Does the brain implement the Kalman filter?

2004 ◽  
Vol 27 (3) ◽  
pp. 404-405 ◽  
Author(s):  
Valeri Goussev
Keyword(s):  
Kalman Filter ◽  
Data Processing ◽  
Kalman Filtering ◽  
Brain Structures ◽  
Parameter Evaluation ◽  
Filtering Technique ◽  
Processing Techniques ◽  
The Brain

The Kalman filtering technique is considered as a part of concurrent data-processing techniques also related to detection, parameter evaluation, and identification. The adaptive properties of the filter are discussed as being related to symmetrical brain structures.

Parallel Optimal Kalman Filtering for Stochastic Systems in Multimodeling Form

1999 ◽  
Vol 122 (3) ◽  
pp. 542-550 ◽  
Author(s):  
Cyril Coumarbatch ◽  
Zoran Gajic
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Kalman Filters ◽  
Stochastic Systems ◽  
State Variables ◽  
State Transformation ◽  
Eighth Order ◽  
Reduced Order ◽  
Filtering Technique ◽  
Ill Conditioning

In this paper we show how to completely and exactly decompose the optimal Kalman filter of stochastic systems in multimodeling form in terms of one pure-slow and two pure-fast, reduced-order, independent, Kalman filters. The reduced-order Kalman filters are all driven by the system measurements. This leads to a parallel Kalman filtering scheme and removes ill-conditioning of the original full-order singularly perturbed Kalman filter. The results obtained are valid for steady state. In that direction, the corresponding algebraic filter Riccati equation is completely decoupled and solved in terms of one pure-slow and two pure fast, reduced-order, independent, algebraic Riccati equations. A nonsingular state transformation that exactly relates the state variables in the original and new coordinates (in which the required decomposition is achieved) is also established. The eighth order model of a passenger car under road disturbances is used to demonstrate efficiency of the proposed filtering technique. [S0022-0434(00)01703-2]

Improving the accuracy of a vibratory sensor using Kalman filtering

Robotica ◽  
1993 ◽  
Vol 11 (2) ◽  
pp. 129-138 ◽  
Author(s):  
D.T. Pham ◽  
K. Hafeez
Keyword(s):  
Kalman Filter ◽  
Kalman Filtering ◽  
Natural Frequencies ◽  
Industrial Robot ◽  
Sensor System ◽  
Location Information ◽  
Location Determination ◽  
Sensor Signals ◽  
Filtering Technique ◽  
Position And Orientation

SUMMARYThis paper presents a Kalman filtering technique for reducing errors in locating 3-D objects using a sensor system. The location information is employed to control the motion of an industrial robot to pick up the objects. The sensor consists of a rigid platform mounted on a flexible column. Each object to be located is placed on the sensor. The static deflections and natural frequencies of vibrations of the sensor are measured and processed to determine the position and orientation of the object. In practice, the sensor signals obtained are corrupted with noise leading to errors in location determination. A Kalman filter is used to reduce the noise present in the sensor system.

Dynamic Data Processing with Kaiman Filter

1970 ◽  
Vol 111 (5) ◽  
pp. 33-36 ◽  
Author(s):  
A. Kluga ◽  
J. Kluga
Keyword(s):  
Information Processing ◽  
Kalman Filter ◽  
Data Processing ◽  
Kalman Filtering ◽  
Estimation Error ◽  
Gps Receiver ◽  
Adaptive Kalman Filter ◽  
Dynamic Data ◽  
Kaiman Filter ◽  
Movement Change

In this work research results of dynamic data processing with adaptive Kalman filter are presented. In filtering process parameters of filter are change on results of difference between measuring and expected coordinate. Modeling results of adaptive Kalman filtering are comparing with classic Kalman filter. Modeling results using adaptive Kalman filtering for coordinate estimation shows that estimation error decrease in points when parameters of movement change very rapidly. Results of adaptive Kalman filter modeling and filter use for GPS receiver information processing are described. Ill. 11, bibl. 4 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.111.5.351

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

scholarly journals The Salted Kalman Filter: Kalman filtering on hybrid dynamical systems

Automatica ◽  
2021 ◽  
Vol 131 ◽  
pp. 109752
Author(s):  
Nathan J. Kong ◽  
J. Joe Payne ◽  
George Council ◽  
Aaron M. Johnson
Keyword(s):  
Kalman Filter ◽  
Dynamical Systems ◽  
Kalman Filtering ◽  
Hybrid Dynamical Systems

Review of data processing techniques for density profile evaluation from broadband FM-CW reflectometry on ASDEX Upgrade

2006 ◽  
Vol 46 (9) ◽  
pp. S693-S707 ◽  
Author(s):  
P Varela ◽  
M.E Manso ◽  
A Silva ◽  
the CFN Team ◽  
the ASDEX Upgrade Team
Keyword(s):  
Data Processing ◽  
Density Profile ◽  
Processing Techniques

scholarly journals Peptides Derived from Growth Factors to Treat Alzheimer’s Disease

2021 ◽  
Vol 22 (11) ◽  
pp. 6071
Author(s):  
Suzanne Gascon ◽  
Jessica Jann ◽  
Chloé Langlois-Blais ◽  
Mélanie Plourde ◽  
Christine Lavoie ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Growth Factors ◽  
Signaling Pathways ◽  
Brain Structures ◽  
Therapeutic Strategies ◽  
Native Proteins ◽  
Receptor Sites ◽  
The Brain

Alzheimer’s disease (AD) is a devastating neurodegenerative disease characterized by progressive neuron losses in memory-related brain structures. The classical features of AD are a dysregulation of the cholinergic system, the accumulation of amyloid plaques, and neurofibrillary tangles. Unfortunately, current treatments are unable to cure or even delay the progression of the disease. Therefore, new therapeutic strategies have emerged, such as the exogenous administration of neurotrophic factors (e.g., NGF and BDNF) that are deficient or dysregulated in AD. However, their low capacity to cross the blood–brain barrier and their exorbitant cost currently limit their use. To overcome these limitations, short peptides mimicking the binding receptor sites of these growth factors have been developed. Such peptides can target selective signaling pathways involved in neuron survival, differentiation, and/or maintenance. This review focuses on growth factors and their derived peptides as potential treatment for AD. It describes (1) the physiological functions of growth factors in the brain, their neuronal signaling pathways, and alteration in AD; (2) the strategies to develop peptides derived from growth factor and their capacity to mimic the role of native proteins; and (3) new advancements and potential in using these molecules as therapeutic treatments for AD, as well as their limitations.

scholarly journals The Brain Resting-State Functional Connectivity Underlying Violence Proneness: Is It a Reliable Marker for Neurocriminology? A Systematic Review

2019 ◽  
Vol 9 (1) ◽  
pp. 11 ◽  
Author(s):  
Ángel Romero-Martínez ◽  
Macarena González ◽  
Marisol Lila ◽  
Enrique Gracia ◽  
Luis Martí-Bonmatí ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Effective Treatment ◽  
Resting State ◽  
Brain Structures ◽  
Prevention Programs ◽  
Angular Gyrus ◽  
Resting State Functional Connectivity ◽  
Treatment And Prevention ◽  
Reliable Marker ◽  
The Brain

Introduction: There is growing scientific interest in understanding the biological mechanisms affecting and/or underlying violent behaviors in order to develop effective treatment and prevention programs. In recent years, neuroscientific research has tried to demonstrate whether the intrinsic activity within the brain at rest in the absence of any external stimulation (resting-state functional connectivity; RSFC) could be employed as a reliable marker for several cognitive abilities and personality traits that are important in behavior regulation, particularly, proneness to violence. Aims: This review aims to highlight the association between the RSFC among specific brain structures and the predisposition to experiencing anger and/or responding to stressful and distressing situations with anger in several populations. Methods: The scientific literature was reviewed following the PRISMA quality criteria for reviews, using the following digital databases: PubMed, PsycINFO, Psicodoc, and Dialnet. Results: The identification of 181 abstracts and retrieval of 34 full texts led to the inclusion of 17 papers. The results described in our study offer a better understanding of the brain networks that might explain the tendency to experience anger. The majority of the studies highlighted that diminished RSFC between the prefrontal cortex and the amygdala might make people prone to reactive violence, but that it is also necessary to contemplate additional cortical (i.e. insula, gyrus [angular, supramarginal, temporal, fusiform, superior, and middle frontal], anterior and posterior cingulated cortex) and subcortical brain structures (i.e. hippocampus, cerebellum, ventral striatum, and nucleus centralis superior) in order to explain a phenomenon as complex as violence. Moreover, we also described the neural pathways that might underlie proactive violence and feelings of revenge, highlighting the RSFC between the OFC, ventral striatal, angular gyrus, mid-occipital cortex, and cerebellum. Conclusions. The results from this synthesis and critical analysis of RSFC findings in several populations offer guidelines for future research and for developing a more accurate model of proneness to violence, in order to create effective treatment and prevention programs.

scholarly journals Glia Not Neurons: Uncovering Brain Dysmaturation in a Rat Model of Alzheimer’s Disease

Biomedicines ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 823
Author(s):  
Ekaterina A. Rudnitskaya ◽  
Tatiana A. Kozlova ◽  
Alena O. Burnyasheva ◽  
Natalia A. Stefanova ◽  
Nataliya G. Kolosova
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prefrontal Cortex ◽  
Brain Structures ◽  
Time Frame ◽  
Oxys Rats ◽  
Unknown Etiology ◽  
Suitable Model ◽  
Model Of Alzheimer’S Disease ◽  
The Brain

Sporadic Alzheimer’s disease (AD) is a severe disorder of unknown etiology with no definite time frame of onset. Recent studies suggest that middle age is a critical period for the relevant pathological processes of AD. Nonetheless, sufficient data have accumulated supporting the hypothesis of “neurodevelopmental origin of neurodegenerative disorders”: prerequisites for neurodegeneration may occur during early brain development. Therefore, we investigated the development of the most AD-affected brain structures (hippocampus and prefrontal cortex) using an immunohistochemical approach in senescence-accelerated OXYS rats, which are considered a suitable model of the most common—sporadic—type of AD. We noticed an additional peak of neurogenesis, which coincides in time with the peak of apoptosis in the hippocampus of OXYS rats on postnatal day three. Besides, we showed signs of delayed migration of neurons to the prefrontal cortex as well as disturbances in astrocytic and microglial support of the hippocampus and prefrontal cortex during the first postnatal week. Altogether, our results point to dysmaturation during early development of the brain—especially insufficient glial support—as a possible “first hit” leading to neurodegenerative processes and AD pathology manifestation later in life.

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