From Chirps to Random-FM Excitations in Pulse Compression Ultrasound Systems

Pulse compression is often practiced in ultrasound Non Destructive Testing (NDT) systems using chirps. However, chirps are inadequate for setups where multiple probes need to operate concurrently in Multiple Input Multiple Output (MIMO) arrangements. Conversely, many coded excitation systems designed for MIMO miss some chirp advantages (constant envelope excitation, easiness of bandwidth control, etc.) and may not be easily implemented on hardware originally conceived for chirp excitations. Here, we propose a system based on random-FM excitations, capable of enabling MIMO with minimal changes with respect to a chirp-based setup. Following recent results, we show that random-FM excitations retain many advantages of chirps and provide the ability to frequency-shape the excitations matching the transducers features.Comment: 4 pages, 4 figures. Post-print from conference proceedings. Note that paper in conference proceedings at http://dx.doi.org/10.1109/ULTSYM.2012.0117 has some rendering issue

Multi Detector Fusion of Dynamic TOA Estimation using Kalman Filter

In this paper, we propose fusion of dynamic TOA (time of arrival) from multiple non-coherent detectors like energy detectors operating at sub-Nyquist rate through Kalman filtering. We also show that by using multiple of these energy detectors, we can achieve the performance of a digital matched filter implementation in the AWGN (additive white Gaussian noise) setting. We derive analytical expression for number of energy detectors needed to achieve the matched filter performance. We demonstrate in simulation the validity of our analytical approach. Results indicate that number of energy detectors needed will be high at low SNRs and converge to a constant number as the SNR increases. We also study the performance of the strategy proposed using IEEE 802.15.4a CM1 channel model and show in simulation that two sub-Nyquist detectors are sufficient to match the performance of digital matched filter

Adaptive Investment Strategies For Periodic Environments

In this paper, we present an adaptive investment strategy for environments with periodic returns on investment. In our approach, we consider an investment model where the agent decides at every time step the proportion of wealth to invest in a risky asset, keeping the rest of the budget in a risk-free asset. Every investment is evaluated in the market via a stylized return on investment function (RoI), which is modeled by a stochastic process with unknown periodicities and levels of noise. For comparison reasons, we present two reference strategies which represent the case of agents with zero-knowledge and complete-knowledge of the dynamics of the returns. We consider also an investment strategy based on technical analysis to forecast the next return by fitting a trend line to previous received returns. To account for the performance of the different strategies, we perform some computer experiments to calculate the average budget that can be obtained with them over a certain number of time steps. To assure for fair comparisons, we first tune the parameters of each strategy. Afterwards, we compare the performance of these strategies for RoIs with different periodicities and levels of noise.Comment: Paper submitted to Advances in Complex Systems (November, 2007) 22 pages, 9 figure

The bistatic continuous-wave radar method for the study of planetary surfaces Scientific report no. 13

Bistatic continuous-wave radar for mapping surface of planet