Robust spatio-temporal partial-response signaling over a frequency-selective fading MIMO channel with imperfect CSI

Partial-response signaling is known to facilitate the equalizer design because a controlled amount of residual interference is permitted. The design of the target impulse response of the partial-response precoder often assumes perfect channel state information, which is unfortunately not available at the transmitter in most practical applications. Consequently, this contribution focuses instead on the robust and joint design of a spatio-temporal target impulse response and the equalization coefficients for a frequency-selective fading multiple-input multiple-output communication channel based on current and/or previous noisy channel estimates. More precisely, the error in the channel estimates is statistically modeled, and robustness is achieved by minimizing the mean-squared estimation error averaged over the joint distribution of the actual channel and the available channel estimates. Numerical results of the bit error rate confirm that the proposed robust partial-response signaling not only provides a significant performance gain compared to traditional full-response signaling, but also outperforms the naive approach, which ignores channel estimation errors

The design of digital-adaptive controllers for VTOL aircraft

Design procedures for VTOL automatic control systems have been developed and are presented. Using linear-optimal estimation and control techniques as a starting point, digital-adaptive control laws have been designed for the VALT Research Aircraft, a tandem-rotor helicopter which is equipped for fully automatic flight in terminal area operations. These control laws are designed to interface with velocity-command and attitude-command guidance logic, which could be used in short-haul VTOL operations. Developments reported here include new algorithms for designing non-zero-set-point digital regulators, design procedures for rate-limited systems, and algorithms for dynamic control trim setting

Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts

The aim of the research is to compare VaR methods/models for commodities. For risk measurement Conditional Autoregressive Value at Risk models (CAViaR), implied quantile model and encompassing method are used. The aim is to check whether simultaneous use of information both from historical time series and regarding markets' expectation can improve accuracy of forecasts. For this purpose four methods of combining forecasts are used: a simple average combining, an unrestricted linear combination, a weighted averaged combining and a weighted averaged combining using exponential weighting. In the case of the commodities neither the encompassing method nor the combining forecast method improve VaR forecasts. The method of choosing the most adequate model leads to simple CAViaR-SAV model as the source of most optimal measure of risk forecasts. The Kupiec test, the Christoffersen and the Dynamic Quantile test indicate the model as an adequate to forecast VaR for gold and oil for short positions at the 0.01 and the 0.05 significance level, and for a long position at the 0.05 significance level

Synchronization Techniques for Burst-Mode Continuous Phase Modulation

Synchronization is a critical operation in digital communication systems, which establishes and maintains an operational link between transmitter and the receiver. As the advancement of digital modulation and coding schemes continues, the synchronization task becomes more and more challenging since the new standards require high-throughput functionality at low signal-to-noise ratios (SNRs). In this work, we address feedforward synchronization of continuous phase modulations (CPMs) using data-aided (DA) methods, which are best suited for burst-mode communications. In our transmission model, a known training sequence is appended to the beginning of each burst, which is then affected by additive white Gaussian noise (AWGN), and unknown frequency, phase, and timing offsets. Based on our transmission model, we derive the Cramer-Rao bound (CRB) for DA joint estimation of synchronization parameters. Using the CRB expressions, the optimum training sequence for CPM signals is proposed. It is shown that the proposed sequence minimizes the CRB for all three synchronization parameters asymptotically, and can be applied to the entire CPM family. We take advantage of the simple structure of the optimized training sequence in order to design a practical synchronization algorithm based on the maximum likelihood (ML) principles. The proposed DA algorithm jointly estimates frequency offset, carrier phase and symbol timing in a feedforward manner. The frequency offset estimate is first found by means of maximizing a one dimensional function. It is then followed by symbol timing and carrier phase estimation, which are carried out using simple closed-form expressions. We show that the proposed algorithm attains the theoretical CRBs for all synchronization parameters for moderate training sequence lengths and all SNR regions. Moreover, a frame synchronization algorithm is developed, which detects the training sequence boundaries in burst-mode CPM signals. The proposed training sequence and synchronization algorithm are extended to shaped-offset quadrature phase-shift keying (SOQPSK) modulation, which is considered for next generation aeronautical telemetry systems. Here, it is shown that the optimized training sequence outperforms the one that is defined in the draft telemetry standard as long as estimation error variances are considered. The overall bit error rate (BER) plots suggest that the optimized preamble with a shorter length can be utilized such that the performance loss is less than 0.5 dB of an ideal synchronization scenario

Global crop production forecasting data system analysis

The author has identified the following significant results. Findings led to the development of a theory of radiometric discrimination employing the mathematical framework of the theory of discrimination between scintillating radar targets. The theory indicated that the functions which drive accuracy of discrimination are the contrast ratio between targets, and the number of samples, or pixels, observed. Theoretical results led to three primary consequences, as regards the data system: (1) agricultural targets must be imaged at correctly chosen times, when the relative evolution of the crop's development is such as to maximize their contrast; (2) under these favorable conditions, the number of observed pixels can be significantly reduced with respect to wall-to-wall measurements; and (3) remotely sensed radiometric data must be suitably mixed with other auxiliary data, derived from external sources

System Development for Geolocation in Harsh Environments

Wireless sensor networks (WSN) consist of a set of distributed devices equipped with multiple sensors, which can be employed in different environments of varying characteristics. Nowadays, node localization has become one of their most basic and important requirements. Due to the nature of certain environments, typical positioning systems, such as Global Navigation Satellite System (GNSS), cannot be employed. Therefore, in recent years several alternative positioning mechanisms have risen. ROMOVI is a project which has as its main goal the development of low cost autonomous robots capable of monitoring and perform logistic tasks on the steep slopes of the Douro river vineyards. Integrated in this project, this dissertation proposes the development of a full-custom wireless communication system for geolocation purposes in harsh environments. Using a Symmetric Double Sided Two Way Ranging (SDS-TWR) algorithm, it is possible to achieve ranging measures between nodes, thus providing accurate relative positioning. This work focuses mainly on the study of the SDS-TWR algorithm and its major error sources, such as those due to digital clock drift, among others. A preamble based on Frank-Zadoff-Chu sequence was developed and, due to its good periodic autocorrelation properties, a system employing the transmission and reception of this preamble was implemented in hardware, through a field programmable gate array (FPGA). By employing an embedded logic processor, the Altera Nios II, control over the complete procedure of the aforementioned algorithm is possible, to perform and analyze the main advantages of the SDS-TWR algorithm. Finally, a medium access control (MAC) layer frame format was defined, in order to enable future development of communication among multiple nodes, to enhance the original algorithm and, as such, provide the capability of trilateration

Outlook of solar energy in Europe based on economic growth characteristics

Solar power production in Europe has raised from about 130 MW to 110 GW of installed capacity (corresponding to 90 GWh to 120 TWh in annual electricity generation) during the present century. Together with wind power, it constitutes the largest growth within renewable energy sources in the last decades. At present however, clear signs of saturation can be observed in the leading areas of solar power in Europe. Here, the development of solar power in Europe is analysed and, for the three leading countries (Germany, Italy, Spain) a logistic growth path at the national level and a proportionality between saturation level of the growth curve of each country and its gross domestic product (GDP) is found. The sum of the next three countries (France, UK, Belgium) is well described by a logistic path with a time offset relative to the first group of three, and the sum of the two logistic paths, representing in total about 85% of European solar power production, describes the growth pattern in the corresponding area very well. Based on this, an estimate of a future saturation level for solar power in Europe is obtained by extrapolation. Finally, a model based on logistic growth patterns and learning curves that links solar power production data to investment data, is proposed. The proposed model is validated and calibrated on historical European data and extrapolated into the future. In a future scenario where European investments continue to decrease, a saturation level that is fully in line with our GDP based 200 TWh/y estimate is found and the application of the findings is discussed in a global context