Roadside LiDAR (Light Detection and Ranging) sensors promise safer and faster traffic management and vehicular operations. However, occlusion and small view angles are significant challenges to widespread use of roadside LiDARs. We consider fusing data from multiple LiDARs at a traffic intersection to better estimate traffic parameters than one can estimate from a single LiDAR. The key challenge is to calibrate multiple LiDARs both in time and space. The problem is more complex when heterogeneous sensors differ in resolution and are positioned arbitrarily on a traffic intersection.

Roadside LiDAR (Light Detection and Ranging) sensors are recently being explored for intelligent transportation systems aiming at safer and faster traffic management and vehicular operations. A key challenge in such systems is to efficiently transfer massive point-cloud data from the roadside LiDAR devices to the edge connected through a 5G network for real-time processing. In this paper, we consider the problem of compressing roadside (i.e. static) LiDAR data in real-time that provides a unique condition unexplored by current methods. Existing point-cloud compression methods assume moving LiDARs (that are mounted on vehicles) and do not exploit spatial consistency across frames over time.

This paper introduces a new pattern mining task that considers aligning or joining a set of time series based on an arbitrary number of subsequences (i.e., patterns) with arbitrary lengths. Joining multiple time series along common patterns can be pivotal in clustering and summarizing large time series datasets. An exact algorithm to join hundreds of time series based on multi-length patterns is impractical due to the high computational costs. This paper proposes a fast algorithm named MultiPAL to join multiple time series at interactive speed to summarize large time series datasets. The algorithm exploits Matrix Profiles of the individual time series to enable a greedy search over possible joins. The algorithm is orders of magnitude faster than the exact solution and can utilize hundreds of Matrix Profiles. We evaluate our algorithm for sequential mining on data from various real-world domains, including power management and bioacoustics monitoring.

A conventional power grid is criticized by its poor capability of power usage management, especially in handling dynamically varying power demands over time. The concept of smart grid has been introduced to mitigate this problem by satisfying not only real-time power demands, but also by restricting power usage within the capacity. Its consistent outperformance and new perspective in computer intelligence to control the grid for autonomous power consumption has been gradually replacing the conventional power grid. However, even in smart grid, providing high satisfaction to users often leads smart grid operator (SGO) to loss and vice versa. In this paper, we develop an optimal dynamic pricing mechanism for trading-off (ODPT), for SGOs that tradeoff between user utility and operator profit in smart grid systems. It allows the operator to purchase power from multiple energy producers and to set selling price to users dynamically following the demand-supply theory of economics. It also exploits an artificial neural network model to more accurately predict the power usage. The simulation results, carried out on a commercially available optimization modeling tool using practical power usage data, prove the effectiveness of the proposed ODPT in increasing the operator profit while satisfying user demands.