Integral image is the sum of input image pixel values. It is mainly used to speed up the process of a box filter operation, such as Haar-like features. However, large memory capacity for integral image data can be an obstacle in an embedded environment with limited hardware. In a previous research, [5] reduced the size of integral image memory using 2×2 block structure with additional calculations. It can be easily extended to n×n block structure for further reduction, but it requires more additional calculations. In this paper, we propose a new block structure for the integral image by modifying the location of the reference pixel in the block. It results in much less additional calculations by reducing the number of memory accesses, while keeping the same amount of memory as the original block structure.

An integral image is the sum of input image pixel values. It is mainly used to speed up the process of a box filter operation, such as Haar-like features. However, large memory for integral image data can be an obstacle in an embedded environment with limited hardware. Therefore, an efficient method to store the integral image is necessary. In this paper, we propose a memory size reduction method for integral image. The method uses four types image information: an integral image, a row integral image, a column integral image, and an input image. Using this method, integral image memory can be reduced by 42.6% on a 640×480 8-bit gray-scale input image. The same idea can be applied for bigger size images.

Fine-grained image analysis, such as pixel-level approaches, improves threat detection in x-ray security images. In the practical setting, the cost of obtaining complete pixel-level annotations increases significantly, which can be reduced by partially labeling the dataset. However, handling partially labeled datasets can lead to training complicated multi-stage networks. In this paper, we propose a new end-to-end object separation framework that trains a single network on a partially labeled dataset while also alleviating the inherent class imbalance at the data and object proposal level. Empirical results demonstrate significant improvement over existing approaches.

We propose a new method that can speed up the modular multiplication by physically partitioning the key size into two slices. By using LSB-first and MSB-first approach on two respective partitioned hardware module in parallel, we reduce the number of iterations in modular multiplication from k to k/2+1 for k-bit operands, and the resulting performance is doubled when contrasted with an implementation purely by LSB-first or MSB-first approach.

Threat object recognition in x-ray security images is one of the important practical applications of computer vision. However, research in this field has been limited by the lack of available dataset that would mirror the practical setting for such applications. In this paper, we present a novel GAN-based anomaly detection (GBAD) approach as a solution to the extreme class-imbalance problem in multi-label classification. This method helps in suppressing the surge in false positives induced by training a CNN on a non-practical dataset. We evaluate our method on a large-scale x-ray image database to closely emulate practical scenarios in port security inspection systems. Experiments demonstrate improvement against the existing algorithm.

Using third-party intellectual properties (3PIP) has been a norm in IC design development process to meet the time-to-market demand and at the same time minimizing the cost. But this flow introduces a threat, such as hardware trojan, which may compromise the security and trustworthiness of underlying hardware, like disclosing confidential information, impeding normal execution and even permanent damage to the system. In years, different detections methods are explored, from just identifying if the circuit is infected with hardware trojan using conventional methods to applying machine learning where it identifies which nets are most likely are hardware trojans. But the performance is not satisfactory in terms of maximizing the detection rate and minimizing the false positive rate. In this paper, a new hardware trojan detection approach is proposed where gate-level netlist is segmented into regions first before analyzing which nets might be hardware trojans. The segmentation process depends on the nets' connectivity, more specifically by looking on each fanout points. Then, further analysis takes place by means of computing the structural similarity of each segmented region and differentiate hardware trojan nets from normal nets. Experimental results show 100% detection of hardware trojan nets inserted on each benchmark circuits and an overall average of 1.38% of false positive rates which resulted to a higher accuracy with an average of 99.31%.

Field Programmable Gate Array (FPGA) is gaining popularity because of their reconfigurability which brings in security concerns like inserting hardware trojan. Various detection methods to overcome this threat have been proposed but in the ASIC's supply chain and cannot directly apply to the FPGA application. In this paper, the authors aim to implement a structural feature-based detection method for detecting hardware trojan in a cell-level netlist, which is not well explored yet, where the nets are segmented into smaller groups based on their interconnection and further analyzed by looking at their structural similarities. Experiments show positive performance with an average detection rate of 95.41%, an average false alarm rate of 2.87% and average accuracy of 96.27%.

This paper presents an approach for developing an algorithm for automatic license plate recognition system (ALPR) on complex scenes. A plate-style classification method is also proposed in this paper to address the inherent challenges for ALPR in a system that uses multiple plate-styles (e.g., different fonts, multiple plate lay-out, variations in character sequences) which is the case in the current Philippine license plate system. Methods are proposed for each ALPR module: plate detection, character segmentation, and character recognition. K-nearest neighbor (KNN) is used as a classifier for character recognition together with a proposed confidence scoring to rate the decision made by the classifier. A small dataset of Philippine license plates but with relevant features of complex scenarios for ALPR is prepared. Using the proposed system on the prepared dataset, the performance of the system is evaluated on different categories of complex scenes. The proposed algorithm structure shows promising results and yielded an overall accuracy higher than the existing ALPR systems on the dataset consisting mostly of complex scenes.