Browsing by Subject "Demodulation"

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Open Access
A new detection method for capacitive micromachine ultrasonic transducers
(IEEE, 2001) Ergun, A. S.; Temelkuran, B.; Özbay, Ekmel; Atalar, Abdullah
Capacitive micromachine ultrasonic transducers (cMUT) have become an alternative to piezoelectric transducers in the past few years. They consist of many small circular membranes that are connected in parallel. In this work, we report a new detection method for cMUTs. We model the membranes as capacitors and the interconnections between the membranes as inductors. This kind of LC network is called an artificial transmission line. The vibrations of the membranes modulate the electrical length of the transmission line, which is proportional to the frequency of the signal through it. By measuring the electrical length of the artificial line at a high RF frequency (in the gigahertz range), the vibrations of the membranes can be detected in a very sensitive manner. For the devices we measured, we calculated the minimum detectable displacement to be in the order of 10 -5 Å/√Hz with a possible improvement to 10 -7 Å/√Hz.
Open Access
Optimal joint modulation classification and symbol decoding
(IEEE, 2019-05) Kazıklı, Ertan; Dulek, Berkan; Gezici, Sinan
In this paper, modulation classification and symbol decoding problems are jointly considered and optimal strategies are proposed under various settings. In the considered framework, there exist a number of candidate modulation formats and the aim is to decode a sequence of received signals with an unknown modulation scheme. To that aim, two different formulations are proposed. In the first formulation, the prior probabilities of the modulation schemes are assumed to be known and a formulation is proposed under the Bayesian framework. This formulation takes a constrained approach in which the objective function is related to symbol decoding performance whereas the constraint is related to modulation classification performance. The second formulation, on the other hand, addresses the case in which the prior probabilities of the modulation schemes are unknown, and provides a method under the minimax framework. In this case, a constrained approach is employed as well; however, the introduced performance metrics differ from those in the first formulation due to the absence of the prior probabilities of the modulation schemes. Finally, the performance of the proposed methods is illustrated through simulations. It is demonstrated that the proposed techniques improve the introduced symbol detection performance metrics via relaxing the constraint(s) on the modulation classification performance compared with the conventional techniques in a variety of system configurations.