As Integrated Circuits (ICs) increase in complexity, there is a growing need for more comprehensive representations within deep learning approaches to support Electronic Design Automation (EDA) tasks in the backend stage, such as routing congestion prediction. However, current methods in congestion prediction often fail to fully combine both topological and geometrical information, limiting their ability to effectively represent and optimize circuits. Here we propose a novel architecture named TGFN that effectively merges topological information from the entire circuit and diverse geometrical information from layouts to enhance prediction accuracy. This model is specially designed to accommodate various data structures, integrating them into a cohesive latent representation that comprehensively models the circuit. Experimental results demonstrate that our method not only leverages both types of information effectively but also outperforms alternative baseline approaches. Notably, it achieves up to 6.87%, 11.24% and 15.96% improvements in Pear-son, Spearman and Kendall correlation coefficients respectively.

Vision Transformers (ViTs) have emerged as the dominant architecture for visual processing tasks, demonstrating excellent scalability with increased training data and model size. However, recent work has identified the emergence of artifact tokens in ViTs that are incongruous with local semantics. These anomalous tokens degrade ViT performance in tasks that require fine-grained localization or structural coherence. An effective mitigation of this issue is the addition of register tokens to ViTs, which implicitly ''absorb'' the artifact term during training. Given the availability of existing large-scale pre-trained ViTs, in this paper we seek to add register tokens to existing models without retraining the models from scratch, which is infeasible considering their size. Specifically, we propose Post Hoc Registers (**PH-Reg**), an efficient self-distillation method that integrates registers into an existing ViT without requiring additional labeled data and full retraining. PH-Reg initializes both teacher and student networks from the same pre-trained ViT. The teacher remains frozen and unmodified, while the student is augmented with randomly initialized register tokens. By applying test-time augmentation to the teacher’s inputs, we generate denoised dense embeddings free of artifacts, which are then used to optimize only a small subset of unlocked student weights. We show that our approach can effectively reduce the number of artifact tokens, improving the segmentation and depth prediction of the student ViT under zero-shot and linear probing.

Directional brightness temperature (DBT) plays an important role in surface energy balance and urban climate. How to determinate the components' proportion efficiently is one of the key factors to accurate simulation of DBT. Usually, determining the proportion of each component in the sensor's field of view (FOV) uses the radiosity method. This approach is more complicated and highly accurate, however, the computational complexity increases dramatically with the decreasing splitting scale of the involved facet area. To solve this problem, this research uses message passing interface (MPI) programming model to design and implement a parallel Linux cluster-based components' proportion calculation method. The experimental results show that the speedup ratio of the algorithm increases with the decrease of the facet areas' splitting scales, and that the achieved speedup reaches to 100 times or so, i.e., the acceleration effect is satisfactory and meets the application's requirements.

This paper presents a fully functional and highly portable mini Unmanned Aerial Vehicle (UAV) system, HAWK, for conducting aerial localization. HAWK is a programmable mini helicopter Draganflyer X6 armed with a wireless sniffer Nokia N900. We developed custom PI-Control laws to implement a robust waypoint algorithm for the mini helicopter to fly a planned route. A Moore space filling curve is designed as a flight route for HAWK to survey a specific area. A set of theorems were derived to calculate the minimum Moore curve level for sensing all targets in the area with minimum flight distance. With such a flight strategy, we can confine the location of a target of interest to a small hot area. We can recursively apply the Moore curve-based flight route to the hot area for a fine-grained localization of a target of interest. We have conducted extensive experiments to validate the feasibility of HAWK and our theory. A demo of HAWK in autonomous fly is available at http://www.youtube.com/watch?v=ju86xnHbEq0.

In many wireless localization applications, we rotate a directional antenna to derive the angle of arrival (AOA) of wireless signals transmitted from a target mobile device. The AOA corresponds to the direction in which the maximum received signal strength (RSS) is sensed. However, an unanswered question is how to make sure the directional antenna picks up packets producing the maximum RSS while rotating. We propose a set of novel RSS sampling theory to answer this question. We recognize the process that a directional antenna measures RSS of wireless packets while rotating as the process that the radiation pattern of the directional antenna is sampled. Therefore, if RSS samples can reconstruct the antenna's radiation pattern, the direction corresponding to the peak of the radiation pattern is the AOA of the target. We derive mathematical models to determine the RSS sampling rate given the target's packet transmission rate. Our RSS sampling theory is applicable to various types of directional antennas. To validate our RSS sampling theory, we developed BotLoc, which is a programmable and self-coordinated robot armed with a wireless sniffer. We conducted extensive real-world experiments and the experimental results match the theory very well. A video of BotLoc is at www.youtube.com/watch?v=WtUt0IqhXRU&feature=youtu.be.

This paper presents a fully functional and highly portable mini Unmanned Aerial Vehicle (UAV) system, HAWK, for conducting aerial localization. HAWK is a programmable mini helicopter - Draganflyer X6 - armed with a wireless sniffer - Nokia N900. We developed custom PI-Control laws to implement a robust waypoint algorithm for the mini helicopter to fly a planned route. A Moore space filling curve is designed as a flight route for HAWK to survey a specific area. A set of theorems were derived to calculate the minimum Moore curve level for sensing all targets in the area with minimum flight distance. With such a flight strategy, we can confine the location of a target of interest to a small hot area. We can recursively apply the Moore curve based flight route to the hot area for a fine-grained localization of a target of interest. Therefore, HAWK does not rely on a positioning infrastructure for localization. We have conducted extensive experiments to validate the feasibility of HAWK and our theory. A demo of HAWK in autonomous fly is available at http://www.youtube.com/watch?v=ju86xnHbEq0.

The network address translation technique (NAT) is widely used in wireless routers. It is a low cost solution to IPv4 address space limitations. However, cyber criminals may abuse NAT and hide behind wireless routers to use mobile devices and conduct crimes. To identify a suspect mobile device, we should be able to map the suspect public traffic on the Internet to the private traffic behind the wireless router in WLAN. In this paper, we propose a suite of novel packet size based traffic marking techniques to identify suspect mobiles in encrypted wireless networks as well as open wireless networks. To cope with severe packet loss during wireless sniffing, we proposed to use error correcting codes to improve detection rate. We conducted extensive analysis and experiments to demonstrate the efficiency and accuracy of our schemes, which achieve high detection rate and very small false positive rate. The proposed strategies can be used for law enforcement for combatting cyber crimes in wireless network crime scene investigations.

Positioning wireless mobiles has been widely studied. However, issues on efficiency and accuracy are not fully considered in the design of most positioning techniques. In this paper, we analyze the performance of localization of wireless mobiles with the knowledge of signal sampling theory. We propose a general approach of locating a wireless mobile device using a directional antenna, and we prove that the correctness of localization depends on the setting of the signal sampling rate. We derived a set of theorems to judge the time cost of localization in order to achieve a certain degree of accuracy. These theorems cover different types of directional antennas for signal strength sampling so that we can utilize them to enhance the positioning accuracy. We conduct extensive simulations to demonstrate the effectiveness and accuracy of our theory.

In this paper, we present 3DLoc: an integrated system of hardware and software toolkits for locating an 802.11-compliant mobile device in a three dimensional (3D) space. 3DLoc features two specialized antennas: an azimuth antenna and an elevation antenna, for detecting the azimuth and elevation angles of a mobile device respectively in real time. To improve positioning accuracy in real-world urban settings, we propose various signal processing techniques such as clustering and wavelet-transform based denoising, and present theoretical analysis of the accuracy of these techniques. With different antenna configurations, 3DLoc is able to track single or multiple targets in one round of azimuth scanning and elevation scanning. We conduct extensive experiments to demonstrate the efficiency and accuracy of 3DLoc. 3DLoc can be used in various applications, including wireless network forensics for locating anonymous criminal mobile devices.

People are benefiting tremendously from pervasively deployed WiFi networks. However, criminals may exploit the anonymity of WiFi communication and wireless routers to access illegal content such as child porn videos. It's becoming an urgent topic as regards to how to preserve and acquire network forensic data from household and small business wireless routers in order to track down criminals. In this paper, we first survey the forensic capacity of nearly all household wireless routers which are available on market. We present our analysis for people who are willing to choose a wireless router to monitor their network. Secondly, we develop a generic network forensic data logging mechanism to monitor traffic into and out of wireless routers which support OpenWrt. Our code running in the wireless routers could log network traffic and send connection information to the administrator via email.