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We consider a variational model for Single Molecule Localisation Microscopy (SMLM) super-resolution. More specifically, we study a generalization of the Continuous Exact $\ell_{0}$ (CELO) penalty, recently introduced to relax the $\ell_{2}-\ell_{0}$ problem, where a weighted-$\ell_{2}$ data fidelity now models signal-dependent Poisson noise. For the numerical solution of the associated non-convex minimisation problem, we propose an iterative reweighted $\ell_{1}$ (IRL1) algorithm, for which efficient parameter computation strategies are detailed. Both qualitative and quantitative molecule localisation results are reported, showing that the proposed weighted-CELO (wCELO) model for Poisson noisy data improves the results obtained by CELO and state-of-the art deep-learning approaches for the high-density SMLM ISBI 2013 dataset.

Secondary Electron Yield (SEY) [3, 5] occurs in a system when a primary electron impinges a material's surface and induces the emission of a 1st and potentially 2nd generation secondary electrons (see Figure 1, Left). The total number of secondary electrons per primary electron is the SEY. This phenomenon fonns a highly challenging problem in many systems, for example in particle accelerators, where significant levels of SEY fonn as an electron cloud and can perturbate the circulating beams and generate a high level of heat load to be absorbed by cooling and cryogenics. The Large Hadron Collider (LHC) lias a 54-km beam pipe [1] in which copper-laminated steel beam-screens are placed in order to shield the beam pipes from heat loads, but inherently result in unwanted SEY. As such, the development of methods which mitigate the SEY are increasingly appealing [2], including surface texturing, shaping the geometry and orientation of patterns etched into the surfaces [3], and carbon-coating of the interior of the beam pipes in the Super Proton Synchrotron (SPS) [4], Previously we have shown that nanosecond pulsed laser treatment of copper surfaces at 532 mn could significantly increase the optical absorbance of the surface [6], and furthennore reduce the SEY to close to 1 [7], More recently we demonstrated that surface structures produced by a picosecond pulsed laser at 532mn exhibited SEY values below 1 and were successfully tested in a dipole magnet in the Super Proton Synchrotron (SPS) accelerator at CERN [8].

A high repetition rate, semiconductor based, Marx generator has been designed and developed for a spark gap system: this system will allow the study of breakdown rate phenomena for different materials and surface finishes, for RF structures for the proposed Compact Linear Collider (CLIC). The Marx circuit is based on new SiC MOSFETs (Metal Oxide Semiconductor Field Effect Transistors), with 15 compact stages, capable of running with capacitive type loads during normal operation and also short-circuit mode at reduced repletion rate. Experimental tests with 150 pF to 650 pF dummy capacitances and also the actual spark gap system are presented: these tests and measurements prove the performance of the Marx generator to generate pulses from 200 ns to 100 μs pulse width, up to 10kV, with frequencies up to 1 kHz and pulse voltage rise times less than 100 ns with a load capacitance of not more than 150 pF. In addition, first breakdown results validating the operation of the Marx generator during breakdown are presented.

Sensor technology that is unobtrusively integrated into the clothing and equipment of an athlete can support the training of sport activities and monitor the athlete;s progress. In this paper, we propose two wearable systems that support ice hockey players in the training of skating and shooting. These assistants measure the motions of players and compare them with reference executions of the same activities by professional players. A third system that we introduce monitors the player;s activities during a hockey game and creates a match report for objective performance measurement. For each of the three proposed applications, we present a prototype setup that we evaluate with amateur and professional players. The main findings are i) that with a skate-worn motion sensor and user-dependent training, eight skating motions can be spotted with an accuracy above 90%, ii) that stick-integrated sensors enable the measurement of relevant shot features, which differentiate professional from amateur athletes, and iii) that it is possible to spot important ice hockey activities in the signals of body-worn motion sensors worn during a game.

Activity recognition systems based on body-worn motion sensors suffer from a decrease in performance during the deployment and run-time phases, because of probable changes in the sensors (e.g. displacement or rotatation), which is the case in many real-life scenarios (e.g. mobile phone in a pocket). Existing approaches to achieve robustness tend to sacrifice information (e.g. by rotation-invariant features) or reduce the weight of the anomalous sensors at the classifier fusion stage (adaptive fusion), ignoring data which might still be perfectly meaningful, although different from the training data. We propose to use adaptation to rebuild the classifier models of the sensors which have changed position by a two-step approach: in the first step, we run an anomaly detection algorithm to automatically detect which sensors are delivering unexpected data; subsequently, we trigger a system self-training process, so that the remaining classifiers retrain the "anomalous" sensors. We show the benefit of this approach in a real activity recognition dataset comprising data from 8 sensors to recognize locomotion. The approach achieves similar accuracy compared to the upper baseline, obtained by retraining the anomalous classifiers on the new data.

Template matching methods using Dynamic Time Warping (DTW) have been used recently for online gesture recognition from body-worn motion sensors. However, DTW has been shown sensitive under the strong presence of noise in time series. In sensor readings, labeling temporal boundaries of daily gestures precisely is rarely achievable as they are often intertwined. Moreover, the variation in daily gesture execution always exists. Therefore, here we propose two template matching methods utilizing the Longest Common Subsequence (LCSS) to improve robustness against such noise for online gesture recognition. Segmented LCSS utilizes a sliding window to define the unknown boundaries of gestures in the continuous coming sensor readings and detects efficiently a possibly shorter gesture within it. WarpingLCSS is our novel variant of LCSS to determine occurrences of gestures without segmenting data and performs one order of magnitude faster than the Segmented LCSS. The WarpingLCSS requires low-resource settings to process new arriving samples, thus it is suitable for real-time gesture recognition implemented directly on the small wearable devices. We compare our methods with the existing template matching methods based on Dynamic Time Warping (DTW) on two real-world gesture datasets from arm-worn accelerometer data. The results demonstrate that the LCSS approaches outperform the existing template matching approaches (about 12% in accuracy) in the dataset that suffers from boundary noise and execution variation.

Human activity recognition is a thriving research field. There are lots of studies in different sub-areas of activity recognition proposing different methods. However, unlike other applications, there is lack of established benchmarking problems for activity recognition. Typically, each research group tests and reports the performance of their algorithms on their own datasets using experimental setups specially conceived for that specific purpose. In this work, we introduce a versatile human activity dataset conceived to fill that void. We illustrate its use by presenting comparative results of different classification techniques, and discuss about several metrics that can be used to assess their performance. Being an initial benchmarking, we expect that the possibility to replicate and outperform the presented results will contribute to further advances in state-of-the-art methods.

The field of research on activity recognition is relatively young compared to others, like computer vision. In more mature fields, algorithms are usually tested on standardized, reference datasets. This way, algorithms coming from different groups can be tested in a fair manner, which accelerates the process of developing new knowledge. Collecting a reference dataset under realistic settings for activity recognition poses many challenges due to the large amount of sensors and sensor modalities which are needed to provide a sufficiently complete playground. We here report on some lessons learned while collecting such a reference dataset with a heterogeneous setup. We argue for the importance of a few principles to obtain a clean dataset, starting from the sampling and acquisition, down to the synchronization and labeling of the data.

Non-stationary data distributions are a challenge in activity recognition from body worn motion sensors. Classifier models have to be adapted online to maintain a high recognition performance. Typical approaches for online learning are either unsupervised and potentially unstable, or require ground truth information which may be expensive to obtain. As an alternative we propose a teacher signal that can be provided by the user in a minimally obtrusive way. It indicates if the predicted activity for a feature vector is correct or wrong. To exploit this information we propose a novel incremental online learning strategy to adapt a k-nearest-neighbor classifier from instances that are indicated to be correctly or wrongly classified. We characterize our approach on an artificial dataset with abrupt distribution change that simulates a new user of an activity recognition system. The adapted classifier reaches the same accuracy as a classifier trained specifically for the new data distribution. The learning based on the provided correct - error signal also results in a faster learning speed compared to online learning from ground truth. We validate our approach on a real world gesture recognition dataset. The adapted classifiers achieve an accuracy of 78.6% compared to the subject independent baseline of 68.3%.

We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity occurrences observed during post-processing, and estimate that over 13000 and 14000 object and environment interactions occurred. We describe the networked sensor setup and the methodology for data acquisition, synchronization and curation. We report on the challenges and outline lessons learned and best practice for similar large scale deployments of heterogeneous networked sensor systems. We evaluate data acquisition quality for on-body and object integrated wireless sensors; there is less than 2.5% packet loss after tuning. We outline our use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations. Eventually this dataset will be made public.

This paper proposes a method for the acoustic rendering of a virtual environment based on a geometric decomposition of the wave-field into multiple elementary acoustic beams, all reconstructed with a loudspeaker array. The point of origin, the orientation and the aperture of each beam is computed according to the geometry of the virtual environment that we want to render and to the location of the sources. Space-time filters are computed with a Least Squares approach to render the desired beam. Experimental results show the feasibility as well as the critical issues of the proposed algorithm.

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific deployed infrastructure. In this paper we introduce the newly started European research project OPPORTUNITY within which we develop mobile opportunistic activity and context recognition systems. We outline the project’s objective, the approach we follow along opportunistic sensing, data processing and interpretation, and autonomous adaptation and evolution to environmental and user changes, and we outline preliminary results.

The selection of candidate materials for the accelerating cavities of the Compact Linear Collider (CLIC) is carried out in parallel with high power RF testing. The maximum DC breakdown field of copper, copper alloys, refractory metals, aluminium and titanium have been measured with a dedicated setup. Higher maximum fields are obtained for refractory metals and for titanium, which exhibits, however, important damages after conditioning. Fatigue behaviour of copper alloys has been studied for surface and bulk by pulsed laser irradiation and ultrasonic excitation, respectively. The selected copper alloys show consistently higher fatigue resistance than copper in both experiments. In order to obtain the best local properties in the device a possible solution is a bi-metallic assembly. Junctions of molybdenum and copper-zirconium UNS C15000 alloy, achieved by HIP (Hot Isostatic Pressing) diffusion bonding or explosion bonding were evaluated for their mechanical strength. The reliability of the results obtained with both techniques should be improved. Testing in DC and radiofrequency (RF) is continued in order to select materials for a bi-metal exhibiting superior properties with respect to the combination C15000-Mo.

The problem of collimation and beam cleaning is one of the most challenging aspects of the LHC project. A collimation system must be designed, built, installed and commissioned with parameters that extend the present state-of-the-art by 2-3 orders of magnitude. Problems include robustness, cleaning efficiency, impedance and operational aspects. A strong design effort has been performed at CERN over the last two years. The adopted phased approach is described. Robust and precisely controllable collimators have been designed. Several LHC prototype collimators have been built and tested with the highest beam intensities that are presently available at CERN. The successful beam tests are presented, including beam-based setup procedures, a 2 MJ robustness test and measurements of the collimator-induced impedance. Finally, an outlook is presented on the challenges that are ahead in the coming years.

The functional specification of the LHC Collimators requires, for the start-up of the machine and the initial luminosity runs (Phase 1), a collimation system with maximum robustness against abnormal beam operating conditions. The most severe cases to be considered in the mechanical design are the asynchronous beam dump at 7TeV and the 450GeV injection error. To ensure that the collimator jaws survive such accident scenarios, low-Z materials were chosen, driving the design towards Graphite or Carbon reinforced Carbon composites. Furthermore, in-depth thermo-mechanical simulations, both static and dynamic, were necessary. This paper presents the results of the numerical analyses performed for the 450GeV accident case, along with the experimental results of the tests conducted on a collimator prototype in CERN TT40 transfer line, impacted by a 450GeV beam of 3.2e13 protons, with impact transverse offsets from 1 to 5 mm.

A 200 MHz single cell elliptical Nb-Cu cavity has been fabricated and tested at 4.2 K and at 2.5 K in a vertical de-war fitted in a radiation shielded pit, 5 m deep and 2.5 m in diameter. The low field Q/sub 0/ reached 1.5 /spl times/ 10/sup 10/ at 4.2 K. Two multipacting barriers show up at E/sub acc/ = 3 MV/m and 1 MV/m. Helium processing is effective to reduce field emission and improve accelerating gradients by a factor of as much as 2. E/sub acc/ reached 11 MV/m at a Q/sub 0/ of 6 /spl times/ 10/sup 8/. The Q-drop is observed but shows a stronger field dependence as compared to expected Q-slope typical for Nb-Cu cavities.

Several authors proposed the construction of superconducting proton linacs using the LEP2 cavities once LEP will be decommissioned. However only a fraction (about half) of these cavities can be used as they are for the high-energy part (/spl beta//spl sim/1) of such a linac, the low energy part requiring the development of accelerating structures optimized for lower values of the particle velocity. At CERN an R&D programme on reduced-/spl beta/ single-cell cavities started in 1996 in order to study and explore the limits of the technology successfully used for the production of LEP2 cavities (copper cavities niobium-plated using the magnetron sputtering technique). Four different geometries were extensively investigated, each representing part of a multicell structure optimized for particles having /spl beta/=0.48, /spl beta/=0.625, /spl beta/=0.66 and /spl beta/=0.8 respectively. The results were encouraging for the last two types and therefore a new phase of R&D aimed at the production of multicell cavities for /spl beta/=0.66 and /spl beta/=0.8 was started. The goal is to demonstrate simultaneously the feasibility of such cavities and the possibility of producing them by low-cost modification of LEP cavities. In the paper, after a brief review of the results obtained on the single-cell cavities, we present in more detail the procedure for the transformation of the LEP cavities, which should allow a realistic estimate of the costs of such operation.