Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item A study of performance scalability by parallelizing loop iterations on multi-core SMPs(2010) Raghavendra, P.S.; Behki, A.K.; Hariprasad, K.; Mohan, M.; Jain, P.; Bhat, S.S.; Thejus, V.M.; Prabhu, V.Today, the challenge is to exploit the parallelism available in the way of multi-core architectures by the software. This could be done by re-writing the application, by exploiting the hardware capabilities or expect the compiler/software runtime tools to do the job for us. With the advent of multi-core architectures ([1] [2]), this problem is becoming more and more relevant. Even today, there are not many run-time tools to analyze the behavioral pattern of such performance critical applications, and to re-compile them. So, techniques like OpenMP for shared memory programs are still useful in exploiting parallelism in the machine. This work tries to study if the loop parallelization (both with and without applying transformations) can be a good case for running scientific programs efficiently on such multi-core architectures. We have found the results to be encouraging and we strongly feel that this could lead to some good results if implemented fully in a production compiler for multi-core architectures. © Springer-Verlag Berlin Heidelberg 2010.Item Bithiophene based red light emitting material - Photophysical and DFT studies(American Institute of Physics Inc. subs@aip.org, 2019) Mohan, M.; Satyanarayan, M.N.; Trivedi, D.Bithiophene based red fluorescent light emitting material BTCN has been synthesized by Schiff base condensation reaction and characterized by standard spectroscopic techniques. The effect of -CN substituted amino pyrazole unit covalently linked to bithiophene moiety enhances the emission intensity in the system. In solid state BTCN exhibits an emission wavelength of 651nm with 170nm FWHM. Cyclic voltammogram shows the HOMO energy level of the BTCN to be -5.59 eV with LUMO around -3.24 eV. DFT optimized geometry of BTCN possesses a high amount of planarity in their structure and TD-DFT estimates the nature of electronic transitions occurring in the system. Overall, BTCN can act as good red light emitting material for organic light emitting applications. © 2019 Author(s).Item Techniques to Secure Address Resolution Protocol(Institute of Electrical and Electronics Engineers Inc., 2020) Selvarajan, S.; Mohan, M.; Chandavarkar, B.R.Address Resolution Protocol was developed to create a standard for translating IP addresses to physical addresses. ARP takes (IP, Protocol) as input and converts to physical address. ARP can be easily spoofed because it lacks security. The inventors of ARP thought that internal to the network threats were minimum, and ARP had to be simple for its efficient and dynamic working. A machine in the network, which can work at the data link layer, can be easily spoofed because of the vulnerability in ARP protocol, leading to a man-in-the-middle attack. Securing ARP is not an easy task because state information should be preserved for authentication of ARP frames. However, the protocol is stateless, and making changes to the ARP protocol itself is not practical since the protocol is currently being widely used. Our objective in this paper is to provide a solution to detect and mitigate ARP spoofing attacks without any changes to the protocol itself. The proposed system provides improvement to an existing solution using ICMP to detect ARP spoofing. © 2020 IEEE.Item A Clustering-based model for the Generation of Diversified Recommendations(Institute of Electrical and Electronics Engineers Inc., 2022) Chaitanya, V.S.; Mohan, M.; Santhi Thilagam, P.S.The primary goal of a recommender system is to generate accurate recommendations according to the user's interests. But the user's satisfaction increases when they get a chance to view the diverse categories of items. There exist several works on the generation of diverse recommendations but the performance of these methods often gets limited due to the issues such as cold start, filter bubble long tail, and grey sheep. Moreover, these methods do not consider the user's preference regarding exploration and exploitation while generating the recommendations. To this extent, this work proposes a model known as the iterative clustering-based diversity model, which can generate diverse recommendations and also solve the above-said issues. It groups the items based on the item description using the TF-IDF algorithm. The model generates two recommendations in such a way that one recommendation is similar and the other is different in comparison with the last interaction made by the user. The model has been evaluated on the benchmark dataset and has achieved promising results. © 2022 IEEE.Item Machine Learning-Based Gap Acceptance Model for Uncontrolled Intersections Under Mixed Traffic Conditions(Springer Science and Business Media Deutschland GmbH, 2023) Arathi, A.R.; Harikrishna, M.; Mohan, M.Uncontrolled intersections are the most common type of intersections in a transportation network. The study modeled minor road driver’s decision of accepting or rejecting a gap at four-legged uncontrolled intersections having similar geometric characteristics using Artificial Neural Network (ANN) model, Logistic Regression (LR) model, and Support Vector Machine (SVM) model. The results reveal that the performance of LR and SVM models are somewhat similar, while the performance of ANN model exceeds the performance of both LR and SVM models with a correct prediction of about 96.2%. Also, the higher values of the goodness of fit measures like F1 score and R2 value together with a lower value of MSE show that ANN model is better in distinguishing between the classes. The variable gap duration has a major influence on model prediction comparing to other variables. The effect of the critical gap, occupancy time, conflicting volume, and vehicle type are also found remarkable. © 2023, Transportation Research Group of India.Item Reduction of Vehicular Emission at Urban Road Junctions Through Traffic Interventions(Springer Science and Business Media Deutschland GmbH, 2023) Rahman, S.; Mohan, M.The road transportation sector is one of the dominant sources of vehicular emission, which is causing very high levels of air pollution. Any technique to effectively control pollution is only possible by precisely estimating vehicle exhaust emissions. The objective of the research is to estimate vehicular emissions near the signalized intersection under the effect of traffic, control, vehicle, and road characteristics. This will enable to establish the link between emissions and the most likely influencing and measurable characteristics of Indian traffic conditions. The simulation results generated in VISSIM are imported into EnViVer to calculate the total emissions and emissions of individual vehicle classes. By simulating various combinations of vehicular, traffic, geometric, and control conditions at the intersection, the researcher will be able to arrive at the optimal combination that will result in minimal vehicular emission. The mathematical models that could be developed out of the research will be helpful to field practitioners in selecting the best strategy to tackle air pollution resulting from vehicular traffic. CO2 emission reduces significantly by 45.79% by decreasing 2W (Two-wheeler) and cars in the traffic stream by 75% and substituting these with busses. Further, a reduction of pollution levels by 91.1% occurs when all conventional cars are replaced by electric vehicles. Hence, encouraging the use of public transportation and the adoption of electric-powered vehicles could be the right step to tackle the ever-increasing pollution levels on Indian roads. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Impact of Leading Vehicles of the Queue on Saturation Flow at Signalized Intersections(Springer Science and Business Media Deutschland GmbH, 2024) Kumar, R.; Mohan, M.Saturation flow is an essential component in the design and capacity estimation of signalized intersections. The saturation flow rate is influenced by several factors, including intersection geometry, speed limit, the proportion of heavy vehicles, parking activities, bus stops adjacent to the intersection area, lane utilization, pedestrian volume, etc. Estimation of saturation flow is well-established for homogeneous traffic conditions. However, measuring and modelling saturation flow are challenging in mixed traffic conditions due to the non-lane-based traffic flow and the presence of multiple types of vehicles. Typically, Passenger Car Unit (PCU) adopted should be enough to account for the variation among the vehicles that constitute the queue while estimating saturation flow. However, field observation at Indian intersections indicated that the vehicles at the front of the queue might profoundly influence the queue discharge at the onset of green phase, thereby exposing the shortcoming of PCUs. This influence is clearly visible when heavy vehicles make up the front of the queue and owing to their inferior operational performance and larger size, they deny the following vehicles the opportunity to attain their desired acceleration. To investigate this, the present study simulated the operation of a typical four-legged signalized intersection in VISSIM software. The subsequent analysis revealed a strong relationship between leaders of the queue and the saturation flow of the approach. This result from the simulation was then verified based on field data. The study concluded that the PCU factors adopted at signalized intersections fail to account for the effect of the queue leader’s composition, which has a marked influence on the saturation flow. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Driver Skill Profiling Using Machine Learning(Springer Science and Business Media B.V., 2024) Akhtar, N.; Mohan, M.Road safety is a critical aspect of public safety, and driving skills are essential to ensuring safety on the road. An accurate understanding of one's driving abilities is crucial in promoting safe driving practices and reducing the risk of accidents. Overconfidence and underestimating road events can lead to a false sense of handling emergencies and may result in a higher risk of traffic offenses and accidents. Young and novice drivers are particularly susceptible to these issues and may overestimate their abilities, leading to a higher risk tolerance. Machine learning is a viable approach that can compare perceived and actual skills to measure subjective driving skills accurately. A scoring system based on machine learning algorithms can quantify driver skills effectively and improve self-awareness, ultimately contributing to increased road safety. The proposed scoring system can give drivers an accurate assessment of their abilities, helping them take necessary corrective actions to work on their weaknesses. Driving style, encompassing violations, errors, and lapses, and driving skills, including perceptual motor skills and safety skills, are the two main components of the human factor in driving. Training sessions may be conducted based on the proposed scoring system using machine learning that can help improve drivers' self-awareness and reduce the risk of accidents. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Factors Influencing Post-encroachment Time of Road Crossing Pedestrians Near Bus Stops Located on Mid-Block Sections(Springer Science and Business Media Deutschland GmbH, 2024) Ajaykrishnan, M.J.; Sethulakshmi, G.; Mohan, M.The increase in road traffic poses a significant threat to vulnerable road users, heightening the demand for their safety. According to the recent Road Accidents Statistics (MoRTH, 2020), out of the total road accidents reported in 2020, 15.8% involved pedestrians, and 65.1% occurred on straight road sections. This highlights the difficulty and lack of safety for pedestrians. Lack of visibility is the major cause of most accidents, and in the case of bus stops, parked buses affect the visibility of crossing pedestrians. In this study, traffic safety of crossing pedestrians is analysed using the proactive approach, which identifies an observable non-crash event that could have led to a conflict. Post-Encroachment Time (PET) is the most popular time-based measure that is the least time-consuming for accurate estimation of surrogate safety. This paper focuses on determining factors which affect PET of pedestrian crossings at mid-block sections with designated or undesignated bus stops. Videographic surveys were conducted at two mid-block sections with bus stops in Kerala, where accidents to pedestrians are frequent. Kinovea software was used to extract data from the recorded video samples, and IBM SPSS software was used to outline the factors influencing PET. The study found PET of crossing pedestrians to have a high positive correlation with the time taken for the vehicle to approach the crossing pedestrian, while other factors like evasive action and compliance behaviour were negatively correlated. The study proposed a model that will enable the easy computation of PET values of crossing pedestrians. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Classification of Longitudinal Driving Events Using Vehicle Response Signals for Profiling Driving Behaviors(Springer Science and Business Media Deutschland GmbH, 2025) Arichandran, R.; Krishnakar, H.S.; Kumar, A.; Mohan, M.Driving behavior profiling (DBP) involves evaluating driving patterns to determine a safety score for drivers. Proper classification of driving events increases the accuracy of profiling driving behaviors. Most driving event classification models consider lateral driving events, such as turning and lane changes. In heavy traffic conditions, it is impossible to perform lateral events independent of the position of other vehicles. This research aims to develop a model for classifying longitudinal driving events (acceleration and braking) and nonevents using vehicle response signals. To develop the model, naturalistic driving data were collected using a passenger car on a 19 km road stretch. Vehicle response signals were collected using Inertial Measurement Unit (IMU) sensors fixed on the test vehicle with a frequency of approximately 200 Hz with timestamps. The driver’s pedal operation was also captured with timestamps using a camera to map the ground truth labels with vehicle response signals. The data were collected from 5 drivers, totaling a dataset for approximately 190 km. The start and end times of all 634 events (444 driving events and 190 nonevents) were used to label the driving events in the IMU sensor data. These labeled driving events were split into the train (476 events) and test (158 events) datasets. Hidden Markov Model (HMM) algorithm was used to develop classification models for the driving events. The models were developed for various combinations of accelerations using the training dataset. The accuracy of these models was then compared to a test dataset. The models achieved 90.99% and 77.08% accuracy, respectively, in classifying events and nonevents using data from the accelerometer. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.