No abstract

Today, it is clearly known that the electronic devices generate electromagnetic radiations unintentionally, which may contain critical information called compromising emanations (CE). CE is also known as TEMPEST radiation, which is a code name firstly used by an U.S government program. Every developed country has a TEMPEST Test Laboratory (TTL) connected to their National Security Agency (NSA). The main objective of these laboratories is to investigate equipment, systems, and platforms processing cryptographic information in terms of CE. TEMPEST tests might take very long time depending on the item under test. In this paper, a complete Automatic TEMPEST Test and Analysis System (ATTAS) developed in TUBITAK, BILGEM TTL is introduced. The system has the following properties, which are automatic system calibration unit, automatic test matrix generator based on the SDIP-27/1 standard, implementation of tunable and nontunable tests, automatic CE investigations, rendering of the CE of video display units, playing of the CE of audio signals, measurement of detection system sensitivity, zoning of TEMPEST equipment based on SDIP-28 standard, and generation of graphical results.

He is currently working as an undergraduate research assistant in the additive manufacturing laboratory under Dr. Fidan. Nick is the student trustee on the Tennessee Tech Board of Trustees and is formally the Tennessee Board of Regents Student Regent. He is also the recipient of the 2017 Rising Renaissance Engineer Spectrum Award. Nick enjoys spending time with his family and trading stocks in his free time. Mr. James Reed Rust, Tennessee Technological University Mr. Reed Rust is a senior in Manufacturing Engineering Technology at Tennessee Tech University. He is currently working as an undergraduate research assistant in the additive manufacturing laboratory under Dr. Fidan. He is also the build team director for the TTU Motorsports Formula SAE team. Reed is also the recipient of the 2017 Rising Renaissance Engineer Spectrum Award. He enjoys spending his time working in the machine shop and working on cars.

The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generativedeep learning model using the conditional GAN architecture to predict glaucoma progression over time. The patient's OCT scan is predicted from three or two prior measurements. The predicted images demonstrate high similarity with the ground truth images. In addition, our results suggest that OCT scans obtained from only two prior visits may actually be sufficient to predict the next OCT scan of the patient after six months.

He is currently working as an undergraduate research assistant in the additive manufacturing laboratory under Dr. Fidan. Nick is the student trustee on the Tennessee Tech Board of Trustees and is formally the Tennessee Board of Regents Student Regent. He is also the recipient of the 2017 Rising Renaissance Engineer Spectrum Award. Nick enjoys spending time with his family and trading stocks in his free time. Mr. James Reed Rust, Tennessee Technological University Mr. Reed Rust is a senior in Manufacturing Engineering Technology at Tennessee Tech University. He is currently working as an undergraduate research assistant in the additive manufacturing laboratory under Dr. Fidan. He is also the build team director for the TTU Motorsports Formula SAE team. Reed is also the recipient of the 2017 Rising Renaissance Engineer Spectrum Award. He enjoys spending his time working in the machine shop and working on cars.