Elephant–train collision has been a major issue for both the railway as well as the forest departments. In this study real-time video data is analysed for detecting elephant to alert the train driver in case of elephants crossing the railway track in which the train is approaching. The HAAR feature extraction and adaptive boosting-based machine learning algorithm are used for detecting elephants from real-time video data. The experimental result shows the average precision of the proposed technique in detecting elephants using real-time video data is more than 96%.

Brain–computer interface (BCI) is an active domain which has attracted attention of the research community in recent years. It offers huge potential as a technology which can estimate the intention of a user by analysis of brain signals and establish a communication channel directly between a human brain and an external device. Electroencephalography (EEG) is the most popular signal acquisition technique due to its ease of use and simplicity. In EEG-based BCI systems, electrodes are placed on specific positions on the scalp of the subject to record electrical activity. The BCI system consists of sequential stages of signal acquisition, its preprocessing, feature extraction and feature classification. It is an active research area which has a focus on improving classification accuracy in motor imagery-based BCI systems. The first stage in a BCI system is to acquire EEG signals from different positions of the scalp of the human subject. The acquired brain signals are preprocessed to remove artifacts before these are fed to feature the extraction stage. In this paper, independent component analysis (ICA) technique is used to remove artifacts from acquired signals. Filter bank common spatial pattern (FBCSP) technique is then used for feature extraction and feature selection. A feature classification approach based on support vector machine (SVM) is proposed in this work and its performance is enhanced by optimizing its polynomial kernel parameters. Selection of kernel parameters is done by grid search method using the fivefold cross-validation procedure. The proposed approach is then executed on publicly available data set 2a of BCI Competition IV. Results show that the proposed approach offers higher classification accuracy and lower misclassification rate as compared to other methods executed on the same dataset, as reported in literature.

Neuro-degenerative diseases (NDD) continue to increase globally and have significant impact on health, developmental and financial fronts. Recent studies have shown that gait impairment as one of the earliest signs of the disease. However, classification of multiple types of NDD becomes more challenging because of the high overlapping symptoms specifically at early stages. This paper entails a composite of signal processing and machine intelligence algorithms to process the gait data captured through multi-sensors for a reliable classification different types of NDD. The captured dataset used in this research consisted of 60 patients’ records representing three different types of NDD. Our simulation results indicated that the proposed approach outperformed existing works in this domain. The proposed work might help the mitigation plans for NDD, reliable monitoring of the disease progression and can assist the evaluation of possible therapy and treatments that would benefit the individuals, associated families, society and healthcare services.

Over the last few years, many optimization algorithms have been developed to predict the optimal secondary structure of ribonucleic acid (RNA) with “minimum free energy” (MFE). These algorithms are either inspired by dynamic programming or by meta-heuristic techniques. RNA participates in several biological activities in the organism. These activities involve protein synthesis, understanding the functional behavior of RNA molecules, coding, decoding and gene expression, carrier of transferring genetic information, formation of protein, catalyst in biomedical reactions and structural molecule in cellular organelles, transcription, etc. Beside the said activities, the major role of RNA is in developing new drugs and understanding several diseases occurred due to genetic disorder and viruses. For the above said activities, it is required to predict the correct RNA secondary structure having minimum free energy with desired prediction accuracy. This paper presents a meta-heuristic optimization algorithm to obtain the optimal secondary structure of RNA with required functionality and requires less time than the others in the literature. The performance of the proposed algorithm is checked with different existing state-of-the-art techniques. It is found that the proposed algorithm gives better results against the other techniques.

Road traffic has been exponentially growing with surging people and vehicle population. Road connectivity infrastructure has not been growing correspondingly and hence the research endeavors for optimal resource allocation and utilization of connectivity resources has gained a lot these days. Therefore, insights-driven real-time traffic management is turning out to be an important component in establishing and sustaining smarter cities across the globe. IT solution and service organizations have come forth with a number of automated traffic management solutions and the primary problem with them is they are unfortunately reactive and hence an inefficient solution for the increasingly connected and dynamic city environments. Therefore, unveiling real-time, adaptive, precision-centric and predictive traffic monitoring, measurement, management and enhancement solutions are being insisted as an indispensable requirement toward sustainable cities. We have come out with a novel approach leveraging a few potential and promising technologies and tools such as a reliable and reusable virtual model for vehicles, a machine learning model, the IoT fog or edge data analytics, a data lake for traffic and vehicle data on public cloud environments, and 5G communication. The paper details all these in a cogent fashion and how these technological advancements come handy in avoiding the frequent traffic congestions and snarls due to various reasons.

One of the internet’s design principles is to make physical distance transparent, which has made it a motor of globalization and economic development. However, the physical world within which it operates is spatial, and reduction of actual spatial distance is a factor of high impact leveraged, e.g., in today’s commercially successful content distribution networks as well as new technologies, such as edge computing. Predictable network behavior is key for reliable intelligent environments. It is therefore worthwhile to study whether and how spatial the internet actually is. We studied a small set of globally dispersed public universities on six continents, recording delays reported by the ping tool to a server in the Pacific Northwest area of the U.S. at four different times of the day (morning, noon, evening, midnight) and over both weekends and weekdays, thus retrieving more than 17,210 data points. We then determined the Pearson’s correlations between distance and delay and between hop count and delay. The experiment yielded that there is an overall correlation of 0.57 (a border case strong correlation) between distance and delay and a weaker correlation of 0.4 (medium strength correlation) between hop count and delay. The results suggest that there is a physical distance effect and that this effect is stronger than the hop count effect.

The ACANTO project is developing robotic assistants to aid the confidence and recovery of older adults. A key requirement of these assistants is aiding with navigation in complex and potentially chaotic environments. Prior work has addressed this for a single user, using a single robotic assistant in an intelligent environment. However, for therapeutic purposes, ACANTO supports social groups and group activities. ACANTO’s robotic assistants must, therefore, be able to plan the motion of groups of older adults walking together. This requires an efficient navigation solution that can handle large numbers of users and that can operate rapidly on embedded computing devices. To increase user confidence, the solution must encourage group cohesion without trying to impose its own rigid structure; it must try to maintain the natural (de facto) group structure despite unpredictable behaviours and environmental conditions. Our on-the-fly group motion planner addresses these challenges by: using intelligent environment information to develop behavioural traces, clustering traces to determine groups, constructing a predictive model of the groups as a whole, and finding an optimal suggested trajectory using statistical model checking. In this work, we describe our proposed approach in detail and validate some of its novel aspects on the ETH Zurich pedestrian motion dataset.

The experience of full automation without explicit user direction may induce anxiety among smart space users. The use of explicit mediators between users and fully automated systems may help to mitigate users’ anxiety. While robots mediators are one possible solution, several issues remain, including high complexity and limited collaboration between robots and smart space platforms, reducing overall system reliability. This paper proposes the Integrated Control Architecture for Robotic mediator in Smart environments (ICARS) as a solution to improve the integration and reliability of robot mediators within automated smart spaces. Assuming relatively thin network robots as robotic mediators to enable a wide distribution with less cost, ICARS provides a well-organized software framework consisting of three layers to integrate robots and smart spaces: a flexible communication/device model, an adaptive service model for the integrated robot control architecture, and a behavior-based high-level collaboration model. In this paper, we also present details of the design, implementation, and an application scenario conducted with ICARS. The results show that ICARS enables flexible integration of the diverse devices associated with robots and environments, adaptive service provision for collaborative services, and easier development of high-level collaborative applications with decent performance.

The internet of things (IoT) envisages an ultralarge-scale network of things, which will offer services to a large number of applications in numerous domains and environments. A middleware can ease application development by providing the necessary functional components for service registration, discovery and composition. Recently, there have been a large number of proposals for IoT middleware solutions, and a few recent studies have surveyed and qualitatively evaluated these IoT middleware proposals against a variety of functional features. This paper is an extension of these studies with an evaluation of four representative middlewares in a small-scale scenario to provide a more in-depth perspective of the state of the art with reference to the scale and heterogeneity of dynamic IoT environments. The results show a number of issues. Service registration, discovery and composition components still require human intervention. With such a large number of available services, these components need to be automated to enable fast deployment and adaptation of IoT applications. The scalability evaluation of new proposals should follow a holistic approach, as opposed to considering only individual components. Finally, support for user’s non-functional requirements is limited to best-effort QoS.

Intelligent environments can be described as hybrid systems, which combine continuous dynamics, modelling the behaviour of physical components, and discrete dynamics, modelling the software components that control the evolution of the physical variables. The growing boom in intelligent environments makes the construction of complex discrete components necessary, which may require the use of more sophisticated concurrent languages. Hy-tccp is a concurrent language for modelling hybrid systems with high-level notations that facilitate the description of computational systems, abstracting away from the implementation details. In this paper, we present an operational semantics based on hybrid automata for Hy-tccp which is a theoretical basis for the analysis and verification of hybrid systems like intelligent environment.