Anthony Griffin

Radio astronomy is uniquely coupled to exponential trends in computation because interferometric optics and spectrometry (i.e., channelization) can now be done digitally. Graphics processing units (GPUs) and inexpensive analog-to-digital converters (ADCs) can sample signals from large numbers of antennas to coherently process wide-field radio data in real time, motivating large- N aperture arrays at moderate cost. We describe the 256-antenna Coherent All-Sky Monitor (CASM-256), a dense aperture array operating at 375–500 MHz, currently being deployed at the Owens Valley Radio Observatory (OVRO) in Big Pine, California. The large field-of-view (FoV ∼ 10 ⁴ deg ² ) and point-source sensitivity of CASM-256 will allow it to detect local Universe fast radio bursts (FRBs). The nearby sample is ideal for unveiling the physical origin of FRBs, measuring the baryonic content of nearby galaxy halos, and discovering prompt multi-wavelength and multi-messenger counterparts to FRBs. CASM will search for fast transients in the Milky Way such as FRB analogs, pulsar giant pulses, and the new source class known as long-period radio transients. We describe the instrument and present on-sky data from the first two dozen antennas, including an operational real-time GPU based FRB search pipeline and a pulsar detection. We emphasize the scalability of the concept and describe paths to a future CASM array with tens of thousands of antennas that could detect one million FRBs.

Farms in many countries use fenced pastures—known as paddocks—for keeping livestock. These farms usually spread over many hectares with paddocks of various shapes and sizes. Farm managers face the difficulty of counting their animals on regular basis and recently the use of an unmanned aerial vehicle (UAV) has been proposed for counting livestock. To get the data from only a respective paddock, the fence lines must also be detected correctly in the aerial images and videos. This will help to keep the data from each paddock separate. A unique system based on a combination of deep learning and machine learning is proposed to accurately detect the fence lines in the videos recorded by a UAV at an altitude of 80 m.

Multiple object tracking (MOT) consists of following the trajectories of different objects in a video with either fixed or moving background. In recent years, the use of deep learning for MOT in the videos recorded by unmanned aerial vehicles (UAVs) has introduced more challenges and hence has a lot of room for extensive research. For the tracking-by-detection method, the three main components, object detector, tracker and data associator, play an equally important role and each part should be tuned to the highest efficiency to increase the overall performance. In this paper, the parameter selection of the Kalman filter and Hungarian algorithm for sheep tracking in paddock videos is discussed. An experimental comparison is presented to show that if the detector is already providing good results, a small change in the system can degrade or improve the tracking capabilities of remaining components. The encouraging results provide an important step in an automated UAV-based sheep tracking system.

In this work we consider the task of detecting sheep onboard an unmanned aerial vehicle (UAV) flying at an altitude of 80 m. At this height, the sheep are relatively small, only about 15 pixels across. Although deep learning strategies have gained enormous popularity in the last decade and are now extensively used for object detection in many fields, state-of-the-art detectors perform poorly in the case of smaller objects. We develop a novel dataset of UAV imagery of sheep and consider a variety of object detectors to determine which is the most suitable for our task in terms of both accuracy and speed. Our findings indicate that a UNet detector using the weighted Hausdorff distance as a loss function during training is an excellent option for detection of sheep onboard a UAV.

Counting livestock is generally done only during major events, such as drenching, shearing or loading, and thus farmers get stock numbers sporadically throughout the year. More accurate and timely stock information would enable farmers to manage their herds better. Additionally, prompt response to any stock in distress is extremely valuable, both in terms of animal welfare and the avoidance of financial loss. In this regard, the evolution of deep learning algorithms and Unmanned Aerial Vehicles (UAVs) is forging a new research area for remote monitoring and counting of different animal species under various climatic conditions. In this paper, we focus on detecting and counting sheep in a paddock from UAV video. Sheep are counted using a model based on Region-based Convolutional Neural Networks and the results are then compared with other techniques to evaluate their performance.

Recently, wireless acoustic sensor networks (WASNs) have received significant attention from the research community and a variety of methods have been proposed for numerous applications, such as location estimation and speech enhancement. The lack of publicly available datasets with signals recorded in WASNs, presents difficulties in obtaining consistent performance indicators across the different approaches. In this paper, we present and release a dataset of real recorded signals in an outdoor WASN comprised of four microphone arrays. Our dataset consists of several speakers recorded at various locations within the WASN and can be used for benchmarking purposes. We also present location estimation results using our real recorded dataset. Our results can serve as a baseline indicator of localization performance of single and multiple sources in a real environment.

Aging populations are stretching existing healthcare systems to their limits in both developing and developed countries. Telemedicine is a promising solution to this challenging problem. Under the conventional data compression paradigm, long-time recording of electroencephalography (EEG) signals still generates excessive amount of data, which requires large data storage and long transmission time. While promoting mobile telemedicine with compressed sensing (CS) as a key system for EEG monitoring, this paper investigates the effect of epoch length on CS to compress EEG signals. Experimental results show that a longer epoch length leads to better signal compression at the expense of larger signal reconstruction time. At a sampling frequency of 256 Hz, a 4-s epoch length is suitable when using a general desktop computer to perform signal reconstruction.

Electroencephalogram (EEG) signals have been widely used to analyze brain activities so as to diagnose certain brain-related diseases. They are usually recorded for a fairly long interval with adequate resolution, consequently requiring a considerable amount of memory space for storage and transmission. Recently compressed sensing (CS) has been proposed in order to effectively compress EEG signals. However, its performance is closely dependent on how a compression dictionary is built. Through our study, we notice that building the best fit over-complete Gabor dictionary plays an important role in this task. In this paper, we evaluate the effect of different time and frequency step sizes in building Gabor atoms on the performance of EEG signal compression using CS with three common EEG databases used by the research community. Taking the Normalized Mean Square Error (NMSE) as a performance metric, we present a quantitative study with an attempt to provide more insight on how to adopt CS in EEG signal compression.

The Square Kilometre Array (SKA) will push the boundaries of radio astronomy. As such, it will need enormous computing power to process the tremendous amount of data it will produce. Significant savings in computing could be made if some of this processing was done in single-precision. This paper presents our end-to-end modelling of the SKA Imaging Pipeline. We model the signals received by the Central Signal Processor (CSP) using our Sky Generator Model. We then process the sky signals with our CSP Correlator Model to generate the visibilities that are passed to the Science Data Processor (SDP). These visibilities are gridded Fourier transformed by our SDP Imaging Model to produce a dirty image. This dirty image is then deconvolved to produce an image of the sky. Through our models we investigate the error introduced with reduced numerical precision, and we perform various tests to explore the limits of single-precision processing.

In this work we present a vision-based road user monitoring system for traffic intersections using a combination of Gaussian Mixture Model (GMM)-based deep learning approaches and geometric warping for further behaviour analysis. GMMs and detection of features in consecutive frames are used in bounding box prediction in order to track objects, while a Fast Regional Convolutional Neural Networks (R-CNN) approach classifies different road users. For better analysis, by knowing the actual coordinates of the intersection, we use geometric warping to map the 3D-plane to the 2D plane. We thus extract the real distance and the approximate real time speed of each road user. The results demonstrate that the proposed region proposal generator method for Fast R-CNN outperforms the other methods considered in terms of both accuracy and computation time.