Sharif Digital Repository

Centralized and cloud computing-based network architectures are the promising tracks of future communication systems where a large scale compute power can be virtualized for various algorithms. These architectures rely on high-performance communication links between the base stations and the central computing systems. On the other hand, massive Multiple-Input Multiple-Output (MIMO) technology is a promising solution for base stations toward higher spectral efficiency. To reduce system complexity and energy consumption, 1-bit analog-to-digital converters (ADCs) are leveraged with the cost of lowering the signal quality. To recover the lost information, more sophisticated algorithms, like... 

We consider the problem of learning a density function from observations of an unknown underlying model in a distributed setting, where the observations are partitioned into different sites. Applying commonly used density estimation methods such as Gaussian Mixture Model (GMM) or Kernel Density Estimation (KDE) to distributed data leads to an extensive amount of communication. A familiar approach to address this issue is to sample a small subset of data and collect them into a central node to run the density estimation algorithms on them. In this paper, we follow an alternative to the sub-sampling approach by proposing the nested Log-Poly model. This model provides an accurate density... 

In-flight aircraft angular motion estimation based on an all-accelerometers inertial measurement unit (IMU) is investigated in this study. The relative acceleration equation as the representative of a rigid-body kinematics is manipulated to present the state and measurement equations of the aircraft dynamics. A decoupled scalar form (DSF) of the system equations, as a free-singularity problem, is derived. Mathematical modeling and simulation of an aircraft dynamics, equipped with an all-accelerometers IMU, are employed to prepare measurement data. Taking into account the modeling of accelerometer error, the measurement data have been simulated as a real condition. Three extended Kalman... 

Orbit estimation (OE) is a required significant task in almost all space missions. Accordingly, a wide variety of sensors and estimation algorithms have been developed within the last few decades to this aim. However, the current study proposes a novel autonomous OE method that is purely based on temperature data of six orthogonal surfaces of a three-axis stabilized satellite as it orbits around the Earth. While the utility of satellite surface temperature data has been recently investigated for satellite attitude estimation (AE) assuming its navigational information, the present paper is focused on OE via only temperature data that has not been attended to in the related literature. To this... 

In many sensor fusion algorithms, the vision based RANdom Sample Consensus (RANSAC) method is used for estimating motion parameters for autonomous robots. Usually such algorithms estimate both translation and rotation parameters together which makes them inefficient solutions for merely rotation estimation purposes. This paper presents a novel 3-point RANSAC algorithm for estimating only the rotation parameters between two camera frames which can be utilized as a high rate source of information for a camera-IMU sensor fusion system. The main advantage of our proposed approach is that it performs less computations and requires fewer iterations for achieving the best result. Despite many... 

Objective: Epileptic seizure detection is a key step for epilepsy assessment. In this work, using the pentylenetetrazole (PTZ) model, seizures were induced in rats, and ECoG signals in interictal, preictal, ictal, and postictal periods were recorded. The recorded ECoG signals were then analyzed to detect epileptic seizures in the epileptic rats. Methods: Two different approaches were considered in this work: thresholding and classification. In the thresholding approach, a feature is calculated in consecutive windows, and the resulted index is tracked over time and compared with a threshold. The moment the index crosses the threshold is considered as the moment of seizure onset. In the... 

Detection and isolation of faults in the exhaust gas path of a turbocharged spark ignition (SI) engine is an essential part of the engine control unit (ECU) strategies to minimize exhaust emission and ensure safe operation of a turbocharger. This paper proposes a novel physics-based strategy to detect and isolate an exhaust manifold leakage and a closed-stuck wastegate fault. The strategy is based on a globally optimal parameter estimation algorithm which detects an effective hole area in the exhaust manifold. The estimation algorithm requires prediction of the exhaust manifold's input and output flows. The input flow is predicted by a nonlinear Luenberger observer which is analytically... 

A new heuristic filter, called Continuous Ant Colony Filter, is proposed for non-linear systems state estimation. The new filter formulates the states estimation problem as a stochastic dynamic optimization problem and utilizes a colony of ants to find and track the best estimation. The ants search the state space dynamically in a similar scheme to the optimization algorithm, known as Continuous Ant Colony System. The performance of the new filter is evaluated for a nonlinear benchmark and the results are compared with those of Extended Kalman Filter and Particle Filter, showing improvements in terms of estimation accuracy  

IN this paper the effect of different channel estimation approaches in OFDMA uplink system which are based on Basis Expansion Model (BEM) and widely used to consider time varying channels are discussed. It has been shown in previous works that modeling error will be reduced by applying oversampled BEM with cost of increasing sensitivity to noise. This problem will be solved by combining oversampled BEM and MMSE channel estimator with cost of increasing computational complexity. In this paper a novel channel estimation approach is represented in which by combining oversampled BEM and low rank MMSE in frequency domain, almost the same performance as the full rank MMSE has been achieved while... 

In this paper, we use nonlinear system identification method to predict and detect process fault of a cement rotary kiln. After selecting proper inputs and output, an input-output model is identified for the plant. To identify the various operation points in the kiln, Locally Linear Neuro-Fuzzy (LLNF) model is used. This model is trained by LOLIMOT algorithm which is an incremental tree-structure algorithm. Then, by using this method, we obtained 3 distinct models for the normal and faulty situations in the kiln. One of the models is for normal condition of the kiln with 15 minutes prediction horizon. The other two models are for the two faulty situations in the kiln with 7 minutes... 

Fractional-pixel accuracy Motion Estimation (ME) has been shown to result in higher quality reconstructed image sequences in hybrid video coding systems. However, the higher quality is achieved by notably increased Motion Field (MF) bitrate and more complex computations. In this paper, new half-pixel block matching ME algorithms are proposed to improve the rate-distortion characteristics of low bitrate video communications. The proposed methods tend to decrease the required video bandwidth, while improving the motion compensation quality. The key idea is to put a deeper focus on the search origin of the ME process, based on center-bias characteristics of low bitrate video MFs. To employ the... 

There are many depletable resource planning problems associated with convex cost function. The Greedy algorithm has been used to find the optimal solution for such problems. But exploiting that algorithm requires a large bulk of computations to find the optimal solution. Thus, in this paper we have established two piecewise linear estimators for the convex cost function: an inner intersection of the function values and an outer tangent. A practical experiment was conducted to compare the performance of the two presented estimators and it was observed that the outer tangent performs more efficiently than the inner intersection. The execution time of the Greedy Algorithm and the estimation... 

Detection and isolation of faults in the exhaust gas path of a turbocharged spark ignition (SI) engine is an essential part of the engine control unit (ECU) strategies to minimize exhaust emission and ensure safe operation of a turbocharger. This paper proposes a novel physics-based strategy to detect and isolate an exhaust manifold leakage and a closed-stuck wastegate fault. The strategy is based on a globally optimal parameter estimation algorithm which detects an effective hole area in the exhaust manifold. The estimation algorithm requires prediction of the exhaust manifold's input and output flows. The input flow is predicted by a nonlinear Luenberger observer which is analytically... 

The effect of complexity level of a lumped kinetic model for heavy residue hydroconversion on estimated values of kinetic parameters was investigated in this work by imposing constraints for the parameter estimation algorithm of a complex six-lump kinetic model and deriving a simpler modified model from the complex model. Kinetic analysis was performed using available experimental data reported in the literature from a study on hydrocracking of Chinese Gudao vacuum residue in a bench-scale reactor using ammonium phosphomolybdate (APM) as a dispersed catalyst. The kinetic models also included coke formation reactions that had previously been ignored by most investigators due to the rather... 

A Bayesian framework for 3-D human pose estimation from monocular images based on sparse representation (SR) is introduced. Our probabilistic approach aims at simultaneously learning two overcomplete dictionaries (one for the visual input space and the other for the pose space) with a shared sparse representation. Existing SR-based pose estimation approaches only offer a point estimation of the dictionary and the sparse codes. Therefore, they might be unreliable when the number of training examples is small. Our Bayesian framework estimates a posterior distribution for the sparse codes and the dictionaries from labeled training data. Hence, it is robust to overfitting on small-size training... 

The problem of extracting low-dimensional structure from high-dimensional data arises in many applications such as machine learning, statistical pattern recognition, wireless sensor networks, and data compression. If the data is restricted to a lower dimensional subspace, then simple algorithms using linear projections can find the subspace and consequently estimate its dimensionality. However, if the data lies on a low-dimensional but nonlinear space (e.g., manifolds), then its structure may be highly nonlinear and, hence, linear methods are doomed to fail. In this paper, we introduce a new technique for dimensionality reduction based on point-wise operators. More precisely, let $ {bf A} n... 

Waveform design for target identification and classification in multiple-input multiple-output (MIMO) radar systems has been studied in several recent works. In previous works, optimal signals for an estimation algorithm are found assuming that only signal- independent noise exists. This work extends previous research by studying the case where clutter is also present. We develop a procedure to design the optimal waveform which minimizes estimation error at the output of the minimum mean squared error (MMSE) estimators in two scenarios. In the first one different transmit antennas see uncorrelated aspects of the target, and we consider the correlated target aspects in the second one.... 

During a Wet End break, the loss of paper feed through the paper machine causes loss of sensory information and the remaining parts of the process are operated in open-loop. This causes the stock composition in the Headbox to deviate substantially from the nominal specifications, causing paper quality (after start up) and paper machine runability issues. In this work, the Base Sheet Ash measurement of the scanner is estimated using a least absolute value (LAV) model which can then be used for control of the chalk valve during the breaks to keep the Headbox Ash within specified limits. The model is computed using a very fast optimization algorithm which is able to compute the LAV solution...