The UK landscape for robotics and autonomous systems

Robotics and Autonomous Systems Special Interest Group Report: Innovate UK - Technology Strategy Board This landscape collates the output from a series of workshops designed to explore the impact on the UK of advances in Robotics and Autonomous Systems (RAS). In overviewing the resulting landscape it is clear that the RAS opportunity, as perceived by the UK community, is extensive and rich and that the UK has the potential to create a strong RAS market. It is also clear that robotics and autonomous systems will impact on each UK market sector and that the total size of this impact will be significantly greater than the size of the RAS sector itself. Across these sectors strong cross cutting themes exist that can be used to drive synergies to build technical capability and market opportunity. Within those sectors that will benefit the most from robotics and autonomous systems technology the potential for disruptive innovation and the need to respond to change through the development of new business models is now obvious. Robotics and autonomous systems do not work in isolation. They will require testing, regulation, standards, innovation, investment and skills together with technical progress and strong collaborative partnerships in order to fully realise the opportunity. The resulting Landscape carries an essential message; that the UK has a unique opportunity to engage with robotics and autonomous systems, to exploit existing expertise within the UK and explore its potential, but that other nations are similarly engaged and the UK must now be bold and invest to win. 41 Individuals listed as contributor

A Unified Deep Learning Approach for Prediction of Parkinson’s Disease

The paper presents a novel approach, based on deep learning, for diagnosis of Parkinson’s disease through medical imaging. The approach includes analysis and use of the knowledge extracted by Deep Convolutional and Recurrent Neural Networks (DNNs) when trained with medical images, such as Magnetic Resonance Images and DaTscans. Internal representations of the trained DNNs constitute the extracted knowledge which is used in a transfer learning and domain adaptation manner, so as to create a unified framework for prediction of Parkinson’s across different medical environments. A large experimental study is presented illustrating the ability of the proposed approach to effectively predict Parkinson’s, using different medical image sets from real environments

Potentials and caveats of AI in Hybrid Imaging

State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research

The internet of food things: Adding values to the digitalisation of the UK food supply chain

The Internet of Things (IoT) is beginning to have a pronounced impact on the digitalised food manufacturing chain. Whilst the food chain is becoming at once more complex and more critical, the application of IoT has begun to be recognised as a key component of this transformation. However, exactly what the IoT is, or is not, is both evolving and provoking a lively debate. New clarifiers are being added to the term such as the Internet of Industrial Things and the Autonomous Internet of Things to ostensibly help clarify the terminology. For the purposes of the Internet of Food Things Network Plus, our conceptual and physical platform is predicated on the idea of a secure and trustworthy network based on open standards, where-possible commodity devices, and underpinned by societally ratified regulatory arrangements

Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage

This paper presents a spatiotemporal deep learning approach for mouse behavioural classification in the home-cage. Using a series of dual-stream architectures with assorted modifications to increase performance, we introduce a novel feature sharing approach that jointly processes the streams at regular intervals throughout the network. To investigate the efficacy of this approach, models were evaluated by dissociating the streams and training/testing in the same rigorous manner as the main classifiers. Using an annotated, publicly available dataset of a singly-housed mice, we achieve prediction accuracy of 86.47% using an ensemble of a Inception-based network and an attention-based network, both of which utilize this feature sharing. We also demonstrate through ablation studies that for all models, the feature-sharing architectures consistently perform better than conventional ones having separate streams. The best performing models were further evaluated on other activity datasets, both mouse and human. Future work will investigate the effectiveness of feature sharing to behavioural classification in the unsupervised anomaly detection domain

Moving to 3D: relationships between coral planar area, surface area and volume.

Coral reefs are a valuable and vulnerable marine ecosystem. The structure of coral reefs influences their health and ability to fulfill ecosystem functions and services. However, monitoring reef corals largely relies on 1D or 2D estimates of coral cover and abundance that overlook change in ecologically significant aspects of the reefs because they do not incorporate vertical or volumetric information. This study explores the relationship between 2D and 3D metrics of coral size. We show that surface area and volume scale consistently with planar area, albeit with morphotype specific conversion parameters. We use a photogrammetric approach using open-source software to estimate the ability of photogrammetry to provide measurement estimates of corals in 3D. Technological developments have made photogrammetry a valid and practical technique for studying coral reefs. We anticipate that these techniques for moving coral research from 2D into 3D will facilitate answering ecological questions by incorporating the 3rd dimension into monitoring

Are Distributed Ledger Technologies the panacea for food traceability?

Distributed Ledger Technology (DLT), such as blockchain, has the potential to transform supply chains. It can provide a cryptographically secure and immutable record of transactions and associated metadata (origin, contracts, process steps, environmental variations, microbial records, etc.) linked across whole supply chains. The ability to trace food items within and along a supply chain is legally required by all actors within the chain. It is critical to food safety, underpins trust and global food trade. However, current food traceability systems are not linked between all actors within the supply chain. Key metadata on the age and process history of a food is rarely transferred when a product is bought and sold through multiple steps within the chain. Herein, we examine the potential of massively scalable DLT to securely link the entire food supply chain, from producer to end user. Under such a paradigm, should a food safety or quality issue ever arise, authorized end users could instantly and accurately trace the origin and history of any particular food item. This novel and unparalleled technology could help underpin trust for the safety of all food, a critical component of global food security. In this paper, we investigate the (I) data requirements to develop DLT technology across whole supply chains, (ii) key challenges and barriers to optimizing the complete system, and (iii) potential impacts on production efficiency, legal compliance, access to global food markets and the safety of food. Our conclusion is that while DLT has the potential to transform food systems, this can only be fully realized through the global development and agreement on suitable data standards and governance. In addition, key technical issues need to be resolved including challenges with DLT scalability, privacy and data architectures

Hybrid Modality Fusion of Planar Scintigrams and CT Topograms to Localize Sentinel Lymph Nodes in Breast Lymphoscintigraphy: Technical Description and Phantom Studies

Lymphoscintigraphy is a nuclear medicine procedure that is used to detect sentinel lymph nodes (SLNs). This project sought to investigate fusion of planar scintigrams with CT topograms as a means of improving the anatomic reference for the SLN localization. Heretofore, the most common lymphoscintigraphy localization method has been backlighting with a 57Co sheet source. Currently, the most precise method of localization through hybrid SPECT/CT increases the patient absorbed dose by a factor of 34 to 585 (depending on the specific CT technique factors) over the conventional 57Co backlighting. The new approach described herein also uses a SPECT/CT scanner, which provides mechanically aligned planar scintigram and CT topogram data sets, but only increases the dose by a factor of two over that from 57Co backlighting. Planar nuclear medicine image fusion with CT topograms has been proven feasible and offers a clinically suitable compromise between improved anatomic details and minimally increased radiation dose

The Reference Image Database to Evaluate Response to Therapy in Lung Cancer (RIDER) Project: A Resource for the Development of Change‐Analysis Software

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110105/1/cptclpt2008161.pd

Unsupervised detection of mouse behavioural anomalies using two-stream convolutional autoencoders

This paper explores the application of unsupervised learning to detecting anomalies in mouse video data. The two models presented in this paper are a dual stream, 3D convolutional autoencoder (with residual connections) and a dual stream, 2D convolutional autoencoder. The publicly available dataset used here contains twelve videos of a single home-caged mice alongside frame level annotations. Under the pretext that the autoencoder only sees normal events, the video data was handcrafted to treat each behaviour as a pseudo-anomaly thereby eliminating them from the others during training. The results are presented for one conspicuous behaviour (hang) and one inconspicuous behaviour (groom). The performance of these models is compared to a single stream autoencoder and a supervised learning model, which are both based on the custom CAE encoder. Both models are also tested on the CUHK Avenue dataset were found to perform as well as some state-of-the-art architectures