The changing face of lithium cells

Despite negative press, the future of lithium-based battery chemistries appears positive.

Formalising human recognition : a fundamental building block for security proofs

A fundamental part of many authentication protocols which authenticate a party to a human involves the human recognizing or otherwise processing a message received from the party. Examples include typical implementations of Verified by Visa in which a message, previously stored by the human at a bank, is sent by the bank to the human to authenticate the bank to the human; or the expectation that humans will recognize or verify an extended validation certificate in a HTTPS context. This paper presents general definitions and building blocks for the modelling and analysis of human recognition in authentication protocols, allowing the creation of proofs for protocols which include humans. We cover both generalized trawling and human-specific targeted attacks. As examples of the range of uses of our construction, we use the model presented in this paper to prove the security of a mutual authentication login protocol and a human-assisted device pairing protocol.

Make-Buy Decisions in the Face of Radical Innovations

Make-buy decisions are an important aspect of the overall strategic plans for most firms, and the introduction of a new and potentially radical technology into an industry should therefore be a cue for managers to review their make-buy policies. Should a company make in-house the components and processes underpinning the technology, or should it buy them from an outside supplier? Earlier attempts to answer this question may have failed to agree on a single verdict because they have overlooked two important market forces: supplier relations and industry clockspeed. Based on an intensive three-year study at the University of Cambridge which analyzed supply chain management practices from a broad range of manufacturers around the world, this book helps to resolve this classic technology outsourcing dilemma and gives managers the tools they will need to determine if they should make or buy the components and processes that go into a potentially radical innovation.

Viruses face a double defense by plant small RNAs

Plants fight viral infections with enzymes that digest viral RNA, but viruses retaliate with proteins that suppress these enzymes. To boost their antiviral response plants deploy enzymes with redundant functions.

Domestic violence and family law : recognition and appropriation

The battered women’s movement in the United States contributed to a sweeping change in the recognition of men’s violence against female intimate partners. Naming the problem and arguing in favour of its identification as a serious problem meriting a collective response were key aspects of this effort. Criminal and civil laws have been written and revised in an effort to answer calls to take such violence seriously. Scholars have devoted significant attention to the consequences of this reframing of violence, especially around the unintended outcomes of the incorporation of domestic violence into criminal justice regimes. Family law, however, has remained largely unexamined by criminologists. This paper calls for criminological attention to family law responses to domestic violence and provides directions for future research.

Autonomous movement-driven place recognition calibration for generic multi-sensor robot platforms

In this paper we present a method for autonomously tuning the threshold between learning and recognizing a place in the world, based on both how the rodent brain is thought to process and calibrate multisensory data and the pivoting movement behaviour that rodents perform in doing so. The approach makes no assumptions about the number and type of sensors, the robot platform, or the environment, relying only on the ability of a robot to perform two revolutions on the spot. In addition, it self-assesses the quality of the tuning process in order to identify situations in which tuning may have failed. We demonstrate the autonomous movement-driven threshold tuning on a Pioneer 3DX robot in eight locations spread over an office environment and a building car park, and then evaluate the mapping capability of the system on journeys through these environments. The system is able to pick a place recognition threshold that enables successful environment mapping in six of the eight locations while also autonomously flagging the tuning failure in the remaining two locations. We discuss how the method, in combination with parallel work on autonomous weighting of individual sensors, moves the parameter dependent RatSLAM system significantly closer to sensor, platform and environment agnostic operation.

Bearing Fault Diagnostics Using the Spectral Pattern Recognition

In the field of diagnostics of rolling element bearings, the development of sophisticated techniques, such as Spectral Kurtosis and 2nd Order Cyclostationarity, extended the capability of expert users to identify not only the presence, but also the location of the damage in the bearing. Most of the signal-analysis methods, as the ones previously mentioned, result in a spectrum-like diagram that presents line frequencies or peaks in the neighbourhood of some theoretical characteristic frequencies, in case of damage. These frequencies depend only on damage position, bearing geometry and rotational speed. The major improvement in this field would be the development of algorithms with high degree of automation. This paper aims at this important objective, by discussing for the first time how these peaks can draw away from the theoretical expected frequencies as a function of different working conditions, i.e. speed, torque and lubrication. After providing a brief description of the peak-patterns associated with each type of damage, this paper shows the typical magnitudes of the deviations from the theoretical expected frequencies. The last part of the study presents some remarks about increasing the reliability of the automatic algorithm. The research is based on experimental data obtained by using artificially damaged bearings installed in a gearbox.

Delirium knowledge and recognition : a randomized controlled trial of a web-based educational intervention for acute care nurses

Delirium is a significant problem for older hospitalized people and is associated with poor outcomes. It is poorly recognized and evidence suggests that a major reason is lack of education. Nurses, who are educated about delirium, can play a significant role in improving delirium recognition. This study evaluated the impact of a delirium specific educational website. A cluster randomized controlled trial, with a pretest/post-test time series design, was conducted to measure delirium knowledge (DK) and delirium recognition (DR) over three time-points. Statistically significant differences were found between the intervention and non-intervention group. The intervention groups' DK scores were higher and the change over time results were statistically significant [T3 and T1 (t=3.78 p=<0.001) and T2 and T1 baseline (t=5.83 p=<0.001)]. Statistically significant improvements were also seen for DR when comparing T2 and T1 results (t=2.56 p=0.011) between both groups but not for changes in DR scores between T3 and T1 (t=1.80 p=0.074). Participants rated the website highly on the visual, functional and content elements. This study supports the concept that web-based delirium learning is an effective and satisfying method of information delivery for registered nurses. Future research is required to investigate clinical outcomes as a result of this web-based education.

Training-free probability models for whole-image based place recognition

Whole-image descriptors such as GIST have been used successfully for persistent place recognition when combined with temporal filtering or sequential filtering techniques. However, whole-image descriptor localization systems often apply a heuristic rather than a probabilistic approach to place recognition, requiring substantial environmental-specific tuning prior to deployment. In this paper we present a novel online solution that uses statistical approaches to calculate place recognition likelihoods for whole-image descriptors, without requiring either environmental tuning or pre-training. Using a real world benchmark dataset, we show that this method creates distributions appropriate to a specific environment in an online manner. Our method performs comparably to FAB-MAP in raw place recognition performance, and integrates into a state of the art probabilistic mapping system to provide superior performance to whole-image methods that are not based on true probability distributions. The method provides a principled means for combining the powerful change-invariant properties of whole-image descriptors with probabilistic back-end mapping systems without the need for prior training or system tuning.

Towards bio-inspired place recognition over multiple spatial scales

This paper presents a new multi-scale place recognition system inspired by the recent discovery of overlapping, multi-scale spatial maps stored in the rodent brain. By training a set of Support Vector Machines to recognize places at varying levels of spatial specificity, we are able to validate spatially specific place recognition hypotheses against broader place recognition hypotheses without sacrificing localization accuracy. We evaluate the system in a range of experiments using cameras mounted on a motorbike and a human in two different environments. At 100% precision, the multiscale approach results in a 56% average improvement in recall rate across both datasets. We analyse the results and then discuss future work that may lead to improvements in both robotic mapping and our understanding of sensory processing and encoding in the mammalian brain.

Towards condition-invariant, top-down visual place recognition

In this paper we present a novel place recognition algorithm inspired by recent discoveries in human visual neuroscience. The algorithm combines intolerant but fast low resolution whole image matching with highly tolerant, sub-image patch matching processes. The approach does not require prior training and works on single images (although we use a cohort normalization score to exploit temporal frame information), alleviating the need for either a velocity signal or image sequence, differentiating it from current state of the art methods. We demonstrate the algorithm on the challenging Alderley sunny day – rainy night dataset, which has only been previously solved by integrating over 320 frame long image sequences. The system is able to achieve 21.24% recall at 100% precision, matching drastically different day and night-time images of places while successfully rejecting match hypotheses between highly aliased images of different places. The results provide a new benchmark for single image, condition-invariant place recognition.

A security system based on human iris identification using wavelet transform

A security system based on the recognition of the iris of human eyes using the wavelet transform is presented. The zero-crossings of the wavelet transform are used to extract the unique features obtained from the grey-level profiles of the iris. The recognition process is performed in two stages. The first stage consists of building a one-dimensional representation of the grey-level profiles of the iris, followed by obtaining the wavelet transform zerocrossings of the resulting representation. The second stage is the matching procedure for iris recognition. The proposed approach uses only a few selected intermediate resolution levels for matching, thus making it computationally efficient as well as less sensitive to noise and quantisation errors. A normalisation process is implemented to compensate for size variations due to the possible changes in the camera-to-face distance. The technique has been tested on real images in both noise-free and noisy conditions. The technique is being investigated for real-time implementation, as a stand-alone system, for access control to high-security areas.

Facial expression recognition experiments with data from television broadcasts and the world wide web

Facial expression recognition (FER) systems must ultimately work on real data in uncontrolled environments although most research studies have been conducted on lab-based data with posed or evoked facial expressions obtained in pre-set laboratory environments. It is very difficult to obtain data in real-world situations because privacy laws prevent unauthorized capture and use of video from events such as funerals, birthday parties, marriages etc. It is a challenge to acquire such data on a scale large enough for benchmarking algorithms. Although video obtained from TV or movies or postings on the World Wide Web may also contain ‘acted’ emotions and facial expressions, they may be more ‘realistic’ than lab-based data currently used by most researchers. Or is it? One way of testing this is to compare feature distributions and FER performance. This paper describes a database that has been collected from television broadcasts and the World Wide Web containing a range of environmental and facial variations expected in real conditions and uses it to answer this question. A fully automatic system that uses a fusion based approach for FER on such data is introduced for performance evaluation. Performance improvements arising from the fusion of point-based texture and geometry features, and the robustness to image scale variations are experimentally evaluated on this image and video dataset. Differences in FER performance between lab-based and realistic data, between different feature sets, and between different train-test data splits are investigated.

Decision fusion from parts and samples for robust iris recognition

Fusion techniques can be used in biometrics to achieve higher accuracy. When biometric systems are in operation and the threat level changes, controlling the trade-off between detection error rates can reduce the impact of an attack. In a fused system, varying a single threshold does not allow this to be achieved, but systematic adjustment of a set of parameters does. In this paper, fused decisions from a multi-part, multi-sample sequential architecture are investigated for that purpose in an iris recognition system. A specific implementation of the multi-part architecture is proposed and the effect of the number of parts and samples in the resultant detection error rate is analysed. The effectiveness of the proposed architecture is then evaluated under two specific cases of obfuscation attack: miosis and mydriasis. Results show that robustness to such obfuscation attacks is achieved, since lower error rates than in the case of the non-fused base system are obtained.