Jo Sep 4, 2023
A research team led by Chae Wi Song, a researcher at the Faculty of Distance Education, has developed an authentication system in which users are authenticated by analysis of keystroke dynamics.
Biometric authentication systems identify individuals by their physiological features (fingerprint, skin, retina, iris, etc.).
Each individual’s keystroke pattern is unique and a unique profile can be constructed by their typing speed, key press release timing, pressure applied, and finger positions on a keyboard.
The user authentication system based on this keystroke pattern is appealing for many reasons: simple and transparent authentication and no need for any extra equipment for feature capture.
The research team chose key press time and key release time among many keystroke patterns.
Key press time and key release time enable extraction of four features ― key hold time, key press latency, intervals between key press and release, and release and press. They used random forest algorithm for user authentication. The basic idea is to construct many trees using random vectors sampled from a data set.
This system guarantees fair online exams by automatically blocking several kinds of cheating attempts such as taking exams in place of examinees, keying in answers read by others, etc.
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Jo Aug 30, 2023
Android-based oscilloscopes have been developed for research and educational purposes because they are able to acquire, transmit, display, and analyze any electrical signals since they are mobile and easy to operate.
Due to the speed limitation of wireless transmission channels, android oscilloscopes have some limited performances on several aspects such as bandwidth, waveform capturing rate (WCR), etc.
Kim Mun Hyok, a section head at the Faculty of Automation Engineering, has proposed a real-time lossless data compression scheme to solve a bottleneck of continuous data flow from a data acquisition device (DAQ) to a smartphone. This scheme consists of triggering a signal and encoding differences between waveforms (DBWs).
He also proposed an advanced structure of android bluetooth oscilloscope (ABO) to implement the proposed compression algorithm with field programmable gate array (FPGA)-based hardware and software.
To evaluate WCR improvement, he first analyzed relationship between WCR and compression ratio, and then, verified the compression efficiency by MATLAB simulations using various waveforms, such as simple sinusoidal, complex periodic, square, and chirp waveforms.
He finally studied the robustness of this instrument within the range of 0–15% noise amplitude and 0–2rad phase offset. All the experimental results showed that the proposed scheme can enhance WCR and at the same time it can also be applied to the wireless transmission fields collecting and displaying electrical waveforms.
If further information is needed, please refer to his paper “A Triggering-and-Encoding Lossless Compression Scheme for Waveform Capturing Rate Enhancement of Android Bluetooth Oscilloscope” in “IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT” (SCI).
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Jo Aug 29, 2023
Nowadays, consumer mobile phones come in with ultra-high resolution cameras, and an incredible number of high-resolution images and videos are created every day. The images and videos have to be downscaled with very large factors to be displayed on general screens.
Deep learning-based downscaling methods show superior performance only for some predetermined integer factors such as 2, 3 and 4. For arbitrary factors, the latest image downscaling algorithms preserve edges and fine details but still suffer from noise amplification. They make undesirable artifacts especially when a downscaling factor is very large.
Kim Su Hyon, a researcher at the Faculty of Information Science and Technology, has proposed an algorithm referred to as NDPID (Noise-free DPID or New DPID) for downscaling ultra-high resolution images to a thumbnail size in real-time without amplifying noise. The proposed algorithm is based on inverse joint bilateral filtering using an area pixel model and moving average.
Unlike the DPID, which employs a rectangular function (box filtering) as the spatial kernel, the NDPID uses two-step 1D APID (Area Pixel model based Image Downscaling) filter. The main reason for employing this 1D spatial kernel is to decompose the proposed downscaling algorithm into two subsequent processes each of which performs capturing pixels’ distinctness for their weights and smoothing of the weights. By these two processes, the algorithm alleviates an isolated noise pixel twice but a thin line (important detail) only once. Consequently, the lines and edges survive while the NDPID alleviates the isolated noise pixel in both horizontal and vertical smoothing processes.
The proposed algorithm is much faster than state-of-the-art downscalers and is free from the restraints of predetermined integer downscaling factors. The experimental results show that the proposed algorithm is about 7.37% faster on average than the DPID, the fastest detail-preserving image downscaler in use. GPU implementation of the algorithm downscales a 2K video to 128-pixel width without temporal artifacts at the speed of 116 frames per second. Moreover, the PSNR and SSIM scores achieved by his method were respectively 35.9% and 16.5% higher on average than the highest values scored by the existing methods when downscaling images contaminated by 5% salt and pepper noise.
If further information is needed, please refer to his paper “A New Rapid and Detail-Preserving Image Downscaling Without Noise Amplification” in “IEEE Access” (SCI).
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Jo Aug 28, 2023
Conventional contrast enhancement algorithms such as histogram equalization and adaptive gamma correction overemphasize images without preserving edges, lose details near edges or amplify noise. These are more obvious in case of enhancing dark images with non-uniform illumination, which contain both bright and dark regions. This is mainly because Histogram Equalization and adaptive gamma correction perform a global transformation considering only pixel brightness level.
Kim Thae Song, a section head at the Faculty of Information Science and Technology, has proposed a new algorithm to enhance dark image contrast with non-uniform illumination preserving edges.
His contributions are as follows.
He defined a new edge intensity histogram which represents local brightness variations for each brightness level. With the edge intensity histogram instead of the luminance histogram, adjusting dynamic range adaptively, he obtained a transformation function for efficient contrast enhancement which preserves edges, details and naturalness. To avoid noise amplification, he decomposed an input image into a base and detailed layer and calculated the edge intensity histogram for only the base layer. He enhanced only the base layer using the edge intensity histogram and combined it with the detailed layer which is linearly transformed.
He compared the performance of his method with existing methods such as HE, GMHE, EGEHE, AGCWD, AGCWHD and LTH using a set of dark images taken from the Caltech-256, NCEA, Kodak. To evaluate the proposed method, he used various metrics such as entropy, MSSIM, GMSD, EBCM and AMBE.
The experimental results showed that the proposed method is very effective for enhancing dark images with non-uniform illumination.
If further information is needed, please refer to his paper “An improved contrast enhancement for dark images with non-uniform illumination based on edge preservation” in “Multimedia Systems” (SCI).
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Jo Aug 26, 2023
With a flood of display devices with different resolutions, there are increasing demands for efficient image scaling algorithms, which can improve performances of real-time applications such as remote desktop and screen sharing. Image downscaling algorithms can be divided into two classes ― content-adaptive and non-adaptive. Non-adaptive algorithms produce aliasing, blur and halo artifacts though they are very fast. Content-adaptive algorithms have been proposed to improve perceptual quality of scaled images at the expense of computational power.
Many of the content-adaptive downscalers employ non-adaptive algorithms like box filtering and Bicubic as fundamental tools. This means that the performance of those adaptive scaling methods can be improved by using a superior non-adaptive algorithm.
Kim Su Hyon, a researcher at the Faculty of Information Science and Technology, has proposed a new non-adaptive spatial filter kernel based on a circular area pixel model to improve the underlying frameworks of many state-of-the-art downscalers.
A pixel in a digital camera is the basic element of a sensor and its shape is rectangular. However, a point of light in a scene is spread by an optical system and creates a blurred circular image onto the pixel. In the spatial domain, an optical PSF (Point Spread Function) describes the degree to which an optical system spreads a point of light. Though the PSF from a circular aperture can be expressed by a sombrero function, it is usually modelled with simpler expressions such as a uniform circular disk. Therefore, from the optical point of view, he used a circular area pixel model rather than a rectangular one.
Since his kernel is one-dimensional, the proposed algorithm has two steps: horizontal and vertical processing. For upscaling, an original and a target pixel are treated as circular regions. For downscaling, only target pixel is treated as an elliptical region.
Abundant objective comparisons showed that the proposed downscaling algorithm is the fastest and has the highest PSNR and SSIM values among the commonest non-adaptive image scaling algorithms. Visual comparisons also showed that his algorithm produces the clearest images without blurring and halo effects. His filter kernel can replace the existing spatial kernels of edge-adaptive image downscalers to improve their performance further.
For further information, you can refer to his paper “ A New Image Downscaling Algorithm based on a Circular Area Pixel Model” in “ ACM Conference Proceedings” (EI).
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Jo Aug 24, 2023
Thae Il Gwang, a researcher at the Faculty of Mining Engineering, has built an experimental jaw crusher for analyzing the working properties of jaw crushers commonly used in stoping faces and concentrating mills.
It is composed of a jaw crusher, sensors and a control circuit.
The crusher consists of a body with a fixed jaw, a movable jaw, an eccentric shaft, a connecting rod, a drawbar and a spring. Some structural modification was made to the drawbar and eccentric shaft to install necessary units for experiments.
Sensors are installed on the connecting rod and the eccentric shaft, respectively. They are used for measuring compressive force on the connecting rod and angular displacement on the eccentric shaft.
The control circuit can be connected with a computer to control the jaw crusher and to collect and send measured data.
The data sent to the computer is analyzed by an application for working properties and the computer displays angular displacement, angular velocity and moment of the eccentric shaft, crushing force on the movable jaw, and power consumption for crushing.
This device is used for analyzing fracture mechanism of different ores or for improving working properties of jaw crushers.
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