Inverse Filtering

Image enhancement techniques aim to extract significant data from blurry or noisy images, making them appear sharper. Due to atmospheric and environmental factors, images often deteriorate, necessitating the use of various image processing methods for restoration. Traditional methods, which rely heavily on pixel intensity transformation, may not be effective against different types of noise. This challenge has sparked significant research interest. Image restoration focuses on reducing blurring and noise to improve image quality. Our work presents an innovative way for obtaining the median value by combining an inverse filter with a median filter utilizing a clockwise route methodology. This combined method aims to enhance the restoration process. We have evaluated the proposed approach, comparing it with the inverse filter and their combination using various performance metrics. The results demonstrate significant improvements, achieving higher PSNR, lower MSE, and better SSIM, along with improvements in RMSE, MAE, and Entropy.

This work presents a method to analyze combustion events in an internal combustion engine, called the torque ratio concept. The method is based on crankshaft torque measurements, but an extension to angular speed measurements is possible. The torque ratio concept provides a parametrized model for the combustion progress from which, e.g. combustion phasing can be extracted. The torque ratio concept is derived mathematically and related theoretically to other combustion analysis methods, such as pressure ratio and net heat release. Finally, analysis on recorded data from a five cylinder spark ignited engine verifies the relationships between the three methods. For combustion phasing, the 50% torque ratio is an equivalent measure to 50% pressure ratio and can be transformed into the 50% net heat release position by using a derived volume ratio function.

The SMC Sound and Music Computing group at KTH (formerly the Music Acoustics group) is part of the Department of Speech Music and Hearing, School of Computer Science and Communication. In this short report we present the current status of the group mainly focusing on its research.

We consider sequential stochastic decision problems in which, at each time instant, an agent optimizes its local utility by solving a stochastic program and, subsequently, announces its decision to the world. Given this action, we study the problem of estimating the agent's private belief (i.e., its posterior distribution over the set of states of nature based on its private observations). We demonstrate that it is possible to determine the set of private beliefs that are consistent with public data by leveraging techniques from inverse optimization. We further give a number of useful characterizations of this set; for example, tight bounds by solving a set of linear programs (under concave utility). As an illustrative example, we consider estimating the private belief of an investor in regimeswitching portfolio allocation. Finally, our theoretical results are illustrated and evaluated in numerical simulations.

Bayesian filtering deals with computing the posterior distribution of the state of a stochastic dynamic system given noisy observations. In this paper, motivated by applications in counter-adversarial systems, we consider the following inverse filtering problem: Given a sequence of posterior distributions from a Bayesian filter, what can be inferred about the transition kernel of the state, the observation likelihoods of the sensor and the measured observations? For finite-state Markov chains observed in noise (hidden Markov models), we show that a least-squares fit for estimating the parameters and observations amounts to a combinatorial optimization problem with nonconvex objective. Instead, by exploiting the algebraic structure of the corresponding Bayesian filter, we propose an algorithm based on convex optimization for reconstructing the transition kernel, the observation likelihoods and the observations. We discuss and derive conditions for identifiability. As an application of our results, we illustrate the design of counter-adversarial systems: By observing the actions of an autonomous enemy, we estimate the accuracy of its sensors and the observations it has received. The proposed algorithms are evaluated in numerical examples.

Twang-like vocal qualities have been related to a megaphone-like shape of the vocal tract (epilaryngeal tube and pharyngeal narrowing, and a wider mouth opening), low-frequency spectral changes, and tighter/increased vocal fold adduction. Previous studies have focused mainly on loud and high-pitched singing, comfortable low-pitched spoken vowels or are based on modeling and simulation. There is no data available related to twang-like voices in loud, low-pitched singing. Purpose: This study investigates the possible contribution of the lower and upper vocal tract configurations during loud twang-like singing on high and low pitches in a real subject. Methods: One male contemporary commercial music singer produced a sustained vowel [a:] in his habitual speaking pitch (B2) and loudness. The same vowel was also produced in a loud twang-like singing voice on high (G4) and low pitches (B2). Computerized tomography, acoustic analysis, inverse filtering, and audio-perceptual assessments were performed. Results: Both loud twang-like voices showed a megaphone-like shape of the vocal tract, being more notable on the low pitch. Also, low-frequency spectral changes, a peak of sound energy around 3 kHz and increased vocal fold adduction were found. Results agreed with audioperceptual evaluation. Conclusions: Loud twang-like phonation seems to be mainly related to low-frequency spectral changes (under 2 kHz) and a more compact formant structure. Twang-like qualities seem to require different degrees of twangrelated vocal tract adjustments while phonating in different pitches. A wider mouth opening, pharyngeal constriction, and epilaryngeal tube narrowing may be helpful strategies for maximum power transfer and improved vocal economy in loud CCM singing and potentially in loud speech. Further studies should focus on vocal efficiency and vocal economy measurements using modeling and simulation, based on real-singers' data.

This paper considers inverse filtering problems for linear Gaussian state-space systems. We consider three problems of increasing generality in which the aim is to reconstruct the measurements and/or certain unknown sensor parameters, such as the observation likelihood, given posteriors (i.e., the sample path of mean and covariance). The paper is motivated by applications where one wishes to calibrate a Bayesian estimator based on remote observations of the posterior estimates, e.g., determine how accurate an adversary's sensors are. We propose inverse filtering algorithms and evaluate their robustness with respect to noise (e.g., measurement or quantization errors) in numerical simulations.

Introduction Conclusion Methods Results References Purpose: This study aims to identify the acoustic signs of supraglottal constriction and effects of some vocal manipulation techniques. It is hypothesized that some task-related acoustic contrasts would differ between voice patients with and without supraglottal constriction due to different vocal tract configurations. Method: Classified through videostroboscopic examinations, 30 participants were gender and age-matched to form two comparison groups ("constricted" and "non-constricted"), with five males and ten females in each group. Participants were asked to sustain a vowel (/a/ or /i/) for approximately three seconds in five tasks, including normal-pitch, low-pitch, high-pitch, /m/-onset (i.e., with the consonant /m/ preceding the vowel at normal pitch), and /h/-onset tasks. Acoustic signals were analyzed to extract measures from the mid-portion of the vowel. Results: The "constricted" group showed a lack of task-related contrasts on signal-to-noise ratio, singing power ratio, frequency of the second formant, and the amplitude difference between the first formant and the first harmonic. Conclusion: Further investigations are needed to assess the predictive power of the proposed task-based acoustic approach for detecting supraglottal constriction.

In this paper, at first a color image is taken Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3.the blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via MATLAB programming. Many methods can restore the noisy and motion blurred image: particularly in this paper inverse filtering as well as wiener filtering are implemented for the restoration purpose consequently, both motion blurred and noisy motion blurred image are restored via inverse filtering as well as wiener filtering techniques and the comparison is made among them.