Estimating Eigenenergies from Quantum Dynamics: A Unified Noise-Resilient Measurement-Driven Approach

Ground state energy estimation in physics and chemistry is one of the most promising applications of quantum computing. In this paper, we introduce a novel measurement-driven approach that finds eigenenergies by collecting real-time measurements and post-processing them using the machinery of dynamic mode decomposition (DMD). We provide theoretical and numerical evidence that our method converges rapidly even in the presence of noise and show that our method is isomorphic to matrix pencil methods developed independently across various scientific communities. Our DMD-based strategy can systematically mitigate perturbative noise and stands out as a promising hybrid quantum-classical eigensolver

Beyond the current noise limit in imaging through turbulent medium

Shift-and-add is an approach employed to mitigate the phenomenon of resolution degradation in images acquired through a turbulent medium. Using this technique, a large number of consecutive short exposures is registered below the coherence time of the atmosphere or other blurring medium. The acquired images are shifted to the position of the brightest speckle and stacked together to obtain high-resolution and high signal-to-noise frame. In this paper we present a highly efficient method for determination of frames shifts, even if in a single frame the object cannot be distinguished from the background noise. The technique utilizes our custom genetic algorithm, which iteratively evolves a set of image shifts. We used the maximal energy of stacked images as an objective function for shifts estimation and validate the efficiency of the method on simulated and real images of simple and complex sources. Obtained results confirmed, that our proposed method allows for the recovery of spatial distribution of objects even only 2% brighter than their background. The presented approach extends significantly current limits of image reconstruction with the use of shift-and-add method. The applications of our algorithm include both the optical and the infrared imaging. Our method may be also employed as a digital image stabilizer in extremely low light level conditions in professional and consumer applications.Comment: 8 pages, 4 figure

Wavelet speech enhancement based on time-scale adaptation

Abstract : We propose a new speech enhancement method based on time and scale adaptation of wavelet thresholds. The time dependency is introduced by approximating the Teager Energy of the wavelet coefficients, while the scale dependency is introduced by extending the principle of level dependent threshold to Wavelet Packet Thresholding. This technique does not require an explicit estimation of the noise level or of the apriori knowledge of the SNR, as is usually needed in most of the popular enhancement methods. Performance of the proposed method is evaluated on speech recorded in real conditions (plane, sawmill, tank, subway, babble, car, exhibition hall, restaurant, street, airport, and train station) and artificially added noise. MELscale decomposition based on wavelet packets is also compared to the common wavelet packet scale. Comparison in terms of Signal-to-Noise Ratio (SNR) is reported for time adaptation and time-scale adaptation thresholding of the wavelet coefficients thresholding. Visual inspection of spectrograms and listening experiments are also used to support the results. Hidden Markov Models Speech recognition experiments are conducted on the AURORA–2 database and show that the proposed method improves the speech recognition rates for low SNRs

A novel fractional-order extended Kalman filtering method for on-line joint state estimation and parameter identification of the high power li-ion batteries.

To ensure the reliability and sustainability of the energy storage system, it is important to accurately estimate the state of charge of the battery management system. The Li-ion battery is established based on fractional-order model, and the model parameters are identified online using particle swarm optimization combined with the forgetting factor recursive least square method. On this basis, a novel fractional-order extended Kalman filter method for on-line joint state estimation and parameter identification is proposed. This method can update the parameter model of Li-ion battery in real-time, which not only improves the accuracy of the battery model but also improves the accuracy of SOC estimation. Finally, to verify the accuracy and superiority of the method, the integral order extended Kalman filter, fractional-order extended Kalman filter are compared with the proposed method under the BBDST test schedule. Experimental results show that the algorithm has the highest SOC estimation accuracy and the smallest estimation error (1.5 %.). The results indicate that the fractional-order model can better describe the dynamic characteristics of Li-ion battery, and the adaptive scheme can significantly suppress noise measurement errors and battery model errors. The algorithm realizes online parameter identification and can be used in engineering applications

Channel ranking based on packet delivery ratio estimation in wireless sensor networks

Wireless sensor networks (WSNs) operating in 2.4 GHz unlicensed bands must explore favorable channels in order to mitigate the effects of induced interference by co-existing wireless systems and frequency selective fading. In this context, we develop a packet delivery ratio (PDR) estimation method for channel ranking in WSNs. The PDR, in general, is defined as a function of signal-to-noise ratio (SNR) and signal-to-interferenceplus-noise ratio (SINR) at the sensor and the packet collision-time distribution of the sensor link. The collision-time distribution depends on the packet size and packet inter-arrival time distributions of both networks. Under limited channel measurements, the collision-time cannot be estimated satisfactorily. In order to bypass the collision-time estimation process, the proposed PDR estimation method utilizes signal level, interference and noise characteristics identified by spectrum measurements adjusted to the intended traffic pattern of the sensor link. The proposed method is validated against the empirical PDR using off-the-shelf sensor platform in emulated multi path wireless fading channels. The results reveal that the method is accurate in modeling the empirical PDR with limited channel energy measurements. In addition, we used the estimated PDR as a metric for channel ranking and verified its effectiveness by ranking the available channels to a WSN under interference from multiple WLANs in a real environment.Peer reviewe

Stochastic Modeling of Short-term Occupancy for Energy Efficient Buildings

The primary energy consumer of smart buildings are Heating, Ventilation, and Air-Conditioning (HVAC) systems, approximately 30% of the building energy use, which usually operate on a fixed schedule. Currently, most modern buildings still condition rooms with a set-point assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Occupancy-based controls can achieve significant energy savings by temporally matching the building energy consumption and building usage, conservative user behavior can save a third of expended energy.  In this paper, we present a simple yet effective algorithm to automatically assign reference temperature set-points based on the occupancy information. Both the binary and detailed occupancy estimation cases are considered. In the first case study, we assume the schedule involves only binary states (occupied or not occupied), i.e. the room is invariant. With long-term observations occupancy levels can be estimated using statistical tools. In the second case study, three techniques are introduced. Firstly, we propose an identification-based approaches. More precisely, we identify the models via Expectation Maximization (EM) approach. The statistical state space model is built in linear form for the mapping between the occupancy measurements and real occupancy states with noise considered. Secondly, we propose a method based on uncertain basis functions for modeling and prediction purposes. In literature, basis functions (e.g., radial basis functions, wavelets) are fixed; instead, we assume that the basis functions are random. We consider basis functions with three different distributions, which are Gaussian, Laplace and Uniform, respectively. Finally, we introduce a novel finite state automata (FSA) which is successfully reconstructed by general systems problem solver (GSPS). As far as we know, no studies have used the finite state machine or general system theory to estimate occupancy in buildings. All above estimates can be used to adaptively update the temperature set-points for HVAC control strategy.  To demonstrate effectiveness of proposed approach, a simulation-based experimental analysis is carried out using occupancy data. We define the estimation accuracy as the total number of correct estimations divided by the total number of estimations, and both Root Mean Squared Error (RMSE) and estimation accuracy analysis are provided. All the proposed estimation techniques could achieve at least 70% accuracy rate. Generally, accuracy for binary states estimation is much higher than that of detailed occupancy. For GSPS model, more training data improves performance of estimation. It should be remarked that although some mismatch exist for non-zero jumps, estimation performance tracks the zero base line (non-occupied status) perfectly. Therefore, the estimation techniques are effective for binary estimation with over 90% accuracy. Finally, the estimated occupancy is applied into temperature set algorithm to generate reference temperature curve. By adjusting temperature set curve, we can achieve significant energy without sacrificing customer’s comfort.  In this paper, we propose three real-time occupancy estimation methods that can be incorporated into HVAC controls . We have shown the effectiveness of all the proposed approaches by simulation examples. We have seen great potential of energy saving by integrating the proposed technique into real HVAC control system.   Â

Development of self-organizing methods for radio spectrum sensing

A problem of wide-band radio spectrum analysis in real time was solved and presented in the dissertation. The goal of the work was to develop a spectrum sensing method for primary user emission detection in radio spectrum by investigating new signal feature extraction and intelligent decision making techniques. A solution of this problem is important for application in cognitive radio systems, where radio spectrum is analyzed in real time. In thesis there are reviewed currently suggested spectrum analysis methods, which are used for cognitive radio. The main purpose of these methods is to optimize spectrum description feature estimation in real-time systems and to select suitable classification threshold. For signal spectrum description analyzed methods used signal energy estimation, analyzed energy statistical difference in time and frequency. In addition, the review has shown that the wavelet transform can be used for signal pre-processing in spectrum sensors. For classification threshold selection in literature most common methods are based on statistical noise estimate and energy statistical change analysis. However, there are no suggested efficient methods, which let classification threshold to change adaptively, when RF environment changes. It were suggested signal features estimation modifications, which let to increase the efficiency of algorithm implementation in embedded system, by decreasing the amount of required calculations and preserving the accuracy of spectrum analysis algorithms. For primary signal processing it is suggested to use wavelet transform based features extraction, which are used for spectrum sensors and lets to increase accuracy of noisy signal detection. All primary user signal emissions were detected with lower than 1% false alarm ratio. In dissertation, there are suggested artificial neural network based methods, which let adaptively select classification threshold for the spectrum sensors. During experimental tests, there was achieved full signals emissions detection with false alarm ratio lower than 1%. It was suggested self organizing map structure modification, which increases network self-training speed up to 32 times. This self-training speed is achieved due to additional inner weights, which are added in to self organizing map structure. In self-training stage network structure changes especially fast and when topology, which is suited for given task, is reached, in further self-training iterations it can be disordered. In order to avoid this over-training, self-training process monitoring algorithms must be used. There were suggested original methods for self-training process control, which let to avoid network over-training and decrease self-training iteration quantity

Interpolated-DFT-Based Fast and Accurate Amplitude and Phase Estimation for the Control of Power

The quality of energy produced in renewable energy systems has to be at the high level specified by respective standards and directives. The estimation accuracy of grid signal parameters is one of the most important factors affecting this quality. This paper presents a method for a very fast and accurate amplitude and phase grid signal estimation using the Fast Fourier Transform procedure and maximum decay sidelobes windows. The most important features of the method are the elimination of the impact associated with the conjugate's component on the results and the straightforward implementation. Moreover, the measurement time is very short - even far less than one period of the grid signal. The influence of harmonics on the results is reduced by using a bandpass prefilter. Even using a 40 dB FIR prefilter for the grid signal with THD = 38%, SNR = 53 dB and a 20-30% slow decay exponential drift the maximum error of the amplitude estimation is approximately 1% and approximately 0.085 rad of the phase estimation in a real-time DSP system for 512 samples. The errors are smaller by several orders of magnitude for more accurate prefilters.Comment: in Metrology and Measurement Systems, 201