LIPSFUS: A neuromorphic dataset for audio-visual sensory fusion of lip reading

This paper presents a sensory fusion neuromorphic dataset collected with precise temporal synchronization using a set of Address-Event-Representation sensors and tools. The target application is the lip reading of several keywords for different machine learning applications, such as digits, robotic commands, and auxiliary rich phonetic short words. The dataset is enlarged with a spiking version of an audio-visual lip reading dataset collected with frame-based cameras. LIPSFUS is publicly available and it has been validated with a deep learning architecture for audio and visual classification. It is intended for sensory fusion architectures based on both artificial and spiking neural network algorithms.Comment: Submitted to ISCAS2023, 4 pages, plus references, github link provide

Case Study - Spiking Neural Network Hardware System for Structural Health Monitoring

This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead

Limited Phosphorous Supply Improved Lipid Content of Chlorella vulgaris That Increased Phenol and 2-Chlorophenol Adsorption from Contaminated Water with Acid Treatment

Phenolic compounds are toxic and ominously present in industrial effluents, which can end up in water bodies, causing potential damage to living organisms. This study employed the dried biomass of freshwater green microalgae Chlorella vulgaris to remove phenol and 2-chlorophenol from an aqueous environment. C. vulgaris was grown under different phosphorus- (P) starved conditions, and biomass was treated with sulfuric acid. It was observed that reducing the P level enhanced the lipid content by 7.8 times while decreasing protein by 7.2 times. P-starved C. vulgaris dried biomass removed phenol and 2-chlorophenol by 69 and 57%, respectively, after 180 min from the contaminated water. Acid-treated P-starved C. vulgaris dried biomass removed phenol and 2-chlorophenol by 77 and 75%, respectively, after 180 min. Thus, an economical and eco-friendly P-starved and acid treated C. vulgaris biomass has better potential to remove phenol and 2-chlorophenol from contaminated ground water and industrial wastewater.This research has been funded by Scientific Research Deanship at University of Ha’il—Saudi Arabia through project number RG-21 105