Background Noise Research Articles

A Randomized Crossover Study in Single-Sided Deafness Comparing a Cartilage Conduction CROS System and an Air-Conduction CROS System.

To investigate if cartilage conduction (CC) rerouting devices are noninferior to air-conduction (AC) rerouting devices for single-sided deafness (SSD) patients by measuring objective and subjective performance using speech-in-noise tests that resemble a realistic hearing environment, sound localization tests, and standardized questionnaires. Prospective, single-subject randomized, crossover study. Anechoic room inside a university. Nine adults between 21 and 58 years of age with severe or profound unilateral sensorineural hearing loss. Patients' baseline hearing was assessed; they then used both the cartilage conduction contralateral routing of signals device (CC-CROS) and an air-conduction CROS hearing aid (AC-CROS). Patients wore each device for 2 weeks in a randomly assigned order. Three main outcome measures were 1) speech-in-noise tests, measuring speech reception thresholds; 2) proportion of correct sound localization responses; and 3) scores on the questionnaires, "Abbreviated Profile of Hearing Aid Benefit" (APHAB) and "Speech, Spatial, and Qualities of Hearing Scale" with 12 questions (SSQ-12). Speech reception threshold improved significantly when noise was ambient, and speech was presented from the front or the poor-ear side with both CC-CROS and AC-CROS. When speech was delivered from the better-ear side, AC-CROS significantly improved performance, whereas CC-CROS had no significant effect. Both devices mainly worsened sound localization, whereas the APHAB and SSQ-12 scores showed benefits. CC-CROS has noninferior hearing-in-noise performance except when the speech was presented to the better ear under ambient noise. Subjective measures showed that the patients realized the effectiveness of both devices.

Audiologist’s perspective on newborn hearing screening in a developing nation: a questionnaire-based survey

Abstract Introduction Hearing is crucial for a child's development, influencing speech, cognition, and emotional well-being. Early detection of hearing disorders is vital, yet India faces challenges in implementing effective newborn hearing screening (NBHS) programs. This research explores the perspectives of audiologists, focusing on the major and minor challenges faced and current protocols followed across diverse setups in India. Method A two-phase study involved questionnaire development and data collection from 116 audiologists nationwide. The survey addressed demographics, current practices, challenges faced, and opinions on NBHS improvements. Results Findings revealed disparities in screening protocols, with 42 variations reported. Challenges included ambient noise control, communication gaps, and lack of interdisciplinary support. Audiologists emphasized the need for sensitizing healthcare professionals, free screening, and increased manpower to enhance NBHS efficacy. Conclusion The study aligns with global challenges in NBHS program implementation. Rural areas face a shortage of audiologists, emphasizing the necessity to train community health workers. The varied screening protocols and delays in screening timeframe underscore the need for standardized guidelines and increased funding. Audiologists play a pivotal role in NBHS success. To overcome challenges, standardizing protocols, sensitizing healthcare professionals, and promoting community-based screening are crucial. The study emphasizes proactive measures among audiologists, continual research, and collaboration to enhance evidence-based practices and policy improvements in newborn hearing screening.

Research on a Wind Turbine Gearbox Fault Diagnosis Method Using Singular Value Decomposition and Graph Fourier Transform

Gearboxes operate in challenging environments, which leads to a heightened incidence of failures, and ambient noise further compromises the accuracy of fault diagnosis. To address this issue, we introduce a fault diagnosis method that employs singular value decomposition (SVD) and graph Fourier transform (GFT). Singular values, commonly employed in feature extraction and fault diagnosis, effectively encapsulate various fault states of mechanical equipment. However, prior methods neglect the inter-relationships among singular values, resulting in the loss of subtle fault information concealed within. To precisely and effectively extract subtle fault information from gear vibration signals, this study incorporates graph signal processing (GSP) technology. Following SVD of the original vibration signal, the method constructs a graph signal using singular values as inputs, enabling the capture of topological relationships among these values and the extraction of concealed fault information. Subsequently, the graph signal undergoes a transformation via GFT, facilitating the extraction of fault features from the graph spectral domain. Ultimately, by assessing the Mahalanobis distance between training and testing samples, distinct defect states are discerned and diagnosed. Experimental results on bearing and gear faults demonstrate that the proposed method exhibits enhanced robustness to noise, enabling accurate and effective diagnosis of gearbox faults in environments with substantial noise.

Improved multi-layer wavelet transform and blind source separation based ECG artifacts removal algorithm from the sEMG signal: in the case of upper limbs

Introduction: Surface electromyogram (sEMG) signals have been widely used in human upper limb force estimation and motion intention recognition. However, the electrocardiogram(ECG) artifact generated by the beating of the heart is a major factor that reduces the quality of the EMG signal when recording the sEMG signal from the muscle close to the heart. sEMG signals contaminated by ECG artifacts are difficult to be understood correctly. The objective of this paper is to effectively remove ECG artifacts from sEMG signals by a novel method.Methods: In this paper, sEMG and ECG signals of the biceps brachii, brachialis, and triceps muscle of the human upper limb will be collected respectively. Firstly, an improved multi-layer wavelet transform algorithm is used to preprocess the raw sEMG signal to remove the background noise and power frequency interference in the raw signal. Then, based on the theory of blind source separation analysis, an improved Fast-ICA algorithm was constructed to separate the denoising signals. Finally, an ECG discrimination algorithm was used to find and eliminate ECG signals in sEMG signals. This method consists of the following steps: 1) Acquisition of raw sEMG and ECG signals; 2) Decoupling the raw sEMG signal; 3) Fast-ICA-based signal component separation; 4) ECG artifact recognition and elimination.Results and discussion: The experimental results show that our method has a good effect on removing ECG artifacts from contaminated EMG signals. It can further improve the quality of EMG signals, which is of great significance for improving the accuracy of force estimation and motion intention recognition tasks. Compared with other state-of-the-art methods, our method can also provide the guiding significance for other biological signals.

Anthropogenic disturbance affects calling and collective behaviour in corvid roosts.

Acoustic communication plays an important role in coordinating group dynamics and collective movements across a range of taxa. However, anthropogenic disturbance can inhibit the production or reception of acoustic signals. Here, we investigate the effects of noise and light pollution on the calling and collective behaviour of wild jackdaws (Corvus monedula), a highly social corvid species that uses vocalizations to coordinate collective movements at winter roosting sites. Using audio and video monitoring of roosts in areas with differing degrees of urbanization, we evaluate the influence of anthropogenic disturbance on vocalizations and collective movements. We found that when levels of background noise were higher, jackdaws took longer to settle following arrival at the roost in the evening and also called more during the night, suggesting that human disturbance may cause sleep disruption. High levels of overnight calling were, in turn, linked to disruption of vocal consensus decision-making and less cohesive group departures in the morning. These results raise the possibility that, by affecting cognitive and perceptual processes, human activities may interfere with animals' ability to coordinate collective behaviour. Understanding links between anthropogenic disturbance, communication, cognition and collective behaviour must be an important research priority in our increasingly urbanized world. This article is part of the theme issue 'The power of sound: unravelling how acoustic communication shapes group dynamics'.

PSO-optimized SSLMS adaptive filter for signal denoising of rolling bearings under small sample condition

Abstract To address the issue that the deep learning-based denoising algorithms can hardly effectively eliminate the background noise under small sample data condition, this paper proposes a new denoising method based on spectral subtraction (SS) and least mean square (LMS) adaptive filtering algorithms. To achieve the adaptive selection for the parameters of SS and LMS algorithms, particle swarm optimization (PSO) approach is employed to search and optimize the parameters in the two algorithms, which is helpful for the two algorithms to play an important role in eliminating the noise components with the different properties. Subsequently, the SS algorithm and the LMS algorithm are appropriately combined, and the SS-processed signal is input into the LMS algorithm as a desired signal to actualize the LMS adaptive filtering function. In this way, the denoising performance of both algorithms can be maximally utilized, which achieves effective noise reduction in vibration signal. The effectiveness and superiority of the proposed method are validated through simulation data and rolling bearing experiment data, respectively. The results demonstrate that the proposed method significantly diminishes noise components and retains precise and reliable fault features under small sample data condition, which provides an effective denoising method for rolling bearing vibration signals under small sample data condition in practical engineering scenarios.

FDS-MOMEDA: Optimization-Blind Deconvolution in Finite High-Dimensional Spaces for Extracting Pulse Signal in Rolling Bearing Fault Diagnosis

Abstract Rolling bearing fault diagnosis is crucial for ensuring the safe and reliable operation of mechanical equipment. Detecting faults directly from measurement signals is challenging due to severe noise and interference. Blind deconvolution, as a preferred method, effectively recovers periodic pulses from the measured vibration signals of faulty bearings. This study introduces a simulated annealing-based blind deconvolution approach to enhance the pulse signal components reflecting faults in vibration signals measured on rolling bearings. This method iteratively searches for the optimal coordinates in a high-dimensional orthogonal optimization space, where the optimal coordinates reflect the combination of the inverse filter coefficients. Compared to the generalized spherical optimization space used in the “Optimization-Blind Deconvolution” method in previous works, the proposed finite high-dimensional optimization space helps overcome the problem of inverse filter coefficient convergence, allowing for the design of inverse filters without limit of its shape. To better accommodate the cyclostationarity characteristics of bearing signal measured in reality, the proposed method employs a target vector that allows for uncertainty in pulse occurrence moments, thus overcomes challenges introduced by pseudo-periodic phenomena resulting from bearing slippage. Numerical simulations and experimental results on real bearing vibration signals confirm that the proposed method can design more flexible filters to enhance pulse-like patterns in signals, effectively utilize limited filter resources. Its capacity to tolerate inaccurate fault period estimates, high background noise, and pulse randomness enables it to effectively address vibration measurement signals in real-world scenarios.

Automatic detection of cardiac conditions from photos of electrocardiogram captured by smartphones

BackgroundResearchers have developed machine learning-based ECG diagnostic algorithms that match or even surpass cardiologist level of performance. However, most of them cannot be used in real-world, as older generation ECG...

Improving Polyp Segmentation with Boundary-Assisted Guidance and Cross-Scale Interaction Fusion Transformer Network

Efficient and precise colorectal polyp segmentation has significant implications for screening colorectal polyps. Although network variants derived from the Transformer network have high accuracy in segmenting colorectal polyps with complex shapes, they have two main shortcomings: (1) multi-level semantic information at the output of the encoder may result in information loss during the fusion process and (2) failure to adequately suppress background noise during segmentation. To address these challenges, we propose a cross-scale interaction fusion transformer for polyp segmentation (CIFFormer). Firstly, a novel feature supplement module (FSM) supplements the missing details and explores potential features to enhance the feature representations. Additionally, to mitigate the interference of background noise, we designed a cross-scale interactive fusion module (CIFM) that combines feature information between different layers to obtain more multi-scale and discriminative representative features. Furthermore, a boundary-assisted guidance module (BGM) is proposed to help the segmentation network obtain boundary-enhanced details. Extensive experiments on five typical datasets have demonstrated that CIFFormer has an obvious advantage in segmenting polyps. Specifically, CIFFormer achieved an mDice of 0.925 and an mIoU of 0.875 on the Kvasir-SEG dataset, achieving superior segmentation accuracy to competing methods.

Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation

Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications.

Evaluating benefits of remote microphone technology for adults with hearing loss using behavioural and predictive metrics

Objective To investigate the benefit of remote-microphone (RM) systems for adults with sensory hearing loss. Design Speech recognition in quiet and in background noise was assessed. Participants with hearing loss underwent testing in two device conditions: hearing aids (HAs) alone and HAs with a RM. Normal hearing participants completed testing in the unaided condition. Predictive speech intelligibility modelling using the Hearing-Aid Speech Perception Index (HASPI) was also performed on recordings of HA processed test material. Study Sample Twenty adults with sensory hearing loss and 10 adults with normal hearing participated. Results Speech recognition for participants with hearing loss improved significantly when using the RM compared to HAs alone fit to Phonak’s proprietary prescription. Largest benefits were observed in the most challenging conditions. At the lowest signal-to-noise ratio, participants with hearing loss using a RM outperformed normal hearing listeners. Predicted intelligibility scores produced by HASPI were strongly correlated to behavioural results. Conclusions Adults using HAs who have significant difficulties understanding speech in noise will experience considerable benefits with the addition of a RM. Improvements in speech recognition were observed for all participants using RM systems, including those with relatively mild hearing loss. HASPI modelling reliably predicted the speech perception difficulties experienced.

GGMNet: Pavement-Crack Detection Based on Global Context Awareness and Multi-Scale Fusion

Accurate and comprehensive detection of pavement cracks is important for maintaining road quality and ensuring traffic safety. However, the complexity of road surfaces and the diversity of cracks make it difficult for existing methods to accomplish this challenging task. This paper proposes a novel network named the global graph multiscale network (GGMNet) for automated pixel-level detection of pavement cracks. The GGMNet network has several innovations compared with the mainstream road crack detection network: (1) a global contextual Res-block (GC-Resblock) is proposed to guide the network to emphasize the identities of cracks while suppressing background noises; (2) a graph pyramid pooling module (GPPM) is designed to aggregate the multi-scale features and capture the long-range dependencies of cracks; (3) a multi-scale features fusion module (MFF) is established to efficiently represent and deeply fuse multi-scale features. We carried out extensive experiments on three pavement crack datasets. These were DeepCrack dataset, with complex background noises; the CrackTree260 dataset, with various crack structures; and the Aerial Track Detection dataset, with a drone’s perspective. The experimental results demonstrate that GGMNet has excellent performance, high accuracy, and strong robustness. In conclusion, this paper provides support for accurate and timely road maintenance and has important reference values and enlightening implications for further linear feature extraction research.

Age-related differences in processing of emotions in speech disappear with babble noise in the background

ABSTRACT Older adults process emotional speech differently than young adults, relying less on prosody (tone) relative to semantics (words). This study aimed to elucidate the mechanisms underlying these age-related differences via an emotional speech-in-noise test. A sample of 51 young and 47 older adults rated spoken sentences with emotional content on both prosody and semantics, presented on the background of wideband speech-spectrum noise (sensory interference) or on the background of multi-talker babble (sensory/cognitive interference). The presence of wideband noise eliminated age-related differences in semantics but not in prosody when processing emotional speech. Conversely, the presence of babble resulted in the elimination of age-related differences across all measures. The results suggest that both sensory and cognitive-linguistic factors contribute to age-related changes in emotional speech processing. Because real world conditions typically involve noisy background, our results highlight the importance of testing under such conditions.

Relative Seismic Velocity Variations at Axial Seamount Observed With Ambient Seismic Noise Capture Transition Point in Volcanic Inflation

AbstractTemporal changes in seismic velocity estimated from ambient seismic noise can be utilized to infer subsurface properties at volcanic systems. In this study, we process 7 years of continuous seismic noise at Axial Seamount and use cross‐correlation functions to calculate the relative seismic velocity changes (dv/v) beneath the caldera. We find a long‐term trend of decreasing velocity during rapid inflation, followed by slight increase in velocities as background seismicity increases and inflation rate decreases. Furthermore, we observe small short‐term increases in dv/v which coincide with short‐term deflation events. Our observations of changes in dv/v and their correlation with other geophysical data provide insights into how the top ∼1 km of the crust at Axial Seamount changes in response to subsurface magma movement and capture the transition from a period of rapid reinflation to a period where the caldera wall faults become critically stressed and must rupture to accommodate further inflation.

Enhanced Underwater Single Vector-Acoustic DOA Estimation via Linear Matched Stochastic Resonance Preprocessing

Underwater acoustic vector sensors (UAVSs) are increasingly utilized for remote passive sonar detection, but the accuracy of direction-of-arrival (DOA) estimation remains a challenging problem, particularly under low signal-to-noise ratio (SNR) conditions and complex background noise. In this paper, a comprehensive theoretical analysis is conducted on UAVS signal preprocessing subjected to gain-phase uncertainties for average acoustic intensity measurement (AAIM) and complex acoustic intensity measurement (CAIM)-based vector DOA estimation, aiming to explain the theoretical restrictions of intensity-based vector acoustic preprocessing approaches. On this basis, a generalized vector acoustic preprocessing optimization model is established in which the principle can be described as “maximizing the denoising performance under the constraints of an equivalent amplitude-gain response and phase-bias response”. A novel vector acoustic preprocessing method named linear matched stochastic resonance (LMSR) is proposed within the framework of matched stochastic resonance theory, which can naturally guarantee the linear gain-phase restrictions, as well achieving effective denoising performance. Numerical analyses demonstrate the superior vector DOA estimation performance of our proposed LMSR-AAIM and LMSR-CAIM methods in comparison to classical intensity-based AAIM and CAIM methods, especially under low-SNR conditions and non-Gaussian impulsive noise circumstances. Experimental verification conducted in the South China Sea further verifies its the effectiveness for practical application. This work can lay a solid foundation to break through the challenges of underwater remote vector acoustic DOA estimation under low-SNR conditions and complex ocean ambient noise and can provide important guidance for future research work.

Power System Signal-Detection Method Based on the Accelerated Unsaturated Stochastic Resonance Principle

The classical bistable stochastic resonance algorithm has an inherent output saturation defect that restricts the amplitude of the output signal. This paper examines the causes of this phenomenon and its negative impact on the detection of weak signals. Proposing the Unsaturated Bistable Stochastic Resonance (UBSR) detection algorithm involves constructing a segmented potential function using a linear function to eliminate the effect of higher-order terms in the classical stochastic resonance algorithm. A new type of segmented potential function has been created by combining exponential and linear functions. This new function helps to eliminate the impact of higher-order terms in classical algorithms while also improving the noise immunity of the stochastic resonance system. This results in the development of the accelerated stochastic resonance (ASR) detection algorithm. In this paper, the Kramers escape rate and output signal-to-noise ratio of two improved stochastic resonance algorithms are theoretically derived and compared with the classical bistable stochastic resonance algorithms, and the proposed algorithms are able to effectively avoid the output saturation phenomenon and have more excellent detection performance under strong background noise.

In-situ co-creative soundscape augmentation with fountains: Balancing sound preference and road traffic noise masking in urban parks

In this work, a co-creative in-situ methodology is presented augmenting the sonic environment of a highway noise dominated urban park through the incorporation of fountains. Pre-recorded water feature sounds were played with a flexible loudspeaker setup positioned at the pond’s bank. This simplified approach to fountain auralization enabled the integration of water feature sound scenarios with the actual traffic noise background during a representative moment in the park, at a design spot. In a first phase, the (pure) water sounds were evaluated by a consciously listening panel (N = 33, mainly consisting of nearby dwellers) at a high signal-to-noise ratio relative to the park’s background noise. The most preferred water feature sounds were identified and were further analyzed for their perceived affective quality using soundscape dimensional analysis. The second part of the listening experiment concentrated on the most favored fountain sounds in conjunction with the necessary aerators. This involved exploring various positioning scenarios in the pond by tuning the loudspeakers to predicted exposure levels. The most pleasant augmented soundscape for the park was shown to be a delicate balance between preference for specific fountain sounds and the reduction of road traffic audibility.

Into the red: An M-band study of the chemistry and rotation of β Pictoris b at high spectral resolution

Abstract High-resolution cross-correlation spectroscopy (HRCCS) combined with adaptive optics has been enormously successful in advancing our knowledge of exoplanet atmospheres, from chemistry to rotation and atmospheric dynamics. This powerful technique now drives major science cases for ELT instrumentation including METIS/ELT, GMTNIRS/GMT and MICHI/TMT, targeting biosignatures on rocky planets at 3–5 μm, but remains untested beyond 3.5 μm where the sky thermal background begins to provide the dominant contribution to the noise. We present 3.51–5.21 μm M-band CRIRES+/VLT observations of the archetypal young directly imaged gas giant β Pictoris b, detecting CO absorption at S/N = 6.6 at 4.73 μm and H2O at S/N = 5.7, and thus extending the use of HRCCS into the thermal background noise dominated infrared. Using this novel spectral range to search for more diverse chemistry we report marginal evidence of SiO at S/N = 4.3, potentially indicative that previously proposed magnesium-silicate clouds in the atmosphere are either patchy, transparent at M-band wavelengths, or possibly absent on the planetary hemisphere observed. The molecular detections are rotationally broadened by the spin of β Pic b, and we infer a planetary rotation velocity of vsin(i) = 22 ± 2 km s−1 from the cross-correlation with the H2O model template, consistent with previous K-band studies. We discuss the observational challenges posed by the thermal background and telluric contamination in the M-band, the custom analysis procedures required to mitigate these issues, and the opportunities to exploit this new infrared window for HRCCS using existing and next-generation instrumentation.

A feasibility study of Boree Salt body mapping in the Adavale Basin using passive seismic data

Hydrogen plays a pivotal role in the global energy transition and may require underground storage. So far salt cavern storage is the only proven technology for underground hydrogen storage. The Boree Salt in the Adavale Basin, mostly at depths from 1 to 2.5 km and up to 550 m thick, consists predominantly of halite and is deemed suitable for hydrogen storage. However, current maps are inadequate. Recently passive seismic data (ambient noise) have received much interest for subsurface imaging. The main signal from passive data is surface waves (usually below 2 Hz). The capability of surface waves for the Boree Salt body mapping is examined. Parameters of seismic sensor spacing, the dominant frequencies of the surface waves, and data noise levels are all considered. It is demonstrated that surface waves from ambient noise can map the Boree Salt bodies with a survey distance of ~40 km. Between frequencies of 0.12 and 0.25 Hz, results from the latter have better resolution because of a shorter wavelength. Moving to higher frequencies of 0.5 and 1 Hz, however, the resolution becomes worse, because the depth sensitivity of surface waves moves to the shallower part of the model with increasing frequencies, rendering them incapable of effectively probing the targeted depths. For signal/noise ratio above five, station spacing can be as large as 1 km without compromising quality. Therefore, cost-effective and environmentally friendly passive seismic data can be a good alternative to the traditional active-source data for deep salt body imaging.

Modeling and Sentiment Analysis of Social Relationships in Elderly Smart Homes Based on Graph Neural Networks

With the expansion of Speech Emotion Recognition in the consumer domain, several devices, particularly those designed for managing smart home personal assistants for the elderly, have been widely available on the market. The increasing processing power and connection, together with the growing need to facilitate longer residency through technological interventions, highlight the potential benefits of smart home assistants. Enabling these assistants to recognize human emotions would greatly improve user-assistant communication, allowing the assistant to deliver more constructive and customized feedback to the user. In this research work, Modeling and Sentiment Analysis of Social Relationships in Elderly Smart Homes Based on Graph Neural Networks (SASR-MBHNN-BBOA) is proposed. The input data are collected from Social Recommendation Dataset. Then, input data are pre-processed utilizing Inverse Optimal Safety Filters (IOSF) for cleaning the data and removing the background noise. Then the pre-processed data are given to Memristive Bi-neuron Hopfield Neural Network (MBHNN) for predicting the sentiments like positive, negative and neutral. In general, MBHNN doesn’t express some adoption of optimization approaches for determining optimal parameters to predicting the sentiments accurately. Hence BBOA is proposed to optimize MBHNN classifier which precisely predicts the sentiments in elderly smart home. The proposed SASR-MBHNN-BBOA method is implemented in Python, and it assessed with numerous performance metrics such as accuracy, precision, recall, F1-score, ROC. The outcomes show SASR-MBHNN-BBOA attains 20.8%, 19.5%, and 29.6% higher Accuracy, 28.8%, 22.5%, and 32.6% higher Precision, 15.5%, 27.4%, and 18.2% higher Recall are analysed with existing methods such as, Emotional speech analysis in real time for smart home assistants.(SASR-CNN-SHA), Machine Learning to Investigate Elderly Care Requirements in China via the Lens of Family Caregivers (SASR-ML-IECR),Identifying User Emotions via Audio Conversations with Smart Assistants (SASR-DNN-EASA) methods respectively.