Design and development of microstrip patch antenna with circular and rectangular slot for structural health monitoring

Structural health monitoring (SHM) is the technique to identify the damage in the building or physical structures. Several structural health monitoring has been proposed in recent years. Increase in number of sensors and its wiring mechanism causes issue in signal processing demand, efficiency, and unreliability. In current state, already present sensors cannot be used for Structural Health Monitoring because of its high cost and low battery capacity, although none of these techniques are capable of monitoring the condition of complex physical structures. The measurement of strain can be done by using patch antenna as strain sensors. In currently available sensors only the wireless range. The main objective of this paper is to investigate the feasibility of using circular and rectangular slotted microstrip patch antenna as a strain sensor for structural health monitoring without these afore mentioned problems. These antenna sensors’ (slotted rectangular and circular microstrip patch antennas) simulation results are confirmed. From the simulation results, the sensor provides information about crack orientation.     

Matrix method for two-dimensional waveguide mode solution

In this paper, we show that the transfer matrix theory of multilayer optics can be used to solve the modes of any two-dimensional (2D) waveguide for their effective indices and field distributions. A 2D waveguide, even composed of numerous layers, is essentially a multilayer stack and the transmission through the stack can be analysed using the transfer matrix theory. The result is a transfer matrix with four complex value elements, namely A, B, C and D. The effective index of a guided mode satisfies two conditions: (1) evanescent waves exist simultaneously in the first (cladding) layer and last (substrate) layer, and (2) the complex element D vanishes. For a given mode, the field distribution in the waveguide is the result of a ‘folded’ plane wave. In each layer, there is only propagation and absorption; at each boundary, only reflection and refraction occur, which can be calculated according to the Fresnel equations. As examples, we show that this method can be used to solve modes supported by the multilayer step-index dielectric waveguide, slot waveguide, gradient-index waveguide and various plasmonic waveguides. The results indicate the transfer matrix method is effective for 2D waveguide mode solution in general.     

Assessment of early onset of driver fatigue using multimodal fatigue measures in a static simulator

Driver fatigue is an important contributor to road accidents. This paper reports a study that evaluated driver fatigue using multimodal fatigue measures, i.e., surface electromyography (sEMG), electroencephalography (EEG), seat interface pressure, blood pressure, heart rate and oxygen saturation level. Twenty male participants volunteered in this study by performing 60 min of driving on a static simulator. Results from sEMG showed significant physical fatigue (ρ < 0.05) in back and shoulder muscle groups. EEG showed significant (ρ < 0.05) increase of alpha and theta activities and a significant decrease of beta activity during monotonous driving. Results also showed significant change in bilateral pressure distribution on thigh and buttocks region during the study. These findings demonstrate the use of multimodal measures to assess early onset of fatigue. This will help us understand the influence of physical and mental fatigue on driver during monotonous driving.     

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space–time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBL-McRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multi-modal Human Action Database (MHAD).     

Surface treatments for pistons and their effect on engine performance

Surface treatment alters the frictional behaviour of pistons in I.C. engines and can be used to improve engine performance. Surface treatments applied to aluminium alloy pistons of a high speed diesel engine and their effect on the engine performance are described. Certain piston surface treatments improve engine performance and also reduce the run-in period.     

Detailed analysis on emission and performance characteristics of neat biofuel-fuelled diesel engine

ABSTRACT

There is an entanglement over the rapid exhaust of fossil fuel and soreness of environmental changes. Biofuels are acting as an alternative resource for petroleum products and also salve of emissions control and engine performance improvement. Scholars have seen the supreme use of bio-fuel apparent, as it will influence greenhouse effect. Investigation results show the diminished heating value in congruence with conventional pabulum, so it had depleted more in brake mean effective-fuel power ratio and proliferated NO_x compared with diesel fuel. The article mainly focuses on the selection – process of biofuel and analysis of performance (BSFC, EGT and brake thermal efficiency), emissions (CO, NO_x, CO₂, PM and HC) and combustion (NHR and CP) of the engine are exclusively discussed and summarised. Finally, stability, opportunity, and restraint of a selection of alternative fuel and investigation and study on the engine were asserted to guide further future exploration and evolution in that domain.     

Anomaly detection in large-scale data stream networks

This paper addresses the anomaly detection problem in large-scale data mining applications using residual subspace analysis. We are specifically concerned with situations where the full data cannot be practically obtained due to physical limitations such as low bandwidth, limited memory, storage, or computing power. Motivated by the recent compressed sensing (CS) theory, we suggest a framework wherein random projection can be used to obtained compressed data, addressing the scalability challenge. Our theoretical contribution shows that the spectral property of the CS data is approximately preserved under a such a projection and thus the performance of spectral-based methods for anomaly detection is almost equivalent to the case in which the raw data is completely available. Our second contribution is the construction of the framework to use this result and detect anomalies in the compressed data directly, thus circumventing the problems of data acquisition in large sensor networks. We have conducted extensive experiments to detect anomalies in network and surveillance applications on large datasets, including the benchmark PETS 2007 and 83 GB of real footage from three public train stations. Our results show that our proposed method is scalable, and importantly, its performance is comparable to conventional methods for anomaly detection when the complete data is available.     

Preparation and evaluation of radioiodinated thermoresponsive polymer based on poly(N‐isopropyl acrylamide) for radiotherapy

A thermoresponsive polymer based on poly N‐isopropylacrylamide (PNIPAM) was synthesized and radioiodinated to explore its potential use in localized radiotherapy. The synthesized PNIPAM polymer was functionalized with L ‐tyrosinamide to facilitate radioiodination. The content of tyrosinamide groups in the polymer was assayed spectrophotometrically (λ = 275nm). The functionalized polymer showed a cloud point temperature of 29–31°C and phase separation at 35°C, as revealed by Differential Scanning Calorimetry (DSC) and Dynamic Light Scattering (DLS). The phase transition temperature is conducive for preferential localization of the polymer at the site of injection due to changes in the polymer conformation at body temperature. For in vivo demonstration, the biodistribution studies of radioiodinated polymer were carried out in Swiss mice bearing fibrosarcoma tumor. Biodistribution studies showed a retention of 30% of the injected labeled polymer, PNIPAM‐¹²⁵I‐tyrosinamide, in the solid tumor tissues after 2 h of intratumoral injection. Although the activity decreased with time, 3–4% of the injected dose (i.d) was found to be retained in the tumor on 5 d post injection. The results suggest the potential use of thermosensitive polymer based on poly N‐isopropylacrylamide for locoregional radionuclide therapy. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 860‐868, 2013     

Bulk temperature estimation during wear of a polymer composite pin

Various models have been proposed to estimate the bulk temperatures along the axis of a pin wearing against a disc in a pin-on-disc tribometer. These have been generally addressing the estimation of temperature for monolithic materials. The current investigation addresses the estimation of the bulk temperatures for a composite material using the rule of mixtures and corroborates the same with experimental verification.