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The ever-increasing use of artificial intelligence (AI) to optimize embedding a watermark or find the optimal location in the host image for inserting a watermark might solve some problems in this field. However, the main problem, which is finding the optimum trade-off point among several watermarking criteria, has still not been investigated by researchers in this field, especially for speech signals. This paper aims to find the best trade-off among the watermarking requirements such as capacity, inaudibility, and robustness by applying an AI model. Moreover, a novel watermarking technique is proposed by modification of the probability density function (PDF) of the linear predictive (LP) residual and wavelet detail coefficient. For this method, a mathematical model is developed based on applying higher-order statistics for embedding and extracting the watermark. Sinh-arcsinh is used to shape the skewness and kurtosis of normal distribution for the LP residual or the wavelet high-frequency sub-bands, respectively, based on the watermark bits. Experimental results will show that although LP residual is not robust and it shows random behavior for modeling its PDF, the wavelet high-frequency band is quite robust and it can model the PDF of the wavelet. Moreover, it is demonstrated that AI has the capability to compromise among the watermarking criteria. Conclusions are drawn based on theoretical (maximum likelihood) and AI (machine learning) approaches, which confirm the effectiveness of the proposed model. Finally, in conclusion, several potential areas are discussed for further exploration. [ABSTRACT FROM AUTHOR]

The path planning strategies of multi-objective tasks for autonomous agents are applied more and more widely in the areas of resource exploration, factory transportation, post-disaster relief, etc. These tasks typically require access to all target points within a limited time and can be formulated by signal temporal logic (STL) formulas with multiple logic AND. This paper presents a dual-layer network deep reinforcement learning algorithm to address the decision problem of the multi-objective STL tasks. First, the multi-objective optimization problem is represented by iterable UNTIL operators, and each sub-task is encoded into an one-hot vector. In the control layer, one-hot vector is updated according to the ongoing sub-task. Based on the coupling input of agent states and one-hot vector, the neural network controller is trained to complete the sub-tasks in the given order strictly. In the order layer, robust-based weighted entropy is assigned according to the agent trajectory robust value of each sub-task. By maximizing the entropy, the order priority of access to the target points is updated periodicity and is passed into the control layer. Simulation and comparison with the traditional method show that our proposed dual-layer network algorithm has the reliability for strictly completing sub-tasks of multi-objective task reformed by UNTIL operators and the effectiveness of the access order planning for multi-objective tasks with improved real-time performance. [ABSTRACT FROM AUTHOR]

In this paper, a novel method is introduced to diagnose the cardiac arrhythmias. Automatic diagnosis of cardiac arrhythmias is crucial for successful treatment of heart diseases, and nowadays, machine learning is used to facilitate this purpose. For accurate classification of arrhythmias, it is vital to extract some appropriate features which distinguish different classes. In this research, a deep convolutional neural network is used to extract the features. Since the heart rates of various patients are very different, arrhythmias of a specific class will be placed in different locations of feature space. To reduce the intra-class variations, each patient's heart rate is mapped with a dedicated function to increase its similarity to the heart rate of a training patient. The proposed patient-specific mapping decreases the intra-class variation and significantly increases the classification accuracy of cardiac arrhythmias. The calculated accuracy is about 91.08%, which shows the better performance compared to other similar works. [ABSTRACT FROM AUTHOR]

This work investigates the significance of Hilbert domain characterization of wavelet packets in classifying different emotion of speech signal. The goal of this paper is to create a new emotional speech database and introduce a new feature extraction approach that can recognize various emotions. The proposed feature, wavelet cepstral coefficients (WCC) are based on Hilbert spectrum analysis of the wavelet packet of the speech signal. The speaker-independent machine learning models are developed using multiclass support vector machine (SVM) and k-nearest neighbourhood (KNN) classifier. The approach is tested with newly developed Telugu Indian database and the EMOVO (Italian emotional speech) database. Our proposed wavelet features achieve a peak accuracy of 73.5%, further boosted by NCA feature selection by 3–5%, resulting in an improved unweighted average recall (UAR) of 78% for database 1 and 87.50% for database 2, employing optimal wavelet features in conjunction with SVM classification. The proposed features outperformed the baseline Mel-frequency cepstral coefficients (MFCC) feature. The performance of newly formulated features is better than other existing methodologies tested with different language databases. [ABSTRACT FROM AUTHOR]

Several works report on overcoming the challenge of complex general-purpose signal processing algorithms that result in quick utilization of chip area resources (like FPGA in implementing system on chip or network on chip). However, with the advent of machine learning and deep learning algorithms, reducing this complexity and immense use of resources is a challenging task that can improve the system's performance in real time. One of the approaches is algorithm optimization, which consumes fewer resources and produces an equivalent performance. This approach may likely be accepted in less-sensitive applications, like voice or video processing. Recently, an optimized algorithm of correlation-less Wiener–Hopf-based adaptive channel equalizer is reported, in which the overall complexity of the system is reduced by employing a more compact way of weight optimization. This paper is the continuation of that work that reports the modification in the Steepest-Descent-based adaptive channel equalizer algorithm by keeping the autocorrelation matrix out of the algorithm. The proposed design is simulated and tested at equivalent spectral performance and produces comparable performance with fewer chip resources when translated on FPGA. The proposed design supports that algorithm-level optimization may be accepted extensively for the optimal hardware-based design of DSP systems. [ABSTRACT FROM AUTHOR]

This paper presents a least-mean-square-based (LMS-based) background calibration algorithm with reinforcement learning optimization to calibrate the capacitor mismatch in successive approximation-register (SAR) analog-to-digital converters (ADCs). When calibrating capacitor mismatch, the convergence speed and stability of the conventional LMS-based algorithm cannot be balanced due to the fixed iteration step size. To improve the calibration effect, nine different iteration step sizes are proposed for calibration. An iteration step size selection strategy is obtained by training the Q-learning algorithm with the conventional LMS-based calibration iteration process. Leading by this strategy, the proposed calibration algorithm performs better convergence speed and stability than the conventional LMS-based calibration algorithm. A 12-bit sub-radix-2 redundant SAR ADC is used to test our proposed calibration algorithm for better evaluation. Behavioral simulations show that the proposed calibration algorithm converges at 0.065 M sampling points, and the standard deviation of the effective number of bits (ENOB) in the convergence state is only 0.034-bit. [ABSTRACT FROM AUTHOR]

Sound source localization is a technique that utilizes microphone arrays to detect the position of sound sources. It has a wide range of applications in areas such as smart homes, robot navigation, and conference recording. However, due to the complexity of the acoustic environment and the impact of noise interference, the accuracy of localization algorithms has always been a core concern in this field. Traditional time difference of arrival (TDOA) techniques struggle to achieve high precision and efficiency in localization results. To address this issue, this paper proposes a microphone array sound source localization method based on incremental broad learning (Enhance) algorithm. This method extracts shallow and deep features from audio signals and maps them to feature nodes and enhancement nodes in the broad learning system (BLS); a neural network model is constructed, which allows for fast adjustment of network structure and parameters. The model employs ridge regression to calculate connection weights and utilizes enhancement nodes to modify and optimize the feature nodes, thus achieving accurate prediction of sound source locations. The proposed method is experimentally validated using the NOIZEUS dataset and compared with the single-structure broad learning (One-shot) algorithm, back-propagation neural network (BP) algorithm, and recurrent neural network (RNN) algorithm. In the experiments, a microphone array consisting of four microphones is used, with a room size of 5 m × 4 m × 3 m. Different reverberation times (T60) and signal-to-noise ratios (SNRs) are employed to simulate various acoustic environments, and the performance of the four algorithms is evaluated in terms of outlier percentage and mean-squared error (MSE). The results demonstrate that under high reverberation (T60 = 700 ms) and low SNR (SNR = 0 dB) conditions, the proposed method achieves outlier percentage of only 0.308% and MSE of 0.92°. Compared to the other three algorithms, Enhance algorithm exhibits superior localization accuracy, noise robustness, and stability, thus holding significant research and practical value. [ABSTRACT FROM AUTHOR]

In recent times, the use of chest X-ray (CXR) images in telemedicine has increased exponentially. However, the transfer of these images on an unsecured channel poses a serious threat to their authenticity and copyright protection. To address this problem, a chest X-ray image watermarking scheme (CXRmark) is presented in this paper using an online sequential reduced kernel extreme learning machine (OS-RKELM). U-Net is used to segment the lung area into the region of non-interest (RONI) and region of interest (ROI). An OS-RKELM is trained using the approximation coefficients (obtained by transforming training images using discrete wavelet transform) and the corresponding quantized coefficients. Subsequently, it is used to modulate the approximation coefficients. In order to avoid inadvertent deterioration in the ROI and to achieve high robustness, the proposed watermarking scheme uses different embedding strengths for ROI and RONI. Further, to enhance the security of the watermark, it has been encoded using the logistic chaotic map. To evaluate the performance of the proposed scheme, we experimented with a set of 461 CXR images from a publicly available repository: COVID-19 Image Data Collection. It has been demonstrated that CXRmark results in high perceptual quality of the signed CXRs and is robust to common interference. Further, the reversibility of the proposed CXRmark scheme is demonstrated by recovering the undistorted CXR image from the signed CXR. The comparison of the proposed scheme with state-of-the-art schemes shows that CXRmark outperforms its competitors in terms of imperceptibility and robustness. [ABSTRACT FROM AUTHOR]

Machine learning-based direction-of-arrival (DOA) estimation methods can have a good predictive ability even in complex scenarios. However, their estimation performance in the face of unknown data is poor because they rely on the generalization ability of the algorithm itself. Therefore, this paper proposes a DOA estimation method based on the Light Gradient Boosting Machine (LightGBM) algorithm. Using the histogram algorithm, gradient-based one-sided sampling and exclusive feature bundling measures, the LightGBM algorithm can reduce the time to find the best segmentation point, reduce the amount of data and the number of features in the dataset, thus reducing the model training time, and achieve high prediction accuracy. Applying the LightGBM algorithm to the DOA estimation problem and using a large dataset for training can improve the estimation accuracy while reducing the training cost. Simulation and real experimental results verify the effectiveness of the method. [ABSTRACT FROM AUTHOR]