Security scanning at 94GHz - art. no. 62110C

It is well known that millimetre waves can pass through clothing. In short range applications such as in the scanning of people for security purposes, operating at W band can be an advantage. The size of the equipment is decreased when compared to operation at Ka band and the equipments have similar performance.

In this paper a W band mechanically scanned imager designed for imaging weapons and contraband hidden under clothing is discussed. This imager is based on a modified folded conical scan technology previously reported. In this design an additional optical element is added to give a Cassegrain configuration in image space. This increases the effective focal length and enables improved sampling of the image and provides more space for the receivers. This imager is constructed from low cost materials such as polystyrene, polythene and printed circuit board materials. The trade off between image spatial resolution and thermal sensitivity is discussed.

Cable Crossings: The Aran Jumper as Myth and Merchandise

This article considers the Aran jumper as a cultural artefact from an anthropological perspective. As an internationally recognized symbol of Irishness that comes with its own myth of origin, the Aran jumper carries emotionally charged ideas about kinship and nativeness. Whether read as an ID document, family tree, representation of the landscape or reference to Christian or pre-Christian spirituality, the Aran jumper’s stitch patterns seem to invite interpretation. Emerging at a particular period in the relationship between Ireland and America, this garment and the story that accompanies it have been shaped by migration and tourism, but may be understood very differently on either side of the Atlantic. The resilience of the myth of a fisherman lost at sea, whose corpse is identifiable only by designs his relatives have stitched into his clothing, is explained in light of its resonance with diasporic narratives and transnational longings.

Child Poverty in Northern Ireland: Results from the Poverty and Social Exclusion Study

- There is general agreement that the measurement of child poverty is based on both low income and deprivation.

- Adjusting incomes for different household types, measuring before or after housing costs, and the selection of deprivation items all have an impact on poverty rates.

- The consensual poverty method, which takes account of what the population considers to be basic necessities, is described. The study found a high level of agreement on the basic necessities for children.

- The study found that only a few children lacked very basic necessities such as three meals a day and adequate clothing, but a third of all children (150,000) were deprived of an annual holiday and 75,000 children are growing up in cold and damp homes.

- Overall, the study found that 24% or 106,000 children are living in low income households and are deprived of four or more items.

- The study found those who had a ‘high experience’ of the conflict were significantly more deprived than those with no conflict experience and that a fifth of all children are living with an adult/s who have ‘high experience’.

Just a piece of cloth? German courts and employees with headscarves

On 10 October 2002, and on 24 September 2003, the German Federal Labour Court and the German Federal Constitutional Court each delivered a decision on the consequences of wearing a headscarf for employees. Both courts appeared to protect the individual rights of the woman in question. The Federal Labour Court invalidated the dismissal of a salesperson based on the wearing of a headscarf; the Federal Constitutional Court held that a school teacher must not be denied employment on grounds of wearing a headscarf. However, both courts also left some room for manoeuvre in favour of clothing policies or laicism principles which could be used to justify head-scarf bans. This note discussed the potential and drawback of these cases, especially as regards intersectional inequalities along the lines of gender, religion and ethnicity.

Patient preferences for dental clinical attire: a cross-sectional survey in a dental hospital

AIMS AND OBJECTIVES: This cross sectional descriptive study was designed to survey patient opinion towards dental clinical attire, name badges and commonly used cross-infection control measures.

METHOD: Patients attending a dental hospital for a consultation appointment were asked to complete a questionnaire accompanied by photographs of models portraying a range of clinical attire. A representative sample of patients completed 188 questionnaires over a four week period.

RESULTS: The study found that the majority of patients felt clinical attire was important and that they preferred dental professionals to wear name badges. The majority of patients also preferred dentists to use both safety glasses and face masks. When asked to indicate which clinical attire was most appropriate for a consultant/specialist to wear, the overwhelming opinion was that of smart dress accompanied with a white coat. In addition, most respondents wished their dentist to wear a traditional white, dental tunic.

CONCLUDING REMARKS: It is hoped that this study will be informative for the dental team and that the results will be taken into consideration when considering appropriate clinical attire in accordance with patient opinions.

On covariate factor detection and removal for robust gait recognition

We propose a novel bolt-on module capable of boosting the robustness of various single compact 2D gait representations. Gait recognition is negatively influenced by covariate factors including clothing and time which alter the natural gait appearance and motion. Contrary to traditional gait recognition, our bolt-on module remedies this by a dedicated covariate factor detection and removal procedure which we quantitatively and qualitatively evaluate. The fundamental concept of the bolt-on module is founded on exploiting the pixel-wise composition of covariate factors. Results demonstrate how our bolt-on module is a powerful component leading to significant improvements across gait representations and datasets yielding state-of-the-art results.

Person Re-Identification using Deep Convnets with Multi-task Learning

Person re-identification involves recognizing a person across non-overlapping camera views, with different pose, illumination, and camera characteristics. We propose to tackle this problem by training a deep convolutional network to represent a person’s appearance as a low-dimensional feature vector that is invariant to common appearance variations encountered in the re-identification problem. Specifically, a Siamese-network architecture is used to train a feature extraction network using pairs of similar and dissimilar images. We show that use of a novel multi-task learning objective is crucial for regularizing the network parameters in order to prevent over-fitting due to the small size the training dataset. We complement the verification task, which is at the heart of re-identification, by training the network to jointly perform verification, identification, and to recognise attributes related to the clothing and pose of the person in each image. Additionally, we show that our proposed approach performs well even in the challenging cross-dataset scenario, which may better reflect real-world expected performance.