MATHEMATICAL BASIS FOR ANALYSING SIGNALS REMOVED FROM FROM THE HUMAN CEREBRAL CORTEX USING DOBESHI AND HAAR METHODS

The article presents the results of the study of signals taken from the human cerebral cortex, and presents the mathematical foundations of analysis using the methods of Daubechy and Haar. A comparative analysis of the method of the Daubechy and Haar wavelet transform implemented in MATLAB and developed using the C++ programming language in the course of the study on the example of a recorded audio signal with natural interference is given.

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Lightweight feature extraction method for efficient acoustic-based animal recognition in wireless acoustic sensor networks

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-020-01878-z ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Fatima Al-Quayed ◽

Adel Soudani ◽

Saad Al-Ahmadi

Keyword(s):

Feature Extraction ◽

Sensor Networks ◽

Recognition Task ◽

Audio Signal ◽

Haar Wavelet ◽

Acoustic Sensor ◽

Haar Wavelet Transform ◽

Feature Extraction Method ◽

Remote Server ◽

Acoustic Sensor Networks

AbstractWireless acoustic sensor networks represent an attractive solution that can be deployed for animal detection and recognition in a monitored area. A typical configuration for this application would be to transmit the whole acquired audio signal through multi-hop communication to a remote server for recognition. However, continuous data streaming can cause a severe decline in the energy of the sensors, which consequently reduces the network lifetime and questions the viability of the application. An efficient solution to reduce the sensor's radio activity would be to perform the recognition task at the source sensor then to communicate the result to the remote server. This approach is intended to save the energy of the acoustic source sensor and to unload the network from carrying, probably, useless data. However, the validity of this solution depends on the energy efficiency of performing on-sensor detection of a new acoustic event and accurate recognition. In this context, this paper proposes a new scheme for on-sensor energy-efficient acoustic animal recognition based on low-complexity methods for feature extraction using the Haar wavelet transform. This scheme achieves more than 86% in recognition accuracy while saving 71.59% of the sensor energy compared with the transmission of the raw signal.

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C-fos, ERK1/2, MAP2, NOTCH1 Proteins Expression Patterns in Human Cerebral Cortex Neurons after Ischemic Stroke

Annals of the Russian academy of medical sciences ◽

10.15690/vramn1295 ◽

2020 ◽

Vol 75 (3) ◽

pp. 226-233

Author(s):

Svetlana P. Sergeeva ◽

Aleksey V. Lyundup ◽

Valery V. Beregovykh ◽

Petr F. Litvitskiy ◽

Aleksey A. Savin ◽

...

Keyword(s):

Cerebral Cortex ◽

Ischemic Stroke ◽

Expression Patterns ◽

Immunohistochemical Method ◽

Left Middle Cerebral Artery ◽

Contralateral Hemisphere ◽

Human Cerebral Cortex ◽

Fos Protein ◽

Urgent Task ◽

Indirect Immunoperoxidase

Background. The search for protein (these include c-fos, ERK1/2, MAP2, NOTCH1) expression that provide neuroplasticity mechanisms of the cerebral cortex after ischemic stroke (IS) patterns is an urgent task. Aims to reveal c-fos, ERK1/2, MAP2, NOTCH1 proteins expression patterns in human cerebral cortex neurons after IS. Materials and methods. We studied 9 left middle cerebral artery (LMCA) IS patients cerebral cortex samples from 3 zones: 1 the zone adjacent to the necrotic tissue focus; 2 zone remote from the previous one by 47 cm; 3 zone of the contralateral hemisphere, symmetric to the IS focus. Control samples were obtained from 3 accident died people. Identification of targeted proteins NSE, c-fos, ERK1/2, MAP2, NOTCH1 was performed by indirect immunoperoxidase immunohistochemical method. Results. Moving away from the ischemic focus, there is an increase in the density of neurons and a decrease in the damaged neurons proportion, the largest share of c-fos protein positive neurons in zone 2, NOTCH1 positive neurons in zone 1, smaller fractions of ERK1/2 and MAP2 positive neurons compared to the control only in samples of zone 1. Conclusions. With the IS development, the contralateral hemisphere is intact tissue increased activation zone, while the zones 1 and 2 have pathological activation signs. In zone 1 of the range, the adaptive response of the tissue decreases, and in zone 2 it expands. Therefore, a key target for therapeutic intervention is zone 2.

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