents of children with ECC seeking dental care in Mangalore, India. Data were collected through focus group discussions using an interview guide. Manual line-by-line coding and content analysis methods were used for data analysis. Three categories were generated from data obtained through 5 focus groups of 27 participants: dental care visiting patterns, the significance of dental visits, and challenges to dental care utilization. Parents would only visit the dentist when they perceived their children's tooth problems. The challenges faced include time constraints, misinformation, lack of awareness and motivation, costs, fear, and anxiety of the children and the parents themselves. Parents perceived improvements in the awareness of their children's oral health and quality of life after dental visits. Attending dental services to treat their children's tooth problems was the primary way of attaining awareness about prevention among the parents of children with ECC. Increased oral health awareness and improved quality of life can motivate parents to seek further dental care for their children. Collaboration with multidisciplinary stakeholders is required to improve oral health awareness among parents of children with ECC. The study's results will help plan targeted preventive programs for children with ECC. The study suggests dental care utilization by children with ECC is based on parental perceptions of their children's tooth problems. It outlines the challenges that affect dental visits of children with ECC and the role of multidisciplinary stakeholders. Improved oral health awareness and quality of life following dental treatment can facilitate further dental visits.

A Multimode Neuromorphic Vision Sensor With Improved Brightness Measurement Performance by Pulse Coding Method

A Multimode Neuromorphic Vision Sensor With Improved Brightness Measurement Performance by Pulse Coding Method

Adaptive Combined Channel Network Coding for Cognitive Radio Networks with Cooperative Relay

Cognitive radio is one of the new technologies for 4G/5G applications, so cooperative relay communication and network coding are considered as certain methods to help improve their re-spective applications. A primary broadcast system for multimedia video streaming applications that broad¬casts data to primary users and an auxiliary cooperative relay secondary cognitive ra-dio system are considered. The secondary cooperative overlay system can use multiple error control coding methods for point-to-point data retransmission, such as channel coding, network coding, and combi¬ned coding methods to improve system performance under variable link con-ditions. A new technique of adaptive coding of AC2NC combined channel network for data re-transmission is proposed. The new AC2NC first analyzes the channel feedback information and then selects the best retransmission coding technique based on the target bandwidth or trans-mission time optimization. This is instead of using a single static channel or a network coding technique with dynamic channel conditions. The proposed AC2NC improves system through-put, reduces retransmission time, and provides more spectrum access opportunities for the sec-ondary sys¬tem's own data transmissions. AC2NC's relative throughput and time saving capabil-ities for cognitive radio users have been shown to exceed ninety percent, under certain channel conditions, compared to some static coding methods.

A deep learning-based steganography method for high dynamic range images

A deep learning-based steganography method for high dynamic range images

Efficient color image steganography based on new adapted chaotic dynamical system with discrete orthogonal moment transforms

In today's rapidly evolving digital landscape, safeguarding the secure transmission of sensitive information is paramount. Traditional steganography methods, while effective at data concealment, have grown vulnerable to brute force attacks in our age of powerful computing. This paper addresses the pressing need to bolster data transmission security. To address the challenges posed by limited key space and security concerns associated with 1-D chaotic maps in data transmission, we introduce a novel 1-D chaotic map, the Multi-Parameter Chebyshev Map (MCM). The MCM boasts four control parameters, each with a broad chaotic range and high chaos level, significantly enhancing security. Furthermore, we present an innovative steganography technique for color images that leverages the MCM in conjunction with discrete moment transforms. This method chaotically embeds the secret message within the moment coefficients of a cover color image using MCM and quantization rules. The expansive chaotic range and high chaos level of the MCM substantially reinforce the security of this approach. The considered moment transforms include Tchebichef, Krawtchouk, Hahn, Charlier, Meixner, Shmaliy, Racah, and Dual-Hahn. The performance of the proposed steganography method is thoroughly evaluated and compared with other state-of-the-art methods in terms of hiding capacity and image quality in both spatial and transform domains. The quantization of moment coefficients demonstrates its superior performance. This method holds crucial implications for practical applications requiring secure communication of color images, such as military and medical contexts. The combination of MCM and moment transforms in this method presents a promising avenue for future research in the field of information hiding.

Conservativeness Study on the Seismic Analysis Method for Research Reactor Plant Structure Based on TMSR-LF1

The seismic performance analysis of research reactor plants is crucial for ensuring the safety of the entire reactor system. This paper analyzes the plant structure seismic performance of the 2WMt Thorium Molten Salt Reactor-Liquid Fuel 1 (TMSR-LF1) at the Shanghai Institute of Applied Physics, Chinese Academy of Sciences under the action of frequent earthquakes, fortification earthquakes, and rare earthquakes was analyzed by finite element software PKPM based on the seismic design method of civil code +1°. On this basis, a comparison was made between the seismic responses of structural pushover analysis and elastoplastic time history analysis under the action of rare earthquakes, and the conservatism of these two commonly used methods for elastoplastic analysis was systematically analyzed. The results indicate that the TMSR-LF1 plant structure exhibits well bearing and deformation capacity. It meets the seismic design goal of “no damage under small earthquakes,” “no unrepairable damage under medium earthquakes,” and “no collapse under large earthquakes (using static pushover analysis),” and the critical regions are entirely within the elastic range. Furthermore, the analysis reveals that the elastoplastic time history analysis method produced a significantly higher seismic response than the pushover analysis method, which indicates the conservativeness of the time history method. Therefore, it is recommended to use the elastoplastic time history analysis method to evaluate the seismic performance of research reactor plants under rare earthquake actions. The research in this paper provides important references for the seismic performance analysis of other Class II research reactor plants.

Iris Liveness Detection using Fusion of Thepade SBTC and Triangle Thresholding Features with Machine Learning Algorithms

Conventional security systems are often plagued by inherent flaws, leading to frequent security breaches. To address these vulnerabilities, automated biometric systems have emerged, leveraging individuals' physiological and behavioural traits for precise identification. Among these biometric modalities, iris-based authentication is a highly reliable, distinctive, and contactless method for user recognition. This research endeavours to enhance the accuracy of iris liveness detection by combining features extracted from the TSBTC n-Ary (Thepade’s Sorted Block Truncation Coding) method with those derived from the Triangle Thresholding method. Two distinct datasets, namely IIIT Delhi and Clarkson 2015, have been employed to evaluate the efficacy of these combined features. The study involves extracting features from three sources: TSBTC, TSBTC+Triangle, and Triangle methods. These features are subsequently input into the WEKA tool, which employs various classifiers to assess accuracy. The findings of this investigation reveal a notable increase in the accuracy of Iris Liveness Detection (ILD) by incorporating handcrafted techniques like TSBTC in conjunction with the Thresholding method. In essence, this research underscores the potential for improving the robustness of security systems by harnessing the synergy of distinct biometric methods, thereby mitigating the shortcomings of conventional security systems and fortifying the foundations of secure user authentication.

Image Steganography:

Steganography is the practice of hiding secret information within other media, such as images, audio, video, and text. It has become increasingly important in today's society as a way to enable private and secure communication. This research project focuses on image steganography techniques that are used to evade detection of the secret message through statistical steganalysis techniques. The aim of this research is to compare and evaluate different image steganography methods, study their implementation complexity, and propose a framework to improve current approaches. The research will provide a comparison of the efficiency of different steganography techniques in avoiding detection by steganalysis and may lead to the development of better steganography techniques in the future. This paper focuses on the three steganography methods in the spatial domain: Least Significant Bit (LSB), Pixel-Value Difference (PVD) and Edge-based Data Embedding (EBE) methods. A simple experiment has been conducted to encrypt several images using these three methods, and the distortion measure for LSB using mean square error (MSE) and peak-to-noise ratio (PSNR) has been investigated. Although the distortion measure result is considered acceptable for LSB method in the experiment, all methods resulted in significant difference in the file capacity. This shows that further enhancement in the security of the encryption is necessary so that the secret message will not be easily discovered. Thus, in this paper, we propose a conceptualized enhancement in the security using Morse Code, Base 64, SHA-245, and Advanced Encryption Standard (AES) before encrypting using PVD.

Meditation as an Online Pedagogical Method through the Lens of Online Adult Learners’ Experiences

The purpose of this study is to understand the ways in which meditation practice can be used as an online pedagogical method based on adult learners’ experiences with various forms of meditation practices. To arrive at this purpose, the researcher found it necessary to gather data about frequency of use, preferences, and related transformative experiences of the participants. Through qualitative inquiry, this study explored adult learners’ perceptions, attitudes, and reflections on using meditative practices while engaged in asynchronous online learning. Participants utilized prerecorded online meditation videos and biofeedback devices designed for meditation which provided immediate feedback and analytics. The data collection methods included surveys, participants’ meta-reflections, and a focus group interview. Data analyses included coding and constant comparative methods along with descriptive statistical analysis for the Likert-scale responses. Analyses led to themes describing participants’ self-awareness, preferred meditation practices, barriers to meditation, and suggestions for incorporating meditation as an online pedagogical method.

Child and caregiver voices about inpatient care: What do they report as positive and as needing improvement?

PurposeIn pediatric healthcare, patient satisfaction queries exclude children and solicit quantitative ratings from caregivers. We sought satisfaction perspectives from hospitalized children 7 to 17 years and their caregivers by qualitatively analyzing interview responses. Design and methodsEnglish and Spanish-speaking children and their parents on five inpatient units completed two open-ended questions about their satisfaction at hospital discharge (T1, face to face) and 7 to 10 days later (T2, telephone). The questions asked about what was good and what could be better at the hospital. Responses were analyzed using descriptive semantic content analysis and consensus coding methods. ResultsPatients' mean age was 11.9 years (SD = 3.17); 51% were male. At T1, 362 patients offered 833 responses; 600 parents offered 1496 responses. At T2, 252 patients offered 552 responses; 488 parents offered 1290 responses. At T1 and T2, the most frequent patient response to what was good was ‘Staff took good care of me’ and for caregivers was ‘Staff behaving professionally’. At T1, the most frequent patient response about what could be better was ‘more activities for kids’, and at T2, ‘Nothing’ and ‘More food options and better food quality’; for caregivers at T1, ‘Nothing’ and ‘Not liking the physical space’, and at T2, ‘Nothing’, and ‘More communication and professionalism from hospital staff’. ConclusionsPediatric patients and their caregivers are willing and able to offer perspectives about satisfaction with care, and suggestions for improvement before discharge. Practice implicationsPediatric patients and their caregivers' perspectives about care yield actionable recommendations for hospital systems.

Multifocal multiview imaging and data compression based on angular-focal-spatial representation.

Multifocal multiview (MFMV) is an emerging high-dimensional optical data that allows to record richer scene information but yields huge volumes of data. To unveil its imaging mechanism, we present an angular-focal-spatial representation model, which decomposes high-dimensional MFMV data into angular, spatial, and focal dimensions. To construct a comprehensive MFMV dataset, we leverage representative imaging prototypes, including digital camera imaging, emerging plenoptic refocusing, and synthesized Blender 3D creation. It is believed to be the first-of-its-kind MFMV dataset in multiple acquisition ways. To efficiently compress MFMV data, we propose the first, to our knowledge, MFMV data compression scheme based on angular-focal-spatial representation. It exploits inter-view, inter-stack, and intra-frame predictions to eliminate data redundancy in angular, focal, and spatial dimensions, respectively. Experiments demonstrate the proposed scheme outperforms the standard HEVC and MV-HEVC coding methods. As high as 3.693 dB PSNR gains and 64.22% bitrate savings can be achieved.

Beliefs in engineering education research: A systematic scoping review for studying beliefs beyond the most popular constructs

AbstractBackgroundBeliefs are a complex research construct with deep connections to innumerable different research areas and agendas. Engineering education researchers are increasingly studying beliefs, and synergy across these efforts can lead to a greater impact in translating beliefs research into educational practice.PurposeOur purpose was to enable any researcher in engineering education to productively research beliefs as a construct. Specifically, we aimed to synthesize the different purposes for studying beliefs, and the extent to which researchers have operationalized beliefs.Scope/MethodWe conducted a systematic scoping review of beliefs following the PRISMA protocol. We extracted and mapped data from the 79 academic included manuscripts. We performed additional analysis using both inductive and deductive coding methods to synthesize how beliefs have been researched. We included studies about the beliefs of engineering students in post‐secondary education beyond the four most popular types of beliefs (i.e., self‐efficacy, mindset, epistemic, and goal orientation beliefs).ResultsGiven the diverse nature of beliefs in engineering education, we found that the findings of the included studies could not be coherently synthesized. Instead, we present (1) a synthesis of researchers' purpose(s) for studying beliefs, and (2) a detailed representation of the many ways in which researchers have operationalized beliefs using different theories and methodological approaches.ConclusionsWe recommend that researchers studying beliefs work to align their stated purpose for studying beliefs with their research contribution and build understanding of how beliefs ultimately relate to behavior. We also identified an opportunity for researchers to carefully and explicitly operationalize beliefs as a research construct.