The High-Performance Computing and Embedded Systems Laboratory at Coastal Carolina is actively pursuing new research in several systems-related areas. Our principal goal is to engage undergraduate computer science students in research and expose them to the research process.

Our group focuses on problems in the following areas:

High Performance Computing

Writing software which takes advantage of the features of modern computer systems is a challenging task. Over the past several years, computing hardware has evolved substantially through the introduction and widespread adoption of multicore systems. This evolution has thrust parallel computing into the mainstream. Our work at Coastal Carolina has three main objectives:

Field Programmable Gate Arrays

At Coastal Carolina, we use FPGA development systems in our organization and architecture courses. We have found that FPGA-based systems can be used in a wide range of courses across the computer science curriculum. Since FPGAs are flexible hardware systems, they allow adaptation to a variety of specific courses. Additionally, by using these devices in multiple courses, students gain greater familiarity with them and can leverage this knowledge throughout their coursework.

Overview of FPGA Systems at Coastal Carolina


With the recent popularity of hardware such as the Arduino processor and large set of actuators and sensors that it interoperates with, sophisticated robotic devices are now both inexpensive and supported with rich development environments. We are in the process of developing an undergraduate research program focusing on autonomous aerial helicopters. These helicopters make for a novel research platform in their own right, but may also be used as a basis for work in computer vision, cooperation, etc.

Overview of Robotics Research at Coastal Carolina

Affective Graph

Automatic Affective Video Indexing

The increased availability of multimedia equipment has resulted in large repositories of publically available, amateur videos. Users need to be able to identify and retrieve videos that contain content of interest. One of the more difficult challenges in video indexing is affective video indexing, which focuses on content that is intended to evoke certain emotions in users. By utilizing low-level video characteristics, the identification of the targeted content can be performed without relying on the emotional responses of humans.