Image encryption is one of the essential tasks in image security. In this paper, we propose a novel approach that integrates a hyperchaotic system, pixel-level Dynamic Filtering, DNA computing, and operations on 3D Latin Cubes, namely DFDLC, for image encryption. Specifically, the approach consists of five stages: (1) a newly proposed 5D hyperchaotic system with two positive Lyapunov exponents is applied to generate a pseudorandom sequence; (2) for each pixel in an image, a filtering operation with different templates called dynamic filtering is conducted to diffuse the image; (3) DNA encoding is applied to the diffused image and then the DNA-level image is transformed into several 3D DNA-level cubes; (4) Latin cube is operated on each DNA-level cube; and (5) all the DNA cubes are integrated and decoded to a 2D cipher image. Extensive experiments are conducted on public testing images, and the results show that the proposed DFDLC can achieve state-of-the-art results in terms of several evaluation criteria.

Birds play a vital role in the study of ecosystems and biodiversity. Accurate bird identification helps monitor biodiversity, understand the functions of ecosystems, and develop effective conservation strategies. However, previous bird sound recognition methods often relied on single features and overlooked the spatial information associated with these features, leading to low accuracy. Recognizing this gap, the present study proposed a bird sound recognition method that employs multiple convolutional neural-based networks and a transformer encoder to provide a reliable solution for identifying and classifying birds based on their unique sounds. We manually extracted various acoustic features as model inputs, and feature fusion was applied to obtain the final set of feature vectors. Feature fusion combines the deep features extracted by various networks, resulting in a more comprehensive feature set, thereby improving recognition accuracy. The multiple integrated acoustic features, such as mel frequency cepstral coefficients (MFCC), chroma features (Chroma) and Tonnetz features, were encoded by a transformer encoder. The transformer encoder effectively extracted the positional relationships between bird sound features, resulting in enhanced recognition accuracy. The experimental results demonstrated the exceptional performance of our method with an accuracy of 97.99%, a recall of 96.14%, an F1 score of 96.88% and a precision of 97.97% on the Birdsdata dataset. Furthermore, our method achieved an accuracy of 93.18%, a recall of 92.43%, an F1 score of 93.14% and a precision of 93.25% on the Cornell Bird Challenge 2020 (CBC) dataset.

Korotkoff sounds are known to change their characteristics during blood pressure (BP) measurement, resulting in some uncertainties for systolic and diastolic pressure (SBP and DBP) determinations. The aim of this study was to assess the variation of Korotkoff sounds during BP measurement by examining all stethoscope sounds associated with each heartbeat from above systole to below diastole during linear cuff deflation. Three repeat BP measurements were taken from 140 healthy subjects (age 21 to 73 years; 62 female and 78 male) by a trained observer, giving 420 measurements. During the BP measurements, the cuff pressure and stethoscope signals were simultaneously recorded digitally to a computer for subsequent analysis. Heartbeats were identified from the oscillometric cuff pressure pulses. The presence of each beat was used to create a time window (1 s, 2000 samples) centered on the oscillometric pulse peak for extracting beat-by-beat stethoscope sounds. A time-frequency two-dimensional matrix was obtained for the stethoscope sounds associated with each beat, and all beats between the manually determined SBPs and DBPs were labeled as “Korotkoff.” A convolutional neural network was then used to analyze consistency in sound patterns that were associated with Korotkoff sounds. A 10fold cross-validation strategy was applied to the stethoscope sounds from all 140 subjects, with the data from ten groups of 14 subjects being analyzed separately, allowing consistency to be evaluated between groups. Next, withinsubject variation of the Korotkoff sounds analyzed from the three repeats was quantified, separately for each stethoscope sound beat. There was consistency between folds with no significant differences between groups of 14 subjects (P = 0.09 to P = 0.62). Our results showed that 80.7% beats at SBP and 69.5% at DBP were analyzed as Korotkoff sounds, with significant differences between adjacent beats at systole (13.1%, P = 0.001) and diastole (17.4%, P <; 0.001). Results reached stability for SBP (97.8%, at sixth beat below SBP) and DBP (98.1%, at sixth beat above DBP) with no significant differences between adjacent beats (SBP P = 0.74; DBP P = 0.88). There were no significant differences at highcuff pressures, but at low pressures close to diastole there was a small difference (3.3%, P = 0.02). In addition, greater within subject variability was observed at SBP (21.4%) and DBP (28.9%), with a significant difference between both (P <; 0.02). In conclusion, this study has demonstrated that Korotkoff sounds can be consistently identified during the period below SBP and above DBP, but that at systole and diastole there can be substantial variations that are associated with high variation in the three repeat measurements in each subject.

Tinnitus is a perception of sound when no external sound is present. It has seriously affected patients' life. Music is an option to relieve tinnitus in clinic, as it can bring enjoyment to listeners. However, existing music used in tinnitus therapies has limited duration, it is usually repetitively played during the long-term treatment and may not be helpful for relaxation. Moreover, individualized preferences of patients are ignored in most cases. Both of them may hinder tinnitus relief. Although existing methods can synthesize specific music that has unlimited duration and is not repetitively played, the synthesized music has defects with pitch mutations and long pitch durations. Moreover, characteristics of these synthesized music have not been confirmed by tinnitus patients. Therefore, this study presents an automatic method to develop the specific music based on music segments from existing music and long short term memory (LSTM). Numerical results indicate that specific music developed in this study not only retains characteristics of original music, but also overcome the defects above. Besides, a total of 30 tinnitus patients and 10 tinnitus-free volunteers participated the auditory experiment. Auditory results are consistent with numerical results and also suggest that tinnitus patients can perceive feelings that are conducive to tinnitus relief after listening preferred music. Therefore, the developed music presents a possible complement to tinnitus treatment in clinic.

Photoplethysmography (PPG) signal has been commonly used in detecting the peripheral blood volume pulse. This kind of optical-based technique is simple, rapid, low-cost and non-invasive. It can be used widely for different purposes. Many useful information can be obtained from the PPG signal, including heart rate, respiratory rate, pulse wave velocity (PWV) and so on. In order to obtain these information, feature points must be extracted first. In this paper eight healthy subjects were studied. One PPG probe was used to record the PPG signal from left ear and the ECG signal has also been recorded simultaneously. These signals were converted into digital signals and stored into computer for off-line analysis. An automatic algorithm based on wavelet approach has been developed to extract feature points from the PPG signal, then compare with the manual measurement. The result shows that the automatic algorithm provided an effective way for PPG signal feature extraction.