Mimicking the process of manual sequence stratigraphy well correlation

Stratigraphic correlation of well logs is based on interactive, interpreter-based pattern recognition. A skilled interpreter identifies similar patterns (such as upward fining and coarsening) in user-defined well sections and links them using either a conscious or subconscious stratigraphic model. This manual stratigraphic correlation of numerous wells in mature fields can be both time consuming and error prone. To expedite the process of stratigraphic correlation, we perform the semi-automatic stratigraphic correlation of wireline logs from multiple wells using the Improved Dynamic Time Warping (IDTW). The IDTW employs semblance, which compares the shape of the well logs, to replace the Euclidean distance in the pairwise error computation. The resulting error matrix is compatible with the lateral nonstationary variation of well logs in the same formation. The workflow begins with interpreting stratigraphic well tops on user-defined well sections that is similar to current process of stratigraphy analysis. The interpreted wells are then treated as reference wells to aid in interpreting well tops for other wells. Necessary manual interventions are incorporated during the process of the semi-automatic stratigraphic correlation. We applied the proposed method to two experimental fields: a sand-rich reservoir and a mud-rich reservoir. The applications illustrate that the proposed method performs well in aggradational strata and successfully predicts the discontinuities with manual interventions.

Mismatch Negativity and Stimulus-Preceding Negativity in Paradigms of Increasing Auditory Complexity: A Possible Role in Predictive Coding

The auditory mismatch negativity (MMN) has been considered a preattentive index of auditory processing and/or a signature of prediction error computation. This study tries to demonstrate the presence of an MMN to deviant trials included in complex auditory stimuli sequences, and its possible relationship to predictive coding. Additionally, the transfer of information between trials is expected to be represented by stimulus-preceding negativity (SPN), which would possibly fit the predictive coding framework. To accomplish these objectives, the EEG of 31 subjects was recorded during an auditory paradigm in which trials composed of stimulus sequences with increasing or decreasing frequencies were intermingled with deviant trials presenting an unexpected ending. Our results showed the presence of an MMN in response to deviant trials. An SPN appeared during the intertrial interval and its amplitude was reduced in response to deviant trials. The presence of an MMN in complex sequences of sounds and the generation of an SPN component, with different amplitudes in deviant and standard trials, would support the predictive coding framework.

Delayed LMS Algorithm for Ballistocardiogram Biomedical Signal

The Ballisto-cardiogram (BCG) is a biomedical signal which is basically a measure of ballistic forces on heart. Just like ECG to detect the abnormalities in heart Ballisto-cardiography technique is used extensively now a day in research to analyze the abnormalities of the patient. When the blood pumps from heart to different parts of the body is represented in the form of graph for each heart beat. The frequency of Ballisto-cardiography signal is 1-20Hz .Ballisto-cardiography is most emerging techniques which is used to test the diseases related to heart called as cardiovascular disease. Various devices [1] like chairs, beds and weighing scales are projected to improvise the extraction of the BCG, but noise is one of the main issues with this BCG signal processing, noise is generated because of motion artifacts, shaking of the devices or may be power line noise. This noise [2] affects the quality of signal which we need to test. In order to overcome such issue this paper proposes a new architecture making use of LMS filtering algorithm Here weight update algorithm is used to update the error in extracted signal. The architecture proposed here includes FIR filter and also error computation blocks. Here the author has implemented 5-tap filtering algorithm. MATLAB and system generators are used to carry out the work.

ON LAUSSEDAT’S CONTRIBUTION TO THE EMERGENCE OF PHOTOGRAMMETRY

The French officer Aimé Laussedat (1819–1907) is often considered as the “father of photogrammetry”. Indeed, he was the first to use photographic images for topographic surveys as early as 1861, based on a technique he called metrophotography, which he had already implemented with hand-drawn perspective views of edifications and mountainous landscapes. However, the development of analog photogrammetry at the beginning of the 20th century is not a direct evolution of Laussedat’s method and we can wonder what is his actual contribution to the emergence of photogrammetry when the first stereocomparators were created. Based on the influence of precursors like Beautemps-Beaupré and Arago, he developed a very innovative method but his main contribution remained mainly instrumental and he ignored some concepts or technologies which were available, like error computation, stereoscopy and aerial photography. These observations allow a more nuanced appreciation of his contribution.

In vivo Feedback Control of an Antithetic Molecular-Titration Motif in Escherichia coli using Microfluidics

SummaryWe study both in silico and in vivo the real-time feedback control of a molecular titration motif that has been earmarked as a fundamental component of antithetic and multicellular feedback control schemes in E. coli. We show that an external feedback control strategy can successfully regulate the average fluorescence output of a bacterial cell population to a desired constant level in real-time. We also provide in silico evidence that the same strategy can be used to track a time-varying reference signal where the set-point is switched to a different value halfway through the experiment. We use the experimental data to refine and parameterize an in silico model of the motif that can be used as an error computation module in future embedded or multicellular control experiments.

A VTA GABAergic computational model of dissociated reward prediction error computation in classical conditioning

Classical Conditioning is a fundamental learning mechanism where the Ventral Striatum is generally thought to be the source of inhibition to Ventral Tegmental Area (VTA) Dopamine neurons when a reward is expected. However, recent evidences point to a new candidate in VTA GABA encoding expectation for computing the reward prediction error in the VTA. In this system-level computational model, the VTA GABA signal is hypothesised to be a combination of magnitude and timing computed in the Peduncolopontine and Ventral Striatum respectively. This dissociation enables the model to explain recent results wherein Ventral Striatum lesions affected the temporal expectation of the reward but the magnitude of the reward was intact. This model also exhibits other features in classical conditioning namely, progressively decreasing firing for early rewards closer to the actual reward, twin peaks of VTA dopamine during training and cancellation of US dopamine after training.

Supercapacitor fractional model − DAQ-based measurements of frequency characteristics and error computation

The study concerns the modelling of a supercapacitor. The frequency characteristics are selected as the measurement basis for the study. The characteristics are obtained through a measurement setup featuring a data acquisition (DAQ) device and an arbitrary function generator (AFG). After the characteristics had been obtained − a parameter estimation procedure is executed. The response of the model is then compared with the measurement results. An error definition determining the agreement of the frequency characteristics is introduced. It takes into account both the real and imaginary parts of the obtained impedances.