Rapid discharge failure prediction model for solar charged lithium-ion batteries

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityLithium-ion batteries are continually being deployed in many appliances. This is due to their high energy density and cost effectiveness. Most of these have been around for years in portable devices such as mobile phones. With the onset of smartphones, there is an ever increasing need to have batteries with superior performance. This can be viewed from the context of the need for fast charging and an ability to support a fully multitasked smartphone. Lithium-ion batteries have become the defacto battery type in many of these and similar applications due to their inherent characteristics. They have found use in not just mobile phones but also in innovative products designed to light homes as well provide for mobile phone charging in rural Africa. These products include a battery pack of Lithium-ion batteries cells charged by solar panels. There are a number of challenges facing the companies dealing with such products. There is a need to provide a superior product while at the same time ensure efficiency in the production line so as to bring down costs. All these need to be done while maintaining the elusive customer loyalty. One of the major issues faced is accelerated degradation which cannot be noticed using conventional approaches. Currently the main mode of triage for failure is visualization of graphs from data collected from the sensors attached to the batteries and observing for irregularities in the charge and discharging patterns. Existing literature talks about models used on linear data for forecasting in various fields of research. It also proposes an approach to predict battery life in batteries used on various applications such as hybrid electric vehicles. The proposed method will take advantage of predictive analytics in time series analysis to predict failure based on data from the batteries. Data from the batteries spanning 30 days was used to generate gradients of daily charging gradients. These were used as the training data with a binary class of faulty and good. We are able to train a model using the nearest neighbor algorithm to obtain over 80% accuracy with only a sample of 200 batteries data

Kinematic State Estimation using Multiple DGPS/MEMS-IMU Sensors

Animals have evolved over billions of years and understanding these complex and intertwined systems have potential to advance the technology in the field of sports science, robotics and more. As such, a gait analysis using Motion Capture (MOCAP) technology is the subject of a number of research and development projects aimed at obtaining quantitative measurements. Existing MOCAP technology has limited the majority of studies to the analysis of the steady-state locomotion in a controlled (indoor) laboratory environment. MOCAP systems such as the optical, non-optical acoustic and non-optical magnetic MOCAP systems require predefined capture volume and controlled environmental conditions whilst the non-optical mechanical MOCAP system impedes the motion of the subject. Although the non-optical inertial MOCAP system allows MOCAP in an outdoor environment, it suffers from measurement noise and drift and lacks global trajectory information. The accuracy of these MOCAP systems are known to decrease during the tracking of the transient locomotion. Quantifying the manoeuvrability of animals in their natural habitat to answer the question “Why are animals so manoeuvrable?” remains a challenge. This research aims to develop an outdoor MOCAP system that will allow tracking of the steady-state as well as the transient locomotion of an animal in its natural habitat outside a controlled laboratory condition. A number of researchers have developed novel MOCAP systems with the same aim of creating an outdoor MOCAP system that is aimed at tracking the motion outside a controlled laboratory (indoor) environment with unlimited capture volume. These novel MOCAP systems are either not validated against the commercial MOCAP systems or do not have comparable sub-millimetre accuracy as the commercial MOCAP systems. The developed DGPS/MEMS-IMU multi-receiver fusion MOCAP system was assessed to have global trajectory accuracy of _0:0394m, relative limb position accuracy of _0:006497m. To conclude the research, several recommendations are made to improve the developed MOCAP system and to prepare for a field-testing with a wild animal from a family of a terrestrial megafauna

Chapter 34 - Biocompatibility of nanocellulose: Emerging biomedical applications

Nanocellulose already proved to be a highly relevant material for biomedical applications, ensued by its outstanding mechanical properties and, more importantly, its biocompatibility. Nevertheless, despite their previous intensive research, a notable number of emerging applications are still being developed. Interestingly, this drive is not solely based on the nanocellulose features, but also heavily dependent on sustainability. The three core nanocelluloses encompass cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC). All these different types of nanocellulose display highly interesting biomedical properties per se, after modification and when used in composite formulations. Novel applications that use nanocellulose includewell-known areas, namely, wound dressings, implants, indwelling medical devices, scaffolds, and novel printed scaffolds. Their cytotoxicity and biocompatibility using recent methodologies are thoroughly analyzed to reinforce their near future applicability. By analyzing the pristine core nanocellulose, none display cytotoxicity. However, CNF has the highest potential to fail long-term biocompatibility since it tends to trigger inflammation. On the other hand, neverdried BNC displays a remarkable biocompatibility. Despite this, all nanocelluloses clearly represent a flag bearer of future superior biomaterials, being elite materials in the urgent replacement of our petrochemical dependence

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Creatine Supplementation for Health and Clinical Diseases

Creatine plays a critical role in cellular metabolism, primarily by binding with phosphate to form phosphocreatine (PCr) as well as shuttling high-energy phosphate compounds in and out of the mitochondria for metabolism. Increasing the dietary availability of creatine increases the tissue and cellular availability of PCr, and thereby enhances the ability to maintain high-energy states during intense exercise. For this reason, creatine monohydrate has been extensively studied as an ergogenic aid for exercise, training, and sport. Limitations in the ability to synthesize creatine and transport and/or store dietary creatine can impair metabolism and is a contributor to several disease states. Additionally, creatine provides an important source of energy during metabolically stressed states, particularly when oxygen availability is limited. Thus, researchers have assessed the role of creatine supplementation on health throughout the lifespan, as well as whether creatine availability may improve disease management and/or therapeutic outcomes. This book provides a comprehensive overview of scientific and medical evidence related to creatine's role in metabolism, health throughout the lifespan, and our current understanding of how creatine can promote brain, heart, vascular and immune health; reduce the severity of musculoskeletal and brain injury; and may provide therapeutic benefits in glucose management and diabetes, cancer therapy, inflammatory bowel disease, and post-viral fatigue