Detecting signals is often presented as a simple and solved problem. However, signal detection is typically not the only goal, as signal localization in time and frequency is often desired. This detection and localization process must also occur in the presence of non-ideal radio frequency (RF) receiver effects such as receiver non-flat noise floors, front-end inphase-quadrature (IQ) imbalance, local oscillator (LO) leakage, and in some cases power saturation. A robust signal detection system must overcome these hardware impairments and report only signals that correspond to true emitter devices. Additionally, some unspoken requirements take the form of detecting signals that are below the noise floor, very narrowband, very wideband, bursty, or frequency hopping. We present Searchlight, a signal detection system that solves these problems by using novel techniques that enable classical signal detection to work well using software defined radios (SDR). Performance results are presented for synthetically generated and over-the-air (OTA) data sets, which were obtained using commercially available hardware in a complicated signal scenario. Searchlight is shown to be an important enabler for true spectral monitoring platforms that desire to detect anomalous signals or apply machine learning (ML) algorithms to classify the various types of wireless activity in a given area.

Street trees play an important role in the urban environment and have positive social impacts, such as improving human health. However, they can potentially be hazardous if not properly maintained. Although periodic tree assessments are necessary in urban forest management tasks, such assessments are often neglected because of the lack of trained professionals in local cities. This study aimed to develop a comparison of image classification to identify the problems associated with trees using a limited number of training samples using image classification, object detection, and semantic segmentation.

Gaze-based interfaces are especially useful for people with disabilities involving the upper limbs or hands. Typically, users select from a number of options (e.g. letters or commands) displayed on a screen by gazing at the desired option. However, in some applications, e.g. gaze-based driving, it may be dangerous to direct gaze away from the environment towards a separate display. In addition, a purely gaze based interface can present a high cognitive load to users, as gaze is not normally used for selection and/or control, but rather for other purposes, such as information gathering. To address these issues, this paper presents a cost-effective multi-modal system for gaze based driving which combines appearancebased gaze estimates derived from webcam images with push button inputs that trigger command execution. This system uses an intuitive “direct interface”, where users determine the direction of motion by gazing in the corresponding direction in the environment. We have implemented the system for both wheelchair control and robotic teleoperation. The use of our system should provide substantial benefits for patients with severe motor disabilities, such as ALS, by providing them with a more natural and affordable method of wheelchair control. We compare the performance of our system to the more conventional and common “indirect” system where gaze is used to select commands from a separate display, showing that our system enables faster and more efficient navigation.

Recent developments have shown FPGAs to be effective for data centre applications, but debugging support in that environment has not evolved correspondingly. This presents an additional barrier to widespread adoption. This work proposes Debug Governors, a new open-source debugger designed for controllability and interactive debugging that can help to locate issues across multiple FPGAs.
A Debug Governor can pause, log, drop, and/or inject data into any streaming interface. These operations enable single-stepping, unit testing, and interfacing with software. Hundreds of Debug Governors can fit in a single FPGA and, because they are transparent when inactive, can be left “dormant'' in production designs.
We show how Debug Governors can be used to resolve functional problems on a real FPGA, and how they can be extended to memory-mapped protocols.