PSO Aided Adaptive Complementary Filter for Attitude Estimation

Attitude estimation is one of the core frame- works used for navigating an unmanned aerial vehicle from one place to the other. This paper presents an Euler-based non-linear complementary filter (CF) whose gain parameters are obtained using particle swarm optimization (PSO) technique. It relieves the user from feeding the K _P and K _I parameters manually and adjust these parameters automatically when the error between the attitude measured from accelerometer and the CF increases above a particular threshold. The measurement unit for this research consists of micro-electro-mechanical-systems (MEMS) based low cost tri-axial rate gyros, accelerometers and magnetometers, without resorting to global positioning system (GPS) data. The efficiency of the CF is experimentally investigated with the help of reference attitude and the raw sensor data obtained from commercial inertial measurement unit (IMU). Simulation results based on the test data show that the proposed PSO aided non-linear complementary filter (PNCF) can automatically obtain the required gain parameters and exhibits promising performance for attitude estimation.     

Stock prediction using deep learning

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. Methods applied in digital signal processing can be applied to stock data as both are time series. Similarly, learning outcome of this paper can be applied to speech time series data. Deep learning for stock prediction has been introduced in this paper and its performance is evaluated on Google stock price multimedia data (chart) from NASDAQ. The objective of this paper is to demonstrate that deep learning can improve stock market forecasting accuracy. For this, (2D)²PCA + Deep Neural Network (DNN) method is compared with state of the art method 2-Directional 2-Dimensional Principal Component Analysis (2D)²PCA + Radial Basis Function Neural Network (RBFNN). It is found that the proposed method is performing better than the existing method RBFNN with an improved accuracy of 4.8% for Hit Rate with a window size of 20. Also the results of the proposed model are compared with the Recurrent Neural Network (RNN) and it is found that the accuracy for Hit Rate is improved by 15.6%. The correlation coefficient between the actual and predicted return for DNN is 17.1% more than RBFNN and it is 43.4% better than RNN.     

Adsorption of resorcinol and catechol on granular activated carbon: Equilibrium and kinetics

Investigations were conducted in the batch mode for studying the adsorption behavior of resorcinol and catechol on granular activated carbon from a basic salt medium (BSM) at pH≈7.1 and temperature≈30 °C. The isotherm data were correlated with six isotherm models, namely Langmuir, Freundlich, Redlich–Peterson, Radke–Prausnitz, Toth, and Fritz–Schlunder’s using a nonlinear regression technique. It is observed that the catechol isotherm data may be represented by Redlich–Peterson, Radke–Prausnitz, Toth, and Fritz–Schlunder models with similar accuracy (max. dev. 12%). And the resorcinol data may be represented by Freundlich, Redlich–Peterson, Radke–Prausnitz, and Fritz–Schlunder models equally well (max. dev. 15%). Freudlich being a simple model is recommended for resorcinol. At the conditions investigated in this study, catechol is adsorbed to a greater extent than resorcinol. This is due to the compound’s solubility and position of the –OH group on the benzene aromatic ring. The kinetics of adsorption have been found to be diffusion controlled and the value of effective particle diffusion coefficients is of the order of 10⁻¹³ m²/s. Three distinct phases of kinetics—rapid, medium, and slow—have been observed. These results should be useful for the design of adsorbers for removing these pollutants.     

Interaction of polystyryl radical with Cu(DMF)2Br2

The kinetics of 2,2′-azobisisobutyronitrile (AIBN) initiated polymerization of styrene in N,N-dimethylformamide (DMF) at 60°C were investigated in the presence of dibromo(N,N-dimethylformamide)copper(II) complex. The complex was prepared in situ by mixing tetrakis (N,N-dimethylformamide)copper(II) perchlorate with LiBr in the molar ratio of 1 : 2. The equilibrium constant for [Cu(DMF)₄]²⁺ + 2Br^? ? Cu(DMF)₂Br₂ + 2DMF was calculated by the limiting logarithmic method as 1.80 × 10³ L² mol^?2. The velocity constant at 60°C for the interaction of polystyryl radical with Cu(DMF)₂Br₂ is 7.46 × 10⁴ L mol^?1 s^?1.     

Effect of percolation on electrical and dielectric properties of acrylonitrile butadiene styrene/graphite composite

Prelocalized Acrylonitrile Butadiene Styrene/graphite composites were prepared by hot compression molding technique. The increased conductivity with increase of graphite content exhibits percolation phenomenon. The current–voltage characteristics are found to change from nonlinear to linear above the percolation threshold. A positive temperature coefficient of resistance is observed in these composites, and this effect is more pronounced in samples having graphite concentration near percolation threshold. The dielectric constant was found to increase slowly up to the percolation concentration and beyond it a sudden increase in its value is observed. The dissipation factor exhibits maxima in the vicinity of percolation threshold. The dielectric properties are discussed in terms of the interfacial Maxwell‐Wagner effects. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 2007     

Real-time adaptation of decision thresholds in sensor networks for detection of moving targets

This paper addresses real-time decision-making associated with acoustic measurements for online surveillance of undersea targets moving over a deployed sensor network. The underlying algorithm is built upon the principles of symbolic dynamic filtering for feature extraction and formal language theory for decision-making, where the decision threshold for target detection is estimated based on time series data collected from an ensemble of passive sonar sensors that cover the anticipated tracks of moving targets. Adaptation of the decision thresholds to the real-time sensor data is optimal in the sense of weighted linear least squares. The algorithm has been validated on a simulated sensor-network test-bed with time series data from an ensemble of target tracks.     

Characterization of fretting wear behavior of Cu–Al coating on Ti–6Al–4V substrate

Fretting wear and fretting fatigue are two commonly observed material damages when two contacting bodies with a clamping load are under the oscillatory motion. In this study, fretting wear damage of Cu–Al coating on titanium alloy, Ti–6Al–4V substrate was investigated using the dissipated energy approach. Fretting tests were conducted with either no fatigue load or the maximum fatigue load of 300 MPa and stress ratio of 0.1 on the substrate (specimen). In order to investigate the effect of contact load and contact size, different pad sizes and contact loads were used in the tests. Accumulated dissipated energy versus wear volume data showed a linear relationship regardless of fatigue loading condition on specimen with the smaller pad size. However, two separate linear relationships were observed based on the fatigue loading condition with the larger pad size, such that a relatively more dissipated energy was required for a certain amount of wear with fatigue load on the specimen. The linear relationship between the accumulated dissipated energy and wear volume for both pad sizes extended from partial to gross slip regimes and was not affected by the applied contact load. Further, fretting tests with and without fatigue load resulted in different shapes of fretting loops when the larger pad size was used.     

Antioxidant and antimicrobial activities of essential oil and various oleoresins of Elettaria cardamomum (seeds and pods)

BACKGROUND: This paper describes the chemical analysis of the essential oil and various oleoresins of Elettaria cardamomum (seeds and pods) by gas chromatography (GC) and gas chromatography/mass spectrometry (GC/MS) techniques. It also compares the effects of the different extraction solvents used (chloroform, methanol, ethanol and diethyl ether) on the antioxidant and antimicrobial activities of the essential oil and oleoresins. RESULTS: The essential oil was found to contain 71 compounds. The major components were α‐terpinyl acetate (44.3%), 1,8‐cineole (10.7%), α‐terpineol (9.8%) and linalool (8.6%). The chloroform and methanol oleoresins both contained α‐terpinyl acetate (21.8 and 25.9% respectively) as the main component, while 5‐hydroxymethylfurfural (28.9%) was the most abundant compound in the ethanol oleoresin. However, very few components (total 0.61%) were found in the diethyl ether oleoresin. The antioxidant activities of the essential oil and oleoresins, studied in mustard oil by monitoring the peroxide value of the oil substrate, were comparable to those of the synthetic antioxidants butylated hydroxyanisole (BHA) and butylated hydroxytoluene (BHT) at 0.02% concentration. The essential oil exhibited strong antibacterial activity against the micro‐organisms Staphylococcus aureus, Bacillus cereus, Escherichia coli and Salmonella typhi at 3000 ppm by the agar well diffusion method. Antifungal activity was tested against the food‐borne fungi Aspergillus terreus, Penicillium purpurogenum, Fusarium graminearum and Penicillium madriti. The methanol and ethanol oleoresins gave the best results against A. terreus at 3000 ppm by the poison food method. CONCLUSION: This study provides important information about the chemistry and antioxidant and antimicrobial properties of E. cardamomum. Copyright © 2007 Society of Chemical Industry     

Whirligig beetles as corralled active Brownian particles

We study the collective dynamics of groups of whirligig beetles Dineutus discolor (Coleoptera: Gyrinidae) swimming freely on the surface of water. We extract individual trajectories for each beetle, including positions and orientations, and use this to discover (i) a density-dependent speed scaling like v ∼ ρ^−ν with ν ≈ 0.4 over two orders of magnitude in density (ii) an inertial delay for velocity alignment of approximately 13 ms and (iii) coexisting high and low-density phases, consistent with motility-induced phase separation (MIPS). We modify a standard active Brownian particle (ABP) model to a corralled ABP (CABP) model that functions in open space by incorporating a density-dependent reorientation of the beetles, towards the cluster. We use our new model to test our hypothesis that an motility-induced phase separation (MIPS) (or a MIPS like effect) can explain the co-occurrence of high- and low-density phases we see in our data. The fitted model then successfully recovers a MIPS-like condensed phase for N = 200 and the absence of such a phase for smaller group sizes N = 50, 100.     

Collimation testing by use of the Lau effect coupled with moiré readout

We present an easy, simple, and inexpensive technique for checking the quality of the collimation of optical beams using the Lau effect combined with moiré readout. The experimental arrangement consists of a modified Lau-based interferometer in which a white-light incoherent source illuminates a set of two gratings. A collimating lens is placed between the two gratings such that the self-images of the second grating are formed. The third grating is positioned at one of the self-imaging planes forming moiré fringes. The type of the moiré fringe demonstrates the quality of collimation of the optical beam. The necessary theoretical background is presented and the results of our experimental investigation are reported. The technique can also be used for accurate determination of the focal length of a collimating lens using low-cost components.