Estimation and Filtering Theory Research Papers (original) (raw)

This paper presents a frame work for hardware acceleration for post video processing system implemented on FPGA. The deblocking filter algorithms ported on SOC having Altera NIOS-II soft core processor.SOC designed with the help of SOPC builder .Custom instructions are chosen by identifying the most frequently used tasks in the algorithm and the instruction set of NIOS-II processor has been extended. Deblocking filter new instruction added to the processor that are implemented in hardware and interfaced to the NIOS-II processor. New instruction added to the processor to boost the performance of the deblocking filter algorithm. Use of custom instructions the implemented tasks have been accelerated by 5.88%. The benefit of the speed is obtained at the cost of very small hardware resources.

- by
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- Sociology, Political Sociology, Social Movements, Social Theory

A high order signal model is proposed in which the states are Kronecker tensor products of probability distributions. This model enables an optimal linear filter to be specified. A minimum residual error variance criterion may be used to select the number of discretizations and Kronecker products. The filtering of LIDAR data from a coal shiploader environment is investigated. It is demonstrated that the proposed method can outperform conventional Kalman and hidden Markov model filters.

—The concept of information fusion has gained a widespread interest in many fields due to its complementary properties. It makes systems more robust against uncertainty. This paper presents a new approach for the well logging estimation problem by using a fusion methodology. Natural gamma-ray tool (NGT) is considered as an important instrument in the well logging. The NGT detects changes in natural radioactivity emerging from the variations in concentrations of micronutrients as uranium (U), thorium (T h), and potassium (K). The main goal of this study is to have precise estimation of the concentrations of U , T h and K. Four types of Kalman filters are designed to estimate the elements using the NGT sensor. Then, a fusion of the Kalman filters is utilized into an integrated framework by ordered weighted averaging (OWA) operator to enhance the quality of the estimations. A real covariance of the output error based on innovation matrix is utilized to design weighting factors for the OWA operator. The simulation studies indicate not only a reliable performance of the proposed method as compared to the individual Kalman filters, but also a better response in contrast with previous fusion methodologies.

- by Sina Soltani and +1
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- Estimation and Filtering Theory, Kalman Filter, Well Logging, Data Fusion

Multi-sensor networks can alleviate the need for high-cost, high-accuracy, single-sensor tracking in favor of an abundance of lower-cost and lower-accuracy sensors to perform multi-sensor tracking. The use of a multi-sensor network gives rise to the need for a fusion step that combines the outputs of all sensor nodes into a single probabilistic state description. When considering Gaussian uncertainties, the well-known covariance intersection technique may be used. In the more general, non-Gaussian case, covariance intersection is not sufficient. This paper examines a fusion method based on logarithmic opinion pools and develops algorithms for multi-sensor data fusion as well as investigates weight selection schemes for the opinion pool. The proposed fusion rules are applied to the tracking of a space object using multiple ground-based optical sensors. Non-Gaussian orbit determination methods are applied to each sensor individually, and the fusion rule is applied to the combined outputs of each sensor node. It is shown that the multi-sensor fusion rule leads to an increase of nearly two orders of magnitude in the position tracking accuracy as compared to the traditional single-sensor tracking method.

Convolving the output of Discontinuous Galerkin computations with symmetric Smoothness-Increasing Accuracy-Conserving (SIAC) filters can improve both smoothness and accuracy. To extend convolution to the boundaries, several one-sided spline filters have recently been developed. This paper interprets these filters as instances of a general class of position-dependent (PSIAC) spline filters that can have non-uniform knot sequences and skip B-splines of the sequence. PSIAC filters with rational knot sequences have rational coefficients. For prototype knot sequences , such as integer sequences that may have repeated entries, PSIAC filters can be expressed in symbolic form. Based on the insight that filters for shifted or scaled knot sequences are easily derived by non-uniform scaling of one prototype filter, a single filter can be re-used in different locations and at different scales. Computing a value of the convolution then simplifies to forming a scalar product of a short vector with the local output data. Restating one-sided filters in this form improves both stability and efficiency compared to their original formulation via numerical integration. PSIAC filtering is demonstrated for several established and one new boundary filter.

The sliding mode control of the Ball on a Beam system is dealt with in this paper. Static and dynamic slidingmode controllers are designed using the complete model of the Ball on a Beam system. Simulation results indicate that the proposed controllers work well. The dynamic sliding mode controller is better in performance than the static and also it greatly helps in mitigating the chattering.

The Kalman filter is commonly used in neural interface systems to decode neural activity and estimate the desired movement kinematics. We analyze a low-complexity Kalman filter implementation in which the filter gain is approximated by its steady-state form, computed offline before real-time decoding commences. We evaluate its performance using human motor cortical spike train data obtained from an intracortical recording array as part of an ongoing pilot clinical trial. We demonstrate that the standard Kalman filter gain converges to within 95% of the steady-state filter gain in 1.5 ± 0.5 s (mean ±s.d.). The difference in the intended movement velocity decoded by the two filters vanishes within 5 s, with a correlation coefficient of 0.99 between the two decoded velocities over the session length. We also find that the steady-state Kalman filter reduces the computational load (algorithm execution time) for decoding the firing rates of 25±3 single units by a factor of 7.0±0.9. We expect that the gain in computational efficiency will be much higher in systems with larger neural ensembles. The steady-state filter can thus provide substantial runtime efficiency at little cost in terms of estimation accuracy. This far more efficient neural decoding approach will facilitate the practical implementation of future large-dimensional, multisignal neural interface systems.

A simple UV-Visible spectrophotometric method has been
developed for the determination of Atorvastatin in its pure form as
well as pharmaceutical dosage form using Methyl Orange reagent.
The method is based on the measurement of absorbance of
Atorvastatin in methanol at 410 nm. The Beer’s law is obeyed
over the linear range 50-300μg /ml of Atorvastatin. All the
variables were studied to optimize the reaction conditions. No
interference was observed in the presence of common
pharmaceutical excipients. The validity of the method was tested
by analyzing the drug in its pharmaceutical preparations. Good
recoveries were also obtained. Assay for the tablet preparation was
performed using UV-Visible spectrophotometric method and the
results were found to be within acceptable limits.

This paper presents an application of the Dual Extended Kalman Filter (DEKF) algorithm for the estimation of vehicle/ tyre dynamics states and parameters. The developed algorithm-Adap-tyre relies on online adaptation of simple tyre models available in the literature and is used in this paper for vehicle handling estimation. The performance of Adap-tyre is assessed by comparing estimated vehicle characteristics (vehicle body sideslip angle and tyre lateral forces) and tyre parameters with characteristics of a nonlinear vehicle planar model with tyres modeled according to the Magic Formula. Simulation results indicate that the proposed algorithm is very efficient in estimating critical vehicle states and tyre parameters.

- by Caglar Baslamisli
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- Estimation and Filtering Theory

"This book provides several flight-validated formulations and algorithms, fortified by thousands of hours working with real GPS and inertial data. The material, not yet widely used only due to its originality, is beginning to appear in classrooms and (soon) in operational systems. Considerable improvement is offered in multiple areas, including:
• transition from pre-GNSS nmi/hr to today's cm/sec for inertial
navigation
• full usage for “fractured” (intermittent and permanently
ambiguous) carrier phase
• rigorous integrity for separate SVs, with integrity validation
extended in several ways
• unprecedented robustness and situation awareness
• state-of-the-art performance with low cost IMUs
• usage of raw data from GPS (carrier phase and pseudorange)
& from IMU (gyro and accelerometer increments)
• "cookbook" steps to obtain nav (position/velocity/attitude)
estimates in all three dimensions from raw data
• user empowerment – complete flexibility and capability for
versatile operation
• new interoperability features, readily mixing observables from
any channel on any constellation (GPS, GLONASS, etc.)
• new insights for much easier implementation.
• formulations for an extensive set of tracking applications
and supporting operations"

Pokok Pembahasan: Ilustrasi Pengambilan Sampel, Field Table, dan Kriteria Kerapatan Mangrove,.

- by Muhammad Imron Ubaidillah
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- Estimation and Filtering Theory, Mangroves, Estimasi

A novel modification is proposed to the Kalman filter for the case of non-Gaussian measurement noise. We model the non-Gaussian data as outliers. Measurement data is robustly discriminated between Gaussian (valid data) and outliers by Robust Sequential Estimator (RSE). The measurement update is carried out for the valid data only. The modified algorithm proceeds as follows. Initially, the robust parameter and scale estimates of the measurement data are obtained for a sample of data using maximum likelihood estimates for a t-distribution error model through Iteratively Reweighted Least Squares (IRLS). The sample is dynamically updated with each new observation. Sequential classification of each new measurement is decided through a weighting scheme determined by RSE. State updates are carried out for the valid data only. Simulations provide satisfactory results and a significant improvement in mean square error with the proposed scheme.

- by Muhammad Mirza
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- Statistics, Machine Learning, Estimation and Filtering Theory

Personal positioning is a challenging topic in the area of navigation mainly because of the cost, size and power consumption constraints imposed on the hardware. Satel- lite based positioning techniques can meet the requirements for many applications, but cover well only outdoor environment. Problems like weak satellite signals make the positioning impossible indoors. Urban canyons are also difficult areas for GNSS based navigation because of large multipath errors and satellite signal outages. Many applications require seamless positioning in all environments. However, there is no overall solution for navigation in GNSS denied environment, which is reliable, ac- curate, cost effective and quickly installed. Recently developed systems for indoor positioning often require pre-installed infrastructure.
Another approach is to use fully autonomous navigation systems based on self-con- tained sensors and street or indoor maps. This thesis is concerned with autonomous personal navigation devices, which do not rely on the reception of external informa- tion, like satellite or terrestrial signals. The three proposed algorithms can be integ- rated into personal navigation systems.
The first algorithm computes positioning for a map aided navigation system designed for land vehicles traveling on road network. The novelty is in application of particle filtering to vehicle navigation using road network database. The second algorithm is aimed at map aided vehicle navigation indoors. The novelty is in the method for correction of position and heading. The third algorithm computes solution for pedestrian navigation system, which is based on body mounted inertial measurement unit and models of human gait.

Target tracking performance is determined by the fidelity of target mobility model (F, Q), tracking sensor measurement quality (R), and sensor-to-target geometry (H). A tracking sensor manager has choices in sensor selection/placement (H), waveform design (R), and filter tuning (F and Q), thus affecting the tracking performance in many ways. This paper concerns with the geometry aspect of sensor placement so as to optimize the tracking performance. Recently, a considerable amount of work has been published on optimal conditions for instantaneous placement of homogeneous sensors (same type and same measurement quality) in which the targets are either assumed perfectly known or the target location uncertainty is averaged out via the expected value of the determinant of the Fisher information matrix. In this paper, we derive conditions for optimal placement of heterogeneous sensors based on maximization of the updated Fisher information matrix from an arbitrary prior characterizing the uncertainty about the initial target location. The heterogeneous sensors can be of the same or different types (ranging sensors, bearing-only sensors, or both). The sensors can also make, over several time steps, multiple independent measurements of different qualities.

- by Erik Blasch
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- Control Systems Engineering, Aerospace Engineering, Mathematics, Computer Science

Center of Mass (CoM) estimation realizes a crucial role in legged locomotion. Most walking pattern generators and real-time gait stabilizers commonly assume that the CoM position and velocity are available for feedback. In this thesis we present one of the first
3D-CoM state estimators for humanoid robot walking. The proposed estimation scheme fuses effectively joint encoder, inertial, and feet pressure measurements with an Extended Kalman Filter (EKF) to accurately estimate the 3D-CoM position, velocity, and external
forces acting on the CoM. Furthermore, it directly considers the presence of uneven terrain and the body’s angular momentum rate and thus effectively couples the frontal with the lateral plane dynamics, without relying on feet Force/Torque (F/T) sensing.
Nevertheless, it is common practice to transform the measurements to a world frame of reference and estimate the CoM with respect to the world frame. Consequently, the robot’s base and support foot pose are mandatory and need to be co-estimated. To this end, we extend a well-established in literature floating mass estimator to account for the
support foot dynamics and fuse kinematic-inertial measurements with the Error State Kalman Filter (ESKF) to appropriately handle the overparametrization of rotations. In such a way, a cascade state estimation scheme consisting of a base and a CoM estimator
is formed and coined State Estimation RObot Walking (SEROW). Additionally, we employ Visual Odometry (VO) and/or LIDAR Odometry (LO) measurements to correct the kinematic drift caused by slippage during walking. Unfortunately, such measurements suffer from outliers in a dynamic environment, since frequently it is assumed that only the
robot is in motion and the world around is static. Thus, we introduce the Robust Gaussian ESKF (RGESKF) to automatically detect and reject outliers without relying on any prior knowledge on measurement distributions or finely tuned thresholds. Therefore, SEROW
is robustified and is suitable for dynamic human environments. In order to reinforce further research endeavors, SEROW is released to the robotic community as an open-source ROS/C++ package.
Up to date control and state estimation schemes readily assume that feet contact status is known a priori. Contact detection is an important and largely unexplored topic in contemporary humanoid robotics research. In this thesis, we elaborate on a broader question: in which gait phase is the robot currently in? To this end, we propose a holistic frame-
work based on unsupervised learning from proprioceptive sensing that accurately and efficiently addresses this problem. More specifically, we robustly detect one of the three gait-phases, namely Left Single Support (LSS), Double Support (DS), and Right Single Support (RSS) utilizing joint encoder, IMU, and F/T measurements. Initially, dimensionality reduction with Principal Components Analysis (PCA) or autoencoders is performed to extract useful features, obtain a compact representation, and reduce the noise. Next, clustering is performed on the low-dimensional latent space with Gaussian Mixture Models (GMMs) and three dense clusters corresponding to the gait-phases are obtained. Interestingly, it is
demonstrated that the gait phase dynamics are low-dimensional which is another indication pointing towards locomotion being a low dimensional skill. Accordingly, given that the proposed framework utilizes measurements from sensors that are commonly available
on humanoids nowadays, we offer the Gait-phase Estimation Module (GEM), an open-source ROS/Python implementation to the robotic community.
SEROW and GEM have been quantitatively and qualitatively assessed in terms of accuracy and efficiency both in simulation and under real-world conditions. Initially, a simulated robot in MATLAB and NASA’s Valkyrie humanoid robot in ROS/Gazebo were employed to establish the proposed schemes with uneven/rough terrain gaits. Subsequently,
the proposed schemes were integrated on a) the small size NAO humanoid robot v4.0 and b) the adult size WALK-MAN v2.0 for experimental validation. With NAO, SEROW was implemented on the robot to provide the necessary feedback for motion planning and real-
time gait stabilization to achieve omni-directional locomotion even on outdoor/uneven terrains. Additionally, SEROW was used in footstep planning and also in Visual SLAM with the same robot. Regarding WALK-MAN v2.0, SEROW was executed onboard with kinematic-inertial and F/T data to provide base and CoM feedback in real-time. Furthermore, VO has also been considered to correct the kinematic drift while walking and facilitate possible footstep planning. GEM was also employed to estimate the gait phase in WALK-MAN’s dynamic gaits.
Summarizing, a robust nonlinear state estimator is proposed for humanoid robot walking. Nevertheless, this scheme can be readily extended to other type of legged robots such
as quadrupeds, since they share the same fundamental principles.

ABSTRAK Suara merupakan salah satu media komunikasi yang paling sering dan paling umum digunakan oleh manusia. Suara yang dikeluarkan harus sampai ke tujuan dengan jelas dan dapat dimengerti, hanya saja lingkungan suara tidak selalu mendukung dalam penyampaian informasi suara, karena adanya noise yang mengganggu datangnya suara. Noise mengakibatkan suara yang diterima mengalami kerusakan bahkan menghilangkan informasi suara yang dibawa. Hal ini tentu saja mengakibatkan kualitas suara yang diterima menjadi kurang bagus, sehingga diperlukan pengolahan sinyal suara untuk menghilangkan noise tersebut. Salah satu permasalahan pengenalan suara yang sangat rentan dengan noise adalah pengenalan suara rekaman kalimat pembicaraan seseorang, karena noise dapat mengganggu dalam proses pengenalan suara yang keluarkan, sehingga suara yang diterima menjadi kurang bagus. Salah satu alternatif penyelesaian masalah sinyal suara yang terganggu oleh noise dapat diselesaikan oleh sebuah filter digital, yaitu Finite Impulse Response (FIR). Metode penelitian yang dilakukan adalah dengan melakukan simulasi perancangan dengan menggunakan pemrograman Matlab (Matrix Laboratory). Hasil yang diinginkan adalah keluaran sinyal suara yang bersih dari noise. Besarnya noise yang telah dihilangkan bisa dilihat melalui nilai Signal to Noise Ratio (SNR) dan pendekatan visual berupa gambar keluaran sinyal suara. Kata kunci : Suara, Finite Impulse Response (FIR), Matlab (Matrix Laboratory), Signal to Noise Ratio (SNR).

- by Yeffry Handoko and +1
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- Estimation and Filtering Theory

Today very important means of communication is the e-mail that allows people all over the
world to communicate, share data, and perform business. Yet there is nothing worse than an
inbox full of spam; i.e., information crafted to be delivered to a large number of recipients
against their wishes. In this paper, we present a numerous anti-spam methods and solutions that
have been proposed and deployed, but they are not effective because most mail servers rely on
blacklists and rules engine leaving a big part on the user to identify the spam, while others rely
on filters that might carry high false positive rate.

Vocabularies in Tendering and Estimating

- by Banjo Segun
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- Estimation and Filtering Theory, Vocabulary, Vocabulary Learning

Sensors can be used to measure the position of an object. In the present thesis the effects
which limit the usage of sensors in high dynamic positioning applications on a nanometer
level are discussed. Various sensor principles and their properties are investigated and
compared. Sensors based on the measurement of i.a. magnetic fields, illumination, or
even strain are characterized, as well as their range, bandwidth, resolution, linearity
and disturbance rejection is determined.
It will be shown that the simultaneous use of multiple sensors and the specific
combination of sensors’ data (fusion) enables a higher performance primarily in terms
of resolution and dynamics. Several techniques for the fusion are discussed under
consideration of various aspects, however the ultimate aim of sensor fusion is similar.
The methods of feedforward control, complementary filtering, Kalman filtering and
optimal filtering (robust control) are developed and verified on practical problems in
position sensor systems. To treat various challenges in sensor filtering and sensor fusion
a methodological approach, containing separable steps of
• problem formulation with well-defined prerequisits and simplifications,
• theory discussion with approach to find a solution,
• analytical proof or reasoning by statistical values out of numerical simulations,
• experiment design, and
• verification on a real time platform
are realized.

This paper reviews an important result in estimation theory, now known as the Kalman filter, named after Rudolf E. Kalman. The Kalman filter solves the least-squares estimation problem recursively, and in a computationally ecient manner. The problem is carefully stated in the second section and is later solved using matrix derivatives. The resulting solution, the linear Kalman filter, is collected into an algorithm that has a number of applications where one wants to estimate a collection of related, measurable values as data is collected.

- by Brent Perreault
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- Estimation and Filtering Theory, Adaptive Filtering, Kalman Filter

— Electrical devices often use RC, LC or RLC circuit to design power amplifiers, filters and mixers etc. Complex iterative algorithms are used for the simulation of these circuits.. This paper illustrates numerical experiments on growth of rounding off error in simulation of simple RC circuit using fourth order Runge-Kutta method (RK) and the Runge-Kutta-Fehlberg with adaptive step size control method (RKF). Our analysis indicates that in simulations with RK method, round off error grows ~ 80% with 10-15 iterations and ~96-98% within 100 iterations with different step sizes and double precision. In the simulations with RKF method round off error grows to 70% with 10-20 iterations and ~80% within 100 iterations. It does not exceed 80% for 1000 of iterations for single and double precision. This indicates that growth of round off error in RKF method is less and it should be used to minimize round off error.

Problemi estimativi seguenti alla risoluzione contrattuale per grave inadempimento.
Il contratto di locazione finanziaria può essere risolto per grave inadempimento qualora l’utilizzatore non provveda al pagamento di almeno sei canoni mensili o due canoni trimestrali, anche non consecutivi, o di importo equivalente. In questo caso, ai sensi dell’art.
1, comma 138 della l. 124/2017 il concedente procede alla risoluzione del contratto con il diritto alla restituzione del bene; tuttavia rimane obbligato a corrispondere all’utilizzatore quanto ricavato dalla vendita o da altra collocazione del bene, effettuata ai valori di mercato, dedotte la somma pari all’ammontare dei canoni scaduti e non pagati alla data della risoluzione, dei canoni a scadere, solo in linea capitale, e del prezzo pattuito per l’esercizio dell’opzione finale di acquisto, nonché le spese anticipate per il recupero del bene, la stima e la sua conservazione per il tempo necessario alla vendita

- by massimo moncelli
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- Agronomy, Estimation and Filtering Theory, Evaluation, Parameter estimation

This chapter presents the minimum-variance filtering results simplified for the case when the model parameters are time-invariant and the noise processes are stationary. The filtering objective remains the same, namely, the task is to estimate a signal in such as way to minimise the filter error covariance.A somewhat naïve approach is to apply the standard filter recursions using the time-invariant problem parameters. Although this approach is valid, it involves recalculating the Riccati difference equation solution and filter gain at each time-step, which is computationally expensive. A lower implementation cost can be realised by recognising that the Riccati difference equation solution asymptotically approaches the solution of an algebraic Riccati equation. In this case, the algebraic Riccati equation solution and hence the filter gain can be calculated before running the filter.
The steady-state discrete-time Kalman filtering literature is vast and some of the more accessible accounts [1] – [14] are canvassed here. The filtering problem and the application of the standard time-varying filter recursions are described in Section 5.2. An important criterion for checking whether the states can be uniquely reconstructed from the measurements is observability. For example, sometimes states may be internal or sensor measurements might not be available, which can result in the system having hidden modes. Section 5.3 describes two common tests for observability, namely, checking that an observability matrix or an observability gramian are of full rank. The subject of Riccati equation monotonicity and convergence has been studied extensively by Chan [4], De Souza [5], [6], Bitmead [7], [8], Wimmer [9] and Wonham [10], which is discussed in Section 5.4. Chan, et al [4] also showed that if the underlying system is stable and observable then the minimum-variance filter is stable. Section 6 describes a discrete-time version of the Kalman-Yakubovich-Popov Lemma, which states for time-invariant systems that solving a Riccati equation is equivalent to spectral factorisation. In this case, the Wiener and Kalman filters are the same.
Since the optimal filter is model-based, any unknown model parameters need to be estimated (as explained in Chapter 7) prior to implementation. The estimated parameters can be inexact which leads to degraded filter performance. An iterative frequency weighting procedure is described in Section 5.5 for mitigating the performance degradation.

—In many contemporary engineering problems, model uncertainty is inherent because accurate system identification is virtually impossible owing to system complexity or lack of data on account of availability, time, or cost. The situation can be treated by assuming that the true model belongs to an uncertainty class of models. In this context, an intrinsically Bayesian robust (IBR) filter is one that is optimal relative to the cost function (in the classical sense) and the prior distribution over the uncertainty class (in the Bayesian sense). IBR filters have previously been found for both Wiener and granulometric morphological filtering. In this paper, we derive the IBR Kalman filter that performs optimally relative to an uncertainty class of state-space models. Introducing the notion of Bayesian innovation process and the Bayesian orthogonality principle, we show how the problem of designing an IBR Kalman filter can be reduced to a recursive system similar to the classical Kalman recursive equations, except with " effective " counterparts, such as the effective Kalman gain matrix. After deriving the recursive IBR Kalman equations for discrete time, we use the limiting method to obtain the IBR Kalman-Bucy equations for continuous time. Finally, we demonstrate the utility of the proposed framework for two real world problems: sensor networks and gene regulatory network inference.

Abstract We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models. GPs are gaining increasing importance in signal processing, machine learning, robotics, and control for representing unknown system functions by posterior probability distributions.

— This article presents a complete formulation of the challenging task of stable humanoid robot omnidirectional walk based on the Cart and Table model for approximating the robot dynamics. For the control task, we propose two novel approaches: preview control augmented with the inverse system for negotiating strong disturbances and uneven terrain and linear model-predictive control approximated by an orthonormal basis for computational efficiency coupled with constraints for stability. For the generation of smooth feet trajectories, we present a new approach based on rigid body interpolation, enhanced by adaptive step correction. Finally, we present a sensor fusion approach for sensor-based state estimation and an effective solution to sensors' noise, delay, and bias issues, as well as to errors induced by the simplified dynamics and actuation imperfections. Our formulation is applied on a real NAO humanoid robot, where it achieves real-time onboard execution and yields smooth and stable gaits.

The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist-rate samples. Interestingly, it has been shown that random projections are a near-optimal measurement scheme. This has inspired the design of hardware systems that directly implement random measurement protocols. However, despite the intense focus of the community on signal recovery, many (if not most) signal processing problems do not require full signal recovery. In this paper, we take some first steps in the direction of solving inference problems-such as detection, classification, or estimation-and filtering problems using only compressive measurements and without ever reconstructing the signals involved. We provide theoretical bounds along with experimental results.

In this paper, a low power bulk-driven quasi-floating gate MOSFET based Miller compensated Operational Transconductance Amplifier (OTA) is proposed required particularly in design of Gm-C filter. The analysis of amplifier is compared with low power bulk-driven technique. The performance comparison indicates that bulk-driven quasi floating gate configuration offers better performance. In this configuration the combination of bulk-driven input with quasi-floating gate results in improved transconductance and
hence results in high gain and UGB of the OTA. Moreover, simulation of the bulk-driven quasi-floating gate OTA does not suffer from DC convergence problem. A voltage mode multifunction 2nd order filter design based on proposed BDQFG OTA is also presented. The analysis of all the circuits have been carried out in industry specific node UMC 0.18 micron technology with the help of HSpice simulator.

The induction machine, because of its robustness and low-cost, is commonly used in the industry. Nevertheless, as every type of electrical machine, this machine suffers of some limitations. The most important one is the working temperature which is the dimensioning parameter for the definition of the nominal working point and the machine lifetime. Due to a strong demand concerning thermal monitoring methods appeared in the industry sector. In this context, the adding of temperature sensors is not acceptable and the studied methods tend to use sensorless approaches such as observators or parameters estimators like the extended Kalman Filter (EKF). Then the important criteria are reliability, computational cost ad real time implementation.

Attitude determination system (ADS) is a process to control the orientation of satellite to make sure that the orientation of satellite is relative to inertial reference frame such as Earth. Earth Centered Inertial (ECI) is one of reference frame for satellite that determines the attitude in three dimensional spacecraft. Since RazakSAT orbits on earth, ECI coordinate system will be used for satellite relative to earth rotation. This paper is about the analysis on attitude position of ECI and velocity at X, Y and Z axis based on RazakSAT data. Satellite Tools Kit (STK) is used to estimate the attitude and velocity based on Two Line Elements (TLE) of RazakSAT. The result is compared with RazakSAT measurement data to observe the accuracy of estimation by using STK.

In this paper we develop a nonlinear vehicle sideslip observer design that is based on a lateral dynamics vehicle model. We utilize a novel simplified rational tire model to compute the lateral wheel forces. This tire model is significantly simpler than the well known Magic Formula, yet it provides sufficient detail over a wide range of operating conditions for the purpose of estimating the sideslip angle. The input to the nonlinear observer are typical signals that are available within lateral stability control systems, which include vehicle speed, steer angle, lateral acceleration and yaw rate. Inspired by methods introduced in recent papers by we show that the suggested nonlinear sideslip observer is stable. Finally, we provide numerical simulations to demonstrate the efficacy of our nonlinear observer based estimation technique.

- by Caglar Baslamisli and +1
- •
- Engineering, Estimation and Filtering Theory, Mathematical Sciences, Advanced manufacturing technology

Nowadays, Wireless Sensor Networks are
becoming ubiquitous and increasingly attract investigators to
focus on different aspects of them. In terms of localization
estimation system, location algorithm play an important role to
reach high accuracy. Many algorithms have been developed to
improve the accuracy of the system, but so far none has been able
to deliver complete satisfactory and reliable result. This paper
addresses a filter for smoothing the received signal strength
index (RSSI) based on Principle Component Analysis. To
enhance the accuracy of the system and overcome unwanted
signals, the proposed algorithm is utilized to reduce the noise for
triangulation localization approach instead of reducing the
database in fingerprint approach. Our algorithm divided each 10
received packets to a group and replaced a representative signal
for each group. A realistic demo system of the CC2430/CC2431
has been implemented in the fifth floor of Xi’an JiaotongLiverpool
University to represent the feasibility and accuracy of
our algorithm. The results show the enhancement in localization
accuracy in normal speed by at least 2 meters.

- by Cheng Zhang and +1
- •
- Electrical Engineering, Electronic Engineering, Computer Science, Information Technology

High efficiency filtration equipment have largely resulted from the development of filtration media with increased area-to-volume ratio and reduced the pore size. The pore size usually exceeds the particle size, particularly in fabric or nonwoven filters. These media are collectors in which particles are removed from the air stream by three different mechanisms: inertial interception, Brownian diffusion, and flowline interception. Nowadays, the same philosophy can be exploited by producing composite filter media consisting of two parts: nonwoven microfibers coupled with a nonwoven submicron or nanofibers. The nanofibers layer influences filtration properties, whereas the substrate allows the use of conventional filter media pleating equipment. The simplest way to produce polymer-based fibers with submicron size is the electrospinning process. In this work a statistical analysis of multilayer structures is given. The experimental results indicate that the use of thin layer of nanofibre mat has a significant effect on the air permeability of the resultant structures either in the case of woven or nonwoven multilayer filters.

- by Magdi El Messiry
- •
- Estimation and Filtering Theory, Kalman Filter

This study presents a strategy for estimating the states and the load torque to implement a feedback linearisation controller for induction motor drives. The multivariable control is carried out using input–output linearisation feedback law in order to track profiles of the rotational speed and the rotor flux amplitude. The unknown load torque is compensated by an estimator based on the speed error. The state estimation requires only the measurements of the stator voltages– currents. The estimation method is not invasive as no mechanical sensors are needed. Experimental platform equipped with sensors at the load side, for measuring the speed and the torque of the motor driven by the Opal-RT real-time system, was implemented to verify the accuracy of the proposed estimation method to implement the multivariable control.

- by Adel Merabet and +1
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- Estimation and Filtering Theory, Control Systems, Induction Motor Drives

Today very important means of communication is the e-mail that allows people all over the world to communicate, share data, and perform business. Yet there is nothing worse than an inbox full of spam; i.e., information crafted to be delivered to a large number of recipients against their wishes. In this paper, we present a numerous anti-spam methods and solutions that have been proposed and deployed, but they are not effective because most mail servers rely on blacklists and rules engine leaving a big part on the user to identify the spam, while others rely on filters that might carry high false positive rate.

This paper investigates the anti-synchronization of identical hyperchaotic Xu systems (Xu, Cai and Zheng, 2009) via sliding mode control. The stability results derived in this paper for the anti-synchronization of identical hyperchaotic Xu systems are established using Lyapunov stability theory. Since the Lyapunov
exponents are not required for these calculations, the sliding mode control method is very effective and convenient to achieve anti- synchronization of the identical hyperchaotic Xu systems. Numerical simulations are shown to illustrate and validate the anti-synchronization schemes derived in this paper for the identical hyperchaotic Xu systems.

Contact detection is an important topic in contemporary humanoid robotic research. Up to date control and state estimation schemes readily assume that feet contact status is known in advance. In this work, we elaborate on a broader question: in which gait phase is the robot currently in? We introduce an unsupervised learning framework for gait phase estimation based solely on proprioceptive sensing, namely joint encoder, inertial measurement unit and force/torque data. Initially , a meaningful physical explanation on data acquisition is presented. Subsequently, dimensionality reduction is performed to obtain a compact low-dimensional feature representation followed by clustering into three groups, one for each gait phase. The proposed framework is qualitatively and quantitatively assessed in simulation with ground-truth data of uneven/rough terrain walking gaits and insights about the latent gait phase dynamics are drawn. Additionally, its efficacy and robustness is demonstrated when incorporated in leg odometry computation. Since our implementation is based on sensing that is commonly available on humanoids today, we release an open-source ROS/Python package to reinforce further research endeavors.

- by Stylianos Piperakis and +1
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- Robotics, Control Systems Engineering, Machine Learning, Estimation and Filtering Theory

Usually, standard inertial navigation unit (INU) with global positioning system (GPS) provides relatively poor accuracy in altitude estimation, while autonomous landing of unmanned aerial vehicles (UAVs) requires accurate position estimation. In this paper, a UAV navigation system with aid from an external camera for landing is investigated. This paper presents: (i) a sensor fusion algorithm for passive monocular vision and INU based on the extended Kalman filter (EKF) considering measurement delay to improve the accuracy of position estimates, and (ii) a robust object-detection vision algorithm using optical flow. Pilot controlled landing experiments on a NASA UAV platform and the filter simulations validate the feasibility and performance of the proposed approach.

- by Corey Ippolito
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- Robotics, Control Systems Engineering, Computer Vision, Optimal Control