Maide Bucolo | University of Catania (original) (raw)

Papers by Maide Bucolo

Microfluidics and Nanofluidics, 2014

The recent advancements in bubble logic computation based on two-phase microfluidics bring into l... more The recent advancements in bubble logic computation based on two-phase microfluidics bring into light the possibility that the use of bubbles in microfluidic devices can carry on-chip process control. In this paper four computational models implementing two different AND-OR logic gates, one logic NOT and, a Flip-Flop are presented. More specifically the numerical approach used combines the Navier-Stokes equation with the Phase-Field method. All reported models are based on generally accepted and already tested experimentally chip designs. A parametric T-junction model has been designed to be connected to the logic gate models as a droplet generator. The wider framework on the logic-gate behavior in different operating conditions reveals the relevance of these models in the microfluidics chip design. Moreover, the advantage of using a simulation platform for the investigation of electrical circuits equivalent of microfluidic processes is illustrated. In this context the focus of this paper was not only the definition of CFD models of logic gates but the attempt to establish a workbench easy accessible for the study of the two-phase microfluidic processes.

Microfluidics and Nanofluidics, 2011

In this experimental study, the effects on twophase flow dynamics in a microfluidic snake channel... more In this experimental study, the effects on twophase flow dynamics in a microfluidic snake channel due to periodic forcing were considered. Time series analysis was exploited to investigate on the obtained bubbles' flow considering two aspects: the role of driven frequency through Fourier analysis and the nonlinear behavior through the evaluation of d-infinite and Largest Lyapunov exponent. Phase diagrams summarize the results: the two nonlinear parameters are plotted versus the air fraction and the frequency of the input flow rates. The identified relation maps allow the classification of the flow dynamics, opening the way for the control of bubble flow through signal analysis.

An approach based on nonlinear dynamical systems theory is used in this work to identify the comp... more An approach based on nonlinear dynamical systems theory is used in this work to identify the complex temporal patterns in air bubbles flow carried by water in a snake microfluidic channel with two inlets. Air and water were pumped in with periodic flow. Different experimental campaigns have been designed varying the frequency of the flow rate alternatively for the water and for the air and maintaining fixed the other fluid flow. Microfluidic bubble flows were optically acquired by means of a photodiode-based system and converted into time series. In relation to the input control parameters (flow rate, frequency), the diversity of bubbles' temporal dynamic patterns was identified through nonlinear methodologies. Relationships between nonlinear parameters, volume fraction of fluids and capillary number were found suggesting the chaotic behavior of the system. This work is a fundamental step toward the control of bubble based operations in microfluidics.

The functional connectivity of various brain regions has been studied here using the knowledge fr... more The functional connectivity of various brain regions has been studied here using the knowledge from two different scientific fields. The methods of Synchronization Likelihood (SL) and network theory are applied to magnetoencephalography (MEG) data in an effort to study the brain as a complex network. In this paper the SL method has been used to characterize the functional interactions as ''functional connectivity'', by performing measures of statistical interdependencies between brain activity signals. The underlying assumption is that such correlations, at least in part, reflect the functional interactions between different brain regions. Methods applied in this study investigate the occurrence of small-world phenomenon in MEG data by considering the application of the SL method and the characterization of the respective graphs obtained by varying the threshold T. The data set used here is from a single subject performing a yogic breathing exercise. In the results w...

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 2003

Nonlinear spatio-temporal analysis has been performed on neural activity recorded using 148-chann... more Nonlinear spatio-temporal analysis has been performed on neural activity recorded using 148-channel whole-head Magnetoencephalography. The analysis consists of two phases: artifact removal and nonlinear feature evaluation. In the first phase, known artifacts, produced by cardiac and eye movement, and unknown artifacts have been isolated from intrinsic neural activity by using two adaptive filters in a cascade configuration. In the second one, phase space reconstruction of multivariate Magnetoencephalography measurements have been performed by using both temporal and spatial embedding. Indices of nonlinear dynamics have been defined and evaluated showing invariant features both in time and space.

The functional connectivity of the brain has been studied here using knowledge from two different... more The functional connectivity of the brain has been studied here using knowledge from two different scientific fields. The methods of Synchronization Likelihood and network theory are applied to magnetoencephalography (MEG) data in an effort to analyze the brain as a complex network. These studies show an interesting small-world phenomenon in functional connectivity. Network and head-map images of the results are presented.

2010 IEEE Workshop on Health Care Management (WHCM), 2010

The automatic detection of various differences in brain dynamics has been studied here using two ... more The automatic detection of various differences in brain dynamics has been studied here using two approaches, nonlinear time series analysis, and the clustering method. Six data sets of whole-head 148 channel MEG activity were collected from a single subject performing a yogic breathing protocol in three two-day experiments repeated with a time lag of one month. The MEG signals have been analyzed by evaluating five nonlinear indicators, two statistical measures and the power for each minute. The trends of eight parameters in time and space have been used in an effort to explore the classification schemes using Growing Hierarchical Self Organizing Maps. The results show the utility of this approach for distinguishing the different phases of the yogic breathing protocol and for observing brain activity changes at successive months.

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 2003

The analysis of MEG reference spatial modes has resulted in two groups of basis spatial patterns:... more The analysis of MEG reference spatial modes has resulted in two groups of basis spatial patterns: invariant and variable. The first spatial modes are invariant in respect to the protocol phases and have been assumed as characteristic features of intrinsic neural activity. The second ones are coherent with the timing of the controlled breathing exercise.

Mathematical Biosciences and Engineering, 2006

Magnetoencephalography (MEG) brain signals are studied using a method for characterizing complex ... more Magnetoencephalography (MEG) brain signals are studied using a method for characterizing complex nonlinear dynamics. This approach uses the value of d ∞ (d-infinite) to characterize the system's asymptotic chaotic behavior. A novel procedure has been developed to extract this parameter from time series when the system's structure and laws are unknown. The implementation of the algorithm was proven to be general and computationally efficient. The information characterized by this parameter is furthermore independent and complementary to the signal power since it considers signals normalized with respect to their amplitude. The algorithm implemented here is applied to whole-head 148 channel MEG data during two highly structured yogic breathing meditation techniques. Results are presented for the spatiotemporal distributions of the calculated d∞ on the MEG channels, and they are compared for the different phases of the yogic protocol. The algorithm was applied to six MEG data sets recorded over a three-month period. This provides the opportunity of verifying the consistency of unique spatio-temporal features found in specific protocol phases and the chance to investigate the potential long term effects of these yogic techniques. Differences among the spatio-temporal patterns related to each phase were found, and they were independent of the power spatio-temporal distributions that are based on conventional analysis. This approach also provides an opportunity to compare both methods and possibly gain complementary information.

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007

Magnetoencephalography (MEG) brain signals are characterized using both linear and nonlinear dyna... more Magnetoencephalography (MEG) brain signals are characterized using both linear and nonlinear dynamical methods. The linear approach employs the power analysis in a spatial visualization. The nonlinear approach estimates the value of d ∞ (d-infinite) to characterize the system's asymptotic chaotic behavior using a computationally less onerous method than the conventional one for d ∞ . Both methods are applied here to study a female patient with obsessive compulsive disorder and an age-sex matched normal subject. MEG time series were obtained using dual 37-channel bio-magnetometers (4-D Neuroimaging, San Diego, CA).

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001

Independent Component Analysis (ICA) is applied to the Magnetoencephalography (MEG) data of a sub... more Independent Component Analysis (ICA) is applied to the Magnetoencephalography (MEG) data of a subject performing a yoga breathing exercise specific for the treatment of obsessive compulsive disorder. The spatio-temporal dynamics observed using a whole-head 148-channel MEG instrument are split into the fundamental modes, thus isolating separate brain activity signals. Experiments were performed on data from different brain regions. Spectral analysis of the more significant signals are presented. Moreover a new tool is developed as a Matlab toolbox to support the scientist both in the visualization and computation phases.

2007 IEEE International Symposium on Circuits and Systems, 2007

Magneto Encephalographic (MEG) brain signals are studied using a method for characterizing nonlin... more Magneto Encephalographic (MEG) brain signals are studied using a method for characterizing nonlinear dynamics. This approach uses the value of d ∞ (d-infinite) to characterize the system's asymptotic chaotic behavior. A novel procedure was developed to extract this parameter from time series when the system's structure and laws are unknown. The implementation of the algorithm has proven to be general and computationally efficient. The information characterized by this parameter is furthermore independent and complementary to the signal power since it considers signals normalized with respect to their amplitude. The algorithm implemented here is applied to whole-head 148 channel MEG data during two highly structured yogic breathing meditation techniques. Results, which are relative to spatio-temporal distributions of the calculated d∞ on the MEG channels, are analyzed and compared during different phases of the yogic protocol.

Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications, 2000

In this paper a cellular neural network (CNN) based system to perform a real-time, parallel proce... more In this paper a cellular neural network (CNN) based system to perform a real-time, parallel processing of magetoencephalographic data is proposed. In particular, a nonlinear approach to blind sources separation, instead of the linear procedure performed by independent component analysis, is introduced. Moreover, the characteristic spatial distribution of the cells in the CNN system has been exploited to reproduce the topology of the acquisition channels over the scalp.

Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03., 2000

The effects of parameter spatial disorder are investigated in quantum arrays focusing on collecti... more The effects of parameter spatial disorder are investigated in quantum arrays focusing on collective behaviors and communication between connected units. The amount of information exchanged has been correlated to the global dynamics and the parameters of the complex systems. A two-cell Quantum Cellular Neural Networks (QCNNs) oscillator is chosen as fundamental unit; chaotic dynamics characterizes the oscillators coupled through Coulomb

International Journal of Bifurcation and Chaos, 2005

The paper stresses the universal role that Cellular Nonlinear Networks (CNNs) are assuming today.... more The paper stresses the universal role that Cellular Nonlinear Networks (CNNs) are assuming today. It is shown that the dynamical behavior of 3D CNN-based models allows us to approach new emerging problems, to open new research frontiers as the generation of new geometrical forms and to establish some links between art, neuroscience and dynamical systems.

... Conference Editor: Huijberts, Henri. Authors Sapuppo, Francesca; Schembri, Florinda; Bucolo, ... more ... Conference Editor: Huijberts, Henri. Authors Sapuppo, Francesca; Schembri, Florinda; Bucolo, Maide. ... Predictability in the large: an extension of the concept of Lyapunov exponent, J. Phys. A 30, 1. [2] Bonasera, A., Bucolo M., Fortuna L., Frasca M., Rizzo A. (2003). ...

ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357), 1999

ABSTRACT

The first step towards a heart attack is a condition called Ventricular Arrhythmia. This paper pr... more The first step towards a heart attack is a condition called Ventricular Arrhythmia. This paper proposes a system that uses the advanced digital signal processing techniques to analyse electrocardiogram (ECG) signals and recognise the Arrhythmia condition. In addition, proposed method can differentiate the more dangerous condition, Ventricular Arrhythmia from a simple Arrhythmia. The proposed technique combines the classical ECG signal parameters (e.g. Heart Rate Variability) with the standard statistical signal parameters, nonlinear parameters used in the fields of Chaos Theory and parameters obtained using Symbolic Analysis techniques. Linear Discriminant Analysis (LDA) is employed in order to reduce the size of ECG parameter set and is followed by a clustering algorithm.

An approach based on nonlinear dynamical systems theory is used in this work to identify the comp... more An approach based on nonlinear dynamical systems theory is used in this work to identify the complex temporal patterns in air bubbles flow carried by water in a snake microfluidic channel with two inlets. Air and water were pumped in with periodic flow. Different experimental campaigns have been designed varying the frequency of the flow rate alternatively for the water and for the air and maintaining fixed the other fluid flow. Microfluidic bubble flows were optically acquired by means of a photodiode-based system and converted into time series. In relation to the input control parameters (flow rate, frequency), the diversity of bubbles' temporal dynamic patterns was identified through nonlinear methodologies. Relationships between nonlinear parameters, volume fraction of fluids and capillary number were found suggesting the chaotic behavior of the system. This work is a fundamental step toward the control of bubble based operations in microfluidics.

Microfluidics and Nanofluidics, 2014

The recent advancements in bubble logic computation based on two-phase microfluidics bring into l... more The recent advancements in bubble logic computation based on two-phase microfluidics bring into light the possibility that the use of bubbles in microfluidic devices can carry on-chip process control. In this paper four computational models implementing two different AND-OR logic gates, one logic NOT and, a Flip-Flop are presented. More specifically the numerical approach used combines the Navier-Stokes equation with the Phase-Field method. All reported models are based on generally accepted and already tested experimentally chip designs. A parametric T-junction model has been designed to be connected to the logic gate models as a droplet generator. The wider framework on the logic-gate behavior in different operating conditions reveals the relevance of these models in the microfluidics chip design. Moreover, the advantage of using a simulation platform for the investigation of electrical circuits equivalent of microfluidic processes is illustrated. In this context the focus of this paper was not only the definition of CFD models of logic gates but the attempt to establish a workbench easy accessible for the study of the two-phase microfluidic processes.

Microfluidics and Nanofluidics, 2011

In this experimental study, the effects on twophase flow dynamics in a microfluidic snake channel... more In this experimental study, the effects on twophase flow dynamics in a microfluidic snake channel due to periodic forcing were considered. Time series analysis was exploited to investigate on the obtained bubbles' flow considering two aspects: the role of driven frequency through Fourier analysis and the nonlinear behavior through the evaluation of d-infinite and Largest Lyapunov exponent. Phase diagrams summarize the results: the two nonlinear parameters are plotted versus the air fraction and the frequency of the input flow rates. The identified relation maps allow the classification of the flow dynamics, opening the way for the control of bubble flow through signal analysis.

An approach based on nonlinear dynamical systems theory is used in this work to identify the comp... more An approach based on nonlinear dynamical systems theory is used in this work to identify the complex temporal patterns in air bubbles flow carried by water in a snake microfluidic channel with two inlets. Air and water were pumped in with periodic flow. Different experimental campaigns have been designed varying the frequency of the flow rate alternatively for the water and for the air and maintaining fixed the other fluid flow. Microfluidic bubble flows were optically acquired by means of a photodiode-based system and converted into time series. In relation to the input control parameters (flow rate, frequency), the diversity of bubbles' temporal dynamic patterns was identified through nonlinear methodologies. Relationships between nonlinear parameters, volume fraction of fluids and capillary number were found suggesting the chaotic behavior of the system. This work is a fundamental step toward the control of bubble based operations in microfluidics.

The functional connectivity of various brain regions has been studied here using the knowledge fr... more The functional connectivity of various brain regions has been studied here using the knowledge from two different scientific fields. The methods of Synchronization Likelihood (SL) and network theory are applied to magnetoencephalography (MEG) data in an effort to study the brain as a complex network. In this paper the SL method has been used to characterize the functional interactions as ''functional connectivity'', by performing measures of statistical interdependencies between brain activity signals. The underlying assumption is that such correlations, at least in part, reflect the functional interactions between different brain regions. Methods applied in this study investigate the occurrence of small-world phenomenon in MEG data by considering the application of the SL method and the characterization of the respective graphs obtained by varying the threshold T. The data set used here is from a single subject performing a yogic breathing exercise. In the results w...

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 2003

Nonlinear spatio-temporal analysis has been performed on neural activity recorded using 148-chann... more Nonlinear spatio-temporal analysis has been performed on neural activity recorded using 148-channel whole-head Magnetoencephalography. The analysis consists of two phases: artifact removal and nonlinear feature evaluation. In the first phase, known artifacts, produced by cardiac and eye movement, and unknown artifacts have been isolated from intrinsic neural activity by using two adaptive filters in a cascade configuration. In the second one, phase space reconstruction of multivariate Magnetoencephalography measurements have been performed by using both temporal and spatial embedding. Indices of nonlinear dynamics have been defined and evaluated showing invariant features both in time and space.

The functional connectivity of the brain has been studied here using knowledge from two different... more The functional connectivity of the brain has been studied here using knowledge from two different scientific fields. The methods of Synchronization Likelihood and network theory are applied to magnetoencephalography (MEG) data in an effort to analyze the brain as a complex network. These studies show an interesting small-world phenomenon in functional connectivity. Network and head-map images of the results are presented.

2010 IEEE Workshop on Health Care Management (WHCM), 2010

The automatic detection of various differences in brain dynamics has been studied here using two ... more The automatic detection of various differences in brain dynamics has been studied here using two approaches, nonlinear time series analysis, and the clustering method. Six data sets of whole-head 148 channel MEG activity were collected from a single subject performing a yogic breathing protocol in three two-day experiments repeated with a time lag of one month. The MEG signals have been analyzed by evaluating five nonlinear indicators, two statistical measures and the power for each minute. The trends of eight parameters in time and space have been used in an effort to explore the classification schemes using Growing Hierarchical Self Organizing Maps. The results show the utility of this approach for distinguishing the different phases of the yogic breathing protocol and for observing brain activity changes at successive months.

Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439), 2003

The analysis of MEG reference spatial modes has resulted in two groups of basis spatial patterns:... more The analysis of MEG reference spatial modes has resulted in two groups of basis spatial patterns: invariant and variable. The first spatial modes are invariant in respect to the protocol phases and have been assumed as characteristic features of intrinsic neural activity. The second ones are coherent with the timing of the controlled breathing exercise.

Mathematical Biosciences and Engineering, 2006

Magnetoencephalography (MEG) brain signals are studied using a method for characterizing complex ... more Magnetoencephalography (MEG) brain signals are studied using a method for characterizing complex nonlinear dynamics. This approach uses the value of d ∞ (d-infinite) to characterize the system's asymptotic chaotic behavior. A novel procedure has been developed to extract this parameter from time series when the system's structure and laws are unknown. The implementation of the algorithm was proven to be general and computationally efficient. The information characterized by this parameter is furthermore independent and complementary to the signal power since it considers signals normalized with respect to their amplitude. The algorithm implemented here is applied to whole-head 148 channel MEG data during two highly structured yogic breathing meditation techniques. Results are presented for the spatiotemporal distributions of the calculated d∞ on the MEG channels, and they are compared for the different phases of the yogic protocol. The algorithm was applied to six MEG data sets recorded over a three-month period. This provides the opportunity of verifying the consistency of unique spatio-temporal features found in specific protocol phases and the chance to investigate the potential long term effects of these yogic techniques. Differences among the spatio-temporal patterns related to each phase were found, and they were independent of the power spatio-temporal distributions that are based on conventional analysis. This approach also provides an opportunity to compare both methods and possibly gain complementary information.

2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007

Magnetoencephalography (MEG) brain signals are characterized using both linear and nonlinear dyna... more Magnetoencephalography (MEG) brain signals are characterized using both linear and nonlinear dynamical methods. The linear approach employs the power analysis in a spatial visualization. The nonlinear approach estimates the value of d ∞ (d-infinite) to characterize the system's asymptotic chaotic behavior using a computationally less onerous method than the conventional one for d ∞ . Both methods are applied here to study a female patient with obsessive compulsive disorder and an age-sex matched normal subject. MEG time series were obtained using dual 37-channel bio-magnetometers (4-D Neuroimaging, San Diego, CA).

2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001

Independent Component Analysis (ICA) is applied to the Magnetoencephalography (MEG) data of a sub... more Independent Component Analysis (ICA) is applied to the Magnetoencephalography (MEG) data of a subject performing a yoga breathing exercise specific for the treatment of obsessive compulsive disorder. The spatio-temporal dynamics observed using a whole-head 148-channel MEG instrument are split into the fundamental modes, thus isolating separate brain activity signals. Experiments were performed on data from different brain regions. Spectral analysis of the more significant signals are presented. Moreover a new tool is developed as a Matlab toolbox to support the scientist both in the visualization and computation phases.

2007 IEEE International Symposium on Circuits and Systems, 2007

Magneto Encephalographic (MEG) brain signals are studied using a method for characterizing nonlin... more Magneto Encephalographic (MEG) brain signals are studied using a method for characterizing nonlinear dynamics. This approach uses the value of d ∞ (d-infinite) to characterize the system's asymptotic chaotic behavior. A novel procedure was developed to extract this parameter from time series when the system's structure and laws are unknown. The implementation of the algorithm has proven to be general and computationally efficient. The information characterized by this parameter is furthermore independent and complementary to the signal power since it considers signals normalized with respect to their amplitude. The algorithm implemented here is applied to whole-head 148 channel MEG data during two highly structured yogic breathing meditation techniques. Results, which are relative to spatio-temporal distributions of the calculated d∞ on the MEG channels, are analyzed and compared during different phases of the yogic protocol.

Proceedings of the 2002 7th IEEE International Workshop on Cellular Neural Networks and Their Applications, 2000

In this paper a cellular neural network (CNN) based system to perform a real-time, parallel proce... more In this paper a cellular neural network (CNN) based system to perform a real-time, parallel processing of magetoencephalographic data is proposed. In particular, a nonlinear approach to blind sources separation, instead of the linear procedure performed by independent component analysis, is introduced. Moreover, the characteristic spatial distribution of the cells in the CNN system has been exploited to reproduce the topology of the acquisition channels over the scalp.

Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03., 2000

The effects of parameter spatial disorder are investigated in quantum arrays focusing on collecti... more The effects of parameter spatial disorder are investigated in quantum arrays focusing on collective behaviors and communication between connected units. The amount of information exchanged has been correlated to the global dynamics and the parameters of the complex systems. A two-cell Quantum Cellular Neural Networks (QCNNs) oscillator is chosen as fundamental unit; chaotic dynamics characterizes the oscillators coupled through Coulomb

International Journal of Bifurcation and Chaos, 2005

The paper stresses the universal role that Cellular Nonlinear Networks (CNNs) are assuming today.... more The paper stresses the universal role that Cellular Nonlinear Networks (CNNs) are assuming today. It is shown that the dynamical behavior of 3D CNN-based models allows us to approach new emerging problems, to open new research frontiers as the generation of new geometrical forms and to establish some links between art, neuroscience and dynamical systems.

... Conference Editor: Huijberts, Henri. Authors Sapuppo, Francesca; Schembri, Florinda; Bucolo, ... more ... Conference Editor: Huijberts, Henri. Authors Sapuppo, Francesca; Schembri, Florinda; Bucolo, Maide. ... Predictability in the large: an extension of the concept of Lyapunov exponent, J. Phys. A 30, 1. [2] Bonasera, A., Bucolo M., Fortuna L., Frasca M., Rizzo A. (2003). ...

ICECS'99. Proceedings of ICECS '99. 6th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.99EX357), 1999

ABSTRACT

The first step towards a heart attack is a condition called Ventricular Arrhythmia. This paper pr... more The first step towards a heart attack is a condition called Ventricular Arrhythmia. This paper proposes a system that uses the advanced digital signal processing techniques to analyse electrocardiogram (ECG) signals and recognise the Arrhythmia condition. In addition, proposed method can differentiate the more dangerous condition, Ventricular Arrhythmia from a simple Arrhythmia. The proposed technique combines the classical ECG signal parameters (e.g. Heart Rate Variability) with the standard statistical signal parameters, nonlinear parameters used in the fields of Chaos Theory and parameters obtained using Symbolic Analysis techniques. Linear Discriminant Analysis (LDA) is employed in order to reduce the size of ECG parameter set and is followed by a clustering algorithm.

An approach based on nonlinear dynamical systems theory is used in this work to identify the comp... more An approach based on nonlinear dynamical systems theory is used in this work to identify the complex temporal patterns in air bubbles flow carried by water in a snake microfluidic channel with two inlets. Air and water were pumped in with periodic flow. Different experimental campaigns have been designed varying the frequency of the flow rate alternatively for the water and for the air and maintaining fixed the other fluid flow. Microfluidic bubble flows were optically acquired by means of a photodiode-based system and converted into time series. In relation to the input control parameters (flow rate, frequency), the diversity of bubbles' temporal dynamic patterns was identified through nonlinear methodologies. Relationships between nonlinear parameters, volume fraction of fluids and capillary number were found suggesting the chaotic behavior of the system. This work is a fundamental step toward the control of bubble based operations in microfluidics.