Contains fulltext : 308793.pdf (Publisher’s version ) (Open Access)

For widespread adoption, public security and surveillance systems must be accurate, portable, compact, and real-time, without impeding the privacy of the individuals being observed. Current systems broadly fall into two categories – image-based which are accurate, but lack privacy, and RF signal-based, which preserve privacy but lack portability, compactness and accuracy. Our paper proposes mmSense, an end-to-end portable miniaturised real-time system that can accurately detect the presence of concealed metallic objects on persons in a discrete, privacy-preserving modality. mm-Sense features millimeter wave radar technology, provided by Google’s Soli sensor for its data acquisition, and TransDope, our real-time neural network, capable of processing a single radar data frame in 19 ms. mmSense achieves high recognition rates on a diverse set of challenging scenes while running on standard laptop hardware, demonstrating a significant advancement towards creating portable, cost-effective real-time radar based surveillance systems.

Specialised metabolites from microbial sources are well-known for their wide range of biomedical applications, particularly as antibiotics. When mining paired genomic and metabolomic data sets for novel specialised metabolites, establishing links between Biosynthetic Gene Clusters (BGCs) and metabolites represents a promising way of finding such novel chemistry. However, due to the lack of detailed biosynthetic knowledge for the majority of predicted BGCs, and the large number of possible combinations, this is not a simple task. This problem is becoming ever more pressing with the increased availability of paired omics data sets. Current tools are not effective at identifying valid links automatically, and manual verification is a considerable bottleneck in natural product research. We demonstrate that using multiple link-scoring functions together makes it easier to prioritise true links relative to others. Based on standardising a commonly used score, we introduce a new, more effective score, and introduce a novel score using an Input-Output Kernel Regression approach. Finally, we present NPLinker, a software framework to link genomic and metabolomic data. Results are verified using publicly available data sets that include validated links.

We demonstrate that front-of-screen targeting on mobile phones can be predicted from back-of-device grip manipulations. Using simple, low-resolution capacitive touch sensors placed around a standard phone, we outline a machine learning approach to modelling the grip modulation and inferring front-of-screen touch targets. We experimentally demonstrate that grip is a remarkably good predictor of touch, and we can predict touch position 200ms before contact with an accuracy of 18mm.

OBJECTIVE: The array of available brain-computer interface (BCI) paradigms has continued to grow, and so has the corresponding set of machine learning methods which are at the core of BCI systems. The latter have evolved to provide more robust data analysis solutions, and as a consequence the proportion of healthy BCI users who can use a BCI successfully is growing. With this development the chances have increased that the needs and abilities of specific patients, the end-users, can be covered by an existing BCI approach. However, most end-users who have experienced the use of a BCI system at all have encountered a single paradigm only. This paradigm is typically the one that is being tested in the study that the end-user happens to be enrolled in, along with other end-users. Though this corresponds to the preferred study arrangement for basic research, it does not ensure that the end-user experiences a working BCI. In this study, a different approach was taken; that of a user-centered design. It is the prevailing process in traditional assistive technology. Given an individual user with a particular clinical profile, several available BCI approaches are tested and - if necessary - adapted to him/her until a suitable BCI system is found. METHODS: Described is the case of a 48-year-old woman who suffered from an ischemic brain stem stroke, leading to a severe motor- and communication deficit. She was enrolled in studies with two different BCI systems before a suitable system was found. The first was an auditory event-related potential (ERP) paradigm and the second a visual ERP paradigm, both of which are established in literature. RESULTS: The auditory paradigm did not work successfully, despite favorable preconditions. The visual paradigm worked flawlessly, as found over several sessions. This discrepancy in performance can possibly be explained by the user's clinical deficit in several key neuropsychological indicators, such as attention and working memory. While the auditory paradigm relies on both categories, the visual paradigm could be used with lower cognitive workload. Besides attention and working memory, several other neurophysiological and -psychological indicators - and the role they play in the BCIs at hand - are discussed. CONCLUSION: The user's performance on the first BCI paradigm would typically have excluded her from further ERP-based BCI studies. However, this study clearly shows that, with the numerous paradigms now at our disposal, the pursuit for a functioning BCI system should not be stopped after an initial failed attempt.

Conversation partners on mobile phones can align their walking gait without physical proximity or visual feedback. We investigate gait synchronization, measured by accelerometers while users converse via mobile phones. Hilbert transforms are used to infer gait phase angle, and techniques from synchronization theory are used to infer level of alignment. Experimental conditions include the use of vibrotactile feedback to make one conversation partner aware of the other's footsteps. Three modes of interaction are tested: reading a script, discussing a shared image and spontaneous conversation. The vibrotactile feedback loop on its own is sufficient to create synchronization, but there are complex interference effects when users converse spontaneously. Even without vibration crosstalk, synchronisation appeared for long periods in the spontaneous speech condition, indicating that users were aligning their walking behaviour from audible cues alone.