A recursive maximum a posteriori estimator

Kalman Filter (KF) is the optimal state estimator for linear dynamical systems in the presence of zero mean white Gaussian noise. It is a minimum mean square error (MMSE) estimator. In the present work a recursive maximum a posteriori estimator (RMAPE) has been developed from basic principles of estimation. This estimator may be used for realtime state estimation of linear dynamical systems in presence of zero mean white Gaussian noise. It is further shown here that the KF can be derived from this RMAPE algorithm, i.e. this work shows an alternative method way to derive the KF. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

Illumination and scale invariant relevant visual features with hypergraph-based learning for multi-shot person re-identification

Person re-identification which aims at matching people across disjoint cameras has received increasing attention due to the widespread use of video surveillance applications. Existing methods concentrate either on robust feature extraction or view-invariant feature transformation. However, the extracted features suffer from various limitations such as color inconsistency and scale variations. Besides, during matching, a probe is compared against each gallery instance which represents only the pairwise relationship and ignores the high order relationship among them. To address these issues, we propose a multi-shot person re-identification framework that first performs a preprocessing task on images to address illumination variations for maintaining the color consistency. Subsequently, we formulate an approach to handle scale variations in the pedestrian appearances for keeping them with relatively a fixed scale ratio. Overlapped visual patches representing appearance cues are then extracted from the processed images. A structured multi-class feature selection approach is employed to select a set of relevant patches that simultaneously discriminates all distinct persons. These selected patches use a hypergraph to represent the visual association among a probe and gallery images. Finally, for matching, we formulate a hypergraph-based learning scheme, which considers both the pairwise and high-order association among the probe and gallery images. The hypergraph structure is then optimized to yield an improved similarity score for a probe against each gallery instance. The effectiveness of our proposed framework is validated on three public datasets and comparison with state-of-the-art methods shows the superior performance of our framework.

     

The defluoridation of water by acidic alumina

Fluoride is considered as a major inorganic pollutant present in drinking water. To remove this excess fluoride, defluoridation was done by alumina. In the present study, alumina used was acidic in nature and hence considered as a good fluoride removing adsorbent. Characterization of the adsorbent was done by XRD, SEM, BET and FTIR with BET surface area of 144.27 m²/g. Systematic adsorption experiments were carried out with different process parameters such as contact time, adsorbent mass, pH, temperature and stirring speed. Fluoride adsorption by alumina was highly pH dependent. Maximum fluoride was removed from water at pH 4.4. At very low and very high pH, fluoride removal efficiency was affected. The study of thermodynamic parameters inferred that physical adsorption was dominant with activation energy of 95.13 kJ/mol and endothermic behavior of the process. The kinetics study concluded that pseudo second order kinetics was followed by the adsorption process. Adsorption equilibrium was studied with Langmuir and Freundlich isotherm models. The adsorption process followed Langmuir isotherm with an adsorption capacity of 8.4 mg/g. A regeneration study was proposed in order to reuse the adsorbent for better economy of the process. Finally, a process design calculation was reported to know the amount of adsorbent required for efficient removal of fluoride from aqueous medium.     

Anticipated velocity based guidance strategy for wheeled mobile evader amidst stationary and moving obstacles in bounded environment

This paper is concerned with a class of pursuit‐evasion game problems amidst stationary and moving obstacles in a bounded environment. We concentrate on evader's strategy taking into account the following challenges: (i) pursuer and evader are nonholonomic wheeled mobile robots and the evader is slower than the pursuer; (ii) pursuer follows a proportional navigation law; and (iii) geometry of the environment is not known to the players, a priori. We propose an efficient evader‐centric anticipated velocity based guidance strategy. Pursuer's trajectory is anticipated at each step by the evader using quadratic polynomial interpolation. The aim of the evader is to escape interception with the pursuer for maximum possible time. To deal with static obstacles, a technique based on a well‐known tangent bug algorithm is presented. While dealing with dynamic obstacles, a recently introduced reciprocal orientation method is employed to avoid collision in situations when the dynamic obstacle also cooperates in the process. In case dynamic obstacles do not participate in the process of collision avoidance, a well‐known velocity obstacle method is employed for planning safe collision‐free paths. Efficiency of the proposed algorithms is analyzed with respect to the interception time and the distance traveled by the players. Copyright © 2014 John Wiley & Sons, Ltd.     

Terahertz interferometric and synthetic aperture imaging

The stand-off imaging properties of a terahertz (THz) interferometric array are examined. For this application, the imaged object is in the near-field region limit of the imaging array. In this region, spherical and circular array architectures can compensate for near-field distortions and increase the field of view and depth of focus. Imaging of THz point sources is emphasized to demonstrate the imaging method and to compare theoretical predictions to experimental performance.     

Structural and electrical properties of Bi5Ti3FeO15 ceramics

Bi₅Ti₃FeO₁₅ (BTF) is a multiferroic material of Aurivillius structural family with (perovskite) layered structure. This material has special interest and position in the family because it is a combination of multiferroic BiFeO₃ and ferroelectric Bi₄Ti₃O₁₂, and can be used as new magneto-electric material for devices. The compound (Bi₅Ti₃FeO₁₅) was synthesized by a standard and widely used a high-temperature solid-state reaction method using high purity oxides. Preliminary structural analysis of the compound from the room temperature X-rays diffraction data confirmed the formation and good quality of the material. The nature of the microstructure (i.e., distribution, size and shape of grains, etc.) of sample recorded at room temperature using scanning electron microscopy exhibits formation of high-density sample. Studies of capacitive (permittivity and tangent loss) and resistive (impedance, electrical modulus and electrical conductivity) properties of the material as a function of frequency (1–1,000 kHz) at different temperatures (30–500 °C) using a complex impedance spectroscopy technique have provided many interesting and vital information on contribution of grains, grain boundary and interface in the material.     

ARTIFICIAL NEURAL NETWORK ANALYSIS IN INTERFEROMETRIC THz IMAGING FOR DETECTION OF LETHAL AGENTS

In the context of a non-invasive, non-contact method to detect concealed lethal agents employing stand-off imaging in the Terahertz (THz) range using an interferometric detector array, the techniques of image analysis are discussed. To complement the experimental testing, extensive modeling simulates reconstructed images of lethal agents at different THz frequencies applying interferometric techniques. The composite images are consequently analyzed using artificial neural network to make positive identification of the lethal agent. This work also attempts to address issues related to the presence of barriers and isolate some suitable numerical techniques required for rapid and successful recognition of lethal agents during security screening in THz range.