Bunch Length Measurement and the Beam Spectra Observation Through High Power RF Waveguide System
A wide-band pick-up, which is mounted on the wall of waveguide between the klystron and the RF cavity, has been developed for measuring beam spectra in KEKB. The spectrum between 5 GHz and 20GHz is taken to calculate the bunch length. And the synchrotron oscillation is observed as the side band of revolution peak. The bunch length and synchrotron frequency are measured under the different momentum compaction factors {alpha} and different total RF voltages Vc. Measured bunch length is compared with the measurement of streak camera.
KEKB beam instrumentation systems
For the stable high-luminosity operation and luminosity increase, the electron and positron storage rings of the KEK B-Factory (KEKB) is equipped with various beam instrumentations, which have been working well since the start of the commissioning in December, 1998. Details and performance of the beam-position monitor system based on the spectrum analysis using DSPs, the turn-by-turn BPM with four-dimensional function available for measurements of the individual bunch position, phase and intensity, the parametric beam-DCCTs designed so as to avoid the magnetic-core-selection problems for the parametric flux modulation, the bunch-by-bunch feedback system indispensable to suppress the strong multibunch instabilities in KEKB, the various optical beam diagnostic systems, such as synchrotron radiation interferometers for precise beam-size measurement, the tune meters, the bunch length monitors and the beam-loss monitors are described. Delicate machine tuning of KEKB is strongly supported by these instrumentations.
Benchmark database for fine-grained image classification of benthic macroinvertebrates
Abstract Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.