@article {358, title = {Technologies Shaping Transportation of the Future and Smart Cities}, year = {2018}, abstract = {

Connected vehicles and smart cities are two massively transformational technologies. We are already seeing an impact with the investments in economy and society in the connected vehicles and cities. While the private-sector leads the connected car technology investments, the technological development and smart cities innovation is led by state and local governments.\ 

}, author = {Davis, Jeff and Johnson, Steve and LeVine, Minna and Ram Dantu and Rojas-Levi, Elizabeth} } @conference {35, title = {Are You a Safe Driver?}, booktitle = {2009 International Conference on Computational Science and Engineering}, year = {2009}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Washington, DC, USA}, abstract = {

As the world continues to enhance and strengthen its emergency services, mobile phones may be used to aid in safe driving practices and detection of emergencies. To our knowledge, no work has been reported in understanding vehicle motion using accelerometers/compass in cell phones. In this paper, we used the multiple sensors in a Google phone to classify safe versus unsafe driving. In particular, we used breaking distance, acceleration, and deceleration for detecting safe verses unsafe braking. Next, we calculated the displacement in the axis perpendicular to the trajectory of vehicle and used it to classify safe and unsafe lane changes. The direction of the phone with respect to the motion of the vehicle is important during calibration of the above measurements, so we used 2D and 3D rotation matrices for transforming device orientation. Future work includes calibration of braking distance, lane changes, and reliable transformation of phone orientation with respect to trajectory of the vehicle.

}, keywords = {Accelerometer, Android, driving, emergency, mobile phone, orientation, safe, unsafe}, isbn = {978-0-7695-3823-5}, doi = {10.1109/CSE.2009.331}, url = {http://dx.doi.org/10.1109/CSE.2009.331}, author = {Langle, Lonnie and Ram Dantu} } @inbook {46, title = {Behavioral Entropy of a Cellular Phone User}, booktitle = {Social Computing, Behavioral Modeling, and Prediction}, year = {2008}, pages = {160{\textendash}167}, publisher = {Springer US}, organization = {Springer US}, address = {Boston, MA}, abstract = {

The increase of advanced service offered by cellular networks draws lots of interest from researchers to study the networks and phone user behavior. With the evolution of Voice over IP, cellular phone usage is expected to increase exponentially. In this paper, we analyze the behavior of cellular phone users and identify behavior signatures based on their calling patterns. We quantify and infer the relationship of a person{\textquoteright}s randomness levels using information entropy based on the location of the user, time of the call, inter-connected time, and duration of the call. We use real-life call logs of 94 mobile phone users collected at MIT by the Reality Mining Project group for a period of nine months. We are able to capture the user{\textquoteright}s calling behavior on various parameters and interesting relationship between randomness levels in individual{\textquoteright}s life and calling pattern using correlation coeffi- cients and factor analysis. This study extends our understanding of cellular phone user behavior and characterizes cellular phone users in forms of randomness level.

}, isbn = {978-0-387-77672-9}, doi = {10.1007/978-0-387-77672-9_18}, url = {http://dx.doi.org/10.1007/978-0-387-77672-9_18}, author = {Phithakkitnukoon, Santi and Husna, Husain and Ram Dantu}, editor = {Liu, Huan and Salerno, John J. and Young, Michael J.} } @conference {34, title = {Classification of Attributes and Behavior in Risk Management Using Bayesian Networks}, booktitle = {2007 IEEE International Conference on Intelligence and Security Informatics (ISI) }, year = {2007}, month = {05/2007}, address = {New Brunswick, NJ}, abstract = {

Security administration is an uphill task to implement in an enterprise network providing secured corporate services. With the slew of patches being released by network component vendors, system administrators require a barrage of tools for analyzing the risk due to vulnerabilities in those components. In addition, criticalities in patching some end hosts raises serious security issues about the network to which the end hosts are connected. In this context, it would be imperative to know the risk level of all critical resources keeping in view the everyday emerging new vulnerabilities. We hypothesize that sequence of network actions by attackers depends on their social and attack profile (behavioral resources such as skill level, time, and attitude). To estimate the types of attack behavior, we surveyed individuals for their ability and attack intent. Using the individuals{\textquoteright} responses, we determined their behavioral resources and classified them as having opportunist, hacker, or explorer behavior. The profile behavioral resources can be used for determining risk by an attacker having that profile. Thus, suitable vulnerability analysis and risk management strategies can be formulated to efficiently curtail the risk from different types of attackers.

}, keywords = {attack behaviour, attack graphs, attack intent, Bayesian network, behavior, belief networks, explorer behavior, hacker behavior, opportunist behavior, profile behavioral resource classification, risk management, Security, security administration, vulnerability analysis}, doi = {10.1109/ISI.2007.379536}, author = {Ram Dantu and Kolan, Prakash and Akl, Robert and Loper, Kall} } @conference {11, title = {Risk Management Using Behavior Based Attack Graphs}, booktitle = {Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC{\textquoteright}04) Volume 2 - Volume 2}, year = {2004}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Washington, DC, USA}, abstract = {

Security administration is an uphill task to implement inan enterprise network providing secured corporateservices. With the slew of patches being released byMicrosoft, HP and other vendors, system administratorsrequire a barrage of tools for analyzing the risk due tothese vulnerabilities. In addition to this, criticalities inpatching some end hosts (eg., in hospitals) raises serioussecurity issues about the network to which the end hostsare connected. In this context, it would be imperative toknow the risk level of all critical resources (e.g., OracleServer in HR department) keeping in view the everydayemerging new vulnerabilities. We hypothesize thatsequence of network actions by an attacker depends onthe social behavior (e.g., skill level, tenacity, financialability). By verifying our hypothesis on hacker emailcommunications, we extended this methodology andcalculated risk level for a small network. Towards thisgoal, we formulated a mechanism to estimate the risklevel of critical resources that may be compromisedbased on attacker behavior. This estimation isaccomplished using behavior based attack graphs. Thesegraphs represent all the possible attack paths to all thecritical resources. Based on these graphs, we calculatethe risk level of a critical resource using Bayesianmethodology and periodically update the subjectivebeliefs about the occurrence of an attack Such acalculated risk level would be a measure of thevulnerability of the resource and it forms an effectivebasis for a system administrator to perform suitablechanges to network configuration. Thus suitablevulnerability analysis and risk management strategiescan be formulated to efficiently curtail the risk fromdifferent types of attacker (script kiddies, hackers,criminals and insiders).

}, isbn = {0-7695-2108-8}, url = {http://dl.acm.org/citation.cfm?id=977403.978389}, author = {Ram Dantu and Loper, Kall and Kolan, Prakash} } @article {275, title = {Parallel algorithms for low-level vision on the homogeneous multiprocessor}, journal = {Computers \& Electrical Engineering}, volume = {20}, year = {1994}, pages = {51 - 60}, abstract = {

In this work, we present the parallel implementation of three low-level vision algorithms, namely smoothing, histogram generation and edge detection by using the Sobel operator on the Homogeneous Multiprocessor. These algorithms were run on the simulator specifically developed for the Homogeneous Multiprocessor and the simulation experiments were used to establish the performance of these algorithms on the proposed architecture.

}, keywords = {Computer architecture, image processing, MIMO architecture, Multiprocessing, pattern recognition, vision}, issn = {0045-7906}, doi = {https://doi.org/10.1016/0045-7906(94)90006-X}, url = {http://www.sciencedirect.com/science/article/pii/004579069490006X}, author = {Ram Dantu and Dimopoulos, Nikitas and Li, Kinfu and Patel, Rajni and Al-Khalili, Asim} }