In this poster, we present TaintGrep, a novel static analysis approach to detect vulnerabilities of Android applications. This approach combines the advantages of semantic pattern matching and taint analysis to get better accuracy and be able to detect cross-function vulnerabilities. Compared with many traditional tools, TaintGrep does not require the full source code or building environment to analyze. Moreover, it supports users in defining their customized matching rules using their vulnerability mining experience, which makes this approach more flexible and scalable. In the preliminary experiment, we give a detailed analysis of the rules of two typical vulnerabilities: generic DoS and arbitrary file read/write, and have detected 77 0day vulnerabilities with these rules in 16 well-known Android applications.

Atomic force microscopy (AFM) is a powerful measurement instrument, which has been widely implemented in various fields. To enhance the maneuverability, an AFM can be modified and its cantilever can be controlled as a robotic end-effector. To precisely control the nanorobots, their scanners inherent hysteresis, which is the main disadvantage, should be compensated sufficiently. Mostly, hysteresis compensators used in AFM control system only employ the symmetric models, which cannot well represent the asymmetric hysteresis phenomena of piezo-scanners. However, under many cases, the smart material based scanners possess asymmetric hysteresis slightly or seriously, which is far more complex than the regular symmetric case; in addition, for commercial or customized AFMs, the scanners are usually controlled without feedback. These drawbacks tackle further improvement of positioning accuracy of AFM systems. To effectively describe and further reduce the hysteresis of general cases, we propose a new type of generalized Prandtl-Ishlinskii (PI) operator based superposition model, named unparallel PI (UPI) model. The flexible edge of the UPI operator can be tilted freely within a defined range, which enables it to capture asymmetric hysteresis efficiently. To cancel the hysteresis effect in an open-loop system, three inverse compensation approaches are proposed, compared, and the corresponding stability is analyzed. Experiments with AFM based nanorobot verified the proposed approaches with convincing performance.

Conventional scanning probe microscopy (SPM)-based nanomanipulations always have to face scanner accuracy problems such as hysteresis, nonlinearity, and thermal drift. Although some scanners consist of internal position sensors, the sensitivity is not high enough to monitor high-resolution nanomanipulations. Additionally, once the scan size decreases to a nanolevel such as less than 100 nm, the noise brought by sensors is large enough to affect the performance of the closed-loop motion control system. In this paper, a non-vector space control strategy based on compressive feedback is proposed in order to improve the accuracy of SPM-based nanomanipulations. In this approach, local images (or compressive data) are used as both the reference input and feedback for a non-vector space closed-loop controller which considers the local image (or compressive data) as a set. The controller is designed in non-vector space, and it requires no prior information on features or landmarks which are widely used in traditional visual servoing. In this paper, the atomic force microscopy is used as an example of SPM to implement the non-vector space control strategy for nanomanipulations. The motivation of designing such a non-vector space controller is to solve the accuracy problem in nanomanipulation. Without this technique, the SPM-based nanomanipulations, such as nanomeasurement and nanosurgery, are difficult to conduct, with accuracy controlled under several nanometers. In order to illustrate the contributions and potential applications of this non-vector controller, at the end of this paper, an application of carbon nanotube local electrical property characterization based on a non-vector space motion control is shown to clearly verify the concept. Compared with other research in the local electrical property characterization, the non-vector space controller can ensure that the measurement accuracy (position error) is controlled within a few nanometers, which also ensures the reliability of measurement results. Additionally, this non-vector space control method can be implemented into any kind of SPM to realize a real-time control for nanomanipulation such as nanofabrication and nanoassembly.

AFM based nanomanipulations have been successfully applied in various areas such as physics, biology and so forth in nano scale. Traditional nanomanipulations always have to approach the problems such as hysteresis, nonlinearity and thermal drift of the scanner, and the noise brought by the position sensor. In this research, a compressive feedbacks based non-vector space control approach is proposed for improving the accuracy of AFM based nanomanipulations. Instead of sensors, the local image was used as the feedback to a non-vector space controller to generate a closed-loop control for manipulation. In this paper, there are four research topics: First, local scan strategy was used to get a local image. Second, since the feedback is an image, a non-vector space controller was designed to deal with the difficulty in vector space such as calibration and coordinate transformation. Third, in order to further decrease the time spent on local scan, compressive sensing was introduced to this system. Finally, to overcome the disadvantage that compressive sensing costs time on reconstructing the original signal, we directly use the compressive data as the feedback. Both theoretical analysis and experimental results have shown that the system has a good performance on AFM tip motion control. Therefore, the non-vector space control method can make visual servoing easier, and the compressive feedback could make a high speed real-time control of nanomanipulation possible. In addition, this new method can be applied to nano-assembly, nano-imaging and nanomanipulation.

This paper presents our recently developed humanoid neck system that can effectively mimic motion of human neck with very low motion noises. The feature of low motion noises allows our system to work like a real human head/neck. Thus the level of acoustic noises from wearable equipments, such as donning respirators or chemical-resistant jackets, induced by human head motion can be simulated and investigated using such a system. The objective of this investigation is to facilitate using head-worn communication devices for the person who wears the protective equipment/uniform that usually produces communication-noise when the head/neck moves. Our low motion-noise humanoid neck system is based on the spring structure, which can generate 1 Degree of Freedom (DOF) jaw movement and 3DOF neck movement. To guarantee the low-noise feature, no noise-makers like gear and electro-driven parts are embedded in the head/neck structure. Instead, the motions are driven by seven cables, and the actuators pulling the cables are sealed in a sound insulation box. Furthermore, statics analysis of the system has been processed completely. Experimental results validate the analysis, and clearly show that the head/neck system can greatly mimic the motions of human head with an A-weighted noise level of 30 dB or below.

Nanomanipulation with Atomic force microscopy (AFM) is one of the fundamental tools for nano-manufacturing. The control of the nanomanipulation system requires accurate feedback from the piezoelectric actuator and high frequency response of the control system. We designed and implemented two distinct control schemes by using real-time Linux. The aim is to study various factors in the control of the AFM based nanomanipulation system. By integrating the original controller with the external Linux real-time controller, we achieved a stable system with high response frequency. Finally this multiple input single output (MISO) system is validated to be an effective and efficient tool for the controlling of the nanolithography operation through a haptic device.

We present the design and implementation of a new jumping robot for mobile sensor network. Unlike other jumping robots, the robot is based on a simple two-mass-spring model. After we throw it on ground, it can stabilize itself and then jump once. The detailed mechanism design including the load holding and self-stabilization are presented. Jumping heights and distances with different robot weights are measured and compared with calculated values from the two-mass-spring model.