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Experiments with antiprotons often require the tracking of charged particles emerging from the annihilation process. The Atomic Spectroscopy And Collisions Using Slow Antiprotons (ASACUSA) collaboration at the CERN Antiproton Decelerator (AD) used several panels of scintillating bars placed around the interaction region to detect the passage of charged pions and determine the annihilation vertex position and time. The panels were composed by extruded scintillating bars and the light was collected using WaveLength Shifting (WLS) fibers and multi-anode PhotoMultiplier Tubes (PMTs). After operating for several years, the fiber-PMT coupling quality had degraded and a major upgrade of the light readout system was planned. The PMTs will be replaced by Silicon PhotoMultipliers (SiPMs) and the front-end electronics changed accordingly. An improvement is expected in the efficiency and the uniformity of the detector response. In this contribution the commissioning of the upgrade will be described, including the results of preliminary tests with cosmic rays.

We perform a fitting procedure to differential elastic scattering cross section data of antiproton at intermediate energy on three different nuclei to evaluate parameters of a six-parameters Woods-Saxon optical potential. Then, with these values, we calculate the reaction cross sections at different momenta and evaluate the differential elastic cross sections for low-momenta antiproton ($p\le 100\mathrm{\text{MeV/c}}$). We find that our analysis underestimates the reaction cross sections for data over the momentum-range 50 MeV/c to 1000 MeV/c. We predict measurable effect for antiprotons with momentum above 50 MeV/c at large angles ($𝜃>90°$), in agreement with other recent results. Finally, we consider possible improvements to the model and the analysis.

In this paper, we present a new learning algorithm used to implement associative memories on digital cellular neural networks. The algorithm can be easily implemented in hardware or simulated on a digital computer without numerical errors. These attractive features come from the finite precision of connection weights, automatically taken into account as a design constraint; moreover, no multiplication is needed for weight computation.

In this paper, we present an innovative physical sensor interface based on neural network that allows an electronic music composer to plan and conduct the musical expressivity of a performer. For musical expressivity we mean all those execution techniques and modalities that a performer has to follow in order to satisfy common musical aesthetics. The proposed sensor interface is composed by a gestural transducer, that measure motion acceleration and angular velocity, and a mapping module, that transform few physical measured parameters into a lot of specific sound synthesis parameters. It is able to transform six physical input parameters in seventeen sound synthesis parameters. In this work, we focus our attention on mapping strategies based on Neural Network to solve the problem of electronic music expressivity.

In this paper, a new kinematic sensor is presented, which performs the detection of incipient pathologies in human beings by means of the analysis of the sit-to-stand locomotion task. The sensor is based on the frequency analysis of acceleration measurements supplied by a homemade transducer and on the exploitation of some of the most effective classification strategies at this time. Results show the capability of distinguishing between pathological and non pathological subjects.

Moving object detection starting from a video sequence is a fundamental task in many computer vision applications, such as autonomous robotics, traffic control, driver assistance and surveillance systems. In this paper we propose a method, based on stereo vision, that improves separation ability. Two image sequences of the same scene are taken using two cameras with slight horizontal displacement. Stereo processing of these sequences gives both detection of movement and depth information, which can be used to distinguish moving objects with different distance from the observer. Information on the distance of a given point of the image can be useful in presence of moving objects appearing close in a 2D image but with different distances from the observer.

The fabrication methods of the microelectronics industry have been refined to produce ever smaller devices, but will soon reach their fundamental limits. A promising alternative route to even smaller functional systems with nanometre dimensions is the autonomous ordering and assembly of atoms and molecules on atomically well-defined surfaces. This approach combines ease of fabrication with exquisite control over the shape, composition and mesoscale organization of the surface structures formed. Once the mechanisms controlling the self-ordering phenomena are fully understood, the self-assembly and growth processes can be steered to create a wide range of surface nanostructures from metallic, semiconducting and molecular materials.