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The paper describes work relating to the laser line triangulation technique which has been used to inspect the edges of overlapping shoe components prior to the sewing operation. The laser line triangulation technique involves projecting a laser line on to a surface which can be viewed using an area camera. A surface height transition (edge) causes a discontinuity in the observed laser line. Different approaches for extracting the edge positions in the image co‐ordinate system have been investigated based on the Hough transform, the spatial histogram, polynomial regression and the discrete first derivative. These edge detection algorithms are compared in terms of speed and precision performance. Three‐dimensional scans of typical shoe component parts are presented.

The paper aims to develop a particle matter (PM_2.5) prediction model for open-plan office space using a variety of data sources. Monitoring of PM_2.5 levels is not widely applied in indoor settings. Many reliable methods of monitoring PM_2.5 require either time-consuming or expensive equipment, thus making PM_2.5 monitoring impractical for many settings. The goal of this paper is to identify possible low-cost, low-effort data sources that building managers can use in combination with machine learning (ML) models to approximate the performance of much more costly monitoring devices.

This study identified a variety of data sources, including freely available, public data, data from low-cost sensors and data from expensive, high-quality sensors. This study examined a variety of neural network architectures, including traditional artificial neural networks, generalized recurrent neural networks and long short-term memory neural networks as candidates for the prediction model. The authors trained the selected predictive model using this data and identified data sources that can be cheaply combined to approximate more expensive data sources.

The paper identified combinations of free data sources such as building damper percentages and weather data and low-cost sensors such as Wi-Fi-based occupancy estimator or a Plantower PMS7003 sensor that perform nearly as well as predictions made based on nephelometer data.

This work demonstrates that by combining low-cost sensors and ML, indoor PM_2.5 monitoring can be performed at a drastically reduced cost with minimal error compared to more traditional approaches.

The purpose of this paper is to provide a robust method for automatic detection of seam lines based only on digital images of the garments.

A local standard deviation pre‐processing filter is applied to enhance the contrast between the seam line and the texture and the Prewitt operator extracts the edges of the enhanced image. The seam line is detected by a maximum at the Radon transform. The proposed method is invariant to the illumination intensity and it has been also tested with moving average and fast Fourier transform low‐pass filters used in the pre‐processing module. Extensive experiments are carried out in the presence of additive Gaussian and uniform noise.

The proposed method detects 109 out of 118 seams when the local standard deviation is used at the pre‐processing stage, giving a mean distance error between the real and the estimated line of 2 mm when the image is digitised at 97 dpi. However, in case the images are distorted by additive Gaussian noise at 20 dB signal‐to‐noise ratio, the moving average low‐pass filtering method gives the best results, detecting 104 noisy images.

The proposed method detects seam lines that can be approximated by a continuation of straight lines. The current work can be extended in the detection of the curved parts of seam lines.

Since the method addresses garments instead of seam specimens, the proposed approach can be imported in automatic systems for online quality control of seams.

Local standard deviation belongs to first‐order statistics, which makes it suitable for texture analysis and that is why it is mostly used in web defect detection. The novelty in the approach, however, is that by considering the seam as an abnormality of the texture, the authors applied that method at the pre‐processing stage to enhance the seam before the detection. Moreover, the presented method is illumination invariant, a property that has not been addressed in similar methods.

This paper aims to provide a critical assessment of the Internet of things (IoT) and the social and policy issues raised by its development. While the Internet will continue to become ever more central to everyday life and work, there is a new but complementary vision for an IoT, which will connect billions of objects – “things” like sensors, monitors, and radio-frequency identification devices – to the Internet at a scale that far outstrips use of the Internet as we know it, and will have enormous social and economic implications.

It is based on a review of literature and emerging developments, including synthesis of a workshop and discussions within a special interest group on the IoT.

Nations can harvest the potential of this wave of innovation not only for manufacturing but also for everyday life and work and the development of new information and services that will change the way we do things in many walks of life. However, its success is not inevitable. Technical visions will not lead inexorably to successful public and private infrastructures that support the vitality of an IoT and the quality of everyday life and work. In fact, the IoT could undermine such core values as privacy, equality, trust and individual choice if not designed, implemented and governed in appropriate ways.

There is a need for more multi-disciplinary research on the IoT.

Policymakers and opinion formers need to understand the IoT and its implications.

If the right policies and business models are developed, the IoT will stimulate major social, economic and service innovations in the next years and decades.

This paper pulls together discussions and literature from a social science perspective, as one means to enable more multidisciplinary studies of emerging developments.