Fingerprint-based indoor positioning and intensity classification using an improved machine learning framework

Covid-19 has changed the world in terms of business, public, and many other fields. Millions of livelihoods have been affected by the pandemic. Amidst the upheaval, work from home and restrictions on indoor gatherings have played a significant role in flattening the curve. Even after enforcing restrictions, numbers are still on the rise. Various covid-19 tracing applications have been designed to keep track of positive cases. There is an increased need of tracking positive covid cases to limit the spread of the virus to ordinary people. The continents are trying to flatten the curve and maintain a good economic condition to attain normalcy in the season of chaos. Technology has proved helpful in times of pandemics. Now we have IoT devices and advanced tech, including cameras, Wi-Fi, Bluetooth, RFID etc., which can be used for tracking positive patients. This tracking should be made efficient without exploiting the privacy of users. Vaccination research along with proper tracking seems to be a failsafe solution for evading covid- 19 after effects. Amidst all these available strategies, Indoor localization seems to be one of the required fields of research. This thesis dives into establishing a machine learning framework that can be used across all kinds of IoT (Internet of Things) systems and WSNs (Wireless sensor networks). Distance estimation based on fingerprint has been a widely researched field for indoor localization algorithms. Several traditional approaches have been tried out , including trilateration, triangulation which needs more testing parameters and render them complex. Fingerprinting techniques seems to be helpful. Even though various fingerprinting techniques have been tried out, we do not have a generic framework that can be used for research on fingerprinting. The system has been implemented as a part of cloud remote monitoring solutions and an accurate blend of ensemble bagging and boosting methods for making an accurate distance estimation based on the strength of RSSI fingerprints. The framework hopes to serve as a base platform for all kinds of indoor localization research. It encompasses a BLE-based system that acquires data from leak detection systems, relays it to the cloud via BLE gateway, accumulates data on a cloud database, and is passed as alert notifications to users via the use of the cloud-designed app. At the other end, the database aids in the creation of a location dataset for machine learning which is used for training the model. A regression machine learning model is deployed for the prediction of distances based on fingerprint strength which can be utilized for various fingerprint algorithms. A classification machine learning model is deployed for fingerprint intensity classification to evaluate fingerprint levels in different environments

BRAND: A platform for closed-loop experiments with deep network models.

Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved <600 microsecond latency between processes when sending large quantities of data (1024 channels of 30 kHz neural data in 1 ms chunks). BRAND runs a brain-computer interface with a recurrent neural network (RNN) decoder with less than 8 ms of latency from neural data input to decoder prediction. In a real-world demonstration of the system, participant T11 in the BrainGate2 clinical trial (ClinicalTrials.gov Identifier: NCT00912041) performed a standard cursor control task, in which 30 kHz signal processing, RNN decoding, task control, and graphics were all executed in BRAND. This system also supports real-time inference with complex latent variable models like Latent Factor Analysis via Dynamical Systems.Significance.By providing a framework that is fast, modular, and language-agnostic, BRAND lowers the barriers to integrating the latest tools in neuroscience and machine learning into closed-loop experiments