Search Results (14)

This study investigates the integration, usability, and learning patterns associated with voice assistant technology among older adults, focusing on the “Amazon Echo Show 10, 3rd generation” as a case study. Conducted with 32 participants aged 55 and above in senior and complementary households, this research employs a mixed-method approach, incorporating qualitative interviews and quantitative voice command logging over a twelve-week period. Our findings reveal a high level of learnability and usability of the voice assistant, with 90% of participants finding the device easy to learn and use. The study further explores the patterns of voice assistant use, highlighting a preference for listening to music and seeking information, predominantly on weekends. Despite initial reservations, participants reported a high satisfaction level, with most not feeling monitored by the device. Key recommendations for manufacturers include prioritizing the design and user experience to cater to older adults’ needs, aiming to enhance their digital inclusion and participation. This study contributes to the human–computer interaction (HCI) field by providing insights into older adults’ interactions with voice assistant technology, emphasizing the importance of designing accessible and user-friendly digital solutions for the aging population. Full article

Frequency diversity array–multiple-input multiple-output (FDA-MIMO) radar realizes an angle- and range-dependent system model by adopting a slight frequency offset between adjacent transmitter sensors, thereby enabling potential target localization. This paper presents FDA-MIMO radar-based rapid target localization via the reduction dimension root reconstructed multiple signal classification (RDRR-MUSIC) algorithm. Firstly, we reconstruct the two-dimensional (2D)-MUSIC spatial spectrum function using the reconstructed steering vector, which involves no coupling of direction of arrival (DOA) and range. Subsequently, the 2D spectrum peaks search (SPS) is converted into one-dimensional (1D) SPS to reduce the computational complexity using a reduction dimension transformation. Finally, we conduct polynomial root finding to further eliminate computational costs, in which DOA and range can be rapidly estimated without performance degradation. The simulation results validate the effectiveness and superiority of the proposed RDRR-MUSIC algorithm over the conventional 2D-MUSIC algorithm and reduced-dimension (RD)-MUSIC algorithm. Full article

How to enhance the desired signal with low signal-to-noise ratio (SNR) is a difficult problem in the estimation process of the direction-of-arrival (DOA) of the target scattering wave signal. In this paper, the feasibility of spatial spectrum estimation in the Range-Doppler (RD) domain is analyzed in principle, and the SNR gain expression of weak scattering wave signal is derived when constructing multi-snapshots virtual array data. On this basis, the mutual eigenvector singular value decomposition (MESVD) method based on RD domain mode excitation is proposed, which can robustly and effectively estimate the direction of the coherent weak signals. Simulation experiments verify that the RD domain spectral estimation method has the ability to simultaneously obtain the direction of multiple weak target scattering waves, and the direction-finding accuracy can reach the Cramer–Rao bound (CRB) of conventional spectral estimation method. The results of Monte Carlo experiments show that the root-mean-square-error (RMSE) of azimuth estimation of RD domain spatial spectrum estimation method is 5.76° lower than that of a conventional multiple signal classification (MUSIC) method. In addition, the practicability of the proposed method is demonstrated by comparing the DOA estimation results of a set of real data with Automatic Dependent Surveillance-Broadcast (ADS-B) data. Full article

This paper presents a new technique for estimating the two-dimensional direction of departure (2D-DOD) and direction of arrival (2D-DOA) in bistatic uniform planar array Multiple-Input Multiple-Output (MIMO) radar systems. The method is based on the reduced-dimension (RD) MUSIC algorithm, aiming to achieve improved precision and computational efficiency. Primarily, this pioneering approach efficiently transforms the four-dimensional (4D) estimation problem into two-dimensional (2D) searches, thus reducing the computational complexity typically associated with conventional MUSIC algorithms. Then, exploits the spatial diversity of array response vectors to construct a 4D spatial spectrum function, which is crucial in resolving the complex angular parameters of multiple simultaneous targets. Finally, the objective is to simplify the spatial spectrum to a 2D search within a 4D measurement space to achieve an optimal balance between efficiency and accuracy. Simulation results validate the effectiveness of our proposed algorithm compared to several existing approaches, demonstrating its robustness in accurately estimating 2D-DOD and 2D-DOA across various scenarios. The proposed technique shows significant computational savings and high-resolution estimations and maintains high precision, setting a new benchmark for future explorations in the field. Full article

Objective: The impact of listening to music on pain perception has been evaluated using questionnaires and numeric/visual analogue scales. In this study, the impact of music perception on sensory pain functions was measured by means of quantitative sensory testing. Methods: We enrolled 10 female and 10 male healthy subjects (10 of them were professional musicians). All subjects underwent, in total, four quantitative sensory testing measures (first: baseline; second: after pleasant music [Johannes Brahms, 3rd symphony, 3rd movement]; third: after unpleasant music [Krzysztof Penderecki, Threnos]; fourth: after a longer break). The pleasantness of music was evaluated using the Ertel differential scale. Results: After the participants listened to pleasant music, an increased sensitivity to cold stimuli (both threshold and pain), to mechanical stimuli (only for threshold), and to repeated stimuli (wind-up reaction) was noted. Listening to unpleasant music was not associated with changes in sensitivity. We did not observe any significant differences between male and female subjects or between musicians and non-musicians. There was no significant correlation between the rating of the music as pleasant/unpleasant and the different quantitative sensory testing measures. Conclusions: Our data show that listening to music inducing a pleasant feeling can increase the sensitivity to stimuli applied during a quantitative sensory testing session. This should be considered when performing or interpreting quantitative sensory testing examinations. Interestingly, this finding is in contrast to the observation that listening to music can decrease pain perception during painful procedures. Full article

In this paper, we introduce a Reduced-Dimension Multiple-Signal Classification (RD-MUSIC) technique via Higher-Order Orthogonal Iteration (HOOI), which facilitates the estimation of the target range and angle for Frequency-Diverse Array Multiple-Input–Multiple-Output (FDA-MIMO) radars in the unfolded coprime array with unfolded coprime frequency offsets (UCA-UCFO) structure. The received signal undergoes tensor decomposition by the HOOI algorithm to get the core and factor matrices, then the 2D spectral function is built. The Lagrange multiplier method is used to obtain a one-dimensional spectral function, reducing complexity for estimating the direction of arrival (DOA). The vector of the transmitter is obtained by the partial derivatives of the Lagrangian function, and its rotational invariance facilitates target range estimation. The method demonstrates improved operation speed and decreased computational complexity with respect to the classic Higher-Order Singular-Value Decomposition (HOSVD) technique, and its effectiveness and superiority are confirmed by numerical simulations. Full article

Traditional Angle of Arrival (AoA)-based WiFi array indoor localization algorithms do not fuse Channel State Information (CSI) inter-packet data for estimation, which makes WiFi arrays less effective for localization in complex indoor environments. Most algorithms are overburdened leading to inefficient localization. To address these issues, in this article, an indoor positioning algorithm based on Higher-Order Singular Value Decomposition (HOSVD) is proposed. First, the CSI data are reconstructed as a new measurement matrix by borrowing subcarriers, and a third-order tensor is constructed. Next, tensor compression techniques are used to reduce computational complexity and the signal subspace is obtained by HOSVD. Then, the AoA is obtained by the Reduced Dimension Multiple Signal Classification (RD-MUSIC) method. Finally, the coordinates of the target can be obtained by triangulating the AoAs of the three Access Points (APs). According to the simulation experiments, the AoA can be estimated accurately at a low SNR and with low snapshots. In practical experiments, we can successfully estimate the AoA in complex indoor environments with shorter timelines using HOSVD without modifications to commercial hardware and produce a lower AoA error and localization error rates compared to other algorithms. The effectiveness of our proposed algorithm is proven by simulations and practical experiments. Full article

This paper addresses the challenge of implementing Direction of Arrival (DOA) methods for indoor localization using Internet of Things (IoT) devices, particularly with the recent direction-finding capability of Bluetooth. DOA methods are complex numerical methods that require significant computational resources and can quickly deplete the batteries of small embedded systems typically found in IoT networks. To address this challenge, the paper presents a novel Unitary R-D Root MUSIC for L-shaped arrays that is tailor-made for such devices utilizing a switching protocol defined by Bluetooth. The solution exploits the radio communication system design to speed up execution, and its root-finding method circumvents complex arithmetic despite being used for complex polynomials. The paper carries out experiments on energy consumption, memory footprint, accuracy, and execution time in a commercial constrained embedded IoT device series without operating systems and software layers to prove the viability of the implemented solution. The results demonstrate that the solution achieves good accuracy and attains an execution time of a few milliseconds, making it a viable solution for DOA implementation in IoT devices. Full article

Crying is the only means of communication for a newborn baby with its surrounding environment, but it also provides significant information about the newborn’s health, emotions, and needs. The cries of newborn babies have long been known as a biomarker for the diagnosis of pathologies. However, to the best of our knowledge, exploring the discrimination of two pathology groups by means of cry signals is unprecedented. Therefore, this study aimed to identify septic newborns with Neonatal Respiratory Distress Syndrome (RDS) by employing the Machine Learning (ML) methods of Multilayer Perceptron (MLP) and Support Vector Machine (SVM). Furthermore, the cry signal was analyzed from the following two different perspectives: 1) the musical perspective by studying the spectral feature set of Harmonic Ratio (HR), and 2) the speech processing perspective using the short-term feature set of Gammatone Frequency Cepstral Coefficients (GFCCs). In order to assess the role of employing features from both short-term and spectral modalities in distinguishing the two pathology groups, they were fused in one feature set named the combined features. The hyperparameters (HPs) of the implemented ML approaches were fine-tuned to fit each experiment. Finally, by normalizing and fusing the features originating from the two modalities, the overall performance of the proposed design was improved across all evaluation measures, achieving accuracies of 92.49% and 95.3% by the MLP and SVM classifiers, respectively. The MLP classifier was outperformed in terms of all evaluation measures presented in this study, except for the Area Under Curve of Receiver Operator Characteristics (AUC-ROC), which signifies the ability of the proposed design in class separation. The achieved results highlighted the role of combining features from different levels and modalities for a more powerful analysis of the cry signals, as well as including a neural network (NN)-based classifier. Consequently, attaining a 95.3% accuracy for the separation of two entangled pathology groups of RDS and sepsis elucidated the promising potential for further studies with larger datasets and more pathology groups. Full article

To address the problem of expensive computation in traditional two-dimensional (2D) direction of arrival (DOA) estimation, in this paper, we propose a 2D DOA estimation method based on a reduced dimension and root-finding MUSIC algorithm for nested planar arrays (NPAs). Specifically, the algorithm proposed in this paper transforms the problem based on 2D spectral peak search into two one-dimensional estimation problems by reducing the dimension, and then transforms the one-dimensional estimation problem into a problem of polynomial root finding. Finally the parameters are paired to realize the 2D DOA estimation. The proposed algorithm not only performs two root finding operations directly according to the 2D spectral function transformation, avoiding the performance degradation caused by intermediate operations, but can also fully exploit the enlarged array aperture offered by NPAs with reduced computational complexity and no need for virtualization. The superiorities of the proposed algorithm in terms of estimation accuracy and complexity are verified by simulations. Full article

Three-dimensional (3-D) localization information, including elevation angle, azimuth angle, and range, is important for locating a single source with spherical wave-fronts. Aiming to reduce the high computational complexity of the classical 3-D multiple signal classification (3D-MUSIC) localization method, a novel low-complexity reduced-dimension MUSIC (RD-MUSIC) algorithm based on the sparse symmetric cross array (SSCA) is proposed in this article. The RD-MUSIC converts the 3-D exhaustive search into three one-dimensional (1-D) searches, where two of them are obtained by a two-stage reduced-dimension method to find the angles, and the remaining one is utilized to obtain the range. In addition, a detailed complexity analysis is provided. Simulation results demonstrate that the performance of the proposed algorithm is extremely close to that of the existing rank-reduced MUSIC (RARE-MUSIC) and 3D-MUSIC algorithms, whereas the complexity of the proposed method is significantly lower than that of the others, which is a big advantage in practice. Full article

In this paper, we present a new high-resolution algorithm for primary signal processing in High Frequency Surface Wave Radar (HFSWR). The algorithm has been developed to achieve and improve primary signal processing performance in existing HFSWR radars in terms of radar target detection. The proposed algorithm is based on a high-resolution estimate of the Range–Doppler (RD-HR) map using given number of frames in the selected integration period. RD-HR maps are formed at every antenna in receive antenna array. Target detection is based on an RD-HR map averaged across all the antennas. Azimuth estimation is performed by a high-resolution MUSIC-type algorithm that is executed for all detections we found in the RD-HR map. The existence of strong Bragg’s lines in the RD-HR map complicates the detection process but the contrast of the RD-HR map as well as the detectability of targets on the RD-HR map is significantly better compared to the RD-FFT map used by many existing radars, such as WERA. Full article

Young adults with normal hearing may exhibit risk factors for hearing loss. The purpose of this study was to evaluate how self-reported personal music (PM) system volume use, preferred listening level, and self-reported alcohol use affects distortion product otoacoustic emissions (DPOAEs). Two-hundred, sixteen young adults, 161 women and 55 men, participated. Questionnaire data included the PM system and alcohol use. DPOAEs were obtained from 1–6 kHz and collapsed into 1/3rd octave bands and a probe microphone was used to determine preferred listening level. Alcohol was defined as drinks per month (DPM), categorized as No, Light (≤14), and Heavy (>14). Men who reported loud/very loud volume use had statistically significant lower DPOAEs at 1.5, 2, and 3 kHz than men who reported lower volume use. Light and Heavy DPM men had lower DPOAEs at 1.5, 2, and 3 kHz than no DPM men, but this was not statistically significant. There were no DPOAE differences for either variable in women and there was no association between preferred listening level and DPOAEs for women or men. Men who reported loud/very loud volume use and any DPM had poorer mid-frequency DPOAEs. There was not an association for volume use or DPM and DPOAEs in women. Full article

The present work tries to reflect the student perception about their music education through the application of two tests in the classroom with university students at 3rd and 4th year of the Grade of Teacher at the Faculty of Education in the University of Valladolid. They have been realized at the beginning of the year the first one and at the end of the first semester of the academic year 2017/18. The first one concerns their previous formation in music (school and High school as well as formal/unformal music education—conservatories and schools of music - included their perception about the acquisition of basic music skins. The second test ask them specifically about their perception of improve of those skills (music language, tuning, playing instruments, aural education, didactic of music and use of ICT) after their formation in Higher Education with Leemúsica/Readmusic methodology. Full article