Superdirective beamforming can achieve a narrower mainlobe width compared to conventional methods. However, its high sensitivity to errors and interferences limits its practical applications. This paper proposes an efficient implementation method for robust superdirective beamforming in linear arrays. First, a tridiagonal matrix is constructed based on the noise covariance matrix, and the optimal superdirective beam for the linear array is decomposed by exploiting the structural properties of Toeplitz matrices. The resulting eigenbeams exhibit different directivity and robustness. Subsequently, robust superdirective beampatterns of arbitrary orders are synthesized by selecting specific eigenbeams. Simulation results demonstrate that the proposed method achieves robust superdirectivity with a favorable trade-off between robustness and directivity.
This paper introduces an automated method for Japanese pitch-accent transcription that combines Whisper-derived hidden-state embeddings with neural network classifiers. Three architectures—multilayer perceptron (MLP), convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM)—are evaluated on corpora of Tokyo and Osaka dialects under identical preprocessing and modeling protocols. In within-dialect experiments, the MLP attains the best mora-level accuracy for Tokyo, whereas the CNN performs best for Osaka. A cross-dialect transfer condition is also tested in which all Tokyo data are used for training and all Osaka data for evaluation with an unchanged pipeline. Under this setting, the best configuration reaches 69.13% mora accuracy, 0.678 macro-F1, and 22.82% word-level accuracy, with the other configurations slightly lower. These findings indicate that cross-dialect transfer remains challenging without explicit adaptation, motivating future work on simple, reproducible strategies for target-dialect robustness.
Electroencephalography (EEG) signals contain rich person-specific information that can be used for biometric applications. This raises both opportunities and concerns in areas such as personalized health care and data privacy. This paper presents an approach for EEG based subject verification, where a transformer-based model learns to generate subject specific embeddings to later determine whether two EEG recordings belong to the same person or not. The proposed method generates subject-specific embeddings using pre-trained models and performs subject verification using a distance-based matching strategy with threshold tuning. To assess the models generalization, cross-session and cross-dataset experiments are conducted using two EEG datasets with different recording montages with one being made up of subjects with medical pathologies. Results indicate high performance even when trained and evaluated across different subject groups, and recording configurations, reaching accuracies above 80%. These findings show that identity-related information can be extracted from EEG signals independent of health status or recording protocols, while also highlighting the need for privacy preservation of EEG data to protect sensitive subject-specific information.
Intracytoplasmic Sperm Injection (ICSI) is a widely used assisted reproduction technique involving the precise injection of a single spermatozoon into a mature oocyte. One of the most critical and challenging steps in this process is the manual manipulation and positioning of the sperm inside the injection pipette, which is prone to human error, variability, and potential damage to biological material. This work proposes a novel automated system based on Artificial Intelligence (AI) techniques to optimize and control sperm movement and positioning inside the ICSI needle. The system employs real-time AI-driven control algorithms, including reinforcement learning strategies, to enable adaptive and precise manipulation of sperm cells. This approach reduces operator dependency, enhances reproducibility, and improves the accuracy of sperm aspiration and delivery. By integrating AI and automation, the system aims to minimize procedural variability, reduce the risk of cellular damage, and ultimately improve fertilization success rates. This development represents a significant advancement in the automation of micromanipulation tasks within assisted reproductive technologies, opening new avenues for improving clinical outcomes in infertility treatments.
This paper addresses the problem of centralized fusion estimation for signals in the quaternion domain, based on observations from multiple sensors. It is assumed that the observations from each sensor may be affected by possible Denial-of-Service (DoS) attacks, which can block the transmission of sensor data. In such cases, to mitigate these threats, a compensation strategy is employed, where the missing observation is replaced by its predictor, based on the information available up to the previous time instant. Additionally, the correlation between the additive noise of the state and the observations at the same time instant is considered. To combine information from multiple sensors, a centralized fusion method is used. Under C-properness conditions, a recursive centralized fusion filtering algorithm is proposed to obtain the optimal estimator and corresponding error variance, reducing the problem dimension by half and thus significantly decreasing the computational load. Numerical simulation examples are presented to demonstrate the effectiveness and advantages of the proposed algorithm.
