Wavelet-Domain Information-Hiding Technology with High-Quality Audio Signals on MEMS Sensors.

Due to the rapid development of sensor technology and the popularity of the Internet, not only has the amount of digital information transmission skyrocketed, but also its acquisition and dissemination has become easier. The study mainly investigates audio security issues with data compression for private data transmission on the Internet or MEMS (micro-electro-mechanical systems) audio sensor digital microphones. Imperceptibility, embedding capacity, and robustness are three main requirements for audio information-hiding techniques. To achieve the three main requirements, this study proposes a high-quality audio information-hiding technology in the wavelet domain. Due to the fact that wavelet domain provides a useful and robust platform for audio information hiding, this study applies multi-coefficients of discrete wavelet transform (DWT) to hide information. By considering a good, imperceptible concealment, we combine signal-to-noise ratio (SNR) with quantization embedding for these coefficients in a mathematical model. Moreover, amplitude-thresholding compression technology is combined in this model. Finally, the matrix-type Lagrange principle plays an essential role in solving the model so as to reduce the carrying capacity of network transmission while protecting personal copyright or private information. Based on the experimental results, we nearly maintained the original quality of the embedded audio by optimization of signal-to-noise ratio (SNR). Moreover, the proposed method has good robustness against common attacks.

Intelligent Healthcare System Using Mathematical Model and Simulated Annealing to Hide Patients Data in the Low-Frequency Amplitude of ECG Signals.

Healthcare is an important medical topic in recent years. In this study, the novelty we propose is the intelligent healthcare system using an inequality-type optimization mathematical model with signal-to-noise ratio (SNR) and wavelet-domain low-frequency amplitude adjustment techniques to hide patients' confidential data in their electrocardiogram (ECG) signals. The extraction of the hidden patient information also utilizes the low-frequency amplitude adjustment. The detailed steps of establishing the system are as follows. To integrate confidential patient data into ECG signals, we first propose a nonlinear model to optimize the quality of ECG signals with the embedded patients' confidential data including patient name, patient birthdate, date of medical treatment, and medical history. Then, we apply Simulated Annealing (SA) to solve the nonlinear model such that the ECG signals with embedded patients' confidential data have good SNR, good root mean square error (RMSE), and high similarity. In other words, the distortion of the PQRST complexes and the ECG shape caused by the embedded patients' confidential data is very small, and thus the quality of the embedded ECG signals meets the requirements of physiological diagnostics. In the terminals, one can receive the ECG signals with the embedded patients' confidential data. In addition, the embedded patients' confidential data can be received and extracted without the original ECG signals. The experimental results confirm the efficiency that our method maintains a high quality of each ECG signal with the embedded patient confidential data. Moreover, the embedded confidential data shows a good robustness against common attacks.

Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches.

Electrocardiograph (ECG) technology is vital for biometric security, and blood oxygen is essential for human survival. In this study, ECG signals and blood oxygen levels are combined to increase the accuracy and efficiency of human identification and verification. The proposed scheme maps the combined biometric information to a matrix and quantifies it as a sparse matrix for reorganizational purposes. Experimental results confirm a much better identification rate than in other ECG-related identification studies. The literature shows no research in human identification using the quantization sparse matrix method with ECG and blood oxygen data combined. We propose a multi-dimensional approach that can improve the accuracy and reduce the complexity of the recognition algorithm.

ECG Sensor Card with Evolving RBP Algorithms for Human Verification.

It is known that cardiac and respiratory rhythms in electrocardiograms (ECGs) are highly nonlinear and non-stationary. As a result, most traditional time-domain algorithms are inadequate for characterizing the complex dynamics of the ECG. This paper proposes a new ECG sensor card and a statistical-based ECG algorithm, with the aid of a reduced binary pattern (RBP), with the aim of achieving faster ECG human identity recognition with high accuracy. The proposed algorithm has one advantage that previous ECG algorithms lack-the waveform complex information and de-noising preprocessing can be bypassed; therefore, it is more suitable for non-stationary ECG signals. Experimental results tested on two public ECG databases (MIT-BIH) from MIT University confirm that the proposed scheme is feasible with excellent accuracy, low complexity, and speedy processing. To be more specific, the advanced RBP algorithm achieves high accuracy in human identity recognition and is executed at least nine times faster than previous algorithms. Moreover, based on the test results from a long-term ECG database, the evolving RBP algorithm also demonstrates superior capability in handling long-term and non-stationary ECG signals.

Hiding patients confidential datainthe ECG signal viaa transform-domain quantization scheme.

Watermarking is the most widely used technology in the field of copyright and biological information protection. In this paper, we use quantization based digital watermark encryption technology on the Electrocardiogram (ECG) to protect patient rights and information. Three transform domains, DWT, DCT, and DFT are adopted to implement the quantization based watermarking technique. Although the watermark embedding process is not invertible, the change of the PQRST complexes and amplitude of the ECG signal is very small and so the watermarked data can meet the requirements of physiological diagnostics. In addition, the hidden information can be extracted without knowledge of the original ECG data. In other words, the proposed watermarking scheme is blind. Experimental results verify the efficiency of the proposed scheme.

Essential and non-essential elements in tuna and billfish around the world: Distribution patterns and influencing factors.

Tuna and billfish are widely distributed in oceans worldwide. Their survival is relied on a decent share of essential and non-essential elements. We conducted a comprehensive evaluation of essential and non-essential elements in livers of tuna and billfish collected from global oceans. The individual element consistently shown similar orders of magnitude in both tuna and billfish, with essential elements generally being 1-3 orders of magnitude higher than non-essential elements. Various physicochemical properties and behaviors contributed to four distinct clusters of these elements. Also, element distribution pattern indicated the presence of four sample groups based on regions and categories. Nine elements served as characteristic indicators. Among them, fish category was the most important influencing factor. Hg, Fe, Tl, Co, and Se were influenced by body size, trophic level, and feeding habits. Ni was influenced by sampling regions, while Mg, Mn and As were influenced by body size and local primary production.

Global squid contamination by halogenated polycyclic aromatic hydrocarbons and its trade induced risk transfer.

Squid is traded globally as an important food resource. However, the occurrence of carcinogenic halogenated polycyclic aromatic hydrocarbons (HPAHs) in squid and the risk of their transfer through trade is little understood or recognized. Here, we comprehensively evaluated the occurrence and risk transfer by quantifying the congener-specific concentrations of HPAHs in 121 squid samples collected from the Pacific, Atlantic and Indian oceans. This was the first time that nine of the 36 target chlorinated and brominated PAH congeners had been detected in squid. The HPAHs exhibited growth-dilution effects in the squid. The lipid content of squid was the most significant factor influencing HPAH bioaccumulation, while differences in squid growth and local ocean contamination influenced by geographical distribution also affected HPAH bioaccumulation. The redistribution and risk transfers of HPAHs in squid as a food could be affected by international trading. The cancer risks from squid consumption in China and Mexico were increased by 50 % and 30 %, respectively, because of international squid trading.

Trophodynamics of halogenated organic pollutants (HOPs) in aquatic food webs.

Halogenated organic pollutants (HOPs) represent hazardous and persistent compounds characterized by their capacity to accumulate within organisms and endure in the environment. These substances are frequently transmitted through aquatic food webs, engendering potential hazards to ecosystems and human well-being. The trophodynamics of HOPs in aquatic food webs has garnered worldwide attention within the scientific community. Despite comprehensive research endeavors, the prevailing trajectory of HOPs, whether inclined toward biomagnification or biodilution within global aquatic food webs, remains unresolved. Furthermore, while numerous studies have probed the variables influencing the trophic magnification factor (TMF), the paramount determinant remains elusive. Collating a compendium of pertinent literature encompassing TMFs from the Web of Science between 1994 and 2023, our analysis underscores the disparities in attention accorded to legacy HOPs compared to emerging counterparts. A discernible pattern of biomagnification characterizes the behavior of HOPs within aquatic food webs. Geographically, the northern hemisphere, including Asia, Europe, and North America, has demonstrated greater biomagnification than its southern hemisphere counterparts. Utilizing a boosted regression tree (BRT) approach, we reveal that the food web length and type emerge as pivotal determinants influencing TMFs. This review provides a valuable basis for gauging ecological and health risks, thereby facilitating the formulation of robust standards for managing aquatic environments.

Intelligent Healthcare System Using Patients Confidential Data Communication in Electrocardiogram Signals.

With the advent of the aging era, healthcare and elderly care have become the focus of medical care, especially the care of the elderly with dementia. Patients' confidential data hiding is a useful technology for healthcare and patient information privacy. In this study, we implement an intelligent healthcare system using the multiple-coefficient quantization technology in transform domain to hide patients' confidential data into electrocardiogram (ECG) signals obtained by ECG sensor module. In embedding patients' confidential data, we first consider a non-linear model for optimizing the quality of the embedded ECG signals. Next, we apply simulated annealing (SA) to solve the non-linear model so as to have good signal-to-noise ratio (SNR), root mean square error (RMSE), and relative RMSE (rRMSE). Accordingly, the distortion of the PQRST complexes and the ECG amplitude is very small so that the embedded confidential data can satisfy the requirements of physiological diagnostics. In end devices, one can receive the ECG signals with the embedded confidential data and without the original ECG signals. Experimental results confirm the effectiveness of our method, which remains high quality for each ECG signal with the embedded confidential data no matter how the quantization size Q is increased.