Archives in DNA:Exploring implications of an emerging bio-digital technology through design fiction

Continuing developments in DNA-based digital data storage systems promise us a sustainable, techno-utopian future; propositioning bio-digital solutions addressing the ever-increasing global data production, and inadequacies of conventional storage infrastructure to meet the demand. Distinct attributes of DNA make it an attractive archival medium. With its ability to retain high density of digital information cheaply, and to do so over multi-lifespans, DNA-based storage systems are seen as able to radically shape how we archive and use data, across wide-ranging applications. However, while the stakeholders continue to refine and race towards commercialization of the emerging technology, its sociocultural and ethical implications remain unexplored, limiting opportunities to generate insights on how such systems could be better designed and experienced. This workshop begins to explore what our DNA-mediated archival futures may hold. We learn about the fundamental principles governing the new technology and create stories about its pervasion in our lives, mediated through design fiction and structured discourse

Internet of Things: smart ubiquitous architecture of intelligent transport system

By 2020, there will be more than 24 billion smart devices connected in the Internet of Things (IoT). Tremendously augmented motorization, population, and urbanization has not only brought us many amenities but also has increased traffic congestion to its limits. In this paper, we used IOT to design an efficient and congestion free Intelligent Transport System (ITS). A lot of research is done to either improve or change any one aspect of ITS at one time. This paper demonstrates every aspect or features of an efficient ITS. The purpose of this research is to provide developing countries a detailed and easy to follow ITS architecture so that they can create an ITS for their populace

BEV-Locator: An End-to-end Visual Semantic Localization Network Using Multi-View Images

Accurate localization ability is fundamental in autonomous driving. Traditional visual localization frameworks approach the semantic map-matching problem with geometric models, which rely on complex parameter tuning and thus hinder large-scale deployment. In this paper, we propose BEV-Locator: an end-to-end visual semantic localization neural network using multi-view camera images. Specifically, a visual BEV (Birds-Eye-View) encoder extracts and flattens the multi-view images into BEV space. While the semantic map features are structurally embedded as map queries sequence. Then a cross-model transformer associates the BEV features and semantic map queries. The localization information of ego-car is recursively queried out by cross-attention modules. Finally, the ego pose can be inferred by decoding the transformer outputs. We evaluate the proposed method in large-scale nuScenes and Qcraft datasets. The experimental results show that the BEV-locator is capable to estimate the vehicle poses under versatile scenarios, which effectively associates the cross-model information from multi-view images and global semantic maps. The experiments report satisfactory accuracy with mean absolute errors of 0.052m, 0.135m and 0.251$^\circ$ in lateral, longitudinal translation and heading angle degree

A Semantic loT Early Warning System for Natural Environment Crisis Management

An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-type EWS include easier sensor and data source plug-and-play, simpler, richer, and more dynamic metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model.We use lightweight semantics for metadata to enhance rich sensor data acquisition.We use heavyweight semantics for top level W3CWeb Ontology Language ontology models describing multileveled knowledge-bases and semantically driven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a reployed EWS infrastructure

Using a Smart City IoT to Incentivise and Target Shifts in Mobility Behaviour-Is It a Piece of Pie?

The work presented in this paper is a central part of the research and development in the SUNSET project (contract No. 270228), supported by the 7th Framework Research Program funded by the European Commission. The authors also acknowledge the support of other SUNSET consortium members in helping to create and evaluate the SUNSET tripzoom system

The EDEN-IW ontology model for sharing knowledge and water quality data between heterogeneous databases

Abstract The Environmental Data Exchange Network for Inland Water (EDEN-IW) project's main aim is to develop a system for making disparate and heterogeneous databases of Inland Water quality more accessible to users. The core technology is based upon a combination of: ontological model to represent a Semantic Web based data model for IW; software agents as an infrastructure to share and reason about the IW semantic data model and XML to make the information accessible to Web portals and mainstream Web services. This presentation focuses on the Semantic Web or Ontological model. Currently, we have successfully demonstrated the use of our systems to semantically integrate two main database resources from IOW and NERI -these are available on-line. We are in the process of adding further databases and supporting a wider variety of user queries such as Decision Support System queries

A Fuzzy Logic Module to Estimate a Driver’s Fuel Consumption for Reality-Enhanced Serious Games

Reality-enhanced gaming is an emerging serious game genre, that could contextualize a game within its real instruction-target environment. A key module for such games is the evaluator, that senses a user performance and provides consequent input to the game. In this project, we have explored an application in the automotive field, estimating driver performance in terms of fuel consumption, based on three key vehicular signals, that are directly controllable by the driver: throttle position sensor (TPS), engine rotation speed (RPM) and car speed. We focused on Fuzzy Logic, given its ability to embody expert knowledge and deal with incomplete information availability. The fuzzy models – that we iteratively defined based on literature expertise and data analysis – can be easily plugged into a reality-enhanced gaming architecture. We studied four models with all the possible combinations of the chosen variables (TPS and RPM; RPM and speed; TPS and speed; TPS, speed and RPM). Input data were taken from the enviroCar database, and our fuel consumption predictions compared with their estimated values. Results indicate that the model with the three inputs outperforms the other models giving a higher coefficient of determination (R2), and lower error. Our study also shows that RPM is the most important fuel consumption predictor, followed by TPS and speed

A Contactless Health Monitoring System for Vital Signs Monitoring, Human Activity Recognition and Tracking

Integrated sensing and communication technologies provide essential sensing capabilities that address pressing challenges in remote health monitoring systems. However, most of today’s systems remain obtrusive, requiring users to wear devices, interfering with people’s daily activities, and often raising privacy concerns. Herein, we present HealthDAR, a low-cost, contactless, and easy-to-deploy health monitoring system. Specifically, HealthDAR encompasses three interventions: i) Symptom Early Detection (monitoring of vital signs and cough detection), ii) Tracking & Social Distancing, and iii) Preventive Measures (monitoring of daily activities such as face-touching and hand-washing). HealthDAR has three key components: (1) A low-cost, low-energy, and compact integrated radar system, (2) A simultaneous signal processing combined deep learning (SSPDL) network for cough detection, and (3) A deep learning method for the classification of daily activities. Through performance tests involving multiple subjects across uncontrolled environments, we demonstrate HealthDAR’s practical utility for health monitoring

The Agentcities Network Architecture

Agentcities is a worldwide initiative designed to help realize the commercial and research potential of agent-based applications by constructing an open distributed network of platforms hosting diverse agents and services. The ultimate aim of the Agentcities initiative is to enable the dynamic, intelligent and autonomous composition of services to achieve user and business goals, thereby creating compound services to address changing needs. In this paper, we present the progress and current status of the Agentcities Network, six months after the launch of the project. The architecture of the Network, consisting of agents, services and platforms, is described. Finally, the plans and challenges for enhancing the Agentcities Network in the next phase of development are also discussed