Letter from the Special Issue Editor

Editorial work for DEBULL on a special issue on data management on Storage Class Memory (SCM) technologies

Machine learning for accelerating the discovery of high-performance low-cost solar cells

Solar energy has the potential to enhance the operation of electronic devices profoundly and is the solution to the most important challenge facing humanity today. Such devices primarily rely on rechargeable batteries to satisfy their energy needs. However, since photovoltaic (PV) technology is a mature and reliable method for converting the Sun’s vast energy into electricity, innovation in developing new materials and solar cell architectures is becoming more important to increase the penetration of PV technologies in wearable and IoT applications. Moreover, artificial intelligence (AI) is touted to be a game changer in energy harvesting. The thesis aims to optimize solar cell performance using various computational methods, from solar irradiance and solar architecture to cost analysis of the PV system. The thesis explores the PV cell architectures that can be used for optimized cost/efficiency trade-offs. In addition, machine learning (ML) algorithms are incorporated to develop reconfigurable PV cells based on switchable complementary metal-oxide-semiconductor (CMOS) addressable switches, such that the output power can be optimized for different light patterns and shading. The first part of the thesis presents a critical literature review of a range of ML techniques applied for estimating solar irradiance, followed by a review on accurately predicting the levelized cost of electricity (LCOE) and return on investment (ROI) of a PV system and lastly, presents a systematic review (SR) on the discovery of solar cells. Furthermore, the literature review consists of a thorough systematic review that reveals that ML techniques can speed up the discovery of new solar cell materials and architectures. The review covers a broad range of ML techniques that focus on producing low-cost solar cells. Additionally, a new classification method is introduced based on data synthesis, ML algorithms, optimization, and fabrication process. The review finds that Gaussian Process Regression (GPR) ML technique with Bayesian Optimization (BO) is the most promising method for designing low-cost organic solar cell architecture. Therefore, the first part of the thesis critically evaluates the existing ML techniques and guides researchers in discovering solar cells using ML techniques. The literature review also discusses the recent research work done for predicting solar irradiance and evaluating the LCOE and ROI of the PV system using various time-series forecasting techniques under ML algorithms. Secondly, the thesis proposes an ML algorithm for accurately predicting solar irradiance using the wireless sensor network (WSN) relying on batteries that need constant replacement and are hazardous waste. Therefore, WSNs with solar energy harvesters that scavenge energy from the Sun are proposed as an alternative solution. Consequently, the ML algorithms that enable WSN nodes to accurately predict the amount of solar irradiance are presented so that the node can intelligently manage its energy. The nodes use the panel’s energy to power its internal electronic components, such as the processor and transmitter, and charge its battery. Accordingly, this helps the node access an exact amount of solar irradiance predictions to plan its energy utilization more efficiently, thereby adjusting the operation schedule depending on the expected solar energy availability. The ML models were based on historical weather datasets from California, USA, and Delhi, India, from 2010 to 2020. In addition, the process of data pre-processing, followed by feature engineering, identification of outliers, and grid search to determine the most optimized ML model, is evaluated. Compared with the linear regression (LR) model, the support vector regression (SVR) model showed accurate solar irradiance forecasting. Moreover, from the predicted output calculated results, it was also found that the models with time duration of 1 year and 1 month have much better forecasting results than 10 years and 1 week, with both root square mean error (RMSE) and mean absolute error (MAE) less than 7% for California, USA. Consecutively, the third part of the thesis evaluates the parameter LCOE using demographic variables. Moreover, LCOE facilitates economic decisions and quantitative comparisons between energy generation technologies. Previous methods for calculating the LCOE were based on fixed singular input values that do not capture the uncertainty associated with determining the financial feasibility of a PV project. Instead, a dynamic model that considers important demographic, energy, and policy data that include interest rates, inflation rates, and energy yield is proposed. All these parameters will undoubtedly vary during a PV system’s lifetime and help determine a more accurate LCOE value. Furthermore, comparisons between different ML algorithms revealed that the ARIMA model gave an accuracy of 93.8% for predicting the consumer price of electricity. Moreover, the proposed model with two case studies from the United States and the Philippines is evaluated in detail. Results from these case studies revealed that LCOE values for the State of California could be almost 30% different (5.03 ¢/kWh for singular values in comparison to 7.09¢/kWh using our ML model), which can distort the risk or economic feasibility of a PV power plant. Additionally, the ML model predicts the ROI of a grid-connected PV plant in the Philippines to be 5.37 years instead of 4.23 years which gives a clear indication to the client for making an accurate estimation for the cost analysis of a PV plant

Synthesis and characterization of patterned surfaces and catalytically relevant binary nanocrystalline intermetallic compounds

As devices and new technologies continue to shrink, nanocrystalline multi-metal compounds are becoming increasingly important for high efficiency and multifunctionality. However, synthetic methods to make desirable nanocrystalline multi-metallics are not yet matured. In response to this deficiency, we have developed several solution-based methods to synthesize nanocrystalline binary alloy and intermetallic compounds. This dissertation describes the processes we have developed, as well as our investigations into the use of lithographically patterned surfaces for template-directed self-assembly of solution dispersible colloids. We used a modified polyol process to synthesize nanocrystalline intermetallics of late transition and main-group metals in the M-Sn, Pt-M’, and Co-Sb systems. These compounds are known to have interesting physical properties and as nanocrystalline materials they may be useful for magnetic, thermoelectric, and catalytic applications. While the polyol method is quite general, it is limited to metals that are somewhat easy to reduce. Accordingly, we focused our synthetic efforts on intermetallics comprised of highly electropositive metals. We find that we can react single-metal nanoparticles with zero-valent organometallic Zinc reagents in hot, coordinating amine solvents via a thermal decomposition process to form several intermetallics in the M’’-Zn system. Characterization of the single-metal intermediates and final intermetallic products shows a general retention of morphology throughout the reaction, and changes in optical properties are also observed. Following this principle of conversion chemistry, we can employ the high reactivity of nanocrystals to reversibly convert between intermetallic phases within the Pt-Sn system, where PtSn2 ↔ PtSn ↔ Pt3Sn. Our conversion chemistry occurs in solution at temperatures below 300 °C and within 1 hour, highlighting the high reactivity of our nanocrystalline materials compared to the bulk. Some evidence of the generality for this process is also presented. Our nanocrystalline powders are dispersible in solution, and as such are amenable to solution-based processing techniques developed for colloidal dispersions. Accordingly, we have investigated the use of lithographically patterned surfaces to control the self-assembly of colloidal particles. We find that we can rapidly crystallize 2-dimensional building blocks, as well as use epitaxial templates to direct the formation of interesting superlattice structures comprised of a bidisperse population of particles

On the Edge of Secure Connectivity via Software-Defined Networking

Securing communication in computer networks has been an essential feature ever since the Internet, as we know it today, was started. One of the best known and most common methods for secure communication is to use a Virtual Private Network (VPN) solution, mainly operating with an IP security (IPsec) protocol suite originally published in 1995 (RFC1825). It is clear that the Internet, and networks in general, have changed dramatically since then. In particular, the onset of the Cloud and the Internet-of-Things (IoT) have placed new demands on secure networking. Even though the IPsec suite has been updated over the years, it is starting to reach the limits of its capabilities in its present form. Recent advances in networking have thrown up Software-Defined Networking (SDN), which decouples the control and data planes, and thus centralizes the network control. SDN provides arbitrary network topologies and elastic packet forwarding that have enabled useful innovations at the network level. This thesis studies SDN-powered VPN networking and explains the benefits of this combination. Even though the main context is the Cloud, the approaches described here are also valid for non-Cloud operation and are thus suitable for a variety of other use cases for both SMEs and large corporations. In addition to IPsec, open source TLS-based VPN (e.g. OpenVPN) solutions are often used to establish secure tunnels. Research shows that a full-mesh VPN network between multiple sites can be provided using OpenVPN and it can be utilized by SDN to create a seamless, resilient layer-2 overlay for multiple purposes, including the Cloud. However, such a VPN tunnel suffers from resiliency problems and cannot meet the increasing availability requirements. The network setup proposed here is similar to Software-Defined WAN (SD-WAN) solutions and is extremely useful for applications with strict requirements for resiliency and security, even if best-effort ISP is used. IPsec is still preferred over OpenVPN for some use cases, especially by smaller enterprises. Therefore, this research also examines the possibilities for high availability, load balancing, and faster operational speeds for IPsec. We present a novel approach involving the separation of the Internet Key Exchange (IKE) and the Encapsulation Security Payload (ESP) in SDN fashion to operate from separate devices. This allows central management for the IKE while several separate ESP devices can concentrate on the heavy processing. Initially, our research relied on software solutions for ESP processing. Despite the ingenuity of the architectural concept, and although it provided high availability and good load balancing, there was no anti-replay protection. Since anti-replay protection is vital for secure communication, another approach was required. It thus became clear that the ideal solution for such large IPsec tunneling would be to have a pool of fast ESP devices, but to confine the IKE operation to a single centralized device. This would obviate the need for load balancing but still allow high availability via the device pool. The focus of this research thus turned to the study of pure hardware solutions on an FPGA, and their feasibility and production readiness for application in the Cloud context. Our research shows that FPGA works fluently in an SDN network as a standalone IPsec accelerator for ESP packets. The proposed architecture has 10 Gbps throughput, yet the latency is less than 10 µs, meaning that this architecture is especially efficient for data center use and offers increased performance and latency requirements. The high demands of the network packet processing can be met using several different approaches, so this approach is not just limited to the topics presented in this thesis. Global network traffic is growing all the time, so the development of more efficient methods and devices is inevitable. The increasing number of IoT devices will result in a lot of network traffic utilising the Cloud infrastructures in the near future. Based on the latest research, once SDN and hardware acceleration have become fully integrated into the Cloud, the future for secure networking looks promising. SDN technology will open up a wide range of new possibilities for data forwarding, while hardware acceleration will satisfy the increased performance requirements. Although it still remains to be seen whether SDN can answer all the requirements for performance, high availability and resiliency, this thesis shows that it is a very competent technology, even though we have explored only a minor fraction of its capabilities

Cogitator : a parallel, fuzzy, database-driven expert system

The quest to build anthropomorphic machines has led researchers to focus on knowledge and the manipulation thereof. Recently, the expert system was proposed as a solution, working well in small, well understood domains. However these initial attempts highlighted the tedious process associated with building systems to display intelligence, the most notable being the Knowledge Acquisition Bottleneck. Attempts to circumvent this problem have led researchers to propose the use of machine learning databases as a source of knowledge. Attempts to utilise databases as sources of knowledge has led to the development Database-Driven Expert Systems. Furthermore, it has been ascertained that a requisite for intelligent systems is powerful computation. In response to these problems and proposals, a new type of database-driven expert system, Cogitator is proposed. It is shown to circumvent the Knowledge Acquisition Bottleneck and posess many other advantages over both traditional expert systems and connectionist systems, whilst having non-serious disadvantages.KMBT_22

Silicon Nanodevices

This book is a collection of scientific articles which brings research in Si nanodevices, device processing, and materials. The content is oriented to optoelectronics with a core in electronics and photonics. The issue of current technology developments in the nanodevices towards 3D integration and an emerging of the electronics and photonics as an ultimate goal in nanotechnology in the future is presented. The book contains a few review articles to update the knowledge in Si-based devices and followed by processing of advanced nano-scale transistors. Furthermore, material growth and manufacturing of several types of devices are presented. The subjects are carefully chosen to critically cover the scientific issues for scientists and doctoral students