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A: This paper presents the SAMPA, an ASIC designed for the upgrade of read-out front end electronics of the ALICE Time Projection Chamber (TPC) and Muon Chambers (MCH). SAMPA is made in a 130 nm CMOS technology with 1.25 V nominal voltage supply and includes 32 channels, with selectable input polarity, and five possible combinations of shaping time and sensitivity. Each channel comprises a Charge Sensitive Amplifier, a semi-Gaussian shaper and a 10-bit ADC, followed by a Digital Signal Processor. A prototype in a multi project run was submitted to evaluate the performance of each of these blocks. The experimental results of the tests on these building blocks are presented, showing a substantial agreement with requirements.

A: This paper presents the test results of the second prototype of SAMPA, the ASIC designed for the upgrade of read-out front end electronics of the ALICE Time Projection Chamber (TPC) and Muon Chamber (MCH). SAMPA is made in a 130 nm CMOS technology with 1.25 V nominal voltage supply and provides 32 channels, with selectable input polarity, and three possible combinations of shaping time and sensitivity. Each channel consists of a Charge Sensitive Amplifier, a semi-Gaussian shaper and a 10-bit ADC; a Digital Signal Processor provides digital filtering and compression capability. In the second prototype run both full chip and single test blocks were fabricated, allowing block characterization and full system behaviour studies. Experimental results are here presented showing agreement with requirements for both the blocks and the full chip.

Speaking and presenting in public are critical skills for academic and professional development. These skills are demanded across society, and their development and evaluation are a challenge faced by higher education institutions. There are some challenges to evaluate objectively, as well as to generate valuable information to professors and appropriate feedback to students. In this paper, in order to understand and detect patterns in oral student presentations, we collected data from 222 Computer Engineering (CE) fresh students at three different times, over two different years (2017 and 2018). For each presentation, using a developed system and Microsoft Kinect, we have detected 12 features related to corporal postures and oral speaking. These features were used as input for the clustering and statistical analysis that allowed for identifying three different clusters in the presentations of both years, with stronger patterns in the presentations of the year 2017. A Wilcoxon rank-sum test allowed us to evaluate the evolution of the presentations attributes over each year and pointed out a convergence in terms of the reduction of the number of features statistically different between presentations given at the same course time. The results can further help to give students automatic feedback in terms of their postures and speech throughout the presentations and may serve as baseline information for future comparisons with presentations from students coming from different undergraduate courses.

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