Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

A database of multi-channel intramuscular electromyogram signals during isometric hand muscles contractions.

Hand movement is controlled by a large number of muscles acting on multiple joints in the hand and forearm. In a forearm amputee the control of a hand prosthesis is traditionally depending on electromyography from the remaining forearm muscles. Technical improvements have made it possible to safely and routinely implant electrodes inside the muscles and record high-quality signals from individual muscles. In this study, we present a database of intramuscular EMG signals recorded with fine-wire electrodes alongside recordings of hand forces in an isometric setup and with the addition of spike-sorted metadata. Six forearm muscles were recorded from twelve able-bodied subjects and nine forearm muscles from two subjects. The fully automated recording protocol, based on command cues, comprised a variety of hand movements, including some requiring slowly increasing/decreasing force. The recorded data can be used to develop and test algorithms for control of a prosthetic hand. Assessment of the signals was done in both quantitative and qualitative manners

Medicaid for All?: State-Level Single-Payer Health Care

If single-payer health care is ever to become a reality in the United States, it will very likely be pioneered by a state government, much like Canada’s single-payer system was first adopted in the provinces. Canada’s system operates more like U.S. Medicaid — financed nationally but administered largely by the provinces — than U.S. Medicare. This article describes three basic strategies progressive U.S. state governments are exploring for achieving universal access to high-quality health care and better health outcomes for their residents. First, maximizing eligibility for the existing Medicaid program using matching federal funds. Second, taking up the mantle of Obamacare by adopting state-level replacements for provisions that federal lawmakers repeal, subsidizing and regulating the price of private insurance, and making more affordable coverage available for purchase on state-run health insurance exchanges. Third, I focus particularly on the efforts of states to succeed where federal reformers have failed by adopting a state-level public option or single-payer health care system. Although state-level public-option and single-payer health plans face significant obstacles, they are more feasible than federal reforms. Moreover, I argue, state-level single-payer health care may be preferable from a health justice perspective because it holds greater promise for integrating health care, public health, and social safety net program goals to achieve better health for all. State lawmakers must proceed cautiously, however, particularly with respect to ensuring that people entitled to traditional Medicaid benefits, which offer special coverage for special populations, continue to receive them. Additionally, state lawmakers should carefully assess the role that privatized public coverage currently plays in their health systems and what role, if any, it should play in public-option or single-payer reforms

SensiCut: Material-Aware Laser Cutting Using Speckle Sensing and Deep Learning

Laser cutter users face difficulties distinguishing between visually similar materials. This can lead to problems, such as using the wrong power/speed settings or accidentally cutting hazardous materials. To support users, we present SensiCut, an integrated material sensing platform for laser cutters. SensiCut enables material awareness beyond what users are able to see and reliably differentiates among similar-looking types. It achieves this by detecting materials' surface structures using speckle sensing and deep learning. SensiCut consists of a compact hardware add-on for laser cutters and a user interface that integrates material sensing into the laser cutting workflow. In addition to improving the traditional workflow and its safety1, SensiCut enables new applications, such as automatically partitioning designs when engraving on multi-material objects or adjusting their geometry based on the kerf of the identified material. We evaluate SensiCut's accuracy for different types of materials under different sheet orientations and illumination conditions

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs