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Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor data at a very high pace, making it a challenge to collect and store the data. This scenario brings about the need for effective data compression algorithms to make the data manageable among tiny and battery-powered devices and, more importantly, shareable across the network. Additionally, considering that, very often, wireless communications (e.g., low-power wide-area networks) are adopted to connect field devices, user payload compression can also provide benefits derived from better spectrum usage, which in turn can result in advantages for high-density application scenarios. As a result of this increase in the number of connected devices, a new concept has emerged, called TinyML. It enables the use of machine learning on tiny, computationally restrained devices. This allows intelligent devices to analyze and interpret data locally and in real time. Therefore, this work presents a new data compression solution (algorithm) for the IoT that leverages the TinyML perspective. The new approach is called the Tiny Anomaly Compressor (TAC) and is based on data eccentricity. TAC does not require previously established mathematical models or any assumptions about the underlying data distribution. In order to test the effectiveness of the proposed solution and validate it, a comparative analysis was performed on two real-world datasets with two other algorithms from the literature (namely Swing Door Trending (SDT) and the Discrete Cosine Transform (DCT)). It was found that the TAC algorithm showed promising results, achieving a maximum compression rate of 98.33%. Additionally, it also surpassed the two other models regarding the compression error and peak signal-to-noise ratio in all cases.

Incalculable numbers of patients in hospitals as a result of COVID-19 made the screening of heart patients arduous. Patients who need regular heart monitoring were affected the most. Telecardiology is used for regular remote heart monitoring of such patients. However, the resultant huge electrocardiogram (ECG) data obtained through regular monitoring affects available storage space and transmission bandwidth. These signals can take less space if stored or sent in a compressed form. To recover them at the receiver end, they are decompressed. We have combined telecardiology with automatic ECG arrhythmia classification using CNN and proposed an algorithm named TELecardiology using a Deep Convolution Neural Network (TELDCNN). Discrete cosine transform (DCT), 16-bit quantization, and run length encoding (RLE) were used for compression, and a convolution neural network (CNN) was applied for classification. The database was formed by combining real-time signals (taken from a designed ECG device) with an online database from Physionet. Four kinds of databases were considered and classified. The attained compression ratio was 2.56, and the classification accuracies for compressed and decompressed databases were 0.966 and 0.990, respectively. Comparing the classification performance of compressed and decompressed databases shows that the decompressed signals can classify the arrhythmias more appropriately than their compressed-only form, although at the cost of increased computational time.

Multi-secret image sharing scheme (MSIS) is a technique to share multiple secret images over the internet. Normally, most of the secret image sharing schemes can share only a single secret image. However, due to the rapid development of internet technology, the necessity of sharing multiple images arises. An ( n ,  n ) MSIS is employed to share n images to n authorized participants. All the n participants are required to submit their respective shares to recover the original secret images. If the number of the participants is less than n , then reconstruction of the secret images is impossible. Most of the existing schemes which are in the frequency domain do not have the capability to handle multiple secret images. In this paper, a MSIS that uses the Discrete Cosine Transform is proposed to overcome the limitation present in the existing schemes. Moreover, the proposed scheme requires less computational time than the existing schemes. Security of the proposed scheme is analyzed and shows that the proposed scheme is computationally secure. Also, the proposed scheme can recover the same original secret images.

The image retrieval process grows a massive problem due to the huge number of data exist on the web. So, the accurate retrieval of satellite image of the given query is one of the necessary requirement. The paper propose a classifier of Probabilistic Neural Network based Random Forest (rf-PNN), which is retrieving an exact match of a classified data as per user’s need. Various techniques of Adaptive Median Filter (for pre-processing), Discrete Cosine Transform based Discrete Orthogonal Stockwell Transform (for segmentation) and Linear Binary Pattern (for feature extraction) are presented to process the trained dataset as well as given query. Then, both the feature extracted samples are assigned to compare with the classified network. The experimental setup is demonstrated on MATLAB tool. Then the relevant feature retrieval are analyze under the performance measures of 92% accurate rate, sensitivity for 89.25%, specificity for 94.1% and precision at a rate of 90.08%.

Video steganography approach enables hiding chunks of secret information inside video sequences. The features of video sequences including high capacity as well as complex structure make them more preferable for choosing as cover media over other media such as image, text, or audio. Video steganography is a prominent as well as the evolving field in the information security domain and significant number of video steganography methods are proposed in recent years. This article provides a comprehensive review of video steganography methods proposed in the literature. This article initially reviews various raw domain-based video steganography methods. In particular, the raw domain-based methods include spatial domain approaches such as least significant bits (LSB), transform domain-based methods such as discrete wavelet transform, discrete cosine transform, etc. Furthermore, the article looks into various compressed domain steganography methods. A critical comparative analysis is included in the article to analyze and contrast the steganography methods proposed in the literature. A brief description of various evaluation matrices for video steganography methods is provided in this article. Moreover, a brief introduction to steganalysis and video steganalysis is provided. The article concludes with a discussion focused on the limitations and challenges of the video steganography methods. Further, a brief insight into future directions in video steganography systems is provided.

We introduce a novel image encryption and decryption algorithm for multiple images incorporating multiple parameter fractional discrete cosine transform (MPFrDCT), 3D Arnold transform and RSA cryptosystem. Before encryption, the images are changed into their indexed formats by removing their color maps. The indexed formats of the images are taken as the red, green and blue channel of an RGB image. Firstly, the RGB image is taken as the input of 3D Arnold transform. The 3D Arnold transform not only dislocates the pixel positions, but also changes the pixel values. Mathematically, the 3D map performs both permutation as well as substitution. The distorted image is now encrypted using RSA cryptosystem which is a public key cryptosystem. The RSA cryptosystem makes the image secure in public domain as the hard problem is the factorization of large primes which is unbreakable. Lastly, the domain of the encrypted image is changed to frequency domain using MPFrDCT. If the secret keys are known to an unauthorized person, the encryption algorithm is still secure as the security of the presented cryptosystem depends upon the secret keys and the arrangements of the secret keys. The proposed image encryption algorithm is storage efficient. The statistical and simulation analysis are conducted to evaluate the robustness of the presented encryption and decryption processes.

With the emergence of the Internet of Things (IoT) and many smart gadgets that support artificial intelligence, it is easier than ever to acquire, reproduce, and disseminate a large number of digital data. However, these great technologies have made it possible for intruders to easily violate issues related to copyright protection, identity theft, and privacy leakage. To address such issues, several approaches have been developed, among which image watermarking has been proven to be an ideal solution. In this paper, a blind watermarking approach for RGB color images based on joint Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) is proposed. First, a self-adaptive color selecting strategy is used to select either the blue or green channel of the host image for embedding purpose. Subsequently, the selected color is subdivided into non-overlapping square blocks of size 4 × 4, and then DCT is applied to each block. Afterward, the DC values obtained from each block are decomposed into four sub-bands using DWT, and then the LH middle frequency sub-band is further decomposed into four sub-bands using DWT. Lastly, the LH1 obtained from LH is utilized for watermark embedding. To provide security to the proposed approach, the watermark image is encrypted before embedding using a chaotic sequence originated from a logistic map method. Experimental results reveal that the proposed approach not only enhances watermark invisibility but also provide excellent watermark robustness, meeting the main requirements of image watermarking.

In this paper, a robust blind image watermarking method is proposed for copyright protection of digital images. This hybrid method relies on combining two well-known transforms that are the discrete Fourier transform (DFT) and the discrete cosine transform (DCT). The motivation behind this combination is to enhance the imperceptibility and the robustness. The imperceptibility requirement is achieved by using magnitudes of DFT coefficients while the robustness improvement is ensured by applying DCT to the DFT coefficients magnitude. The watermark is embedded by modifying the coefficients of the middle band of the DCT using a secret key. The security of the proposed method is enhanced by applying Arnold transform (AT) to the watermark before embedding. Experiments were conducted on natural and textured images. Results show that, compared with state-of-the-art methods, the proposed method is robust to a wide range of attacks while preserving high imperceptibility.

Medical image security is a critical concern in the healthcare domain, and various watermarking techniques have been explored to embed imperceptible and secure data within medical images. This paper introduces an innovative frequency-based watermarking technique for medical images, utilizing the Fractional Discrete Cosine Transform (FDCT) and Schur decomposition to ensure robust and secure watermark embedding. The watermark bits are integrated by modulating the obtained Schur coefficients, thereby ensuring robust and secure watermarking without significantly altering the visual quality of the medical images. The experiments conducted on the ocular database demonstrate the capacity, imperceptibility, and robustness of the proposed method. This approach achieved a favorable trade-off between imperceptibility and information embedding capacity for ensuring the authenticity and integrity of medical images during transmission.

Underwater wireless communications (UWC), based on acoustic waves, radio frequency waves, and optical waves, are currently deployed using underwater communications networks. Wireless sensor communications are among the most common UWC technologies because they offer connectivity over long distances. However, the UWC complex problems include restricted bandwidth, multitrack loss, limited battery power, and latency in propagation. Hence in this paper, Artificial Intelligence based Effective Data Interpretation Approach (AI-EDIA) has been proposed to improve the underwater wireless sensor network communication and less computational Time in IoT platform. The proposed AI-EIDA utilizes the discrete cosine transform (DCT) with frequency modulation multiplexing (FMM) for underwater acoustic communication. Underwater acoustic channels are categorized as double Time and frequency distribution channels. Therefore, the reverse DCT structure provides the orthogonal characteristic of the traditional FMM with the additional advantages of reduced execution and improved speed when the actual calculations are needed. Thus the experimental results show that AI-EDIA decreases energy usage and less delay rate to 0.45 s.