Sustainable Environment: Nexus project

Arequipa region is locaed in Southwestern Peru. The Arequipa Nexus Institute for food, energy, water and the environment aims to address the key challenges to a sustainable furture for the people in the region. This roundtable discusses about the sustainable water management, geosaptial analysis and environment sharing, long range sensor network solution for soil health monitoring and data management and sharing in this Nexus project

Prediction model of alcohol intoxication from facial temperature dynamics based on K-means clustering driven by evolutionary computing

Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identification and classification of alcohol-intoxicated people. We adopted a multiregional segmentation procedure to identify and classify symmetrical facial features, which reliably reflects the facial-temperature variations while subjects are drinking alcohol. Such a model can objectively track alcohol intoxication in the form of a facial temperature map. In our paper, we propose the segmentation model based on the clustering algorithm, which is driven by the modified version of the Artificial Bee Colony (ABC) evolutionary optimization with the goal of facial temperature features extraction from the IR (infrared radiation) images. This model allows for a definition of symmetric clusters, identifying facial temperature structures corresponding with intoxication. The ABC algorithm serves as an optimization process for an optimal cluster's distribution to the clustering method the best approximate individual areas linked with gradual alcohol intoxication. In our analysis, we analyzed a set of twenty volunteers, who had IR images taken to reflect the process of alcohol intoxication. The proposed method was represented by multiregional segmentation, allowing for classification of the individual spatial temperature areas into segmentation classes. The proposed method, besides single IR image modelling, allows for dynamical tracking of the alcohol-temperature features within a process of intoxication, from the sober state up to the maximum observed intoxication level.Web of Science118art. no. 99

Generation of large-area arrays of aperiodic functional micro/nano structure using phase shift interferometry

Phase shift interferometry (PSI) derived from interference technique as greater surface characterization technique based on the interference informationrecorded during a controlled phase shift. This research shows the development of micro/nano structures using phase shift interferometry. (PSI) is the process of developing the complex pattern structure using variable phase angle between two or more beams aligned to obtain functional aperiodic arrays. We have designed and modelled the PSI and simulated through MATLAB in 2D and 3D pattern structures. The PSI was performed in two process analysis. First, without PSI referring normal interference technique. Second, with PSI referring position of laser beams in quadrant based alignment. The obtained results show the minimum feature structure was measured as 12 nm. This feature size developed under phase shift interferometry (PSI) produces minimum feature size compared to the existing interferometry technique. This study gives the promising increased fabrication area could develop large area arrays structures.

Sensor-Based Solid Waste Handling Systems: A Survey

As a consequence of swiftly growing populations in the urban areas, larger quantities of solid waste also form rapidly. Since urban local bodies are found to be unable to manage this perilous situation effectively, there is a high probability of risks relative to the environment and public health. A sudden change is indispensable in the existing systems that are developed for the collection, transportation, and disposal of solid waste, which are entangled in turmoil. However, Smart sensors and wireless technology enable cyber-physical systems to automate solid waste management, which will revolutionize the industry. This work presents a comprehensive study on the evolution of automation approaches in solid waste management systems. This study is enhanced by dissecting the available literature in solid waste management with Radio Frequency Identification (RFID), Wireless Sensor Networks (WSN), and Internet of Things (IoT)-based approaches and analyzing each category with a typical architecture, respectively. In addition, various communication technologies adopted in the aforementioned categories are critically analyzed to identify the best choice for the deployment of trash bins. From the survey, it is inferred that IoT-based systems are superior to other design approaches, and LoRaWAN is identified as the preferred communication protocol for the automation of solid waste handling systems in urban areas. Furthermore, the critical open research issues on state-of-the-art solid waste handling systems are identified and future directions to address the same topic are suggested

Generation of Large-area Arrays of Aperiodic Functional Micro/nano Structures Using Phase Shift Interferometry

Phase shift interferometry (PSI) derived from interference technique as greater surface characterization technique based on the interference information recorded during a controlled phase shift. This research shows the development of micro/nano structures using phase shift interferometry. (PSI) is the process of developing the complex pattern structure using variable phase angle between two or more beams aligned to obtain functional aperiodic arrays. We have designed and modelled the PSI and simulated through MATLAB in 2D and 3D pattern structures. The PSI was performed in two process analysis. First, without PSI referring normal interference technique. Second, with PSI referring position of laser beams in quadrant-based alignment. The obtained results show the minimum feature structure was measured as 12 nm. This feature size developed under phase shift interferometry (PSI) produces minimum feature size compared to the existing interferometry technique. This study gives the promising increased fabrication area could develop large area arrays structures

IoT-enabled tip and swap waste management models for smart cities

Current technical advances in sensors, actuators, and wireless networks enable the Internet of Things (IoT) technology. Key features of IoT are the 'smart things, which have significant computational capabilities. In this paper we focus on waste management using dynamic allocation of collection and transfer points with subsequent transporting of waste to processing facilities. Waste management involves a variety of tasks from the collection of the waste in the field to the transport and disposal to the appropriate locations. The proposed waste management system contributes to innovative Smart City (SC) applications with impact in the dynamic allocation management of mobile depots in the SC. We propose a set of models, which advocate for replacing traditional way of tipping waste into larger containers by swapping full waste bins with empty ones. We also propose the concept of mobile depots as intermediate collection and transfer points. Quantitative and qualitative metrics to assess the efficiency of the proposed models are used. We incorporate the CT, TT, L, D and F quantitative metrics and the S qualitative metric. The S metric takes as input the values of the quantitative metrics and gives an output of high or low satisfaction. The models demonstrate their efficiency and potential adoption by SCs

Spatiotemporal authentication system architecture for smart campus safety

Abstract A Smart Campus is a protected area within Smart Cities (Cities 2.0) where physical security of assets is vital for the continuous operation of the university. Concretely, there are specific mission-critical areas on the campus, which should be protected from unauthorized and malicious individuals. This paper describes a sustainable Smart Campus system architecture based on individuals’ spatiotemporal authentication fingerprint, generated by fusing data from mobile GPS devices and CCTV cameras infrastructure to detect malicious user behavior. The system incorporates unobtrusive monitoring to collect data from such individuals. While the system monitors for unauthorized access to restricted locations within the campus area, data are analyzed by an intrusion detection algorithm that sets off alarms and prompts physical evacuation. The efficiency of the proposed system is evaluated by gauging the prediction accuracy of alarms triggered and response time to the actual incidents on the campus. Results are promising for the adoption of the proposed system architecture by universities in Cities 2.0

IoT-enabled tip and swap waste management models for smart cities

No abstract available