Polymerized ionic liquids by condensation polymerization: Stimuli responsive polyurethane gels and dispersions

Ionic liquids have been used in free radical polymerizations to make polymerized ionic liquid (PIL) materials of various types. PIL gels based on imidazolium cations have been found to exhibit an anion-exchange induced stimuli responsiveness. This thesis explores incorporation of ionic liquids in polyurethane (PU) polymers to make PIL PU gels and dispersions through condensation polymerization. PIL gels are synthesized through a single-pot approach that show stimuli response to solvents. This approach allows one to make these gels rapidly and cheaply on-site. These gels can reversibly porate in different solvents and are found to be porous when analyzed by scanning electron microscope (SEM). PIL based resins are also made in two steps, that show self-dispersion properties in water forming thermodynamically stable nano-scale particles. These materials can be transported as 100% solid resins, where they can be transformed into polyurethane dispersions (PUDs) onsite. These particles also show stimuli responsiveness to different anions and solvents

The Fog Makes Sense: Enabling Social Sensing Services With Limited Internet Connectivity

Social sensing services use humans as sensor carriers, sensor operators and sensors themselves in order to provide situation-awareness to applications. This promises to provide a multitude of benefits to the users, for example in the management of natural disasters or in community empowerment. However, current social sensing services depend on Internet connectivity since the services are deployed on central Cloud platforms. In many circumstances, Internet connectivity is constrained, for instance when a natural disaster causes Internet outages or when people do not have Internet access due to economical reasons. In this paper, we propose the emerging Fog Computing infrastructure to become a key-enabler of social sensing services in situations of constrained Internet connectivity. To this end, we develop a generic architecture and API of Fog-enabled social sensing services. We exemplify the usage of the proposed social sensing architecture on a number of concrete use cases from two different scenarios.Comment: Ruben Mayer, Harshit Gupta, Enrique Saurez, and Umakishore Ramachandran. 2017. The Fog Makes Sense: Enabling Social Sensing Services With Limited Internet Connectivity. In Proceedings of The 2nd International Workshop on Social Sensing, Pittsburgh, PA, USA, April 21 2017 (SocialSens'17), 6 page

Reconfigurable Inspection in Manufacturing: State of the Art and Taxonomy

This article provides an overview of the evolution of the product quality and measurement inspection procedure with emphasis on the Reconfigurable Inspection System and Machine. The major components of a reconfigurable manufacturing system have been examined, and the evolution of manufacturing processes has been briefly discussed. Different Reconfigurable Inspection Machines (RIMs) and their arrangement in an assembly line as an inspection system have been carefully studied and the modern inspection system equipped in RMS has been compared to the traditional techniques commonly used in inspection of product quality. A survey of evolving inspection techniques is offered from the standpoint of technological challenges and advancement affecting manufacturing over time. As per authors' knowledge, the review on Reconfigurable Inspection in Manufacturing and taxonomy of reconfigurable inspection systems is rare. Considering the studies done in this domain, there is still resourceful taxonomy for this paradigm. Therefore, different types of inspection procedures have been discussed, their features and applications have been compared to arrive at the taxonomy of the RIS based on the understanding of the nature of a RIS after a critical review.Comment: 7th International Conference on Automation, Control and Robotics (ICACR) 202

Designing Scalable Mechanisms for Geo-Distributed Platform Services in the Presence of Client Mobility

Situation-awareness applications require low-latency response and high network bandwidth, hence benefiting from geo-distributed Edge infrastructures. The developers of these applications typically rely on several platform services, such as Kubernetes, Apache Cassandra and Pulsar, for managing their compute and data components across the geo-distributed Edge infrastructure. Situation-awareness applications impose peculiar requirements on the compute and data placement policies of the platform services. Firstly, the processing logic of these applications is closely tied to the physical environment that it is interacting with. Hence, the access pattern to compute and data exhibits strong spatial affinity. Secondly, the network topology of Edge infrastructure is heterogeneous, wherein communication latency forms a significant portion of the end-to-end compute and data access latency. Therefore, the placement of compute and data components has to be cognizant of the spatial affinity and latency requirements of the applications. However, clients of situation-awareness applications, such as vehicles and drones, are typically mobile – making the compute and data access pattern dynamic and complicating the management of data and compute components. Constant changes in the network connectivity and spatial locality of clients due to client mobility results in making the current placement of compute and data components unsuitable for meeting the latency and spatial affinity requirements of the application. Constant client mobility necessitates that client location and latency offered by the platform services be continuously monitored to detect when application requirements are violated and to adapt the compute and data placement. The control and monitoring modules of off-the-shelf platform services do not have the necessary primitives to incorporate spatial affinity and network topology awareness into their compute and data placement policies. The spatial location of clients is not considered as an input for decision- making in their control modules. Furthermore, they do not perform fine-grained end-to-end monitoring of observed latency to detect and adapt to performance degradations due to client mobility. This dissertation presents three mechanisms that inform the compute and data placement policies of platform services, so that application requirements can be met. M1: Dynamic Spatial Context Management for system entities – clients and data and compute components – to ensure spatial affinity requirements are satisfied. M2: Network Proximity Estimation to provide topology-awareness to the data and compute placement policies of platform services. M3: End-to-End Latency Monitoring to enable collection, aggregation and analysis of per-application metrics in a geo-distributed manner to provide end-to-end insights into application performance. The thesis of our work is that the aforementioned mechanisms are fundamental building blocks for the compute and data management policies of platform services, and that by incorporating them, platform services can meet application requirements at the Edge. Furthermore, the proposed mechanisms can be implemented in a way that offers high scalability to handle high levels of client activity. We demonstrate by construction the efficacy and scalability of the proposed mechanisms for building dynamic compute and data orchestration policies by incorporating them in the control and monitoring modules of three different platform services. Specifically, we incorporate these mechanisms into a topic-based publish-subscribe system (ePulsar), an application orchestration platform (OneEdge), and a key-value store (FogStore). We conduct extensive performance evaluation of these enhanced platform services to showcase how the new mechanisms aid in dynamically adapting the compute/data orchestration decisions to satisfy performance requirements of applicationsPh.D

Continuous-Domain Solutions of Linear Inverse Problems with Tikhonov vs. Generalized TV Regularization

We consider linear inverse problems that are formulated in the continuous domain. The object of recovery is a function that is assumed to minimize a convex objective functional. The solutions are constrained by imposing a continuous-domain regularization. We derive the parametric form of the solution (representer theorems) for Tikhonov (quadratic) and generalized total-variation (gTV) regularizations. We show that, in both cases, the solutions are splines that are intimately related to the regularization operator. In the Tikhonov case, the solution is smooth and constrained to live in a fixed subspace that depends on the measurement operator. By contrast, the gTV regularization results in a sparse solution composed of only a few dictionary elements that are upper-bounded by the number of measurements and independent of the measurement operator. Our findings for the gTV regularization resonates with the minimization of the $l_1$ norm, which is its discrete counterpart and also produces sparse solutions. Finally, we find the experimental solutions for some measurement models in one dimension. We discuss the special case when the gTV regularization results in multiple solutions and devise an algorithm to find an extreme point of the solution set which is guaranteed to be sparse

MACHINE LEARNING ASSISTED OPTIMIZATION AND ITS APPLICATION TO HYBRID DIELECTRIC RESONATOR ANTENNA DESIGN

Machine learning assisted optimization (MLAO) has become very important for improving the antenna design process because it consumes much less time than the traditional methods. These models' accountability can be checked by the accuracy metrics, which tell about the correctness of the predicted result. Machine learning (ML) methods, such as Gaussian Process Regression, Artificial Neural Networks (ANNs), and Support Vector Machine (SVM), are used to simulate the antenna model to predict the reflection coefficient faster. This paper presents the optimization of Hybrid Dielectric Resonator Antenna (DRA) using machine learning models. Several regression models are applied to the dataset for optimization, and the best results are obtained using a random forest regression model with the accuracy of 97%. Additionally, the effectiveness of machine learning based antenna design is demonstrated through comparison with conventional design methods