Warsaw University of Technology

About: Warsaw University of Technology is a education organization based out in Warsaw, Poland. It is known for research contribution in the topics: Microstructure & Optical fiber. The organization has 14293 authors who have published 34362 publications receiving 492211 citations. The organization is also known as: Warsaw Polytechnic & Politechnika Warszawska.

Classification of beverages using a reduced sensor array

Abstract: An electronic tongue based on the sensor array of ion-selective electrodes (ISEs) combined with pattern recognition tools is applied to qualitative analysis of mineral waters and apple juices. The procedure of reducing of total number of the sensors in the array is described. Before and after reducing of number of the sensors, this device is capable of reliable discrimination between different brands of mineral waters and apple juices. Validation of this method is also confirmed using artificial neural networks as a classifier. The ability to recognize different brands of waters and juices after reduction of number of the sensors in the array is comparable to the ability to recognize the same samples by the system before reduction of number of the sensors. This method enables to specify quantitatively the capability of a sensor to discriminate between different classes of samples and can be used for any electronic tongues and noses applications.

Measurement of Production Properties of Positively Charged Kaons in Proton-Carbon Interactions at 31 GeV/c

Abstract: Spectra of positively charged kaons in p+C interactions at 31 GeV/c were measured with the NA61/SHINE spectrometer at the CERN SPS. The analysis is based on the full set of data collected in 2007 with a graphite target with a thickness of 4% of a nuclear interaction length. Interaction cross sections and charged pion spectra were already measured using the same set of data. These new measurements in combination with the published ones are required to improve predictions of the neutrino flux for the T2K long baseline neutrino oscillation experiment in Japan. In particular, the knowledge of kaon production is crucial for precisely predicting the intrinsic electron neutrino component and the high energy tail of the T2K beam. The results are presented as a function of laboratory momentum in 2 intervals of the laboratory polar angle covering the range from 20 up to 240 mrad. The kaon spectra are compared with predictions of several hadron production models. Using the published pion results and the new kaon data, the K+/\pi+ ratios are computed.

A cantilever optical-fiber accelerometer

Abstract: A simple fiber-optic acceleration sensor has been designed and evaluated. It is an amplitude-modulation sensor, which employs inexpensive electronic instrumentation. The construction and technology of the sensor are presented. The influence of some construction parameters on sensor characteristics has been considered and measured. It is proved that simple construction modifications allow some sensor parameters to be tuned. The sensor characteristic is nonlinear. The amplitude-modulation depth reaches 50% at an acceleration of 250 m/s 2 for a cantilever length of 40 mm. The maximum operating frequency is dependent on cantilever length and for length 20 mm is 125 Hz.

VoIP steganography and its Detection—A survey

Abstract: Steganography is an ancient art that encompasses various techniques of information hiding, the aim of which is to embed secret information into a carrier message. Steganographic methods are usually aimed at hiding the very existence of the communication. Due to the rise in popularity of IP telephony, together with the large volume of data and variety of protocols involved, it is currently attracting the attention of the research community as a perfect carrier for steganographic purposes. This article is a first survey of the existing Voice over IP (VoIP) steganography methods and their countermeasures.

New learning algorithm for blind separation of sources

Abstract: A new improved, easily implementible learning algorithm for blind separation of statistically independent unknown source signals is proposed. In contrast to the well known algorithms, two time trajectories of synaptic weights {wij(t)} and {Wij(t)} are computed where Wij(t) is the time average of wij(t). Extensive computer simulation experiments have confirmed that the proposed learning algorithm assures a high convergence speed of the neural network for a blind identification problem, i.e. a quick recovering of unknown signals from the observation of a linear combination (mixture) of them. The algorithm can easily be extended to other applications.