High-performance and hardware-aware computing: proceedings of the second International Workshop on New Frontiers in High-performance and Hardware-aware Computing (HipHaC\u2711), San Antonio, Texas, USA, February 2011 ; (in conjunction with HPCA-17)

High-performance system architectures are increasingly exploiting heterogeneity. The HipHaC workshop aims at combining new aspects of parallel, heterogeneous, and reconfigurable microprocessor technologies with concepts of high-performance computing and, particularly, numerical solution methods. Compute- and memory-intensive applications can only benefit from the full hardware potential if all features on all levels are taken into account in a holistic approach

D4.2 Intelligent D-Band wireless systems and networks initial designs

This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project

Wavelet transforms and efficient implementation on the GPU

Wavelets and wavelet transforms can be applied to various problems concerning signals. The ability to transform the signal into something representing frequencies and to see when the frequencies occurred, can be used in numerous fields. The calculation can be computationally expensive when applied to large datasets. By taking advantage of the computational power of a GPU when implementing a wavelet transform, the time of the computation can be substantially reduced. The goal is to make the application fast enough to solve a problem interactively. This thesis introduces the wavelet transform and addresses differences between some GPU toolkits, looking at development and code efficiency

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

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

Multidimensional Data Processing for Optical Coherence Tomography Imaging

Optical Coherence Tomography (OCT) is a medical imaging technique which distinguishes itself by acquiring microscopic resolution images in-vivo at millimeter scale fields of view. The resulting in images are not only high-resolution, but often multi-dimensional to capture 3-D biological structures or temporal processes. The nature of multi-dimensional data presents a unique set of challenges to the OCT user that include acquiring, storing, and handling very large datasets, visualizing and understanding the data, and processing and analyzing the data. In this dissertation, three of these challenges are explored in depth: sub-resolution temporal analysis, 3-D modeling of fiber structures, and compressed sensing of large, multi-dimensional datasets. Exploration of these problems is followed by proposed solutions and demonstrations which rely on tools from multiple research areas including digital image filtering, image de-noising, and sparse representation theory. Combining approaches from these fields, advanced solutions were developed to produce new and groundbreaking results. High-resolution video data showing cilia motion in unprecedented detail and scale was produced. An image processing method was used to create the first 3-D fiber model of uterine tissue from OCT images. Finally, a compressed sensing approach was developed which we show to guarantee high accuracy image recovery of more complicated, clinically relevant, samples than had been previously demonstrated. The culmination of these methods represents a step forward in OCT image analysis, showing that these cutting edge tools can also be applied to OCT data and in the future be employed in a clinical setting

Chipless RFID sensor systems for structural health monitoring

Ph. D. ThesisDefects in metallic structures such as crack and corrosion are major sources of catastrophic failures, and thus monitoring them is a crucial issue. As periodic inspection using the nondestructive testing and evaluation (NDT&E) techniques is slow, costly, limited in range, and cumbersome, novel methods for in-situ structural health monitoring (SHM) are required. Chipless radio frequency identification (RFID) is an emerging and attractive technology to implement the internet of things (IoT) based SHM. Chipless RFID sensors are not only wireless, passive, and low-cost as the chipped RFID counterpart, but also printable, durable, and allow for multi-parameter sensing. This thesis proposes the design and development of chipless RFID sensor systems for SHM, particularly for defect detection and characterization in metallic structures. Through simulation studies and experimental validations, novel metal-mountable chipless RFID sensors are demonstrated with different reader configurations and methods for feature extraction, selection, and fusion. The first contribution of this thesis is the design of a chipless RFID sensor for crack detection and characterization based on the circular microstrip patch antenna (CMPA). The sensor provides a 4-bit ID and a capability of indicating crack width and orientation simultaneously using the resonance frequency shift. The second contribution is a chipless RFID sensor designed based on the frequency selective surface (FSS) and feature fusion for corrosion characterization. The FSS-based sensor generates multiple resonance frequency features that can reveal corrosion progression, while feature fusion is applied to enhance the sensitivity and reliability of the sensor. The third contribution deals with robust detection and characterization of crack and corrosion in a realistic environment using a portable reader. A multi-resonance chipless RFID sensor is proposed along with the implementation of a portable reader using an ultra-wideband (UWB) radar module. Feature extraction and selection using principal component analysis (PCA) is employed for multi-parameter evaluation. Overall, chipless RFID sensors are small, low-profile, and can be used to quantify and characterize surface crack and corrosion undercoating. Furthermore, the multi-resonance characteristics of chipless RFID sensors are useful for integrating ID encoding and sensing functionalities, enhancing the sensor performance, as well as for performing multi-parameter analysis of defects. The demonstrated system using a portable reader shows the capability of defects characterization from a 15-cm distance. Hence, chipless RFID sensor systems have great potential to be an alternative sensing method for in-situ SHM.Indonesia Endowment Fund for Education (LPDP