Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

New cross-layer techniques for multi-criteria scheduling in large-scale systems

The global ecosystem of information technology (IT) is in transition to a new generation of applications that require more and more intensive data acquisition, processing and storage systems. As a result of that change towards data intensive computing, there is a growing overlap between high performance computing (HPC) and Big Data techniques in applications, since many HPC applications produce large volumes of data, and Big Data needs HPC capabilities. The hypothesis of this PhD. thesis is that the potential interoperability and convergence of the HPC and Big Data systems are crucial for the future, being essential the unification of both paradigms to address a broad spectrum of research domains. For this reason, the main objective of this Phd. thesis is purposing and developing a monitoring system to allow the HPC and Big Data convergence, thanks to giving information about behaviors of applications in a system which execute both kind of them, giving information to improve scalability, data locality, and to allow adaptability to large scale computers. To achieve this goal, this work is focused on the design of resource monitoring and discovery to exploit parallelism at all levels. These collected data are disseminated to facilitate global improvements at the whole system, and, thus, avoid mismatches between layers. The result is a two-level monitoring framework (both at node and application level) with a low computational load, scalable, and that can communicate with different modules thanks to an API provided for this purpose. All data collected is disseminated to facilitate the implementation of improvements globally throughout the system, and thus avoid mismatches between layers, which combined with the techniques applied to deal with fault tolerance, makes the system robust and with high availability. On the other hand, the developed framework includes a task scheduler capable of managing the launch of applications, their migration between nodes, as well as the possibility of dynamically increasing or decreasing the number of processes. All these thanks to the cooperation with other modules that are integrated into LIMITLESS, and whose objective is to optimize the execution of a stack of applications based on multi-criteria policies. This scheduling mode is called coarse-grain scheduling based on monitoring. For better performance and in order to further reduce the overhead during the monitorization, different optimizations have been applied at different levels to try to reduce communications between components, while trying to avoid the loss of information. To achieve this objective, data filtering techniques, Machine Learning (ML) algorithms, and Neural Networks (NN) have been used. In order to improve the scheduling process and to design new multi-criteria scheduling policies, the monitoring information has been combined with other ML algorithms to identify (through classification algorithms) the applications and their execution phases, doing offline profiling. Thanks to this feature, LIMITLESS can detect which phase is executing an application and tries to share the computational resources with other applications that are compatible (there is no performance degradation between them when both are running at the same time). This feature is called fine-grain scheduling, and can reduce the makespan of the use cases while makes efficient use of the computational resources that other applications do not use.El ecosistema global de las tecnologías de la información (IT) se encuentra en transición a una nueva generación de aplicaciones que requieren sistemas de adquisición de datos, procesamiento y almacenamiento cada vez más intensivo. Como resultado de ese cambio hacia la computación intensiva de datos, existe una superposición, cada vez mayor, entre la computación de alto rendimiento (HPC) y las técnicas Big Data en las aplicaciones, pues muchas aplicaciones HPC producen grandes volúmenes de datos, y Big Data necesita capacidades HPC. La hipótesis de esta tesis es que hay un gran potencial en la interoperabilidad y convergencia de los sistemas HPC y Big Data, siendo crucial para el futuro tratar una unificación de ambos para hacer frente a un amplio espectro de problemas de investigación. Por lo tanto, el objetivo principal de esta tesis es la propuesta y desarrollo de un sistema de monitorización que facilite la convergencia de los paradigmas HPC y Big Data gracias a la provisión de datos sobre el comportamiento de las aplicaciones en un entorno en el que se pueden ejecutar aplicaciones de ambos mundos, ofreciendo información útil para mejorar la escalabilidad, la explotación de la localidad de datos y la adaptabilidad en los computadores de gran escala. Para lograr este objetivo, el foco se ha centrado en el diseño de mecanismos de monitorización y localización de recursos para explotar el paralelismo en todos los niveles de la pila del software. El resultado es un framework de monitorización en dos niveles (tanto a nivel de nodo como de aplicación) con una baja carga computacional, escalable, y que se puede comunicar con distintos módulos gracias a una API proporcionada para tal objetivo. Todos datos recolectados se difunden para facilitar la realización de mejoras de manera global en todo el sistema, y así evitar desajustes entre capas, lo que combinado con las técnicas aplicadas para lidiar con la tolerancia a fallos, hace que el sistema sea robusto y con una alta disponibilidad. Por otro lado, el framework desarrollado incluye un planificador de tareas capaz de gestionar el lanzamiento de aplicaciones, la migración de las mismas entre nodos, además de la posibilidad de incrementar o disminuir su número de procesos de forma dinámica. Todo ello gracias a la cooperación con otros módulos que se integran en LIMITLESS, y cuyo objetivo es optimizar la ejecución de una pila de aplicaciones en base a políticas multicriterio. Esta funcionalidad se llama planificación de grano grueso. Para un mejor desempeño y con el objetivo de reducir más aún la carga durante la ejecución, se han aplicado distintas optimizaciones en distintos niveles para tratar de reducir las comunicaciones entre componentes, a la vez que se trata de evitar la pérdida de información. Para lograr este objetivo se ha hecho uso de técnicas de filtrado de datos, algoritmos de Machine Learning (ML), y Redes Neuronales (NN). Finalmente, para obtener mejores resultados en la planificación de aplicaciones y para diseñar nuevas políticas de planificación multi-criterio, los datos de monitorización recolectados han sido combinados con nuevos algoritmos de ML para identificar (por medio de algoritmos de clasificación) aplicaciones y sus fases de ejecución. Todo ello realizando tareas de profiling offline. Gracias a estas técnicas, LIMITLESS puede detectar en qué fase de su ejecución se encuentra una determinada aplicación e intentar compartir los recursos de computacionales con otras aplicaciones que sean compatibles (no se produce una degradación del rendimiento entre ellas cuando ambas se ejecutan a la vez en el mismo nodo). Esta funcionalidad se llama planificación de grano fino y puede reducir el tiempo total de ejecución de la pila de aplicaciones en los casos de uso porque realiza un uso más eficiente de los recursos de las máquinas.This PhD dissertation has been partially supported by the Spanish Ministry of Science and Innovation under an FPI fellowship associated to a National Project with reference TIN2016-79637-P (from July 1, 2018 to October 10, 2021)Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: Félix García Carballeira.- Secretario: Pedro Ángel Cuenca Castillo.- Vocal: María Cristina V. Marinesc

Data Analytics and Performance Enhancement in Edge-Cloud Collaborative Internet of Things Systems

Based on the evolving communications, computing and embedded systems technologies, Internet of Things (IoT) systems can interconnect not only physical users and devices but also virtual services and objects, which have already been applied to many different application scenarios, such as smart home, smart healthcare, and intelligent transportation. With the rapid development, the number of involving devices increases tremendously. The huge number of devices and correspondingly generated data bring critical challenges to the IoT systems. To enhance the overall performance, this thesis aims to address the related technical issues on IoT data processing and physical topology discovery of the subnets self-organized by IoT devices. First of all, the issues on outlier detection and data aggregation are addressed through the development of recursive principal component analysis (R-PCA) based data analysis framework. The framework is developed in a cluster-based structure to fully exploit the spatial correlation of IoT data. Specifically, the sensing devices are gathered into clusters based on spatial data correlation. Edge devices are assigned to the clusters for the R-PCA based outlier detection and data aggregation. The outlier-free and aggregated data are forwarded to the remote cloud server for data reconstruction and storage. Moreover, a data reduction scheme is further proposed to relieve the burden on the trunk link for data uploading by utilizing the temporal data correlation. Kalman filters (KFs) with identical parameters are maintained at the edge and cloud for data prediction. The amount of data uploading is reduced by using the data predicted by the KF in the cloud instead of uploading all the practically measured data. Furthermore, an unmanned aerial vehicle (UAV) assisted IoT system is particularly designed for large-scale monitoring. Wireless sensor nodes are flexibly deployed for environmental sensing and self-organized into wireless sensor networks (WSNs). A physical topology discovery scheme is proposed to construct the physical topology of WSNs in the cloud server to facilitate performance optimization, where the physical topology indicates both the logical connectivity statuses of WSNs and the physical locations of WSN nodes. The physical topology discovery scheme is implemented through the newly developed parallel Metropolis-Hastings random walk based information sampling and network-wide 3D localization algorithms, where UAVs are served as the mobile edge devices and anchor nodes. Based on the physical topology constructed in the cloud, a UAV-enabled spatial data sampling scheme is further proposed to efficiently sample data from the monitoring area by using denoising autoencoder (DAE). By deploying the encoder of DAE at the UAV and decoder in the cloud, the data can be partially sampled from the sensing field and accurately reconstructed in the cloud. In the final part of the thesis, a novel autoencoder (AE) neural network based data outlier detection algorithm is proposed, where both encoder and decoder of AE are deployed at the edge devices. Data outliers can be accurately detected by the large fluctuations in the squared error generated by the data passing through the encoder and decoder of the AE

Algorithms for Fault Detection and Diagnosis

Due to the increasing demand for security and reliability in manufacturing and mechatronic systems, early detection and diagnosis of faults are key points to reduce economic losses caused by unscheduled maintenance and downtimes, to increase safety, to prevent the endangerment of human beings involved in the process operations and to improve reliability and availability of autonomous systems. The development of algorithms for health monitoring and fault and anomaly detection, capable of the early detection, isolation, or even prediction of technical component malfunctioning, is becoming more and more crucial in this context. This Special Issue is devoted to new research efforts and results concerning recent advances and challenges in the application of “Algorithms for Fault Detection and Diagnosis”, articulated over a wide range of sectors. The aim is to provide a collection of some of the current state-of-the-art algorithms within this context, together with new advanced theoretical solutions

Entanglement of spin waves among four quantum memories

Quantum networks are composed of quantum nodes that interact coherently by way of quantum channels and open a broad frontier of scientific opportunities. For example, a quantum network can serve as a `web' for connecting quantum processors for computation and communication, as well as a `simulator' for enabling investigations of quantum critical phenomena arising from interactions among the nodes mediated by the channels. The physical realization of quantum networks generically requires dynamical systems capable of generating and storing entangled states among multiple quantum memories, and of efficiently transferring stored entanglement into quantum channels for distribution across the network. While such capabilities have been demonstrated for diverse bipartite systems (i.e., N=2 quantum systems), entangled states with N > 2 have heretofore not been achieved for quantum interconnects that coherently `clock' multipartite entanglement stored in quantum memories to quantum channels. Here, we demonstrate high-fidelity measurement-induced entanglement stored in four atomic memories; user-controlled, coherent transfer of atomic entanglement to four photonic quantum channels; and the characterization of the full quadripartite entanglement by way of quantum uncertainty relations. Our work thereby provides an important tool for the distribution of multipartite entanglement across quantum networks.Comment: 4 figure