Georgia Institute of Technology

About: Georgia Institute of Technology is a education organization based out in Atlanta, Georgia, United States. It is known for research contribution in the topics: Population & Computer science. The organization has 45387 authors who have published 119086 publications receiving 4651220 citations.

Standards and figure-of-merits for quantifying the performance of triboelectric nanogenerators

Abstract: Triboelectric nanogenerators have been invented as a highly efficient, cost-effective and easy scalable energy-harvesting technology for converting ambient mechanical energy into electricity. Four basic working modes have been demonstrated, each of which has different designs to accommodate the corresponding mechanical triggering conditions. A common standard is thus required to quantify the performance of the triboelectric nanogenerators so that their outputs can be compared and evaluated. Here we report figure-of-merits for defining the performance of a triboelectric nanogenerator, which is composed of a structural figure-of-merit related to the structure and a material figure of merit that is the square of the surface charge density. The structural figure-of-merit is derived and simulated to compare the triboelectric nanogenerators with different configurations. A standard method is introduced to quantify the material figure-of-merit for a general surface. This study is likely to establish the standards for developing TENGs towards practical applications and industrialization.

Predicting cancer outcomes from histology and genomics using convolutional networks

Abstract: Cancer histology reflects underlying molecular processes and disease progression and contains rich phenotypic information that is predictive of patient outcomes. In this study, we show a computational approach for learning patient outcomes from digital pathology images using deep learning to combine the power of adaptive machine learning algorithms with traditional survival models. We illustrate how these survival convolutional neural networks (SCNNs) can integrate information from both histology images and genomic biomarkers into a single unified framework to predict time-to-event outcomes and show prediction accuracy that surpasses the current clinical paradigm for predicting the overall survival of patients diagnosed with glioma. We use statistical sampling techniques to address challenges in learning survival from histology images, including tumor heterogeneity and the need for large training cohorts. We also provide insights into the prediction mechanisms of SCNNs, using heat map visualization to show that SCNNs recognize important structures, like microvascular proliferation, that are related to prognosis and that are used by pathologists in grading. These results highlight the emerging role of deep learning in precision medicine and suggest an expanding utility for computational analysis of histology in the future practice of pathology.

AMNESIA: analysis and monitoring for NEutralizing SQL-injection attacks

Abstract: The use of web applications has become increasingly popular in our routine activities, such as reading the news, paying bills, and shopping on-line. As the availability of these services grows, we are witnessing an increase in the number and sophistication of attacks that target them. In particular, SQL injection, a class of code-injection attacks in which specially crafted input strings result in illegal queries to a database, has become one of the most serious threats to web applications. In this paper we present and evaluate a new technique for detecting and preventing SQL injection attacks. Our technique uses a model-based approach to detect illegal queries before they are executed on the database. In its static part, the technique uses program analysis to automatically build a model of the legitimate queries that could be generated by the application. In its dynamic part, the technique uses runtime monitoring to inspect the dynamically-generated queries and check them against the statically-built model. We developed a tool, AMNESIA, that implements our technique and used the tool to evaluate the technique on seven web applications. In the evaluation we targeted the subject applications with a large number of both legitimate and malicious inputs and measured how many attacks our technique detected and prevented. The results of the study show that our technique was able to stop all of the attempted attacks without generating any false positives.

Cognitive radio sensor networks

Abstract: Dynamic spectrum access stands as a promising and spectrum-efficient communication approach for resource-constrained multihop wireless sensor networks due to their event-driven communication nature, which generally yields bursty traffic depending on the event characteristics. In addition, opportunistic spectrum access may also help realize the deployment of multiple overlaid sensor networks, and eliminate collision and excessive contention delay incurred by dense node deployment. Incorporating cognitive radio capability in sensor networks yields a new sensor networking paradigm (i.e., cognitive radio sensor networks). In this article the main design principles, potential advantages, application areas, and network architectures of CRSNs are introduced. The existing communication protocols and algorithms devised for cognitive radio networks and WSNs are discussed along with the open research avenues for the realization of CRSNs.

Steerable intramedullary fracture reduction device

Abstract: A steerable intramedullary fracture reduction device has an elongated shaft (11) with a steerable tip (13) pivotally mounted to the distal end of the shaft (11) Tip control means (19, 21) near the proximal end of the shaft (11) enable the operator to steer the tip (13) and the shaft (11) into successive segments of the fractured bone, even when the segments are transversely or rotationally displaced so that the segments can be aligned by the shaft (11)