Special Issue “Selected Papers from the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP)”
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Research context
OpenAlex topics: Energy Efficient Wireless Sensor Networks · Advanced Algorithms and Applications · Sensor Technology and Measurement Systems
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Affiliations
Department of Telecommunications, Brno University of Technology, Technicka 3082/12, 616 00 Brno, Czech Republic
Signal Processing for Telecommunications and Economics Laboratory (SP4TE), University of “Roma TRE”, via Vito Volterra 62, 00146 Rome, Italy
College of Engineering and Computing, University of South Carolina, Innovation Center, 550 Assembly Street, Suite 1300, Columbia, SC 29208, USA
References (12)
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1. Global Communications Newsletter: The 2019 42nd International Conference on Telecommunications and Signal Processing (TSP) in Budapest, Hungary10.1109/MCOM.2019.8908543 ↗
2. Hilt, A. (2019). Availability and Fade Margin Calculations for 5G Microwave and Millimeter-Wave Anyhaul Links. Appl. Sci., 9.10.3390/app9235240 ↗
3. Clupek, V., Horvath, T., Munster, P., and Oujezsky, V. (2019). New Security Improvements in Next-Generation Passive Optical Networks Stage 2 †. Appl. Sci., 9.10.3390/app9204430 ↗
4. Boros, T., Bencel, R., and Kotuliak, I. (2019). Transparent Redirections in Content Delivery Networks. Appl. Sci., 9.10.3390/app9245418 ↗
5. Sotner, R., Domansky, O., Jerabek, J., Herencsar, N., Petrzela, J., and Andriukaitis, D. (2019). Integer-and Fractional-Order Integral and Derivative Two-Port Summations: Practical Design Considerations. Appl. Sci., 10.10.3390/app10010054 ↗
6. Istvanek, M., Smekal, Z., Spurny, L., and Mekyska, J. (2020). Enhancement of Conventional Beat Tracking System Using Teager–Kaiser Energy Operator. Appl. Sci., 10.10.3390/app10010379 ↗
7. Ulacha, G., and Wernik, C. (2019). A High Efficiency Multistage Coder for Lossless Audio Compression using OLS+ and CDCCR Method. Appl. Sci., 9.10.3390/app9235218 ↗
8. Datcu, O., Macovei, C., and Hobincu, R. (2020). Chaos Based Cryptographic Pseudo-Random Number Generator Template with Dynamic State Change. Appl. Sci., 10.10.3390/app10020451 ↗
9. Romano, G. (2019). Asymptotic Performances of a Signal-To-Noise Ratio Moment-Based Estimator for Real Sinusoids in Additive Noise. Appl. Sci., 9.10.3390/app9245556 ↗
10. Riid, A., Lõuk, R., Pihlak, R., Tepljakov, A., and Vassiljeva, K. (2019). Pavement Distress Detection with Deep Learning Using the Orthoframes Acquired by a Mobile Mapping System. Appl. Sci., 9.10.3390/app9224829 ↗
11. Arjmand, A., Angelis, C.T., Christou, V., Tzallas, A.T., Tsipouras, M.G., Glavas, E., Forlano, R., Manousou, P., and Giannakeas, N. (2019). Training of Deep Convolutional Neural Networks to Identify Critical Liver Alterations in Histopathology Image Samples. Appl. Sci., 10.10.3390/app10010042 ↗
12. Hanáková, L., Průcha, J., Socha, V., Štengl, M., and Van den Bergh, S. (2020). Effect of High-Induction Magnetic Stimulation on Complex Heart Rate Variability of Sus Scrofa Domesticus under General Anesthesia. Appl. Sci., 10.10.3390/app10020589 ↗