Escape routing for packages and PCBs has been studied extensively in the past. Network flow is pervasively used to model this problem. However, none of the previous works correctly models the diagonal capacity, which is essential for 45° routing in most packages and PCBs. As a result, existing algorithms may either produce routing solutions that violate the diagonal capacity or fail to output a legal routing even though there exists one. In this paper, we propose a new network flow model that guarantees the correctness when diagonal capacity is taken into consideration. This model leads to the first optimal algorithm for escape routing. We also extend our model to handle missing pins.

A correct network flow model for escape routing

The flip-chip package provides a high chip-density solution to the demand for more I/O pads of VLSI designs. In this paper, we present the first routing algorithm in the literature for the pre-assignment flip-chip routing problem with a pre-defined netlist among pads and wire-width and signal-skew considerations. Our algorithm is based on integer linear programming (ILP) and guarantees to find an optimal solution for the addressed problem. It adopts a two-stage technique of global routing followed by detailed routing. In global routing, it first uses two reduction techniques to prune redundant solutions and create a global-routing path for each net. Without loss of the solution optimality, our reduction techniques can further prune the ILP variables (constraints) by 85.5% (98.0%) on average over a recent reduction technique. The detailed routing applies X-based grid- less routing to complete the routing. Experimental results based on five real industry designs show that our router can achieve 100% routability and the optimal global-routing wirelength and satisfy all signal-skew constraints, under reasonable CPU times, while recent related work results in much inferior solution quality.

http://www.gtcad.gatech.edu/www/weekly/fa07/34_1.pdf

An integer linear programming based routing algorithm for flip-chip design

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. It has been broadly used in the literature for source camera identification and image authentication. The abundant information that the sensor pattern noise carries in terms of the frequency content makes it unique, and hence suitable for identifying the source camera and detecting image forgeries. However, the PRNU extraction process is inevitably faced with the presence of image-dependent information as well as other non-unique noise components. To reduce such undesirable effects, researchers have developed a number of techniques in different stages of the process, i.e., the filtering stage, the estimation stage, and the post-estimation stage. In this paper, we present a new PRNU-based source camera identification and verification system and propose enhancements in different stages. First, an improved version of the locally adaptive discrete cosine transform filter is proposed in the filtering stage. In the estimation stage, a new weighted averaging technique is presented. The post-estimation stage consists of concatenating the PRNUs estimated from color planes in order to exploit the presence of physical PRNU components in different channels. Experimental results on two image data sets acquired by various camera devices have shown a significant gain obtained with the proposed enhancements in each stage as well as the superiority of the overall system over related state-of-the-art systems.

/pdf/sensor-pattern-noise-estimation-based-on-improved-locally-sbz0eqqzx5.pdf

Sensor Pattern Noise Estimation Based on Improved Locally Adaptive DCT Filtering and Weighted Averaging for Source Camera Identification and Verification

Passive forensics in image and video using noise features

Extracting a fingerprint of a digital camera has fertile applications in image forensics, such as source camera identification and image authentication. In the last decade, photo response non_uniformity (PRNU) has been well established as a reliable unique fingerprint of digital imaging devices. The PRNU noise appears in every image as a very weak signal, and its reliable estimation is crucial for the success rate of the forensic application. In this paper, we present a novel methodical evaluation of 21 state-of-the-art PRNU estimation/enhancement techniques that have been proposed in the literature in various frameworks. The techniques are classified and systematically compared based on their role/stage in the PRNU estimation procedure, manifesting their intrinsic impacts. The performance of each technique is extensively demonstrated over a large-scale experiment to conclude this case-sensitive study. The experiments have been conducted on our created database and a public image database, the “Dresden image database.”

/pdf/on-the-spn-estimation-in-image-forensics-a-systematic-5eeu8zbd33.pdf

On the SPN Estimation in Image Forensics: A Systematic Empirical Evaluation

In this paper, we give the necessary and sufficient condition that all nets can be connected by monotonic routes when a net consists of a finger and a ball and fingers are on the two parallel boundaries of the ball grid array package, and propose a monotonic routing method based on this condition. Moreover, we give a necessary condition and a sufficient condition when fingers are on the two orthogonal boundaries, and propose a monotonic routing method based on the necessary condition.

/pdf/monotonic-parallel-and-orthogonal-routing-for-single-layer-4purwcnohe.pdf

Monotonic parallel and orthogonal routing for single-layer ball grid array packages

Owing to the rapid progress in digital camera technologies, a large amount of image content is distributed on the World Wide Web. Digital camera identification, which is the identification of the source camera of an input image, is becoming increasingly important for presenting evidence in a court and helping police investigations. In recent years, a digital camera identification method using the image sensor's pattern noise has received considerable attention. Photo-response non-uniformity (PRNU) noise is mainly generated by the existence of differences between the sensitivities of pixels, and it is useful as a fingerprint of a camera. However, the PRNU noise of an image is usually contaminated by random noise and scene content and affected by the image processing engine, which inhibits stable identification. In this paper, we propose a novel digital camera identification method using the pairwise magnitude relations of image sensor noise, which are robust to noise contamination. By performing experiments, we demonstrate that the proposed method can identify the source cameras of query images with high accuracy.

Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise

Video surveillance systems are becoming increasingly important for crime investigation and deterrence, and the number of cameras installed in public space is increasing. However, many cameras installed at fixed positions are required to observe a wide and complex area. In order to efficiently observe such a wide area at lower cost, mobile robots are an attractive option. In this paper, we propose a method for determining the traveling route of a mobile surveillance camera. Our method is based on mixed integer linear programming and obtains an optimum traveling route such that a camera with a certain visual angle and visual distance can observe the entire region at the shortest intervals. Through our experiments, we apply this method to several artificially generated data and data for a real university campus and demonstrate that effective patrol courses for specified mobile surveillance cameras can be generated.

Generation of an Optimum Patrol Course for Mobile Surveillance Camera

In advanced semiconductor-process technology, the ability to detect and repair lithography hotspots, which can affect printability, is essential. In this paper, we propose a two-stage cascade classifier for accurate hotspot detection. Our classifier uses a novel layout feature based on the propagation of light passing through a photomask. We performed experiments to evaluate our cascade classifier by applying it to the ICCAD-2012 CAD contest problem. The hotspot detection performance was evaluated according to two indices: (I1) the number of correctly detected hotspots over the number of actual hotspots and (I2) the number of correctly detected hotspots over the number of false hotspots. The results showed that the proposed method gained a 1.15% improvement in I1 and 24.4 times improvement in I2 on average compared to existing state-of-the-art methods, even the one with the best I1.

Lithography hotspot detection by two-stage cascade classifier using histogram of oriented light propagation

Along with the popularization of digital cameras, the reliable identification of digital image source is becoming increasingly important as an evidence in a court and some help of investigations. In this paper, we propose an enhanced digital camera identification method using the pixel non-uniformity (PNU) noise of image sensors. By clustering the PNU noises, the proposed method extracts the robust features of image sensors to the random noise, scene content, and image processing engine such as the noise reduction. In the experiments, the proposed method shows the high identification accuracy even for latest digital cameras.

Yoichi Tomioka

Papers

Monotonic parallel and orthogonal routing for single-layer ball grid array packages

Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise

Generation of an Optimum Patrol Course for Mobile Surveillance Camera

Lithography hotspot detection by two-stage cascade classifier using histogram of oriented light propagation

Digital camera identification based on the clustered pattern noise of image sensors