, Wiley, Hoboken, NJ.  Lyons, R. G., 2004, Understanding digital signalprocessing , Upper Saddle River, A Prentice Hall PTR Publication.  Mardia, K. V., and Jupp, P. E., 2000, Directional Statistics , Wiley, Hoboken, NJ.  Fisher, N., 2000, Statistical analysis of circular data , Cambridge: Cambridge University Press, UK.  Kuts, Y., Protasov, A., Lysenko, I., Dugin, O., Bliznuk, O. and Uchanin V., 2017, IEEE First Ukraine Conference on electrical and Computer engineering, Kyiv, May 29 - June 2, pp. 826-829.  Derhunov, O., Kuts, Y., Monchenko, O
). Spectrum analysis-A modern perspective. In Proc. of the IEEE , 69 (11), 1380-1419. Srinath, M. D., Viswanathan, M. M. (1975). Sequential algorithm for identification of parameters of an autoregressive process. IEEE Trans. Automat. Contr. , 20 (4), 542-546. Manolakis, D. G., Ingle, V. K., Kogon, S. M. (2005). Statistical and Adaptive SignalProcessing. Norwood, MA, USA: Artech House.
International Journal of Food
An Intelligent SignalProcessing Method for
High Speed Weighing System
Huimei He, State Key Laboratory of Industrial Control
Pingjie Huang, State Key Laboratory of Industrial Control
Dibo Hou, State Key Laboratory of Industrial Control
Zhe Liu, State Key Laboratory of Industrial Control
Guangxin Zhang, State Key Laboratory of Industrial
Control Technology,Zhejiang University
, which directly impacts the qualities of grading, packing, and other procedure. The accuracy grade of dynamic weighing primarily depends on two aspects which include the design of the mechanical structure for fruit cups and weighing area, also the selection of weighing signalprocessing system. In circumstances with high sorting speed, for the inherent damping of weighing system, the raw weighing signals need a long time to reach stable status after the fruit cup passing through the weighing sensor [ 3 ]. In signalprocessing module, how to obtain high-precision weight
The paper presents a signal processing system used for nitrogen dioxide detection employing cavity enhanced absorption spectroscopy. In this system, the absorbing gas concentration is determined by the measurement of a decay time of a light pulse trapped in a cavity.
The setup includes a resonance optical cavity, which was equipped with spherical and high reflectance mirrors, the pulsed diode laser (414 nm) and electronic signal processing system. In order to ensure registration of low-level signals and accurate decay time measurements, special preamplifier and digital signal processing circuit were developed.
Theoretical analyses of main parameters of optical cavity and signal processing system were presented and especially signal-to-noise ratio was taken into consideration. Furthermore, investigation of S/N signal processing system and influence of preamplifier feedback resistance on the useful signal distortion were described.
The aim of the experiment was to study potential application of cavity enhanced absorption spectroscopy for construction of fully optoelectronic NO2 sensor which could replace, e.g., commonly used chemical detectors. Thanks to the developed signal processing system, detection limit of NO2 sensor reaches the value of 0.2 ppb (absorption coefficient equivalent = 2.8 × 10−9 cm−1).
References 1. Brown J. L. Jr: On the error in reconstructing a non-bandlimited function by means of the bandpass sampling theorem. Journal Math. Anal. Applied, Vol. 18. 1967, pp. 75-84, Erratum - Vol. 21.1968, p. 699 2. Brown J. L. Jr: On quadrature sampling of bandpass signals. IEEE Transactions on Aerospace and Electronic Systems, vol. 15, No. 3, 1979, pp. 366-371 3. Cordesses L.: Direct digital synthesis: a tool for periodic wave generation (Part 1). IEE SignalProcessing Magazine, July 2004, pp. 50-54 4. Cordesses L.: Direct digital synthesis: a tool for
is now widely accepted as the key technology that may enable microelectronics to break the bottleneck of signalprocessing and transmission in terms of speed and power consumption. Such a technology can also open the doors for new applications, owing to the large bandwidth, low power consumption, parallel signalprocessing and inherent passive computing ability, such as the Mach-Zehnder interferometer (MZI)-based matrix multiplication [ 14 ]. In recent years, many typical woks on all-optical or electronic and photonic hybrid signalprocessing on-chip system, which
References  Hyvärinen A, Karhunen J, Oja E. Independent component analysis. New York: John Wiley & Sons Inc. 2001.  Cichocki A, Amari SI. Adaptive blind signal and image processing. New York: John Wiley & Sons Inc. 2002.  Jolliffe IT. Principal component analysis. Berlin: Springer Verlag 2002.  Papoulis A. Probability, random variables and stochastic processes. New York: McGraw-Hill 1991.  Comon P. Independent component analysis – a new concept? SignalProcess 1994 ; 36 : 287 –314.  Cardoso JF, Souloumiac A. Blind beamforming for non
Processing (ICASSP), pp. 761-764, May 2011.
Luisa F. Polania E. Carrillo Rafael "Compressed Sensing Based Method For ECG Compression" Proceeding of IEEE International Conference on Acoustics, Speech and SignalProcessing (ICASSP) 761 764 2011
 Anna M.R. Dixon, Emily G. Allstot, "Compressed Sensing Reconstruction: Comparative Study with Applications to ECG Bio-Signals", In Proceeding of IEEE International Symposium of Circuits and Systems (ISCAS), pp. 805-808, 2011.
Anna M.R. Dixon G. Allstot Emily "Compressed Sensing Reconstruction
onsfähigkeit usw., bringt. Eine Betriebsart des RKMs ist der sogenannte TREC-Modus (Topography
and RECognition Imaging), der es erlaubt simultan die Oberflächenrauigkeit zu bestimmen also auch
biologische Interaktionsorte (Epitope) zu lokalisieren. Diese Publikation stellt einen digitalen Signal-
verarbeitungsalgorithmus, basierend auf einer DSP-(Digital SignalProcessing)-Realisierung, vor. Die
Anwendung dieser hier vorgestellten Algorithmen wird anhand des biologischen Rezeptor-Liganden
Paars Avidin–Biotin demonstriert.