Discrete time signal is nothing but samples continuous time signal and this process is called as sampling. If inter-sampling distance is constant it is called as uniform sampling and if distance is unequal, it is non-uniform sampling. Irrespective of type of sampling, reconstruction is possible if sampling rate do not exceed Nyquist rate. Sinc interpolation is one of the methods which results i...

Feedback systems with communication channels encounter unique challenges. Communication channels mandate signal sampling and quantization, and introduce errors, data losses, and delays. Consequently, transmitted signals must be estimated. The signal estimation introduces a dynamic system that interacts with communication channels and affects the stability and performance of the feedback system....

The usual paradigm for signal processing is to model a signal as a bandlimited function and capture the signal by means of its time samples. The Shannon-Nyquist theory says that the sampling rate needs to be at least twice the bandwidth. For broadbanded signals, such high sampling rates may be impossible to implement in circuitry. Compressed Sensing is a new area of signal processing whose aim ...

Sampling theorems on the sphere state that all the information of a continuous band-limited signal on the sphere may be contained in a discrete set of samples. For an equiangular sampling of the sphere, the Driscoll & Healy (DH) [1] sampling theorem has become the standard, requiring ∼ 4L samples on the sphere to represent exactly a signal band-limited in its spherical harmonic decomposition at...

In traditional time-based sampling, the sampling mechanism is triggered by predetermined sampling times, which are mostly uniformly spaced (i.e., periodic). Alternatively, in event-based sampling, some predefined events on the signal to be sampled trigger the sampling mechanism; that is, sampling times are determined by the signal and the event space. Such an alternative mechanism, setting the ...

The theoretical basis for conventional acquisition of bandlimited signals typically relies on uniform time sampling and assumes infinite-precision amplitude values. In this paper, we explore signal representation and recovery based on uniform amplitude sampling with assumed infinite precision timing information. The approach is based on the delta-ramp encoder which consists of applying a one-le...

A compression sampling system based on sparse AR (auto regression) model is designed in this paper. The sparse samples are non-uniform sampled using uniform random sampling (URS) method by mono-chip computer. To guarantee the reconstruction effectiveness, a basis matrix is constructed with prior signal information to represent the received signal sparsely. Then the received signal is recovered ...

Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio in mobile communication. Compressed sensing (CS) is introduced to transfer the sampling burden. The standard sparse signal recovery of CS does not consider the distortion in the analogue-to-information converter (AIC). To mitigate performance degeneration casued by the mismatch in least square distortionle...

In this paper it is shown that for the purpose of nonlinear power amplifier behavioural modelling, the sampling rate can be set to the Nyquist rate of the input signal, rather than to the Nyquist rate of the output signal by making use of Zhu’s generalized sampling theorem. This claim is supported by measurements on a basestation power amplifier. The findings are that the model error obtained w...