Random Impulse Response Research Articles

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter

BackgroundIdentification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical proteins can interact with each other and they are the specific type of PPIs. More and more researchers draw attention to the SIPs detection, and several prediction model have been proposed, but there are still some problems. Hence, there is an urgent need to explore a efficient computational model for SIPs prediction.ResultsIn this study, we developed an effective model to predict SIPs, called RP-FIRF, which merges the Random Projection (RP) classifier and Finite Impulse Response Filter (FIRF) together. More specifically, each protein sequence was firstly transformed into the Position Specific Scoring Matrix (PSSM) by exploiting Position Specific Iterated BLAST (PSI-BLAST). Then, to effectively extract the discriminary SIPs feature to improve the performance of SIPs prediction, a FIRF method was used on PSSM. The R’classifier was proposed to execute the classification and predict novel SIPs. We evaluated the performance of the proposed RP-FIRF model and compared it with the state-of-the-art support vector machine (SVM) on human and yeast datasets, respectively. The proposed model can achieve high average accuracies of 97.89 and 97.35% using five-fold cross-validation. To further evaluate the high performance of the proposed method, we also compared it with other six exiting methods, the experimental results demonstrated that the capacity of our model surpass that of the other previous approaches.ConclusionExperimental results show that self-interacting proteins are accurately well-predicted by the proposed model on human and yeast datasets, respectively. It fully show that the proposed model can predict the SIPs effectively and sufficiently. Thus, RP-FIRF model is an automatic decision support method which should provide useful insights into the recognition of SIPs.

Adaptive OFDM Waveform Design for Cognitive Radar in Signal-Dependent Clutter

The problem of adaptive orthogonal frequency division multiplexing (OFDM) waveform design in the case of stochastic extended targets in the presence of strong and nonstationary interferences is an essential part of a cognitive radar system. In this article, a new approach is developed based on maximizing the mutual information between the random target impulse response and the received echoes with respect to the transmitted waveform. We use the advantage of OFDM signals and present an adaptive structure to match the complex weights of OFDM signal with the radar scene for making complete use of the transmission power. The interference reflections are utilized rather than eliminating them to design the weights of the transmitted OFDM signal. The optimization algorithm for designing transmitted signals is based on adaptive learning whose essential information is obtained by a feedback between receiver and transmitter. This feedback includes essential target and interference characteristics which extracted from the reflected echoes. Based on changes in interference features, the weights of the transmitted OFDM signal on each subcarrier are designed and transmitted in the next coherent processing interval. Experimental results reveal that the proposed approach achieves a higher rate of performance compared to nonadaptive waveform design approaches.

Robust OFDM integrated radar and communications waveform design based on information theory

An integrated radar and communications system (IRCS) where a monostatic radar transceiver is employed for target classification while simultaneously used as a communications transmitter is considered. The radar combined propagation-target response (joint response of the radar propagation channel and target) and communications channel response are generally frequency selective but the corresponding frequency response functions are not exactly known. In particular, these frequency response functions are only known to lie in an uncertainty class. To ensure the IRCS simultaneously provides acceptable target classification performance and communications rate, a robust orthogonal frequency division multiplexing (OFDM) integrated radar and communications waveform (IRCW) design method is proposed. The approach finds a waveform that simultaneously provides a sufficiently large weighted sum of the communications data information rate (DIR) and the conditional mutual information (MI) between the observed signal and the random target impulse response over the entire uncertainty class. First, the conditional MI and DIR based on the OFDM IRCW are derived. Then, a robust OFDM IRCW optimization problem based on the minimax design philosophy is developed such that closed-form solution is derived. Finally, several numerical results are presented to demonstrate the effectiveness of the proposed method.

Kalman filter-based centralized controller design for non-square multi-input multi-output processes

In this paper, a simple and effective centralized controller is presented to control non-square multi-input multi-output industrial processes with heavy interactions and significant time-delays. The control objective is to achieve a good load disturbance rejection and set point tracking. The controller and process model equations are represented in a stochastic state-space. Also, the state and measurement equations respectively use general random walk model and finite impulse response model of the plant. To design the controller, a quadratic cost function including the control error vector as well as the input vector and its changes is introduced. A Kalman filter algorithm is used to solve the discrete algebraic Riccati equation and to estimate the state vector of the controller recursively. This novel use of Kalman filter combines the observer and controller in a block. No square down/up, pairing and pseudo-inversing, is required for the proposed control structure. This leads to the simplicity of controller design. To evaluate the performance of the proposed control strategy, it is applied to a simulation example and an industrial case study with heavy interactions and significant time delays, an atmospheric distillation column with 7 inputs and 4 outputs. Simulation results show that the proposed controller has a good performance in both load disturbance rejection and setpoint tracking.

Adaptive OFDM Integrated Radar and Communications Waveform Design Based on Information Theory

To improve the effectiveness of limited spectral resources, an adaptive orthogonal frequency division multiplexing integrated radar and communications waveform design method is proposed. First, the conditional mutual information (MI) between the random target impulse response and the received signal, and the data information rate (DIR) of frequency selective fading channel are formulated. Then, with the constraint on the total power, the optimization problem, which simultaneously considers the conditional MI for radar and DIR for communications, is devised, and the analytic solution is derived. With low transmit power, the designed integrated waveform outperforms the fixed waveform (i.e., equal power allocation). Finally, several simulated experiments are provided to verify the effectiveness of the designed waveform.

A variational approach for restoring images corrupted by noisy blur kernels and additive noise

SummaryIn this paper, we study a deblurring algorithm for distorted images by random impulse response. We propose and develop a convex optimization model to recover the underlying image and the blurring function simultaneously. The objective function is composed of 3 terms: the data‐fitting term between the observed image and the product of the estimated blurring function and the estimated image, the squared difference between the estimated blurring function and its mean, and the total variation regularization term for the estimated image. We theoretically show that under some mild conditions, the resulting objective function can be convex in which the global minimum value is unique. The numerical results confirm that the peak‐to‐signal‐noise‐ratio and structural similarity of the restored images by the proposed algorithm are the best when the proposed objective function is convex. We also present a proximal alternating minimization scheme to solve the resulting minimization problem. Numerical examples are presented to demonstrate the effectiveness of the proposed model and the efficiency of the numerical scheme.

A novel SINR and mutual information based radar jamming technique

The improvements of anti-jamming performance of modern radar seeker are great threats to military targets. To protect the target from detection and estimation, the novel signal-to-interference-plus-noise ratio (SINR)-based and mutual information (MI)-based jamming design techniques were proposed. To interfere with the target detection, the jamming was designed to minimize the SINR of the radar seeker. To impair the estimation performance, the mutual information between the radar echo and the random target impulse response was used as the criterion. The spectral of optimal jamming under the two criteria were achieved with the power constraints. Simulation results show the effectiveness of the jamming techniques. SINR and MI of the SINR-based jamming, the MI-based jamming as well as the predefined jamming under the same power constraints were compared. Furthermore, the probability of detection and minimum mean-square error (MMSE) were also utilized to validate the jamming performance. Under the jamming power constraint of 1 W, the relative decrease of the probability of detection using SINR-based optimal jamming is about 47%, and the relative increase of MMSE using MI-based optimal jamming is about 8%. Besides, two useful jamming design principles are concluded which can be used in limited jamming power situations.

MIMO radar waveform design with peak and sum power constraints

Optimal power allocation for multiple-input multiple-output radar waveform design subject to combined peak and sum power constraints using two different criteria is addressed in this paper. The first one is by maximizing the mutual information between the random target impulse response and the reflected waveforms, and the second one is by minimizing the mean square error in estimating the target impulse response. It is assumed that the radar transmitter has knowledge of the target’s second-order statistics. Conventionally, the power is allocated to transmit antennas based on the sum power constraint at the transmitter. However, the wide power variations across the transmit antenna pose a severe constraint on the dynamic range and peak power of the power amplifier at each antenna. In practice, each antenna has the same absolute peak power limitation. So it is desirable to consider the peak power constraint on the transmit antennas. A generalized constraint that jointly meets both the peak power constraint and the average sum power constraint to bound the dynamic range of the power amplifier at each transmit antenna is proposed recently. The optimal power allocation using the concept of waterfilling, based on the sum power constraint, is the special case of p = 1. The optimal solution for maximizing the mutual information and minimizing the mean square error is obtained through the Karush-Kuhn-Tucker (KKT) approach, and the numerical solutions are found through a nested Newton-type algorithm. The simulation results show that the detection performance of the system with both sum and peak power constraints gives better detection performance than considering only the sum power constraint at low signal-to-noise ratio.

Tensor Analysis-Based Model Structure Determination and Parameter Estimation for Block-Oriented Nonlinear Systems

A block-oriented nonlinear system is composed of a concatenation of blocks representing either memoryless nonlinearities or linear dynamic subsystems. Wiener, Hammerstein, and Wiener-Hammerstein (WH) models are the most commonly used ones for modeling such block-oriented nonlinear systems. In this paper, we develop a tensor analysis-based approach for determining both the structure and the parameters of the most appropriate model among the three above listed models. The structure is deduced from the rank of a tensor obtained by convolving a random finite impulse response (FIR) linear filter with a <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> th-order Volterra kernel ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> > 2), associated with the block-oriented nonlinear system to be identified. The parameters of the linear subsystems are obtained from the PARAllel FACtor analysis (PARAFAC) decomposition of the <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> th-order Volterra kernel associated with the original nonlinear system and/or an extended WH system, whereas those of the nonlinear subsystem are estimated using the least squares method. The performance of the proposed identification scheme is illustrated by means of some simulation results.

Ultrawideband Channel Modeling on the Basis of Information-Theoretic Criteria

We present results of two indoor ultrawideband channel measurement campaigns in the 2-5 GHz frequency band. In measurement campaign I (MC I), the channel is static and we sample it spatially, while in MCII the transmitting and receiving antennas are fixed and channel variation is induced by people moving in the environment. Transmitter and receiver are separated by up to 27 m in MC I and up to 20 m in MC II. To determine suitable small-scale fading distributions for the tap amplitudes of the discrete-time baseband-equivalent channel impulse response, we use Akaike's information criterion (AIC). Despite the large bandwidth, AIC supports the Rayleigh (MCI) or the Rice distribution (MC II). For data from MC II, we estimate the covariance matrix of the random channel impulse response and demonstrate that the number of corresponding significant eigenvalues, and hence the diversity order of the channel, scales approximately linearly with bandwidth. Contrary to the uncorrected scattering assumption, we find that the channel taps are weakly correlated. The ergodic capacity predicted by the Ricean channel model with parameters estimated from MC II shows good agreement with the ergodic capacity obtained by direct evaluation of the measurement results, while the corresponding outage capacities show a worse fit for low outage probabilities because of shadowing.

MIMO radar waveform design based on mutual information and minimum mean-square error estimation

This paper addresses the problem of radar waveform design for target identification and classification. Both the ordinary radar with a single transmitter and receiver and the recently proposed multiple-input multiple-output (MIMO) radar are considered. A random target impulse response is used to model the scattering characteristics of the extended (nonpoint) target, and two radar waveform design problems with constraints on waveform power have been investigated. The first one is to design waveforms that maximize the conditional mutual information (MI) between the random target impulse response and the reflected waveforms given the knowledge of transmitted waveforms. The second one is to find transmitted waveforms that minimize the mean-square error (MSE) in estimating the target impulse response. Our analysis indicates that under the same total power constraint, these two criteria lead to the same solution for a matrix which specifies the essential part of the optimum waveform design. The solution employs water-filling to allocate the limited power appropriately. We also present an asymptotic formulation which requires less knowledge of the statistical model of the target

Acquisition of low-frequency signals immersed in noise by random sampling and finite impulse response filters

This article presents a method for digital acquisition of low-frequency signals immersed in high-frequency noise when noise frequency is higher than the sampling one. This method uses no antialiasing filters, but random sampling and filtering instead. Its formulation is developed for JRS (jitter random sampling) and finite impulse response (FIR) filters. The proposed method is experimentally tested on three FIR filters: moving average, windowed linear phase, and constrained least square.

A linear equalizer for high-speed APD-based integrate-and-dump receivers

A linear minimum mean-square error (LMMSE) equalizer for avalanche-photodiode (APD) based integrate-and- dump receivers is designed to compensate for intersymbol- interference (ISI) introduced by the APD's random impulse response. Recent work on the characterization of the joint probability distribution of the APD's gain and buildup-time are adopted and utilized to determine the mean and correlation matrix of the APD-based receiver's random impulse response. This LMMSE equalizer is shown to improve the bit-error- rate dramatically at high transmission rates, resulting in an improvement in the receiver sensitivity by approximately 2.3 dB at 15GHz. Index Terms—Avalanche photodiodes, equalization, random impulse response, bit-error-rate, receiver sensitivity.

Power spectra of random spike fields and related processes

In this article, we review known results and present new ones concerning the power spectra of large classes of signals and random fields driven by an underlying point process, such as spatial shot noises (with random impulse response and arbitrary basic stationary point processes described by their Bartlett spectra) and signals or fields sampled at random times or points (where the sampling point process is again quite general). We also obtain the Bartlett spectrum for the general linear Hawkes spatial branching point process (with random fertility rate and general immigrant process described by its Bartlett spectrum). We then obtain the Bochner spectra of general spatial linear birth and death processes. Finally, we address the issues of random sampling and linear reconstruction of a signal from its random samples, reviewing and extending former results.

Power spectra of random spike fields and related processes

In this article, we review known results and present new ones concerning the power spectra of large classes of signals and random fields driven by an underlying point process, such as spatial shot noises (with random impulse response and arbitrary basic stationary point processes described by their Bartlett spectra) and signals or fields sampled at random times or points (where the sampling point process is again quite general). We also obtain the Bartlett spectrum for the general linear Hawkes spatial branching point process (with random fertility rate and general immigrant process described by its Bartlett spectrum). We then obtain the Bochner spectra of general spatial linear birth and death processes. Finally, we address the issues of random sampling and linear reconstruction of a signal from its random samples, reviewing and extending former results.

A new approach for computing the bandwidth statistics of avalanche photodiodes

A new approach for characterizing the avalanche-buildup-time-limited bandwidth of avalanche photodiodes (APD's) is introduced which relies on the direct knowledge of the statistics of the random response time. The random response time is the actual duration of the APD's finite buildup limited random impulse response function. A theory is developed characterizing the probability distribution function (PDF) of the random response time. Recurrence equations are derived and numerically solved to yield the PDF of the random response time. The PDF is then used to compute the mean and the standard deviation of the bandwidth. The dependence of the mean and the standard deviation of the bandwidth on the APD mean gain and the ionization coefficient ratio is investigated. Exact asymptotics of the tail of the PDF of the response time are also developed to aid the computation efficiency. The technique can be readily applied to multiplication models which incorporate dead space and can be extended to cases for which the carrier ionization coefficient is position dependent.

New restoration filter for signals distorted by random impulse response function and noise

A new restoration filter is proposed for the restoration of signals distorted by a random impulse response function and additive measurement noise. The filter is derived from the theory of constrained total least squares, an extension of total least squares to the case where linear constraints with additive random components are incorporated into the measurement model. This enables the incorporation of prior information about the original signal, expressed in the form of linear constraints such as smoothness and region of support. Furthermore, the filter does not require knowledge of the second-order statistics of the random impulse response function and noise. Simulation results are provided to demonstrate the effectiveness of the proposed filter.

Robust deblurring of random blur

A robust method for deblurring random blur is introduced. The image is assumed to be distorted by a linear system whose impulse response function is itself random and by additive noise whose spectral density is known only to be in the neighborhood of some specified spectral density. The estimate is motivated from the generalized least-squares and robust statistical methods. Our robust deblurring is considered in the frequency domain and is of the form of weighted least squares, with the most prominent frequencies of the random impulse response being downweighted in a way similar to Huber's robust estimator.

Information theory and radar waveform design

The use of information theory to design waveforms for the measurement of extended radar targets exhibiting resonance phenomena is investigated. The target impulse response is introduced to model target scattering behavior. Two radar waveform design problems with constraints on waveform energy and duration are then solved. In the first, a deterministic target impulse response is used to design waveform/receiver-filter pairs for the optimal detection of extended targets in additive noise. In the second, a random target impulse response is used to design waveforms that maximize the mutual information between a target ensemble and the received signal in additive Gaussian noise. The two solutions are contrasted to show the difference between the characteristics of waveforms for extended target detection and information extraction. The optimal target detection solution places as much energy as possible in the largest target scattering mode under the imposed constraints on waveform duration and energy. The optimal information extraction solution distributes the energy among the target scattering modes in order to maximize the mutual information between the target ensemble and the received radar waveform. >

Statistical properties of the impulse response function of double-carrier multiplication avalanche photodiodes including the effect of dead space

The statistical properties of the impulse response function of double-carrier multiplication avalanche photodiodes (APDs) are determined, including the effect of dead space, i.e., the minimum distance that a newly generated carrier must travel in order to acquire sufficient energy to become capable of causing an impact ionization. Recurrence equations are derived for the first and second moments and the probability distribution function of a set of random variables that are related, in a deterministic way, to the random impulse response function of the APD. The equations are solved numerically to produce the mean impulse response, the standard deviation, and the signal-to-noise ratio (SNR), all as functions of time.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>