Faculty Publications
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Item Bio-fuel variants for use in CI engine at design and off-design regimes: An experimental analysis(2008) Bekal, S.; Ashok Babu, T.P.A.In this work an attempt has been made to study the ester based fuel variants derived from edible and inedible oil sources for identifying the most appropriate fuel variant and operating mode for running a CI engine based on performance and emission parameters. The twenty four fuel variants tested included esters obtained from the edible sunflower oil, inedible pongamia oil, and their higher and lower proportional blends with diesel. Besides, several other fuel variants obtained from the emulsification of water-in-ester (W/E) with different water proportions have been tested. Basing upon three operational variables, namely, injection timing, injection pressure, and load, comparisons are made in aspects of smoke emissions, NOX emissions, BSEC, and exhaust gas temperatures at the best injection timing. 21.5°, 23°, 24.5° and 27.5° bTDC as the four injection timings and 190, 220 and 250 bar as three injection pressures are considered for the overall study. The 264 sets of experiments conducted with these combinations, focussing on the full and partial load characteristics of the engine, show that both sunflower and pongamia oil esters exhibited similar characteristics in their engine performance, and in both the cases the best BSEC occurred with 220 bar injection pressure for most of the fuel variants, and for straight fuels the ideal injection timing found to be slightly retarded (1.5° crank angle) compared to diesel. However, 24.5° bTDC, normal for the engine, was found to be the most appropriate for the lower blends like B2 (2% ester by volume), B5 and emulsion with 10% water proportion. © 2008 Elsevier Ltd. All rights reserved.Item Experimental analysis of SI engine performance and emission characteristics with gasoline-denatured spirit blends as alternative fuels(2010) Hubballi, P.A.; Ashok Babu, T.P.The experimental study focused on investigating benefits of unleaded gasoline (P100) - denatured spirit [DNS (ethanol 93.3% v/v + water 6.7% v/v)] blends as fuel in a four cylinder four stroke SI engine. Performance tests were conducted to study volumetric efficiency (VolE), brake thermal efficiency (BThE), brake power (BP), engine torque (torque), brake specific fuel consumption (BSFC). Engine exhaust emissions were investigated for carbon monoxide (CO), hydrocarbons (HC), oxides of nitrogen (NOx) and carbon dioxide (CO2). Experiments were conducted at different engine speeds between 2500 - 4500 rpm maintaining throttle position of 50% throughout the experiments. The fuel blends used include DNS30P70 (ethanol 28 % + water 2% + gasoline 70 %), DNS50P50 (ethanol 46.65 % + water 3.35 % + gasoline 50 %) and DNS85P15 (ethanol 79.3 % + water 5.7 % + gasoline 15 %) which were compared with base fuel P100. The investigations revealed that blending DNS with P100 increases BThE, VolE, BP, torque and BSFC. The CO, HC, NOx and CO2 emissions in the exhaust decrease when compared to P100 operation. The DNS85P15 blend produced encouraging results in improved engine performance and decreased engine exhaust emission.Item An experimental evaluation of heuristic algorithms for bus-depot matching problem of urban road transport systems(2010) Mathirajan, M.; Hariharakrishnan, C.V.; Ramachandran, V.This research is motivated by a bus-depot matching problem observed in Urban Road Transport Systems (URTS). In URTS, buses are parked overnight at depots. Starting points of routes are usually different from depot locations. A bus has to cover the distance from its depot to the starting point of its route before being engaged on regular service. Likewise, buses usually do not provide service to the depot at the end of the service period. The distance travelled by a bus in a day from a depot to a starting terminus and/or from the ending or last terminus back to the depot without carrying passengers is known as 'dead kilometers'. The dead kilometers can be reduced by efficiently allocating the buses to depots. In the literature this problem is solved using mathematical model and heuristic algorithms. However, there is no detailed computational analysis to highlight the merits and demerits of various solution methodologies, so far addressed in the literature. In this study a set of heuristic algorithms are considered to make an efficient decisions in buses-depots matching problem. A computational experiment is carried out to understand the efficiency of the heuristic algorithms considered in this study for various large size problems in comparison with exact solutions. From the computational analysis, two out of the five heuristic algorithms considered in this study, resulted very close to exact solutions in most of the problem instances. All the heuristic algorithms considered in this study takes very meager computational time in Pentium IV for the large size problem of 30 depots and 5,031 buses considered in this study. © Operational Research Society of India 2011.Item A tree based representation for effective pattern discovery from multimedia documents(Elsevier B.V., 2017) Pushpalatha, K.; Ananthanarayana, A.The growing amount of multimedia documents demanded the efficient knowledge discovery systems. The efficacy of the knowledge discovery systems is influenced by the representation of multimedia documents. The suitable multimedia document representation acts as a platform for multimedia mining tasks. In this paper, a Multimedia Suffix Tree Document model (MSTD) is presented to represent the multimedia documents in a tree based structure. The MSTD model discovers the useful patterns embedded in the multimedia documents and reduces the search time thereby aiding the multimedia mining methods. It provides the complete information of the multimedia documents in one structure. In order to evaluate the proficiency of the proposed MSTD model, the MSTD model based mining methods are proposed. The experiments are conducted with three multimodal multimedia document datasets. The experimental analysis of the proposed methods reveal the significance of MSTD representation for multimedia documents in achieving the significant performance of multimedia mining tasks. © 2016 Elsevier B.V.Item Guided SAR image despeckling with probabilistic non local weights(Elsevier Ltd, 2017) Gokul, J.; Nair, M.S.; Rajan, J.SAR images are generally corrupted by granular disturbances called speckle, which makes visual analysis and detail extraction a difficult task. Non Local despeckling techniques with probabilistic similarity has been a recent trend in SAR despeckling. To achieve effective speckle suppression without compromising detail preservation, we propose an improvement for the existing Generalized Guided Filter with Bayesian Non-Local Means (GGF-BNLM) method. The proposed method (Guided SAR Image Despeckling with Probabilistic Non Local Weights) replaces parametric constants based on heuristics in GGF-BNLM method with dynamically derived values based on the image statistics for weight computation. Proposed changes make GGF-BNLM method adaptive and as a result, significant improvement is achieved in terms of performance. Experimental analysis on SAR images shows excellent speckle reduction without compromising feature preservation when compared to GGF-BNLM method. Results are also compared with other state-of-the-art and classic SAR depseckling techniques to demonstrate the effectiveness of the proposed method. © 2017 Elsevier LtdItem Dynamic video anomaly detection and localization using sparse denoising autoencoders(Springer New York LLC barbara.b.bertram@gsk.com, 2018) Narasimhan, M.G.; Kamath S?, S.The emergence of novel techniques for automatic anomaly detection in surveillance videos has significantly reduced the burden of manual processing of large, continuous video streams. However, existing anomaly detection systems suffer from a high false-positive rate and also, are not real-time, which makes them practically redundant. Furthermore, their predefined feature selection techniques limit their application to specific cases. To overcome these shortcomings, a dynamic anomaly detection and localization system is proposed, which uses deep learning to automatically learn relevant features. In this technique, each video is represented as a group of cubic patches for identifying local and global anomalies. A unique sparse denoising autoencoder architecture is used, that significantly reduced the computation time and the number of false positives in frame-level anomaly detection by more than 2.5%. Experimental analysis on two benchmark data sets - UMN dataset and UCSD Pedestrian dataset, show that our algorithm outperforms the state-of-the-art models in terms of false positive rate, while also showing a significant reduction in computation time. © 2017, Springer Science+Business Media, LLC.Item Experimental analysis on exergy studies of flow through a minichannel using Tio2/Water nanofluids(Elsevier Ltd, 2018) Narendran, G.; Bhat, M.M.; Akshay, L.; Arumuga Perumal, D.A.The present study involves an experimental investigation on rectangular minichannel heat sink for processor cooling of a workstation. The thermal dissipation power of the corresponding system is 25 W. The heat sink is directly in contact to the processor core and subjected to continuous increase in heat flux to the sink depending on the system loading. Water and TiO2 nanofluid with volume fraction of 0.10%, 0.15%, 0.21% and 0.25% is used as the cooling fluid in the experiments with different volume flow rates with a pulsating pump in the range of 210–400 ml/min respectively. The observations were performed with the sink in both horizontal and vertical position in which heat sink is allowed to reach two different temperature limits of 40 °C and 55 °C above which it is subjected to cooling. The Increase in minichannel efficiency was noticed when flowrate increased from 210 ml/min to 280 ml/min with an increment of 53%, but it started to reduce when flow rate approaches 360 ml/min. The outlet exergy and pumping power increases as the flow rate increases to a limit. Furthermore, decrease in efficiency was noticed beyond flow rate of 360 ml/min and the highest outlet exergy was found at a flow rate of 360 ml/min for about 147.52 W. Additionally, exergy analysis is performed for pure fluid under different flow conditions were examined. Further the effect of nanofluid on pressure drop subjected to pulsating flow for varying volume concentrations is also presented. © 2018 Elsevier LtdItem Performance analysis of a semi-active suspension system using coupled CFD-FEA based non-parametric modeling of low capacity shear mode monotube MR damper(SAGE Publications Ltd, 2019) Gurubasavaraju, G.; Kumar, H.; Mahalingam, A.In this work, an approach for formulation of a non-parametric-based polynomial representative model of magnetorheological damper through coupled computational fluid dynamics and finite element analysis is presented. Using this, the performance of a quarter car suspension subjected to random road excitation is estimated. Initially, prepared MR fluid is characterized to obtain a relationship between the field-dependent shear stress and magnetic flux density. The amount of magnetic flux induced in the shear gap of magnetorheological damper is computed using finite element analysis. The computed magnetic field is used in the computational fluid dynamic analysis to calculate the maximum force induced under specified frequency, displacement and applied current using ANSYS CFX software. Experiments have been conducted to verify the credibility of the results obtained from computational analysis, and a comparative study has been made. From the comparison, it was found that a good agreement exists between experimental and computed results. Furthermore, the influence of fluid flow gap length and frequency on the induced force of the damper is investigated using the computational methods (finite element analysis and computational fluid dynamic) for various values. This proposed approach would serve in the preliminary design for estimation of magnetorheological damper dynamic performance in semi-active suspensions computationally prior to experimental analysis. © IMechE 2018.Item Experimental analysis of Android malware detection based on combinations of permissions and API-calls(Springer-Verlag France 22, Rue de Palestro Paris 75002, 2019) Singh, A.K.; Jaidhar, C.D.; M.a, M.A.A.Android-based smartphones are gaining popularity, due to its cost efficiency and various applications. These smartphones provide the full experience of a computing device to its user, and usually ends up being used as a personal computer. Since the Android operating system is open-source software, many contributors are adding to its development to make the interface more attractive and tweaking the performance. In order to gain more popularity, many refined versions are being offered to customers, whose feedback will enable it to be made even more powerful and user-friendly. However, this has attracted many malicious code-writers to gain anonymous access to the user’s private data. Moreover, the malware causes an increase of resource consumption. To prevent this, various techniques are currently being used that include static analysis-based detection and dynamic analysis-based detection. But, due to the enhancement in Android malware code-writing techniques, some of these techniques are getting overwhelmed. Therefore, there is a need for an effective Android malware detection approach for which experimental studies were conducted in the present work using the static features of the Android applications such as Standard Permissions with Application Programming Interface (API) calls, Non-standard Permissions with API-calls, API-calls with Standard and Nonstandard Permissions. To select the prominent features, Feature Selection Techniques (FSTs) such as the BI-Normal Separation (BNS), Mutual Information (MI), Relevancy Score (RS), and the Kullback-Leibler (KL) were employed and their effectiveness was measured using the Linear-Support Vector Machine (L-SVM) classifier. It was observed that this classifier achieved Android malware detection accuracy of 99.6% for the combined features as recommended by the BI-Normal Separation FST. © 2019, Springer-Verlag France SAS, part of Springer Nature.Item PhishDump: A multi-model ensemble based technique for the detection of phishing sites in mobile devices(Elsevier B.V., 2019) Rao, R.S.; Vaishnavi, T.; Pais, A.R.Phishing is a technique in which the attackers trick the online users to reveal the sensitive information by creating the phishing sites which look similar to that of legitimate sites. There exist many techniques to detect phishing sites in desktop computers. In recent years, the number of mobile users accessing the web has increased which lead to a rise in the number of attacks in mobile devices. Existing techniques designed for desktop computers may not be suitable for mobile devices due to their hardware limitations such as RAM, Screen size, low computational power etc. In this paper, we propose a mobile application named PhishDump to classify the legitimate and phishing websites in mobile devices. PhishDump is based on the multi-model ensemble of Long Short Term Memory (LSTM) and Support Vector Machine (SVM) classifier. As PhishDump focuses on the extraction of features from URL, it has several advantages over existing works such as fast computation, language independence and robust to accidental download of malwares. From the experimental analysis, we observed that our proposed multi-model ensemble outperformed traditional LSTM character and word-level models. PhishDump performed better than the existing baseline models with an accuracy of 97.30% on our dataset and 98.50% on the benchmark dataset. © 2019 Elsevier B.V.