Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis

The paradigm of automated waste classification has recently seen a shift in the domain of interest from conventional image processing techniques to powerful computer vision algorithms known as convolutional neural networks (CNN). Historically, CNNs have demonstrated a strong dependency on powerful hardware for real-time classification, yet the need for deployment on weaker embedded devices is greater than ever. The work in this paper proposes a methodology for reconstructing and tuning conventional image classification models, using EfficientNets, to decrease their parameterisation with no trade-off in model accuracy and develops a pipeline through TensorRT for accelerating such models to run at real-time on an NVIDIA Jetson Nano embedded device. The train-deployment discrepancy, relating how poor data augmentation leads to a discrepancy in model accuracy between training and deployment, is often neglected in many papers and thus the work is extended by analysing and evaluating the impact real world perturbations had on model accuracy once deployed. The scope of the work concerns developing a more efficient variant of WasteNet, a collaborative recycling classification model. The newly developed model scores a test-set accuracy of 95.8% with a real world accuracy of 95%, a 14% increase over the original. Our acceleration pipeline boosted model throughput by 750% to 24 inferences per second on the Jetson Nano and real-time latency of the system was verified through servomotor latency analysis

Improving the Deployment of Recycling Classification through Efficient Hyper-Parameter Analysis

The paradigm of automated waste classification has recently seen a shift in the domain of interest from conventional image processing techniques to powerful computer vision algorithms known as convolutional neural networks (CNN). Historically, CNNs have demonstrated a strong dependency on powerful hardware for real-time classification, yet the need for deployment on weaker embedded devices is greater than ever. The work in this paper proposes a methodology for reconstructing and tuning conventional image classification models, using EfficientNets, to decrease their parameterisation with no trade-off in model accuracy and develops a pipeline through TensorRT for accelerating such models to run at real-time on an NVIDIA Jetson Nano embedded device. The train-deployment discrepancy, relating how poor data augmentation leads to a discrepancy in model accuracy between training and deployment, is often neglected in many papers and thus the work is extended by analysing and evaluating the impact real world perturbations had on model accuracy once deployed. The scope of the work concerns developing a more efficient variant of WasteNet, a collaborative recycling classification model. The newly developed model scores a test-set accuracy of 95.8% with a real world accuracy of 95%, a 14% increase over the original. Our acceleration pipeline boosted model throughput by 750% to 24 inferences per second on the Jetson Nano and real-time latency of the system was verified through servomotor latency analysis

Wireless Personal Area Network-Based Assistance for the Visually Impaired

In this dissertation, a system allowing a visually impaired person to interact with his environment is developed using modern, low-power wireless communications techniques. With recent advances in wireless sensor networks, open-source operating systems, and embedded processing technology, low-cost devices have become practically feasible as a personal notification system for impaired people. Additionally, text-to-speech capabilities can now be employed without special application specific integrated circuits (ASICs), allowing low-cost, general-purpose processors to fill a niche that once required expensive semiconductors. The system takes advantage of 802.15.4 and media access control (MAC) protocols offered by the open source operating system TinyOS. Important characteristics of these new standards that make them ideal for interface with humans are short range, low- power, and open-source software. To facilitate research and development in use and integration of such devices, we developed a hardware platform to allow exploration of possible future network architectures with multiple options for interfacing with the user. Our Visually Impaired Notification System (VINS) allows unprecedented awareness of the environment and has been simulated with multiple nodes using a modification of the TinyOS Dissemination protocol. This dissertation outlines the hardware platform, demonstration of a working prototype, and simulations of how the system would work in its intended environment. We envision this system being used as a testbed allowing further research of other communications and message-delivery techniques. Additionally, the research has contributed directly to the TinyOS project and offered new insight into power management in embedded systems. Finally, through the research effort we were able to contribute to the open source movement and have produced software in four languages used in three countries with over 1500 downloads

Flight Test Data System for Strain Measurement

This thesis describes the design and evaluation of two devices to be included in the next generation of the family of devices called the Boundary Layer Data System (BLDS). The first device, called the Quasi-Static Strain Data Acquisition System, is a continuation of the BLDS-M series of devices to be known as the Flight Test Data System (FTDS) that uses a modular approach to acquire non-flow, quasi-static mechanical strain measurements. Various breakout boards and development boards were used to synthesize the device, which were housed by a custom PCB board. The system is controlled by the SimbleeTM System on a Chip (SOC), and strain measurements are acquired using the HX711 analog-to-digital converter (ADC), and acceleration measurements are acquired with the ADXL345 accelerometer. The Arduino IDE was used to program and troubleshoot the device. The second device, called the Dynamic Strain Data Acquisition System, is a laboratory proof-of-concept device that evaluates various methods of acquiring dynamic strain measurements that may be used in future FTDS designs. A custom PCB board was designed that houses the microcontroller and the various passive components and ICs used to acquire and store strain measurements. The system is controlled by the Atxmega128A4U microcontroller, and measurements are acquired using the AD7708 external ADC and the on-board ADC of the microcontroller. Atmel StudioTM was used to program the microcontroller in C/C++ and to troubleshoot the device. Both devices were tested extensively under room temperature and low temperature conditions to prove the reliability and survivability of each device. The quasi-static data acquisition system was validated to acquire and store measurements to a microSD card at 10 Hz, with a peak operating current under 60 mA. The dynamic data acquisition system was proven to acquire a thousand measurements at 1 kHz and store the data to a microSD card, with a peak operating current under 60 mA

A LiDAR Based Semi-Autonomous Collision Avoidance System and the Development of a Hardware-in-the-Loop Simulator to Aid in Algorithm Development and Human Studies

In this paper, the architecture and implementation of an embedded controller for a steering based semi-autonomous collision avoidance system on a 1/10th scale model is presented. In addition, the development of a 2D hardware-in-the-loop simulator with vehicle dynamics based on the bicycle model is described. The semi-autonomous collision avoidance software is fully contained onboard a single-board computer running embedded GNU/Linux. To eliminate any wired tethers that limit the system’s abilities, the driver operates the vehicle at a user-control-station through a wireless Bluetooth interface. The user-control-station is outfitted with a game-controller that provides standard steering wheel and pedal controls along with a television monitor equipped with a wireless video receiver in order to provide a real-time driver’s perspective video feed. The hardware-in-the-loop simulator was developed in order to aid in the evaluation and further development of the semi-autonomous collision avoidance algorithms. In addition, a post analysis tool was created to numerically and visually inspect the controller’s responses. The ultimate goal of this project was to create a wireless 1/10th scale collision avoidance research platform to facilitate human studies surrounding driver assistance and active safety systems in automobiles. This thesis is a continuation of work done by numerous Cal Poly undergraduate and graduate students

Embedded Firmware Solutions

Computer scienc

Energy Saving and Scavenging in Stand-alone and Large Scale Distributed Systems.

This thesis focuses on energy management techniques for distributed systems such as hand-held mobile devices, sensor nodes, and data center servers. One of the major design problems in multiple application domains is the mismatch between workloads and resources. Sub-optimal assignment of workloads to resources can cause underloaded or overloaded resources, resulting in performance degradation or energy waste. This work specifically focuses on the heterogeneity in system hardware components and workloads. It includes energy management solutions for unregulated or batteryless embedded systems; and data center servers with heterogeneous workloads, machines, and processor wear states. This thesis describes four major contributions: (1) This thesis describes a battery test and energy delivery system design process to maintain battery life in embedded systems without voltage regulators. (2) In battery-less sensor nodes, this thesis demonstrates a routing protocol to maintain reliable transmission through the sensor network. (3) This thesis has characterized typical workloads and developed two models to capture the heterogeneity of data center tasks and machines: a task performance model and a machine resource utilization model. These models allow users to predict task finish time on individual machines. It then integrates these two models into a task scheduler based on the Hadoop framework for MapReduce tasks, and uses this scheduler for server energy minimization using task concentration. (4) In addition to saving server energy consumption, this thesis describes a method of reducing data center cooling energy by maintaining optimal server processor temperature setpoints through a task assignment algorithm. This algorithm considers the reliability impact of processor wear states. It records processor wear states through automatic timing slack tests on a cluster of machines with varying core temperatures, voltages, and frequencies. These optimal temperature setpoints are used in a task scheduling algorithm that saves both server and cooling energy.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116746/1/xjhe_1.pd

Sophisticated Batteryless Sensing

Wireless embedded sensing systems have revolutionized scientific, industrial, and consumer applications. Sensors have become a fixture in our daily lives, as well as the scientific and industrial communities by allowing continuous monitoring of people, wildlife, plants, buildings, roads and highways, pipelines, and countless other objects. Recently a new vision for sensing has emerged---known as the Internet-of-Things (IoT)---where trillions of devices invisibly sense, coordinate, and communicate to support our life and well being. However, the sheer scale of the IoT has presented serious problems for current sensing technologies---mainly, the unsustainable maintenance, ecological, and economic costs of recycling or disposing of trillions of batteries. This energy storage bottleneck has prevented massive deployments of tiny sensing devices at the edge of the IoT. This dissertation explores an alternative---leave the batteries behind, and harvest the energy required for sensing tasks from the environment the device is embedded in. These sensors can be made cheaper, smaller, and will last decades longer than their battery powered counterparts, making them a perfect fit for the requirements of the IoT. These sensors can be deployed where battery powered sensors cannot---embedded in concrete, shot into space, or even implanted in animals and people. However, these batteryless sensors may lose power at any point, with no warning, for unpredictable lengths of time. Programming, profiling, debugging, and building applications with these devices pose significant challenges. First, batteryless devices operate in unpredictable environments, where voltages vary and power failures can occur at any time---often devices are in failure for hours. Second, a device\u27s behavior effects the amount of energy they can harvest---meaning small changes in tasks can drastically change harvester efficiency. Third, the programming interfaces of batteryless devices are ill-defined and non- intuitive; most developers have trouble anticipating the problems inherent with an intermittent power supply. Finally, the lack of community, and a standard usable hardware platform have reduced the resources and prototyping ability of the developer. In this dissertation we present solutions to these challenges in the form of a tool for repeatable and realistic experimentation called Ekho, a reconfigurable hardware platform named Flicker, and a language and runtime for timely execution of intermittent programs called Mayfly