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Adaptive Deep Brain Stimulation (aDBS) offers the potential for personalized stimulation strategies for patients with Parkinson's Disease (PD). The closed loop characteristic of this system requires the incorporation of PD relevant biomarkers that determine the patient's need. In order to obtain high quality LFP (Local Field Potential) input signals, the ECG (electrocardiogram) noise should be suppressed. The aim of this project is to study the performance of various algorithmic ECG noise suppression methods. Out of the ADAPT-PD trial, we have taken 9 LFP channels with consistent ECG artefacts for exploring the performance of ECG noise suppression models. As a reference point for filtering performance, we have used survey data (DBS-OFF). Using an externally measured ECG as reference, we have implemented two Adaptive NLMS (Normalized Least Mean Squared) Noise Cancellation algorithms. For the first version, we have used stimulation ramping alone for synchronization of the data sets. The second version includes an extension that aims to improve only the data synchronization feature. Furthermore, we have explored the ECG noise suppression performance of a proposed template subtraction method, using 11 different variations of epoch length. For improved analysis, we have used three data sets, namely personal (#1), patient group (#9) and simulated (#5346) data, using the Perceive toolbox ECG filtering method as the benchmark. Simulated LFPs are based on survey data combined with 9 external ECGs in 11 levels of contamination (100-1100 %). We have conducted analysis in both the time and frequency domain (beta-range 13-30 Hz), in order to estimate the absolute difference from the reference survey. Outcomes in the frequency domain show that, for personal performance, template subtraction tweaking provides an improvement up to 37.6 % over the Perceive toolbox. Furthermore, the outcomes show that, for both the patient and simulation group, optimal performance is obtained using t, BioMedical Engineering

Adaptive Deep Brain Stimulation (aDBS) offers the potential for personalized stimulation strategies for patients with Parkinson's Disease (PD). The closed loop characteristic of this system requires the incorporation of PD relevant biomarkers that determine the patient's need. In order to obtain high quality LFP (Local Field Potential) input signals, the ECG (electrocardiogram) noise should be suppressed. The aim of this project is to study the performance of various algorithmic ECG noise suppression methods. Out of the ADAPT-PD trial, we have taken 9 LFP channels with consistent ECG artefacts for exploring the performance of ECG noise suppression models. As a reference point for filtering performance, we have used survey data (DBS-OFF). Using an externally measured ECG as reference, we have implemented two Adaptive NLMS (Normalized Least Mean Squared) Noise Cancellation algorithms. For the first version, we have used stimulation ramping alone for synchronization of the data sets. The second version includes an extension that aims to improve only the data synchronization feature. Furthermore, we have explored the ECG noise suppression performance of a proposed template subtraction method, using 11 different variations of epoch length. For improved analysis, we have used three data sets, namely personal (#1), patient group (#9) and simulated (#5346) data, using the Perceive toolbox ECG filtering method as the benchmark. Simulated LFPs are based on survey data combined with 9 external ECGs in 11 levels of contamination (100-1100 %). We have conducted analysis in both the time and frequency domain (beta-range 13-30 Hz), in order to estimate the absolute difference from the reference survey. Outcomes in the frequency domain show that, for personal performance, template subtraction tweaking provides an improvement up to 37.6 % over the Perceive toolbox. Furthermore, the outcomes show that, for both the patient and simulation group, optimal performance is obtained using t, Biomedical Engineering

The problem of estimating underlying signals is well studied that has ubiquitous applications, such as signal processing. In network and image analysis, the tasks of signal estimation usually involve denoising a matrix based on structural information. In the situations where the information structure could be modeled by a network, graph trend filtering is a useful estimator with penalizing the $\ell_1$ norm of discrete derivatives based on some graph structures. A special case of graph trend filtering, based on penalizing an incidence matrix of a graph, is called graph fused lasso. In this dissertation, we proposed power graph fused lasso, an approach to denoising a response matrix with a graph, where both rows and columns of the matrix correspond to the vertices in the graph. There are two categories of graph fused lasso algorithms, solving the problem with a fixed hyperparameter and path algorithms solving for every hyperparameter. An overview of projected Newton-type method for graph fused lasso and ADMM-based graph trend filtering algorithms was provided. Ensemble graph fused lasso algorithms were proposed with some numerical results presented. We further discussed graph trend filtering with Kullback-Leibler divergence loss and how to solve the problem with a Bregman ADMM-based algorithm. We demonstrated the desired property that the GTF reconstructed signal is piecewise polynomial with an application to Covid-19 test positive data. Two other applications were also presented.

This research proposes a new differentiator for estimating higher order derivatives of an input signal. The main reason why higher order derivatives are necessary is that Active Inference makes use of generalized coordinates. This means that it keeps internally track of higher order temporal derivatives of states, inputs and measurements. The difficult part of generalized coordinates are the generalized measurements, because these should be measured from a physical system. However, it is not always possible to measure all states of a system and it is definitely not possible to measure all higher order derivatives. A solution to this is to estimate the derivatives of states by using real time differentiators. A short literature study about differentiators is conducted and found that the state of the art differentiator is the algebraic estimation approach differentiator (AEAD). However, this and other differentiators have the problem that when they are converted to discrete time, they cannot track polynomial signals anymore. For that reason, this thesis proposes a new differentiator: the recurrent low pass algebraic differentiator (RLPAD). The proposed differentiator is compared with the (AEAD) in two experiments. The first experiment evaluates the performance based on three analytical inputs with known higher order derivatives. The three inputs are: a polynomial, a sine and a combination of the two. Additionally these three inputs are corrupted by Gaussian white noise. This experiment concludes that the proposed method outperforms the (AEAD) significantly when a polynomial or polynomial combined with sine input is used. They perform similar for sine inputs, although (RLPAD) is considered slightly better. The second experiment evaluates how the two differentiators perform when real sensor data is used. In order to conduct the experiment, the raw data is smoothed by a Savitzky-Golay filter to create a ground truth about the derivatives. T, Mechanical Engineering | Biomechanical Design - BioRobotics

Various improvements which have been made to the vehicle (reduced engine noise, reducedaerodynamic related NVH), have resulted in tire road noise as the dominant source of thevehicle interior noise. Generally, vehicle interior noise has two main sources, 1) travellinglow frequency excitation below 800 Hz from road surface through a structure- borne pathand 2) the high frequency (above 800 Hz) air-borne noise that is caused by air- pumpingnoise caused by tread pattern.The structure-borne waves of the circumference of the tire are generated by excitation atthe contact patch due to the road surface texture and characteristics. These vibrations arethen transferred from the sidewalls of the tire to the rim and then are transmitted throughthe spindle-wheel interface, resulting in high frequency vibration of vehicle body panels andwindows.The focus of this study is to develop several statistical-based models for analyzing the roadsurface and using them to predict the tire-road noise structure-borne component. In order todo this, a new methodology for sensing the road characteristics, such as asperities and roadsurface condition, were developed using virtual sensing and intelligent tire technology. In ad-dition, the spindle forces were used as an indicator to the structure-borne noise of the vehicle.Several data mining and multivariate analysis-based methods were developed to extractfeatures and to develop an empirical model to predict the power of structure-borne noiseunder different operational and road conditions. Finally, multiple data driven models-basedmodels were developed to classify the road types, and conditions and use them for the noisefrequency spectrum prediction.

The problem of estimating underlying signals is well studied that has ubiquitous applications, such as signal processing. In network and image analysis, the tasks of signal estimation usually involve denoising a matrix based on structural information. In the situations where the information structure could be modeled by a network, graph trend filtering is a useful estimator with penalizing the $\ell_1$ norm of discrete derivatives based on some graph structures. A special case of graph trend filtering, based on penalizing an incidence matrix of a graph, is called graph fused lasso. In this dissertation, we proposed power graph fused lasso, an approach to denoising a response matrix with a graph, where both rows and columns of the matrix correspond to the vertices in the graph. There are two categories of graph fused lasso algorithms, solving the problem with a fixed hyperparameter and path algorithms solving for every hyperparameter. An overview of projected Newton-type method for graph fused lasso and ADMM-based graph trend filtering algorithms was provided. Ensemble graph fused lasso algorithms were proposed with some numerical results presented. We further discussed graph trend filtering with Kullback-Leibler divergence loss and how to solve the problem with a Bregman ADMM-based algorithm. We demonstrated the desired property that the GTF reconstructed signal is piecewise polynomial with an application to Covid-19 test positive data. Two other applications were also presented.

Various improvements which have been made to the vehicle (reduced engine noise, reducedaerodynamic related NVH), have resulted in tire road noise as the dominant source of thevehicle interior noise. Generally, vehicle interior noise has two main sources, 1) travellinglow frequency excitation below 800 Hz from road surface through a structure- borne pathand 2) the high frequency (above 800 Hz) air-borne noise that is caused by air- pumpingnoise caused by tread pattern.The structure-borne waves of the circumference of the tire are generated by excitation atthe contact patch due to the road surface texture and characteristics. These vibrations arethen transferred from the sidewalls of the tire to the rim and then are transmitted throughthe spindle-wheel interface, resulting in high frequency vibration of vehicle body panels andwindows.The focus of this study is to develop several statistical-based models for analyzing the roadsurface and using them to predict the tire-road noise structure-borne component. In order todo this, a new methodology for sensing the road characteristics, such as asperities and roadsurface condition, were developed using virtual sensing and intelligent tire technology. In ad-dition, the spindle forces were used as an indicator to the structure-borne noise of the vehicle.Several data mining and multivariate analysis-based methods were developed to extractfeatures and to develop an empirical model to predict the power of structure-borne noiseunder different operational and road conditions. Finally, multiple data driven models-basedmodels were developed to classify the road types, and conditions and use them for the noisefrequency spectrum prediction.

This research proposes a new differentiator for estimating higher order derivatives of an input signal. The main reason why higher order derivatives are necessary is that Active Inference makes use of generalized coordinates. This means that it keeps internally track of higher order temporal derivatives of states, inputs and measurements. The difficult part of generalized coordinates are the generalized measurements, because these should be measured from a physical system. However, it is not always possible to measure all states of a system and it is definitely not possible to measure all higher order derivatives. A solution to this is to estimate the derivatives of states by using real time differentiators. A short literature study about differentiators is conducted and found that the state of the art differentiator is the algebraic estimation approach differentiator (AEAD). However, this and other differentiators have the problem that when they are converted to discrete time, they cannot track polynomial signals anymore. For that reason, this thesis proposes a new differentiator: the recurrent low pass algebraic differentiator (RLPAD). The proposed differentiator is compared with the (AEAD) in two experiments. The first experiment evaluates the performance based on three analytical inputs with known higher order derivatives. The three inputs are: a polynomial, a sine and a combination of the two. Additionally these three inputs are corrupted by Gaussian white noise. This experiment concludes that the proposed method outperforms the (AEAD) significantly when a polynomial or polynomial combined with sine input is used. They perform similar for sine inputs, although (RLPAD) is considered slightly better. The second experiment evaluates how the two differentiators perform when real sensor data is used. In order to conduct the experiment, the raw data is smoothed by a Savitzky-Golay filter to create a ground truth about the derivatives. T, Mechanical Engineering | Biomechanical Design - BioRobotics

This work presents the experimental results of a wavelet transform-based denoising algorithm to improve the signal-to-noise ratio (SNR) in Proton Sound Detectors (ProSDs). ProSDs are thermoacoustic detectors that exploit the acoustic signal generated by pulsed proton beams in energy absorbers to locate energy deposition with sub-millimeter precision, with interesting applications in beam monitoring during oncological hadron therapy treatments. This work shows how the application of a denoising algorithm allows to improve the signal-to-noise ratio and reduce the extra-dose necessary to characterize the beam. The algorithm was validated with the thermoacoustic signal generated by a proton beam at 20 MeV, where it allowed to increase the SNR by 19 dB and decrease the dose necessary to characterize the beam by a factor of 80 compared to the state of the art.

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire., open access

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire., open access

We investigated the use of a Deep Learning approach to radio signal de-noising. This data-driven approach has does not require explicit use of expert knowledge to set up the parameters of the denoising procedure and grants great flexibility across many channel conditions. The core component used in this work is a Convolutional De-noising AutoEncoder, known to be very effective in image processing. The key of our approach consists in transforming the radio signal into a representation suitable to the CDAE: we transform the time-domain signal into a 2D signal using the Short Time Fourier Transform. We report about the performance of the approach in preamble denoising across protocols of the IEEE 802.11 family, studied using simulation data. This approach could be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A perspective advantage of using the AutoEncoders in that pipeline is that they can be co-trained with the downstream classifier, to optimize the classification accuracy.

This paper proposes a Deep Learning approach to radio signal de-noising. This approach is data-driven, thus it allows de-noising signals, corresponding to distinct protocols, without requiring explicit use of expert knowledge, in this way granting higher flexibility. The core component of the Artificial Neural Network architecture used in this work is a Convolutional De-noising AutoEncoder. We report about the performance of the system in spectrogram-based denoising of the protocol preamble across protocols of the IEEE 802.11 family, studied using simulation data. This approach can be used within a machine learning pipeline: the denoised data can be fed to a protocol classifier. A further perspective advantage of using the AutoEncoders in such a pipeline is that they can be co-trained with the downstream classifier (protocol detector), to optimize its accuracy.

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire., open access

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire., open access

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire., open access

Cardiovascular pathologies cause 23.5% of human deaths, worldwide. An auto-diagnostic system monitoring heart activity, which can identify the early symptoms of cardiac illnesses, might reduce the death rate caused by these problems. Phonocardiography (PCG) is one of the possible techniques able to detect heart problems. Nevertheless, acoustic signal enhancement is required since it is exposed to various disturbances coming from different sources. The most common denoising enhancement is based on the Wavelet Transform (WT). However, the WT is highly susceptible to variations in the noise frequency distribution. This paper proposes a new adaptive denoising algorithm, which combines WT and Time Delay Neural Networks (TDNN). The acquired signal is decomposed by means of the WT using the coif five-wavelet basis at the tenth decomposition level and then provided as input to the TDNN. Besides the advantage of adaptive thresholding, the reason for using TDNNs is their capacity of estimating the Inverse Wavelet Transform (IWT). The best parameters of the TDNN were found for a NN consisting of 25 neurons in the first and 15 in the second layer and the delay block of 12 samples. The method was evaluated on several pathological heart sounds and on signals recorded in a noisy environment. The performance of the developed system with respect to other wavelet-based denoising approaches was validated by the online questionnaire., open access

As an interdisciplinary research between watermarking and cryptography, privacy-aware reversible watermarking permits a party to entrust the task of embedding watermarks to a cloud service provider without compromising information privacy. The early development of schemes was primarily based upon traditional symmetric-key cryptosystems, which involve an extra implementation cost of key exchange. Although recent research attentions were drawn to schemes compatible with asymmetric-key cryptosystems, there were notable limitations in the practical aspects. In particular, the host signal must either be enciphered in a redundant way or be pre-processed prior to encryption, which would largely limit the storage efficiency and scheme universality. To relax the restrictions, we propose a novel research paradigm and devise different schemes compatible with different homomorphic cryptosystems. In the proposed schemes, the encoding function is recognised as an operation of adding noise, whereas the decoding function is perceived as a corresponding denoising process. Both online and offline content-adaptive predictors are developed to assist watermark decoding for various operational requirements. A three-way tradeoff between the capacity, fidelity, and reversibility is analysed mathematically and empirically. It is shown that the proposed schemes achieve the state-of-the-art performance.

Filiació URV: SIInclòs a la memòria: SI, The present work attempts to characterize the accelerometric environment of the DSC-DCMIX1 thermodiffusion experiment carried out in the International Space Station, from November 7th 2011 until January 16th 2012. Quasi-steady and vibrational/transient data coming from MAMS and SAMS2 sensors have been downloaded from the database of the PIMS NASA website. To be as exhaustive as possible, simultaneous digital signals coming from different SAMS2 sensors located in the Destiny and Columbus modules have also been considered. In order to detect orbital adjustments, dockings, undockings, as well as, quiescent periods, when the experiment runs were active, we have used the quasi-steady eight hours averaged (XA, YA and ZA) acceleration functions as well as the eight hours RMS ones. To determine the spectral contents of the different signals the Thomson multitaper and Welch methods have been used. On the other hand, to suppress the high levels of noise always existing in the raw SAMS2 signals, denoising techniques have been preferred for comparative reboostings considerations. Finally, the RMS values for specific 1/3 octave frequency bands showed that the International Space Station vibratory limit requirements have not been totally accomplished during both quiescent periods and strong disturbances, specially in the low frequency range.

Cycle spinning (CS) and a'trous algorithms are different implementations of the undecimated wavelet transform (UWT). Both algorithms can be used for UWT and even though the resulting wavelet coefficients are different, they keep a correspondence. This paper describes an analysis of the CS algorithm performed in the z-transform domain, showing the similarities and differences with the a'trous implementation. CS generates more wavelet coefficients than a'trous, but the number of significative and different coefficients is the same in both cases because of the occurrence of a periodic repetition in CS coefficients. Mathematical expressions for the relationship between CS and a'trous coefficients and for CS coefficient periodicities are provided in the z-transform domain. In some wavelet denoising applications, periodicities (present in the coefficients of the CS procedure) can also be found in the performance measure of the processed signals. In particular, in ultrasonic CS denoising applications, periodicities have been appreciated in the signal-to-noise ratio (SNR) of the ultrasonic denoised signals. These periodicities can be used to optimize the number of CS coefficients for an efficient implementation. Two examples showing the periodicities in the SNR are included. A selection of several reduced sets of CS wavelet coefficients has been utilized in the examples, and the SNRs resulting after denoising are analyzed.

Cycle spinning (CS) and a'trous algorithms are different implementations of the undecimated wavelet transform (UWT). Both algorithms can be used for UWT and even though the resulting wavelet coefficients are different, they keep a correspondence. This paper describes an analysis of the CS algorithm performed in the z-transform domain, showing the similarities and differences with the a'trous implementation. CS generates more wavelet coefficients than a'trous, but the number of significative and different coefficients is the same in both cases because of the occurrence of a periodic repetition in CS coefficients. Mathematical expressions for the relationship between CS and a'trous coefficients and for CS coefficient periodicities are provided in the z-transform domain. In some wavelet denoising applications, periodicities (present in the coefficients of the CS procedure) can also be found in the performance measure of the processed signals. In particular, in ultrasonic CS denoising applications, periodicities have been appreciated in the signal-to-noise ratio (SNR) of the ultrasonic denoised signals. These periodicities can be used to optimize the number of CS coefficients for an efficient implementation. Two examples showing the periodicities in the SNR are included. A selection of several reduced sets of CS wavelet coefficients has been utilized in the examples, and the SNRs resulting after denoising are analyzed.

Filiació URV: SIInclòs a la memòria: SI, The present work attempts to characterize the accelerometric environment of the DSC-DCMIX1 thermodiffusion experiment carried out in the International Space Station, from November 7th 2011 until January 16th 2012. Quasi-steady and vibrational/transient data coming from MAMS and SAMS2 sensors have been downloaded from the database of the PIMS NASA website. To be as exhaustive as possible, simultaneous digital signals coming from different SAMS2 sensors located in the Destiny and Columbus modules have also been considered. In order to detect orbital adjustments, dockings, undockings, as well as, quiescent periods, when the experiment runs were active, we have used the quasi-steady eight hours averaged (XA, YA and ZA) acceleration functions as well as the eight hours RMS ones. To determine the spectral contents of the different signals the Thomson multitaper and Welch methods have been used. On the other hand, to suppress the high levels of noise always existing in the raw SAMS2 signals, denoising techniques have been preferred for comparative reboostings considerations. Finally, the RMS values for specific 1/3 octave frequency bands showed that the International Space Station vibratory limit requirements have not been totally accomplished during both quiescent periods and strong disturbances, specially in the low frequency range.

Seismic acquisition is a trade-off between economy and quality. In conventional acquisition the time intervals between successive records are large enough to avoid interference in time. To obtain an efficient survey, the spatial source sampling is therefore often (too) large. However, in blending, or simultaneous acquisition, temporal overlap between shot records is allowed. This additional degree of freedom in survey design significantly improves the quality or the economics or both. Deblending is the procedure of recovering the data as if they were acquired in the conventional, unblended way. A simple least-squares procedure, however, does not remove the interference due to other sources, or blending noise. Fortunately, the character of this noise is different in different domains, e.g., it is coherent in the common source domain, but incoherent in the common receiver domain. This property is used to obtain a considerable improvement. We propose to estimate the blending noise and subtract it from the blended data. The estimate does not need to be perfect because our procedure is iterative. Starting with the least-squares deblended data, the estimate of the blending noise is obtained via the following steps: sort the data to a domain where the blending noise is incoherent; apply a noise suppression filter; apply a threshold to remove the remaining noise, ending up with (part of) the signal; compute an estimate of the blending noise from this signal. At each iteration, the threshold can be lowered and more of the signal is recovered. Promising results were obtained with a simple implementation of this method for both impulsive and vibratory sources. Undoubtedly, in the future algorithms will be developed for the direct processing of blended data. However, currently a high-quality deblending procedure is an important step allowing the application of contemporary processing flows, Geoscience & Engineering, Civil Engineering and Geosciences

Seismic acquisition is a trade-off between economy and quality. In conventional acquisition the time intervals between successive records are large enough to avoid interference in time. To obtain an efficient survey, the spatial source sampling is therefore often (too) large. However, in blending, or simultaneous acquisition, temporal overlap between shot records is allowed. This additional degree of freedom in survey design significantly improves the quality or the economics or both. Deblending is the procedure of recovering the data as if they were acquired in the conventional, unblended way. A simple least-squares procedure, however, does not remove the interference due to other sources, or blending noise. Fortunately, the character of this noise is different in different domains, e.g., it is coherent in the common source domain, but incoherent in the common receiver domain. This property is used to obtain a considerable improvement. We propose to estimate the blending noise and subtract it from the blended data. The estimate does not need to be perfect because our procedure is iterative. Starting with the least-squares deblended data, the estimate of the blending noise is obtained via the following steps: sort the data to a domain where the blending noise is incoherent; apply a noise suppression filter; apply a threshold to remove the remaining noise, ending up with (part of) the signal; compute an estimate of the blending noise from this signal. At each iteration, the threshold can be lowered and more of the signal is recovered. Promising results were obtained with a simple implementation of this method for both impulsive and vibratory sources. Undoubtedly, in the future algorithms will be developed for the direct processing of blended data. However, currently a high-quality deblending procedure is an important step allowing the application of contemporary processing flows, Geoscience & Engineering, Civil Engineering and Geosciences

One application of seismic interferometry is to retrieve the impulse response (Green's function) from crosscorrelation of ambient seismic noise. Various researchers show results for retrieving the surface-wave part of the Green's function. However, reflection retrieval has proven more challenging. We crosscorrelate ambient seismic noise, recorded along eight parallel lines in the Sirte basin east of Ajdabeya, Libya, to obtain shot gathers that contain reflections. We take advantage of geophone groups to suppress part of the undesired surface-wave noise and apply frequency-wavenumber filtering before crosscorrelation to suppress surface waves further. After comparing the retrieved results with data from an active seismic exploration survey along the same lines, we use the retrieved reflection data to obtain a migrated reflection image of the subsurface., Geotechnology, Civil Engineering and Geosciences

One application of seismic interferometry is to retrieve the impulse response (Green's function) from crosscorrelation of ambient seismic noise. Various researchers show results for retrieving the surface-wave part of the Green's function. However, reflection retrieval has proven more challenging. We crosscorrelate ambient seismic noise, recorded along eight parallel lines in the Sirte basin east of Ajdabeya, Libya, to obtain shot gathers that contain reflections. We take advantage of geophone groups to suppress part of the undesired surface-wave noise and apply frequency-wavenumber filtering before crosscorrelation to suppress surface waves further. After comparing the retrieved results with data from an active seismic exploration survey along the same lines, we use the retrieved reflection data to obtain a migrated reflection image of the subsurface., Geotechnology, Civil Engineering and Geosciences

Penalized least-squares (PELS) rules for signal denoising can be obtained via the use of various information criteria (AIC, BIC, etc.) or various minmax LS approaches. Let S denote the set of "significant" parameters in the denoising problem (which is to be determined), let n(S) be the dimension of S, and let n(S)rho denote the penalty term of a PELS criterion. We show that, depending on the expression for rho, the following cases can occur: type-1) If rho does not depend on S, then denoising via the corresponding PELS rule is equivalent to simple thresholding; and type-2) If rho depends on n(S) only, then the equivalence to thresholding no longer holds but the PELS rule can still be implemented quite efficiently. We also show that the use of BIC leads to an existing PELS rule of type-1 when the noise variance in the denoising problem is known, and to a novel PELS rule of type-2 when the noise variance is unknown., QC 20111116

Coherent noise generated by surface waves or ground roll within a heterogeneous near surface is a major problem in land seismic data. Array forming based on single-sensor recordings might reduce such noise more robustly than conventional hardwired arrays. We use the minimum-variance distortionless-response (MVDR) beamformer to remove (aliased) surface-wave energy from single-sensor data. This beamformer is data adaptive and robust when the presumed and actual desired signals are mismatched. We compute the intertrace covariance for the desired signal, and then for the total signal (desired signal+noise) to obtain optimal weights. We use the raw data of only one array for the covariance of the total signal, and the wavenumber-filtered version of a full seismic single-sensor record for the covariance of the desired signal. In the determination of optimal weights, a parameter that controls the robustness of the beamformer against an arbitrary desired signal mismatch has to be chosen so that the results are optimal. This is similar to stabilization in deconvolution problems. This parameter needs to be smaller than the largest eigenvalue provided by the singular value decomposition of the presumed desired signal covariance. We compare results of MVDR beamforming with standard array forming on single-sensor synthetic and field seismic data. We apply 2D and 3D beamforming and show prestack and poststack results. MVDR beamformers are superior to conventional hardwired arrays for all examples., Geotechnology, Civil Engineering and Geosciences

Coherent noise generated by surface waves or ground roll within a heterogeneous near surface is a major problem in land seismic data. Array forming based on single-sensor recordings might reduce such noise more robustly than conventional hardwired arrays. We use the minimum-variance distortionless-response (MVDR) beamformer to remove (aliased) surface-wave energy from single-sensor data. This beamformer is data adaptive and robust when the presumed and actual desired signals are mismatched. We compute the intertrace covariance for the desired signal, and then for the total signal (desired signal+noise) to obtain optimal weights. We use the raw data of only one array for the covariance of the total signal, and the wavenumber-filtered version of a full seismic single-sensor record for the covariance of the desired signal. In the determination of optimal weights, a parameter that controls the robustness of the beamformer against an arbitrary desired signal mismatch has to be chosen so that the results are optimal. This is similar to stabilization in deconvolution problems. This parameter needs to be smaller than the largest eigenvalue provided by the singular value decomposition of the presumed desired signal covariance. We compare results of MVDR beamforming with standard array forming on single-sensor synthetic and field seismic data. We apply 2D and 3D beamforming and show prestack and poststack results. MVDR beamformers are superior to conventional hardwired arrays for all examples., Geotechnology, Civil Engineering and Geosciences