Informatics Publishing Limited

R. Sasikala , K. Usha Kingsly Devi

Abstract

In order to reduce the time to market and increase productivity in SOC design, reuse of previously designed modules is becoming a common practice. Design reuse leads to the development of IP identification circuits. This paper presents the identification of the intellectual-property (IP) in SOC (System-On-Chip) design using watermarking algorithm. IP identification procedures depend on current IP-based design flow. A watermark-generating circuit (WGC) and test patterns are embedded into the IP core at the behavior design level. Therefore, this scheme can also successfully survive synthesis, placement, and routing. It can identify the IP at various design levels. Five methods are used to combine WGC and test patterns. Depending upon these methods, the watermark sequence and test patterns are combined and produces the output. From this, identification of the IP can be done. The identity of the IP is proven during the general test process without implementing any extra extraction flow. After the chip has been manufactured and packaged, it is easy to detect the identification of the IP provider. There is no need of microphotograph. This scheme has the advantages of low hardware overhead, low tracking costs, and low processing-time costs.

Keywords

System-on-Chip, Intellectual Property, Watermark Generating Circuit.

Full Text

     

R. Sasikala ¹, K. Usha Kingsly Devi ²

Affiliations
1 Anna University, Tirunelveli, IN
2 ECE Department, Anna University, Tirunelveli, IN

Abstract

In order to reduce the time to market and increase productivity in SOC design, reuse of previously designed modules is becoming a common practice. Design reuse leads to the development of IP identification circuits. This paper presents the identification of the intellectual-property (IP) in SOC (System-On-Chip) design using watermarking algorithm. IP identification procedures depend on current IP-based design flow. A watermark-generating circuit (WGC) and test patterns are embedded into the IP core at the behavior design level. Therefore, this scheme can also successfully survive synthesis, placement, and routing. It can identify the IP at various design levels. Five methods are used to combine WGC and test patterns. Depending upon these methods, the watermark sequence and test patterns are combined and produces the output. From this, identification of the IP can be done. The identity of the IP is proven during the general test process without implementing any extra extraction flow. After the chip has been manufactured and packaged, it is easy to detect the identification of the IP provider. There is no need of microphotograph. This scheme has the advantages of low hardware overhead, low tracking costs, and low processing-time costs.

Keywords

System-On-Chip, Intellectual Property, Watermark Generating Circuit.

Full Text

     

N. Manikandan ¹, K. Usha Kingsly Devi ²

Affiliations
1 Applied Electronics, Regional Centre Anna University, Tirunelveli, Tamilnadu, IN
2 Applied Electronics, Regional Centre of Anna University, Tirunelveli, Tamilnadu, IN

Abstract

Computer-aided detection (CAD) systems are convenient for the automatic lung nodule detection in computed tomographic (CT) images, as the sheer volume of information present in CT datasets is overwhelming for radiologists to process. First, segmentation scheme is used as a preprocessing step for enhancement. Then, the nodule candidates are detected by Eigen value decomposition of hessian matrix and Multi-scale dot enhancement filtering. After the initial detection of nodule candidates using filtering technique, feature descriptors were extracted. The feature descriptor is refined using the process of wall detection and eradication. An Evolutionary Support Vector Machine (ESVM) is trained to classify nodules and non-nodules. The proposed CAD system is validated on Lung Image Database Consortium (LIDC) data. Experimental results show that the detection scheme achieves 98.3% sensitivity with only 11 false positives per scan.

Keywords

CT, Pulmonary Nodule Detection, CAD, Feature Extraction.

Full Text