Digital filters underpin the performance of coherent optical receivers which exploit digital signal processing (DSP) to mitigate transmission impairments. We outline the principles of such receivers and review our experimental investigations into compensation of polarization mode dispersion. We then consider the details of the digital filtering employed and present an analytical solution to the design of a chromatic dispersion compensating filter. Using the analytical solution an upper bound on the number of taps required to compensate chromatic dispersion is obtained, with simulation revealing an improved bound of 2.2 taps per 1000ps/nm for 10.7GBaud data. Finally the principles of digital polarization tracking are outlined and through simulation, it is demonstrated that 100krad/s polarization rotations could be tracked using DSP with a clock frequency of less than 500MHz.

Digital filters for coherent optical receivers.

by J. Biggs and C. Tang, Maidenhead, England,
Open University Press, 2007, 360 pp., £29.99, ISBN-13: 978-0-335-22126-4

Teaching for quality learning at university

In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.

https://hal-centralesupelec.archives-ouvertes.fr/hal-01337007/document

FPGAs in Industrial Control Applications

Remote laboratories have been introduced during the last few decades into engineering education processes as well as integrated within e-learning frameworks offered to engineering and science students. Remote laboratories are also being used to support life-long learning and student's autonomous learning activities. In this paper, after a brief overview of state-of-the-art technologies in the development of remote laboratories and presentation of recent and interesting examples of remote laboratories in several areas related with industrial electronics education, some current trends and challenges are also identified and discussed.

Current Trends in Remote Laboratories

In the past two decades, advances in programmable device technologies, in both the hardware and software arenas, have been extraordinary. The original application of rapid prototyping has been complemented with a large number of new applications that take advantage of the excellent characteristics of the latest devices. High speed, very large number of components, large number of supported protocols, and the addition of ready- to-use intellectual property cores make programmable devices the preferred choice of implementation and even deployment in mass production quantities. This paper surveys the advanced features, design tools, and application domains for field-programmable gate arrays (FPGAs). The main characteristics and structure of modern FPGAs are first described to show their versatility and abundance of available design resources. Software resources are also discussed, as they are the main enablers for the efficient exploitation of the design capabilities of these devices. Current application domains are described, such as configurable computing, dynamically reconfigurable systems, rapid system prototyping, communication processors and interfaces, and signal processing. This paper also presents the authors' prospective view of how FPGAs will evolve to enter new application domains in the future.

Features, Design Tools, and Application Domains of FPGAs

Building automation systems (BAS), or building control systems (BCS), typically consist of building energy management systems (BEMSs), physical security and access control, fire/life safety, and other systems (elevators, public announcements, and closed-circuit television). BEMSs control heating, ventilation, and air conditioning (HVAC) and lighting systems in buildings; more specifically, they control HVAC?s primary components such as air handling units (AHUs), chillers, and heating elements. BEMSs are essential components of modern buildings, tasked with seemingly contradicting requirements?minimizing energy consumption while maintaining occupants? comfort [1]. In the United States, about 40% of total energy consumption and 70% of electricity consumption are spent on buildings every year. These numbers are comparable to global statistics that about 30% of total energy consumption and 60% of electricity consumption are spent on buildings. Buildings are an integral part of global cyberphysical systems (smart cities) and evolve and interact with their surroundings (Figure 1) [2]. As buildings undergo years of exploitation, their thermal characteristics deteriorate, indoor spaces (especially in commercial buildings) get rearranged, and usage patterns change. In time, their inner (and outer) microclimates adjust to changes in surrounding buildings, overshadowing patterns, and city climates, not to mention building retrofitting [3], [4]. Thus, even in cases of ?ideally? designed BEMS/HVAC systems, because of ever-changing and uncertain indoor and outdoor environments, their performance frequently falls short of expectations. Unfortunately, the complexity of BEMSs, large amounts of constantly changing data, and evolving interrelations among sensor feeds make identifying these suboptimal behaviors difficult [1], [5]. Therefore, traditional data-mining algorithms and data-analysis tools are often inadequate.

Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings

Industrial cyberphysical systems (ICPSs) are the cornerstone research subject in the era of Industry 4.0 [1]. The study of ICPSs has, therefore, become a worldwide research focus [2]-[4]. ICPSs integrate physical entities with cyber networks to build systems that can work more harmoniously, benefiting from integrated design and system-wide optimization [5]. The safety and performance of industrial systems can be improved by developing specific information infrastructure, monitoring, and control approaches aimed at maintaining controllability under external disturbances and unexpected faults [6]. Based on these observations, the design and deployment of ICPSs have both theoretical and practical significance.

Real-Time Monitoring and Control of Industrial Cyberphysical Systems: With Integrated Plant-Wide Monitoring and Control Framework

Field programmable gate arrays (FPGAs) have established themselves as one of the preferred digital implementation platforms in a plethora of current industrial applications, and extensions and improvements are still continuously being included in the devices. This paper reviews recent advancements in FPGA technology, emphasizing the novel features that may significantly contribute to the development of more efficient digital systems for industrial applications. Special attention is paid to the design paradigm shift caused by the availability of increasingly powerful embedded (and soft) processors, which transformed FPGAs from hardware accelerators to very powerful system-on-chip (SoC) platforms. New analog resources, floating-point operators, and hard memory controllers are also described, because of the great advantages they provide to designers. Software tools are being strongly influenced by the design paradigm shift, which requires from them a much better support for software developers. Focusing mainly on this issue, recent advancements in software resources [intellectual property (IP) cores and design tools] are also reviewed. The impact of new FPGA features in industrial applications is analyzed in detail in three main areas, namely digital real-time simulation, advanced control techniques, and electronic instrumentation, with focus on mechatronics, robotics, and power systems design. The way digital systems are being currently designed in these areas is comprehensively reviewed, and a critical analysis of how they could significantly benefit from new FPGA features is presented.

Advanced Features and Industrial Applications of FPGAs—A Review

Intelligent buildings are quickly becoming cohesive and integral inhabitants of cyberphysical ecosystems. Modern buildings adapt to internal and external elements and thrive on ever-increasing data sources, such as ubiquitous smart devices and sensors, while mimicking various approaches previously known in software, hardware, and bioinspired systems. This article provides an overview of intelligent buildings of the future from a range of perspectives. It discusses everything from the prospects of U.S. and world energy consumption to insights into the future of intelligent buildings based on the latest technological advancements in U.S. industry and government.

Juan J. Rodriguez-Andina

Papers

Features, Design Tools, and Application Domains of FPGAs

Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings

Real-Time Monitoring and Control of Industrial Cyberphysical Systems: With Integrated Plant-Wide Monitoring and Control Framework

Advanced Features and Industrial Applications of FPGAs—A Review

Intelligent Buildings of the Future: Cyberaware, Deep Learning Powered, and Human Interacting