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%.

Power consumption has become a critical factor for embedded systems, especially for battery powered ones. Caches in these systems consume a large portion of the whole chip power. Embedded systems usually adopt set-associative caches to get better performance. However, parallel accessed cache ways incur more energy dissipation. This paper proposed a region-based way-partitioning scheme to reduce cache way access, and without sacrificing performance, to reduce the cache power consumption. The stack accesses and non-stack accesses are isolated and redirected to different ways of the L1 data cache. Under way-partitioning, cache way accesses are reduced, as well as the memory reference interference. Experimental results show that the proposed approach could save around 27.5% of L1 data cache energy on average, without significant performance degradation.

As the multimedia contents market continues its rapid expansion, the amount of image contents used in mobile phone services, digital libraries, and catalog service is increasing remarkably. In spite of this rapid growth, users experience high levels of frustration when searching for the desired image. Even though new images are profitable to the service providers, traditional collaborative filtering methods cannot recommend them. To solve this problem, in this paper, we propose feature-based collaborative filtering (FBCF) method to reflect the user's most recent preference by representing his purchase sequence in the visual feature space. The proposed approach represents the images that have been purchased in the past as the feature clusters in the multi-dimensional feature space and then selects neighbors by using an inter-cluster distance function between their feature clusters. Various experiments using real image data demonstrate that the proposed approach provides a higher quality recommendation and better performance than do typical collaborative filtering and content-based filtering techniques.

The manifold-ranking algorithm has been successfully adopted in content-based image retrieval (CBIR) in recent years. However, while the global low-level features are widely utilized in current systems, region-based features have received little attention. In this paper, a novel attention-driven transductive framework based on a hierarchical graph representation is proposed for region-based image retrieval (RBIR). This approach can be characterized by two key properties: (1) Since the issue about region significance is the key problem in region-based retrieval, a visual attention model is chosen here to measure the regions' significance. (2) A hierarchical graph representation which combines region-level with image-level similarities is utilized for the manifold-ranking method. A novel propagation energy function is defined which takes both low-level visual features and regional significance into consideration. Experimental results demonstrate that the proposed approach shows the satisfactory retrieval performance compared to the global-based and the block-based manifold-ranking methods.

Semantic image segmentation and appropriate region content description are crucial issues for region-based image retrieval (RBIR). In this paper, a novel region-based image retrieval method is proposed, which performs fast coarse image segmentation and fine region feature extraction using the decomposition property of image wavelet transform. First, coarse image segmentation is conducted efficiently in the Low-Low(LL) frequency subband of image wavelet transform. Second, the feature vector of each segmented region is hierarchically extracted from all different wavelet frequency subbands, which captures the distinctive feature (e.g., semantic texture) inside one region finely. Experiment results show the efficiency and the effectiveness of the proposed method for region-based image retrieval.

Noise greatly degrades the image quality and performance of image compression algorithms. This paper presents an approach for the representation and compression of noisy synthetic images. A new concept region-based prediction (RBP) model is first introduced, and then the RBP model is utilized on noisy images. In the conventional predictive coding techniques, the context for prediction is always composed of individual pixels surrounding the pixel to be processed. The RBP model uses regions instead of individual pixels as the context for prediction. An algorithm for the implementation of RBP is proposed and applied to noisy synthetic images in our experiments. Using RBP to find the residual data and encoding them, we achieve a bit rate of 1.10 bits/pixel for the noisy synthetic image. The decompressed image achieves a peak SNR of 42.59 dB. Compared with a peak SNR of 41.01 dB for the noisy synthetic image, the quality of the decompressed synthetic image is improved by 1.58 dB in the MSE sense. In contrast to our proposed compression algorithm with its improvement in image quality, conventional coding methods can compress image data only at the expense of lower image quality. At the same bit rate, the image compression standard JPEG provides a peak SNR of 33.17 dB for the noisy synthetic image, and the conventional median filter with a 33 window provides a peak SNR of 25.89 dB.

This paper proposes a robust method for detecting step and ramp edges. In this method, an edge is defined not as a point where there is a large change in intensity, but as a region boundary based on the separability of image features which can be calculated by linear discriminant analysis. Based on this definition of an edge, its intensity can be obtained from the separability, which depends only on the shape of an edge. This characteristic enables easy selection of the optimum threshold value for the extraction of an edge, and this method can be applied to color and texture edge extraction. Experimental results have demonstrated that this proposed method is robust to noise and dulled edges, and, in addition, allows easy selection of the optimum threshold value.