In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to:  • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information.  Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.

IEEE transactions on pattern analysis and machine intelligence

The rise of low-cost sensing for managing air pollution in cities.

An online GPRS-Sensors Array for air pollution monitoring has been designed, implemented, and tested. The proposed system consists of a Mobile Data-Acquisition Unit (Mobile-DAQ) and a fixed Internet-Enabled Pollution Monitoring Server (Pollution-Server). The Mobile-DAQ unit integrates a single-chip microcontroller, air pollution sensors array, a General Packet Radio Service Modem (GPRS-Modem), and a Global Positioning System Module (GPS-Module). The Pollution-Server is a high-end personal computer application server with Internet connectivity. The Mobile-DAQ unit gathers air pollutants levels (CO, NO2, and SO2), and packs them in a frame with the GPS physical location, time, and date. The frame is subsequently uploaded to the GPRS-Modem and transmitted to the Pollution-Server via the public mobile network. A database server is attached to the Pollution-Server for storing the pollutants level for further usage by various clients such as environment protection agencies, vehicles registration authorities, and tourist and insurance companies. The Pollution-Server is interfaced to Google Maps to display real-time pollutants levels and locations in large metropolitan areas. The system was successfully tested in the city of Sharjah, UAE. The system reports real-time pollutants level and their location on a 24-h/7-day basis.

A Mobile GPRS-Sensors Array for Air Pollution Monitoring

Big data and IoT-based applications in smart environments: A systematic review

An animal health monitoring system for monitoring the physiological parameters, such as rumination, body temperature, and heart rate with surrounding temperature and humidity, has been developed. The developed system can also analyze the stress level corresponding to thermal humidity index. The IEEE802.15.4 and IEEE1451.2 standards-based sensor module has been developed successfully. The Zigbee device and PIC18F4550 microcontroller are used in the implementation of sensor module. The graphical user interface (GUI) is implemented in LabVIEW 9 according to the IEEE1451.1 standard. The real-time monitoring of physiological and behavioral parameters can be present on the GUI PC. The device is very helpful for inexpensive health care of livestock. A prototype model is developed and tested with high accuracy results.

A Zigbee-Based Animal Health Monitoring System

An Environmental Air Pollution Monitoring System (EAPMS) for monitoring the concentrations of major air pollutant gases has been developed, complying with the IEEE 1451.2 standard. This system measures concentrations of gases such as CO, NO2, SO2, and O3 using semiconductor sensors. The smart transducer interface module (STIM) was implemented using the analog devices' ADuC812 microconverter. Network Capable Application Processor (NCAP) was developed using a personal computer and connected to the STIM via the transducer independent interface. Three gas sensors were calibrated using the standard calibration methods. Gas concentration levels and information regarding the STIM can be seen on the graphical user interface of the NCAP. Further, the EAPMS is capable of warning when the pollutant levels exceed predetermined maxima and the system can be developed into a low cost version for developing countries.

An Environmental Air Pollution Monitoring System Based on the IEEE 1451 Standard for Low Cost Requirements

In recent times indoor air quality has attracted the attention of policy makers and researchers as an important similar to that of external air pollution. In certain sense, indoor air quality must be paid more attention than outdoor air quality as people spend more time indoors than outdoors. The indoor environments are confined and closed compared to external environments providing less opportunity for the pollutants to dilute. With the advancement of technology, working places have become more automated using machines to carry out the tasks that were hitherto done manually. These devices emit various solids and gases into the environment during their operation. These emissions contain many substances that are harmful to human health, when exposed to them for a prolonged period of time or more than certain levels of concentration. This paper proposes an IoT based indoor air quality monitoring system for tracking the ozone concentrations near a photocopy machine. The experimental system with a semiconductor sensor capable of monitoring ozone concentrations was installed near a high volume photocopier. The IoT device has been programmed to collect and transmit data at an interval of five minutes over blue tooth connection to a gateway node that in turn communicates with the processing node via the WiFi local area network. The sensor was calibrated using the standard calibration methods. As an additional capability, the proposed air pollution monitoring system can generate warnings when the pollution level exceeds beyond a predetermined threshold value.

IoT enabled proactive indoor air quality monitoring system for sustainable health management

In this paper, we present a voice recognition based victim detection system using an Unmanned Aerial Vehicle (UAV). When a large-scale disaster such as a huge earthquake occurs, the building collapses and it is expected that many people will be buried in rubble. Under such circumstances, it is very important to detect victims effectively in order to promptly rescue victims and raise the survival rate. Therefore, in this study, we propose a system for detecting and rescuing victims whose are buried in rubble. In this study, a speaker installed on the UAV makes sounds for victims to react, and victims are detected by capturing their reaction voice. This reaction voice is mixed with drone sound and other sounds in outdoor environment. For this reason, extracting the human voice from this sound mixture is important to detect victims. We first discuss about the development of an UAV with an on-board voice recognition system using a Raspberry pi. Then present about extracting the vic surrounding sounds. We experimentally showed the performance of victim detection using the system. Keywords-victim detection; UAV; voice recognition; Navio 2;

Victim Detection Using UAV with On-board Voice Recognition System

The massive development take place in the field of IoT (Internet of Things) has enabled generation of large set of real time environmental data from low cost sensor networks. This has enabled developing nations to monitor their environments with more economical options. The environmental monitoring network of Sri Lanka is mainly handled by the government bodies such as Meteorological Department, Irrigation Department and Central Environmental Authority. Most of the systems are manually operated and sparsely distributed. The automated systems are restricted to certain main cities of the country. Thus, the data provided by the existing network not capable enough to generate reliable climate or disaster specific forecasts. The available data is mostly retained as hard copies and they are not freely and publicly available. Hence, every time, the researchers and decision makers need to purchase data and required to undergo redundant data collection procedures. In this context, the 4ONSE research project on developing an experimental, open-source, low cost and non-conventional Environmental Monitoring System (EMS) can be considered as the 1st initiative in Sri Lanka which creates a new revolution in the country for environmental data viewing and sharing without any charge. 4ONSE is the acronym for 4 times Open and Non-Conventional technology for Sensing the Environment, which is a joint research between University of Moratuwa and University of Applied Sciences and Arts of Southern Switzerland (SUPSI). Deduru Oya river basin area is the selected study area to deploy the 4ONSE systems. The Deduru Oya is 115km long and covers 2687km2 of catchment area. It carries flash floods during rainy seasons and very low flow during the dry seasons. Hence, the selected case study area is ideal for developing any forecasting application based on the real time environmental data. The project team has already deployed 27 systems in the basin. Temperature, rainfall, atmospheric pressure, solar radiation, relative humidity, soil moisture, wind speed and direction are the parameters measured by the system. In this research we attempt to present the state of the art of open source based environmental monitoring, architecture of the 4ONSE system, motivations behind deployment of the 4ONSE system and finally the lessons for the future research and development.

Building an open-source environmental monitoring system - A review of state-of-the-art and directions for future research

Recently, method of controlling machine with natural control called Natural User Interface has attracted attention. particularly, human hand is considered to be communication media that can communicate information most naturally. In this paper, user hand is detected by following acquired skin color of the user's face, through face detection. After detecting the user hand, hand gesture is determined. Finally, a robot is controlled to have multi movements according to detected several hand gesture patterns. We succeeded in acquiring the skin color information from user's face and detecting both user's hand and hand gesture following that. In this paper, we developed a mobile robot to conduct experiments. We also confirmed that robot can be controlled by human hand gesture.

B. H. Sudantha

Papers

An Environmental Air Pollution Monitoring System Based on the IEEE 1451 Standard for Low Cost Requirements

IoT enabled proactive indoor air quality monitoring system for sustainable health management

Victim Detection Using UAV with On-board Voice Recognition System

Building an open-source environmental monitoring system - A review of state-of-the-art and directions for future research

A Study on Mobile Robot Control by Hand gesture Detection