Nanyang Technological University

About: Nanyang Technological University is a education organization based out in Singapore, Singapore. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 48003 authors who have published 112815 publications receiving 3294199 citations. The organization is also known as: NTU & Universiti Teknologi Nanyang.

Max-mean and max-median filters for detection of small targets

Abstract: This paper deals with the problem of detection and tracking of low observable small-targets from a sequence of IR images against structural background and non-stationary clutter. There are many algorithms reported in the open literature for detection and tracking of targets of significant size in the image plane with good results. However, the difficulties of detecting small-targets arise from the fact that they are not easily discernable from clutter. The focus of research in this area is to reduce the false alarm rate to an acceptable level. Triple Temporal Filter reported by Jerry Silverman et. al., is one of the promising algorithms in this are. In this paper, we investigate the usefulness of Max-Mean and Max-Median filters in preserving the edges of clouds and structural backgrounds, which helps in detecting small-targets. Subsequently, anti-mean and anti-median operations result in good performance of detecting targets against moving clutter. The raw image is first filtered by max-mean/max-median filter. Then the filtered output is subtracted from the original image to enhance the potential targets. A thresholding step is incorporated in order to limit the number of potential target pixels. The threshold is obtained by using the statistics of the image. Finally, the thresholded images are accumulated so that the moving target forms a continuous trajectory and can be detected by using the post-processing algorithm. It is assumed that most of the targets occupy a couple of pixels. Head-on moving and maneuvering targets are not considered. These filters have ben tested successfully with the available database and the result are presented.

Domain Transfer Multiple Kernel Learning

Abstract: Cross-domain learning methods have shown promising results by leveraging labeled patterns from the auxiliary domain to learn a robust classifier for the target domain which has only a limited number of labeled samples. To cope with the considerable change between feature distributions of different domains, we propose a new cross-domain kernel learning framework into which many existing kernel methods can be readily incorporated. Our framework, referred to as Domain Transfer Multiple Kernel Learning (DTMKL), simultaneously learns a kernel function and a robust classifier by minimizing both the structural risk functional and the distribution mismatch between the labeled and unlabeled samples from the auxiliary and target domains. Under the DTMKL framework, we also propose two novel methods by using SVM and prelearned classifiers, respectively. Comprehensive experiments on three domain adaptation data sets (i.e., TRECVID, 20 Newsgroups, and email spam data sets) demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods.

25th anniversary article : hybrid nanostructures based on two-dimensional nanomaterials

Abstract: Two-dimensional (2D) nanomaterials, such as graphene and transition metal dichalcogenides (TMDs), receive a lot of attention, because of their intriguing properties and wide applications in catalysis, energy-storage devices, electronics, optoelectronics, and so on. To further enhance the performance of their application, these 2D nanomaterials are hybridized with other functional nanostructures. In this review, the latest studies of 2D nanomaterial-based hybrid nanostructures are discussed, focusing on their preparation methods, properties, and applications.

Self-assembled cationic peptide nanoparticles as an efficient antimicrobial agent.

Abstract: Antimicrobial cationic peptides are of interest because they can combat multi-drug-resistant microbes. Most peptides form alpha-helices or beta-sheet-like structures that can insert into and subsequently disintegrate negatively charged bacterial cell surfaces. Here, we show that a novel class of core-shell nanoparticles formed by self-assembly of an amphiphilic peptide have strong antimicrobial properties against a range of bacteria, yeasts and fungi. The nanoparticles show a high therapeutic index against Staphylococcus aureus infection in mice and are more potent than their unassembled peptide counterparts. Using Staphylococcus aureus-infected meningitis rabbits, we show that the nanoparticles can cross the blood-brain barrier and suppress bacterial growth in infected brains. Taken together, these nanoparticles are promising antimicrobial agents that can be used to treat brain infections and other infectious diseases.