Search

Collective behavior appearing abundantly in nature at various scales, such as swarming of insects, flocking of birds or schooling of fish, has motivated an interdisciplinary research thrust towards artificial swarms thanks to its numerous advantages. Although a lot of work has been done on coming up with controllers for these multi-agent systems towards optimizing coordination and planning, the inverse problem of identifying behaviors and models from observing a swarm is still not explored enough. Efficient tools for the analysis of the complex spatial-temporal dataset an observed swarm generates, are still missing. In this paper, we propose a methodology to solve the problem of identifying the leader in a swarm governed by leader-follower control framework using only the macroscopic view of the entire swarm. A methodology that combines clustering and probability methods is used to analyze the spatial-temporal data of a swarm of robots to narrow down on a subset of the robots that includes the leader. The clustering parameters are optimized over multiple simulated behaviors. The results show that this automated system can narrow down on a subset of agents (cluster) that includes the leader of the swarm with high accuracy. Applications of the proposed methodology include automatic identification of leader and swarm dynamics in both artificial and biological swarms, which can lead to a better understanding of collective behaviors and predictions of future behaviors.

Effective social robots should leverage humor’s unique ability to improve relationship connections and dispel stress, but current robots possess limited (if any) humorous abilities. In this paper, we aim to supplement one aspect of autonomous robots by giving robotic systems the ability to "read the room" to assess how their humorous statements are received by nearby people in real time. Using a dataset of the audio of crowd responses to a robotic comedian over multiple performances (first presented in past work), we establish human-labeled joke success ground truths and compare individual human rater accuracy against the outputs of lightweight Machine Learning (ML) approaches that are easy to deploy in real-time joke assessment. Our results indicate that all three ML approaches (naive Bayes, support vector machines, and single-hidden-layer feedforward neural networks) performed significantly better than the baseline approach used in our past work. In particular, support vector machines and neural network approaches are comparable to a human rater in the task of assessing if a joke failed or not in certain cases. The products of this work will inform self-assessment techniques for robots and help social robotics researchers test their own assessment methods on realistic data from human crowds.

This paper explores the concept of web animations with the motive to generate exportable video files using a browser. We explain the process underlying animations such as tweening and controlling individual states of an object's animation cycle. We can composite many animations to follow a single timeline and capture individual frames at periodic intervals. Such frames, when stitched together, form a video. We will also explain the integration of an audio layer than runs in synchronization with the animations for better perception.

As HPC progresses toward exascale, writing applications that are highly efficient, portable, and support programmer productivity is becoming more challenging than ever. The growing scale, diversity, and heterogeneity in compute platforms increases the burden on software to efficiently use available distributed parallel resources. This burden has fallen on developers who, increasingly, are experts in application domains rather than traditional computer scientists and engineers. We propose CASPER-Compiler Abstractions Supporting high Performance on Extreme-scale Resources-a novel domain-specific compiler and runtime framework to enable domain scientists to achieve high performance and scalability on complex HPC systems. CASPER extends domain-specific languages with machine learning to map software tasks to distributed, heterogeneous resources, and provides a runtime framework to support a variety of adaptive optimizations in dynamic environments. This paper presents an initial design and analysis of CASPER for synthetic aperture radar and computational fluid dynamics domains.

While heterogeneous systems provide considerable opportunities for accelerating big data applications, the variation in processing capacities and communication latency of different resources makes it challenging to effectively map the applications on the platform. To generate an optimized mapping of the input application on a variety of heterogeneous platforms, we design a flexible annotated task interaction graph based framework which 1) allows modeling of mixed CPU and GPU architectures, and 2) identifies an efficient task-hardware mapping of the input application, given the dependencies and communication costs between tasks that constitute the applications. The annotated task interaction graph (ATIG) representation captures all the information that is necessary to execute the application and the meta-data, such as performance models for estimating runtime on a target resource and communication latencies. Our framework supports solving the problem of mapping tasks in the ATIG onto available resources by including variations of greedy algorithm and LP relaxations with rounding. We show that our framework can achieve high speedup, allowing domain experts to efficiently compile a broad set of programs to parallel and heterogeneous hardware.

Smart education is the constituent of smart cities. Smart education is the use of computers in the classroom. However, there are many other factors to improve a child’s quality of education. One part is the amount of time the child spends on a bus from traveling bus to home. Although, with currently available technologies as the Internet of Things (IOT) and Android, with these advanced technologies we will be able to track or visibility in a child’s life. This paper presents the design and implementation of an IOT based system that permits parents, schools and regulatory bodies to track the conditions of comfort and safety inside a bus in a real-time manner. A variety of reports for various activities for bus comfort and attendance are then generated from this real-time providing complete visibility of buses.

Recent aggressive scientific and technological developments all these focus on a global environmental issue considering air quality system, reveals the fact that India is facing severe health hazards. In recent reports, more than 10 cities in India are listed on top. The air quality index (AQI) in India launched in 2014 under Swachh Bharat Abhiyan monitors air pollution on 10 scales ranging from low (green) to moderate (yellow) to serious (red) through data analysis of various air contaminating matters like pm 2.5, O3, NO2, SO2, CO. The present paper develops an Internet of Things (IoT) that enabled air quality monitoring system mobile in nature analyzing real-time surrounding data measuring Carbon Monoxide, Smoke and PM level. The system can measure local area air contamination and generate analyzed data based on which it alerts the people through a buzzer device integrated into the system. The user-friendly and easy handling of the system technology is such that it can be installed in houses and in small places.

nowadays development of IoT applications with robotics is an ongoing revaluation. This paper mainly focuses on the security, remote surveillance, and monitoring of our homes done by the surveillance robots. Remote surveillance has become the most important research topic over the past decade. Through this paper we put forward a surveillance robot that can be used in domestic areas and many other places. Robots are becoming important in our day to day life activities as they reduce the human labor and probability of error. We can control robots manually or they can be automatic based on the need of people. This paper focuses on design and implementation of mobile robot for obstacle detection and avoidance in a real-time basis.

The least squares based cost functions are sensitive to outliers in the measured data. The presence of outliers is considered as impulsive noise. In practical scenarios, the co channel interference, saturation effects, non linearity of the measuring instruments, atmospheric conditions and malfunction of sensors will result in outliers or impulsive noise. The robust function obtained by considering the error as the linear combination of sign preserving basis functions is found to be robust against outliers in the desired data. In many practical applications, the parameter to be estimated can be sparse in nature, i.e. only a few elements are large values and the rest are insignificantly small. In such sparse systems, if the prior information about the sparsity is known, then the known information can be incorporated in the cost function as a regularization function. A robust sparse diffusion algorithm is proposed in this work, which is robust against outliers in the desired data and performs better than the existing algorithms in sparsity underlying systems. Simulations performed for different cases of outliers conditions and sparsity conditions validate that the proposed method outperforms the state of the art methods.

Starch is a polymer derived from plants that have various applications. There are various types of starch available like potato, maize, tapioca, and wheat, etc. We have studied and successfully differentiated tapioca and maize starch using polarized light microscopy and bright field techniques with the help of Iodine staining. Starch granules are suspended in aqueous or non-aqueous media for polarized light and bright field microscopy. The botanical source of the starch is identified according to the shape and size of granules, granule surface marking, the position of hilum, iodine staining, and the presence of maltase crosses under polarized light.

To improve the reliability of content shared on social media, effective strategies for mitigating the diffusion of fake news are increasingly necessary. Traditionally, to counter false belief a competing cascade approach is used. This approach assumes that the opposite belief is already known, and thus, not applicable to newly spreading fake news. Another approach is to block nodes and links of the network to impede the flow of fake news (rumor/influence blocking). However, a more active way to battle the dissemination of fake news is to propagate the corresponding real news, since people who receive the real news in tandem with the fake news are less likely to believe in fake news. Such a setting is especially useful on a messaging platform such as WhatsApp, where the news item flows as a private message and the correction of fake news and its propagation must be performed by the users within the network as they receive it. To achieve this goal, we propose network-based recruitment for active fake news correction (RecANt) to find a set of individuals of a pre-defined size to be incentivized for actively fact-checking and passing on the real news so as to reach the maximum number of nodes in the network. These individuals should be such that they are likely to receive the fake news so that they can test its credibility, and when they propagate the corresponding real news, it reaches a large number of individuals. We prove that RecANt is NP-Hard with a monotone and submodular objective, leading to a polynomial time greedy algorithm (AFC) which provides a (1 – 1/e – ϵ)-approximation. We further optimize the runtime of AFC by developing a fast graph-pruning heuristic (RAFC) that performs as well as AFC in checking the spread of fake news while reducing the runtime significantly. Simulations on several networks demonstrate that our approach outperforms popular social network centrality measures and state-of-the-art information diffusion algorithm.

Process industrial loads are increasing due to expansion of existing manufacturing units. Increasing of loads will increase the power requirement which is reflected at the distribution level. Feeding the additional loads existing switchgear to be modified and for meeting load requirement additional source of distribution transformer to be added, which it may not be an economical solution. To meet the additional load requirement of industries, two transformers need to be operated in parallel in continuous mode. Bus fault current at LV switchgear is the main constrain with parallel operation of the distribution transformer. Due to increase in fault current, existing switchgears bus bar design capacities may not be adequate, generally in India fault current at LV side switchgear bus bars will be designed for 50kA withstanding capacity. Approach is to reduce the fault current below design fault current with parallel operation of transformers. This approach is mainly safeguard the switchgear equipment during parallel operation of transformers and also avoiding adding additional transformers in distribution network. This paper proposes the method of utilizing series FACTS device Interline Power Flow controller (IPFC), connected in LV side to control the power flow and reduce the fault current on switchgear bus bar during transformer parallel operation. The controller used as reference design fault current method and injected the required voltage in a distribution side during fault condition to achieve desired fault current. The distribution network consider for analysis is one of the Indian 800MW Thermal power plant Water system LV switchgear and analyzed short circuit current on switchgear bus bar with and without IPFC by using ETAP and MATLAB/SIMULINK model and compared the bus short circuit current for different ratings of transformers connected in parallel to LV switchgear.