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We report on the experimental characterization of a mass-loaded silicon nitride membrane-based resonator, which we investigate towards the development of accelerometers for small amplitude acceleration sensing at low frequencies. We experimentally demonstrate a ~1.1 ×10⁻⁶ kg proof mass system achieving a 17,950 mechanical quality factor for a 526 Hz natural resonance frequency, which compares favorably to other optically interrogated on-chip accelerometers. The inferred acceleration noise floor of the device is currently limited by the displacement noise of the optical fiber displacement readout, yielding a noise amplitude spectral density of 1μg/√Hz at 10 Hz., 2022 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), May 8-11, 2022, Avignon, France

A new traceability chain for non-standardized acoustic sensors is proposed by using the absolute measurement method of acoustic pressures. The current traceability chain for sound-in-air measurements is based on the laboratory standard microphone. However, this approach has clear limitations and cannot be applied to non-standard sensors such as MEMS microphones. This is because the unit of sound-in-air measurement is indirectly defined via the sensitivity of laboratory standard microphones. As a method to determine the acoustic pressure in an absolute manner, the optical method based on photon correlation is applied and the new traceability chain is proposed based on the direct traceability of the SI unit. As an example, the calibration of a MEMS microphone is demonstrated with the photon correlation method., 2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd 4.0 & IoT), June 3-5, 2020, Roma, Italy

In this paper a real time, stand-alone wireless Biomechatronic Extended Physiological Proprioception (EPP) teleoperation system was implemented using two Bluetooth Low Energy (BLE) wireless Systems on Chip (SoCs). This system is designed to achieve kinesthetic coupling between the amputee and prosthetic arm without the use of the classic EPP mechanical linkage, but with the use of a wireless implementation of a Master/Slave teleoperation topology. The experimental real-time implementation achieved a high level of transparency with minuscule time delays.

This paper is concerned with the identification of pass-band and baseband Hammerstein channels from cumulants of the noisy observations of the channel output. The channel is excited by communication signals such as QAM, PSK and OFDM. Exact expressions and respective algorithms involving the output cumulants are derived.

Nonparametric identification of baseband and passband complex Volterra systems excited by communication inputs (PSK, QAM and OFDM) is considered. Closed form expressions are established using multidimensional orthogonal polynomials and higher order statistics. First multidimensional orthogonal polynomials are used for baseband and passband Volterra models driven by PSK and QAM inputs and closed form expressions are derived. Baseband Volterra models excited by IID circular Gaussian signals (OFDM) are identified using cross-cumulants. Performance is assessed by simulations.

In the present study a spatial model, which combines GIS with artificial neural networks, has been developed for forecasting changes in land use. The model has been parameterized for the island of Lesvos (NE Greece) for the time period between 1975 and 1999 and employs an artificial neural network for predicting the patterns of development of the island's urban areas and olive groves, based on a series of input parameters such as population density, transportation network, location of urban areas, proximity to the coastline and elevation. In this context, data from 1975 and 1990 have been used as input and the model has been run to project (i) urban land development and (ii) patterns of olive grove cultivations, for the year 1999. Results demonstrate that the model can predict reasonably well the patterns of change of the island's urban areas, however its predictive ability regarding the changes in the extent of coverage of olive cultivations is considerably lower. The overall performance of the model and its advantages and limitations are critically assessed and future improvements are suggested.

This paper is concerned with the blind identification of Volterra-Hammerstein systems. Two identification scenarios are covered. The first scenario assumes that, although the input is not available, the statistics of the input are a priori known. This case appears in communication applications where the input statistics of the transmitter are known to the receiver. The second scenario assumes that the input statistics are unknown. In the case of known input statistics, the input is stationary higher order white noise with arbitrary probability density function. Under the scenario of unknown input statistics, the input is restricted to Gaussian white process. New cumulant-based identification methods are described for the above scenarios. The problem is converted into a linear multivariable form and the output cumulants are calculated using Kronecker products. First, initial conditions are determined by a linear system of equations. These correspond to the boundary values of the Volterra kernels. The remaining kernel coefficients can be determined under both identification schemes from a possibly overdetermined system of linear equations.

The problem of blind identification of a linear system is widely noticed by many researchers in diverse fields, including speech processing, image reconstruction and data communication. Many existing blind channel identification algorithms require accurate estimates of the channel order. This problem has been studied for single input single output (SISO) as well as single input multiple output (SIMO) systems and several algorithms have been developed. Linear multiple input multiple output (MIMO) systems are considered in this paper. A novel algorithm based on subspace projections is proposed for the determination of all different orders, the number of subsystems and the kernels up to an ambiguity of post multiplication with an invertible matrix.

This paper is concerned with the identification of a discrete input output bilinear system driven by a zero mean white Gaussian input. An algorithmic procedure for the direct computation of the unknown model parameters is developed based on cross-cumulant information up to third order.

In this paper, the maximum likelihood (ML) principle in the phase domain is employed to provide a solution to the carrier recovery problem appropriate for reverse channel point-to-multipoint TDMA wireless communication systems. Frequency and phase estimators are derived exhibiting the minimum variance unbiased estimation (MVUE) property under an assumption on the received signal power. Extensions of the basic estimation algorithm are provided for the cases of pattern-based carrier recovery, decision-directed as well as non-decision-directed carrier recovery based on random data. These three techniques are then used in a burst data transmission platform employing either QPSK or 16-QAM modulation format. A scheme for simultaneous clock and carrier recovery is also considered. Root raised cosine transmit and receive filtering is used. Extensive simulations have shown that a 20-symbol preamble is sufficient for providing reliable system parameter recovery in all tested cases. Specifically for the 16-QAM modulated signal, achieving joint carrier and clock recovery with a preamble as short as 20 symbols is an important result, very beneficial for bandwidth-constrained TDMA system design.

This paper is concerned with the blind identification of a class of bilinear systems excited by non-Gaussian higher order white noise. The matrix of coefficients of mixed input-output terms of the bilinear system model is assumed to be triangular in this work. Under the additional assumption that the system output is corrupted by Gaussian measurement noise, we derive an exact parameter estimation procedure based on the output cumulants of orders up to four. Results of the simulation experiments presented in the paper demonstrate the validity and usefulness of our approach.

In heavy rain climatic regionss attenuation caused by rain presents a great problem for satellite communications at frequencies above 10 GHz. In some severe casess double site diversity protection can be proved to be inadequate and consequently triple diversity becomes indispensable. In this papers a theoretical model is presented for estimating the improvement in rain outage performance caused by triple site protection. Experimental results taken from some parts of the United States such as New Jerseys and Florida are compared with the theoretical values and the agreement has been found to be encouraging.