A Hybrid Indoor Altimetry Based on Barometer and UWB.

Accurate altimetry is essential for location-based services in commercial and industrial applications. However, current altimetry methods only provide low-accuracy measurements, particularly in multistorey buildings with irregular structures, such as hollow areas found in various industrial and commercial sites. This paper innovatively proposes a tightly coupled indoor altimetry system that utilizes floor identification to improve height measurement accuracy. The system includes two optimized algorithms that improve floor identification accuracy through activity detection and address the problem of difficult convergence of z-axis coordinates due to indoor coplanarity by applying constraints to iterative least squares (ILS). Two experiments were conducted in a teaching building and a laboratory, including an irregular environment with a hollow area. The results show that our proposed method for identifying floors based on activity detection outperforms other methods. In dynamic experiments, our method effectively eliminates repeated transformations during the up- and downstairs process, and in static experiments, it minimizes the impact of barometric drift. Furthermore, our proposed altimetry method based on constrained ILS achieves significantly improved positioning accuracy compared to ILS, 1D-CNN, and WC. Specifically, in the teaching building, our method achieves improvements of 0.84 m, 0.288 m, and 0.248 m, respectively, while in the laboratory, the improvements are 2.607 m, 0.696 m, and 0.625 m.

Objective Supervised Machine Learning-Based Classification and Inference of Biological Neuronal Networks.

The classification of biological neuron types and networks poses challenges to the full understanding of the human brain's organisation and functioning. In this paper, we develop a novel objective classification model of biological neuronal morphology and electrical types and their networks, based on the attributes of neuronal communication using supervised machine learning solutions. This presents advantages compared to the existing approaches in neuroinformatics since the data related to mutual information or delay between neurons obtained from spike trains are more abundant than conventional morphological data. We constructed two open-access computational platforms of various neuronal circuits from the Blue Brain Project realistic models, named Neurpy and Neurgen. Then, we investigated how we could perform network tomography with cortical neuronal circuits for the morphological, topological and electrical classification of neurons. We extracted the simulated data of 10,000 network topology combinations with five layers, 25 morphological type (m-type) cells, and 14 electrical type (e-type) cells. We applied the data to several different classifiers (including Support Vector Machine (SVM), Decision Trees, Random Forest, and Artificial Neural Networks). We achieved accuracies of up to 70%, and the inference of biological network structures using network tomography reached up to 65% of accuracy. Objective classification of biological networks can be achieved with cascaded machine learning methods using neuron communication data. SVM methods seem to perform better amongst used techniques. Our research not only contributes to existing classification efforts but sets the road-map for future usage of brain-machine interfaces towards an in vivo objective classification of neurons as a sensing mechanism of the brain's structure.

Reconfigurable Filtering of Neuro-Spike Communications Using Synthetically Engineered Logic Circuits.

High-frequency firing activity can be induced either naturally in a healthy brain as a result of the processing of sensory stimuli or as an uncontrolled synchronous activity characterizing epileptic seizures. As part of this work, we investigate how logic circuits that are engineered in neurons can be used to design spike filters, attenuating high-frequency activity in a neuronal network that can be used to minimize the effects of neurodegenerative disorders such as epilepsy. We propose a reconfigurable filter design built from small neuronal networks that behave as digital logic circuits. We developed a mathematical framework to obtain a transfer function derived from a linearization process of the Hodgkin-Huxley model. Our results suggest that individual gates working as the output of the logic circuits can be used as a reconfigurable filtering technique. Also, as part of the analysis, the analytical model showed similar levels of attenuation in the frequency domain when compared to computational simulations by fine-tuning the synaptic weight. The proposed approach can potentially lead to precise and tunable treatments for neurological conditions that are inspired by communication theory.