Two-Stage Human Activity Recognition Using 2D-ConvNet

There is huge requirement of continuous intelligent monitoring system for human activity recognition in various domains like public places, automated teller machines or healthcare sector. Increasing demand of automatic recognition of human activity in these sectors and need to reduce the cost involved in manual surveillance have motivated the research community towards deep learning techniques so that a smart monitoring system for recognition of human activities can be designed and developed. Because of low cost, high resolution and ease of availability of surveillance cameras, the authors developed a new two-stage intelligent framework for detection and recognition of human activity types inside the premises. This paper, introduces a novel framework to recognize single-limb and multi-limb human activities using a Convolution Neural Network. In the first phase single-limb and multi-limb activities are separated. Next, these separated single and multi-limb activities have been recognized using sequence-classification. For training and validation of our framework we have used the UTKinect-Action Dataset having 199 actions sequences performed by 10 users. We have achieved an overall accuracy of 97.88% in real-time recognition of the activity sequences

Real-time indoor air quality (IAQ) monitoring system for smart buildings

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIndoor air quality (IAQ) is a term describing the air quality of a room, it refers to the health and comfort of the occupants. Normally, people spend around 90% of their time in indoor environments where the concentration of air pollutants, such CO, CO2, VOCs, SO2, O3 and NOx, may be two to five times — and occasionally, more than 100 times — higher than outdoor levels. According to the World Health Organization (WHO), the indoor air pollution is responsible for the deaths of 3.8 million people annually. It has been indicated that IAQ in residential areas or buildings is significantly affected by three primary factors: (i) Outdoor air quality, (ii) human activity in buildings, and (iii) building and construction materials, equipment, and furniture. In this contest, this work consist in a real time IAQ system to monitoring and control thermal comfort and gas concentration. The system has a data acquisition stage, where the data is measured by a set of sensors and then stored on InfluxDB database and displayed in Grafana. To track the behavior of the measured parameters, two machine learning algorithms are developed, a mathematical model linear regression, and an artificial intelligence model neural network. In a test made to see how precise were the prediction of the two models, linear regression model performed better then neural network, presenting cases of up to 99.7% and 98.1% of score prediction, respectively. After that, a test with smoke was done to validate the models where the results shows that both learning models can detect adverse cases. Finally, prediction data are storage on InfluxDB and displayed on Grafana to monitoring in real-time measured data and prediction data

Transcriptional Priming of Salmonella Pathogenicity Island-2 Precedes Cellular Invasion

Invasive salmonellosis caused by Salmonella enterica involves an enteric stage of infection where the bacteria colonize mucosal epithelial cells, followed by systemic infection with intracellular replication in immune cells. The type III secretion system encoded in Salmonella Pathogenicity Island (SPI)-2 is essential for intracellular replication and the regulators governing high-level expression of SPI-2 genes within the macrophage phagosome and in inducing media thought to mimic this environment have been well characterized. However, low-level expression of SPI-2 genes is detectable in media thought to mimic the extracellular environment suggesting that additional regulatory pathways are involved in SPI-2 gene expression prior to cellular invasion. The regulators involved in this activity are not known and the extracellular transcriptional activity of the entire SPI-2 island in vivo has not been studied. We show that low-level, SsrB-independent promoter activity for the ssrA-ssrB two-component regulatory system and the ssaG structural operon encoded in SPI-2 is dependent on transcriptional input by OmpR and Fis under non-inducing conditions. Monitoring the activity of all SPI-2 promoters in real-time following oral infection of mice revealed invasion-independent transcriptional activity of the SPI2 T3SS in the lumen of the gut, which we suggest is a priming activity with functional relevance for the subsequent intracellular host-pathogen interaction

Incorporating appliance usage patterns for non-intrusive load monitoring and load forecasting

This paper proposes a novel Non-Intrusive Load Monitoring (NILM) method which incorporates appliance usage patterns (AUPs) to improve performance of active load identi- fication and forecasting. In the first stage, the AUPs of a given residence were learnt using a spectral decomposition based standard NILM algorithm. Then, learnt AUPs were utilized to bias the priori probabilities of the appliances through a specifically constructed fuzzy system. The AUPs contain likelihood measures for each appliance to be active at the present instant based on the recent activity/inactivity of appliances and the time of day. Hence, the priori probabilities determined through the AUPs increase the active load identification accuracy of the NILM algorithm. The proposed method was successfully tested for two standard databases containing real household measurements in USA and Germany. The proposed method demonstrates an improvement in active load estimation when applied to the aforementioned databases as the proposed method augments the smart meter readings with the behavioral trends obtained from AUPs. Furthermore, a residential power consumption forecasting mechanism, which can predict the total active power demand of an aggregated set of houses, five minutes ahead of real time, was successfully formulated and implemented utilizing the proposed AUP based technique

Holter digital ECG

This work presents the design, development and implementation of a portable heart activity monitoring system (ECG HOLTER). It consists of two major blocks: the analogue stage, involving a bio potential amplifier suitable for signal conditioning, and the digital stage: a microcontroller with a built-in ADC (12 bits, 10 effective resolution bits), used for signal digitalization. In addition, the system includes an SD card for data storage, a graphic display for real-time signal representation and a visualization software.Este artículo presenta el diseño, desarrollo y construcción de un sistema de monitoreo ambulatorio de señales electrocardiográficas (HOLTER ECG). El mismo consta de dos grandes bloques. Por un lado, una etapa analógica, conformada por un amplificador de biopotenciales para acondicionamiento de la señal, y por otro, un micro- controlador con conversor ADC integrado (12 bits, 10 bits efectivos) para la digitalización de la misma. Además, se incorpora al sistema una tarjeta SD para almacenamiento de datos, un display gráfico para visualización en tiempo real y un software de visualización de la señal en computadora.Instituto de Investigaciones en Electrónica, Control y Procesamiento de Señale

An FPGA-based infant monitoring system

We have designed an automated visual surveillance system for monitoring sleeping infants. The low-level image processing is implemented on an embedded Xilinx’s Virtex II XC2v6000 FPGA and quantifies the level of scene activity using a specially designed background subtraction algorithm. We present our algorithm and show how we have optimised it for this platform

Design methodology for smart actuator services for machine tool and machining control and monitoring

This paper presents a methodology to design the services of smart actuators for machine tools. The smart actuators aim at replacing the traditional drives (spindles and feed-drives) and enable to add data processing abilities to implement monitoring and control tasks. Their data processing abilities are also exploited in order to create a new decision level at the machine level. The aim of this decision level is to react to disturbances that the monitoring tasks detect. The cooperation between the computational objects (the smart spindle, the smart feed-drives and the CNC unit) enables to carry out functions for accommodating or adapting to the disturbances. This leads to the extension of the notion of smart actuator with the notion of agent. In order to implement the services of the smart drives, a general design is presented describing the services as well as the behavior of the smart drive according to the object oriented approach. Requirements about the CNC unit are detailed. Eventually, an implementation of the smart drive services that involves a virtual lathe and a virtual turning operation is described. This description is part of the design methodology. Experimental results obtained thanks to the virtual machine are then presented