arXiv subjects

Sujit Dey

Publications and source records attributed to Sujit Dey.

5 recordsLinked to original sources

arXiv2026

Power Estimation and Optimal Work-Charging Scheduling of Construction Electric Vehicles via Mobile Charging Stations

Construction electric vehicles (CEVs) are a promising clean alternative to diesel-powered construction equipment, but their adoption is constrained by sparse onsite charging infrastructure, limited CEV mobility, and insufficient understanding of their power consumption. We address these gaps through a field-data-driven framework coupling CEV power estimation with mobile-charging-aware work scheduling. First, using a real-world construction demonstration at the University of California, San Diego, we develop and validate a per-subactivity power estimation model for a compact electric excavator. Manually labeled video is synchronized with coarse battery state-of-charge (SOC) telematics, and constrained nonnegative least squares is used to recover each subactivity's average power consumption. The model predicts held-out test data within $17\%$ normalized mean absolute error (NMAE), and the accompanying dataset is released publicly. Second, leveraging the subactivity power estimates, we formulate a mixed-integer program that jointly optimizes CEV work and charging schedules together with the location, timing, and charging/discharging of mobile charging stations (MCSs) serving the CEVs. The optimization accounts for energy and demand charges, carbon emissions, unmet work penalties, MCS travel, and the physical and operational constraints of the CEVs and MCSs. Across realistic scenarios drawn from the demonstration, the proposed co-optimization attains the lowest operating cost in every case, being $7$--$96\%$ below the best-performing baseline, while solving most instances to proven optimality within an hour. Dataset and scripts are available at https://github.com/ghosh-avik/CEV-MCS-Power-Estimation-and-Joint-Scheduling.

eess.SY↗

arXiv2025

JExplore: Design Space Exploration Tool for Nvidia Jetson Boards

Nvidia Jetson boards are powerful systems for executing artificial intelligence workloads in edge and mobile environments due to their effective GPU hardware and widely supported software stack. In addition to these benefits, Nvidia Jetson boards provide large configurability by giving the user the choice to modify many hardware parameters. This large space of configurability creates the need of searching the optimal configurations based on the user's requirements. In this work, we propose JExplore, a multi-board software and hardware design space exploration tool. JExplore can be integrated with any search tool, hence creating a common benchmarking ground for the search algorithms. Moreover, it accelerates the exploration of user application and Nvidia Jetson configurations for researchers and engineers by encapsulating host-client communication, configuration management, and metric measurement.

cs.AR↗

arXiv2023

Body and Head Orientation Estimation from Low-Resolution Point Clouds in Surveillance Settings

We propose a system that estimates people's body and head orientations using low-resolution point cloud data from two LiDAR sensors. Our models make accurate estimations in real-world conversation settings where subjects move naturally with varying head and body poses, while seated around a table. The body orientation estimation model uses ellipse fitting while the head orientation estimation model combines geometric feature extraction with an ensemble of neural network regressors. Our models achieve a mean absolute estimation error of 5.2 degrees for body orientation and 13.7 degrees for head orientation. Compared to other body/head orientation estimation systems that use RGB cameras, our proposed system uses LiDAR sensors to preserve user privacy, while achieving comparable accuracy. Unlike other body/head orientation estimation systems, our sensors do not require a specified close-range placement in front of the subject, enabling estimation from a surveillance viewpoint which produces low-resolution data. This work is the first to attempt head orientation estimation using point clouds in a low-resolution surveillance setting. We compare our model to two state-of-the-art head orientation estimation models that are designed for high-resolution point clouds, which yield higher estimation errors on our low-resolution dataset. We also present an application of head orientation estimation by quantifying behavioral differences between neurotypical and autistic individuals in triadic (three-way) conversations. Significance tests show that autistic individuals display significantly different behavior compared to neurotypical individuals in distributing attention between conversational parties, suggesting that the approach could be a component of a behavioral analysis or coaching system.

cs.CV↗

arXiv2023

Optimal Vehicle Charging in Bilevel Power-Traffic Networks via Charging Demand Function

Electric vehicle (EV) charging couples the operation of power and traffic networks. Specifically, the power network determines the charging price at various locations, while EVs on the traffic network optimize the charging power given the price, acting as price-takers. We model such decision-making processes by a bilevel program, with the power network at the upper-level and the traffic network at the lower-level. However, since the two networks are managed by separate entities and the charging expense term, calculated as the product of charging price and charging demand, is nonlinear. Solving the bilevel program is nontrivial. To overcome these challenges, we derive the charging demand function using multiparametric programming theory. This function establishes a piecewise linear relationship between the charging price and the optimal charging power, enabling the power network operator to manage EV charging power independently while accounting for the coupling between the two networks. With the derived function, we are also able to replace the nonlinear charging expense term with a piecewise quadratic one, thus guaranteeing solution optimality. Our numerical studies demonstrate that different traffic demands can have an impact on charging patterns and the power network can effectively incentivize charging at low-price nodes through price setting.

eess.SY↗

arXiv2016

On Tie Strength Augmented Social Correlation for Inferring Preference of Mobile Telco Users

For mobile telecom operators, it is critical to build preference profiles of their customers and connected users, which can help operators make better marketing strategies, and provide more personalized services. With the deployment of deep packet inspection (DPI) in telecom networks, it is possible for the telco operators to obtain user online preference. However, DPI has its limitations and user preference derived only from DPI faces sparsity and cold start problems. To better infer the user preference, social correlation in telco users network derived from Call Detailed Records (CDRs) with regard to online preference is investigated. Though widely verified in several online social networks, social correlation between online preference of users in mobile telco networks, where the CDRs derived relationship are of less social properties and user mobile internet surfing activities are not visible to neighbourhood, has not been explored at a large scale. Based on a real world telecom dataset including CDRs and preference of more than $550K$ users for several months, we verified that correlation does exist between online preference in such \textit{ambiguous} social network. Furthermore, we found that the stronger ties that users build, the more similarity between their preference may have. After defining the preference inferring task as a Top-$K$ recommendation problem, we incorporated Matrix Factorization Collaborative Filtering model with social correlation and tie strength based on call patterns to generate Top-$K$ preferred categories for users. The proposed Tie Strength Augmented Social Recommendation (TSASoRec) model takes data sparsity and cold start user problems into account, considering both the recorded and missing recorded category entries. The experiment on real dataset shows the proposed model can better infer user preference, especially for cold start users.

cs.SI↗