Chiman Kwan

Chiman Kwan, Bence Budavari

Since targets are small in long range infrared (IR) videos, it is challenging to accurately detect targets in those videos. In this paper, we propose a high performance approach to detecting small targets in long range and low quality infrared videos. Our approach consists of a video resolution enhancement module, a proven small target detector based on local intensity and gradient (LIG), a connected component (CC) analysis module, and a track association module to connect detections from multiple frames. Extensive experiments using actual mid-wave infrared (MWIR) videos in ranges between 3500 m and 5000 m from a benchmark dataset clearly demonstrated the efficacy of the proposed approach.

Ying Qu, Hairong Qi, Chiman Kwan

In many computer vision applications, obtaining images of high resolution in both the spatial and spectral domains are equally important. However, due to hardware limitations, one can only expect to acquire images of high resolution in either the spatial or spectral domains. This paper focuses on hyperspectral image super-resolution (HSI-SR), where a hyperspectral image (HSI) with low spatial resolution (LR) but high spectral resolution is fused with a multispectral image (MSI) with high spatial resolution (HR) but low spectral resolution to obtain HR HSI. Existing deep learning-based solutions are all supervised that would need a large training set and the availability of HR HSI, which is unrealistic. Here, we make the first attempt to solving the HSI-SR problem using an unsupervised encoder-decoder architecture that carries the following uniquenesses. First, it is composed of two encoder-decoder networks, coupled through a shared decoder, in order to preserve the rich spectral information from the HSI network. Second, the network encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. Third, the angular difference between representations are minimized in order to reduce the spectral distortion. We refer to the proposed architecture as unsupervised Sparse Dirichlet-Net, or uSDN. Extensive experimental results demonstrate the superior performance of uSDN as compared to the state-of-the-art.

Laszlo Bela Kish, Kamran Entesari, Claes-Goran Granqvist et al.

The statistical-physics-based Kirchhoff-law-Johnson-noise (KLJN) key exchange offers a new and simple unclonable system for credit/debit card chip authentication and payment. The key exchange, the authentication and the communication are unconditionally secure so that neither mathematics- nor statistics-based attacks are able to crack the scheme. The ohmic connection and the short wiring lengths between the chips in the card and the terminal constitute an ideal setting for the KLJN protocol, and even its simplest versions offer unprecedented security and privacy for credit/debit card chips and applications of physical unclonable functions.

Minh Dao, Xiang Xiang, Bulent Ayhan et al.

In this paper, we propose a burnscar detection model for hyperspectral imaging (HSI) data. The proposed model contains two-processing steps in which the first step separate and then suppress the cloud information presenting in the data set using an RPCA algorithm and the second step detect the burnscar area in the low-rank component output of the first step. Experiments are conducted on the public MODIS dataset available at NASA official website.

Laszlo B. Kish, Chiman Kwan

Weak physical uncloneable function (WPUF) encryption key means that the manufacturer of the hardware can clone the key but anybody else is unable to so that. Strong physical uncloneable function (SPUF) encryption key means that even the manufacturer of the hardware is unable to clone the key. In this paper, first we introduce a "ultra"-strong PUF with intrinsic dynamical randomness, which is not only not cloneable but it also gets renewed to an independent key (with fresh randomness) during each use via the unconditionally secure key exchange. The solution utilizes the Kirchhoff-law-Johnson-noise (KLJN) method for dynamical key renewal and a one-time-pad secure key for the challenge/response process. The secure key is stored in a flash memory on the chip to provide tamper-resistance and non-volatile storage with zero power requirements in standby mode. Simplified PUF keys are shown: a strong PUF utilizing KLJN protocol during the first run and noise-based logic (NBL) hyperspace vector string verification method for the challenge/response during the rest of its life or until it is re-initialized. Finally, the simplest PUF utilizes NBL without KLJN thus it can be cloned by the manufacturer but not by anybody else.