Experience of Teaching Advanced Touch Sensing Technologies

A touchscreen is an electronic visual display that users can control by touching the screen with a special stylus or fingers. It allows the rapid, accurate and direct interaction by the user with display contents, which existing keyboard and mouse systems cannot. Touchscreens are popular in many information appliances such as tablet computers, smartphones, and personal digital assistants (PDAs). In fact, many display manufactures and chip vendors around the world, such as Samsung, Chimei, Atmel, ST Microelectronics, and Texas Instruments, have recognized the trend of using touchscreens as a highly desirable user interface component and started to integrate the touch-sensing technology into their products. There are many touch sensing technologies. Among them, analog resistive, surface capacitive, projected capacitive, infrared grid, optical imaging, and surface acoustic wave are the most important ones. We feel the importance and need to teach engineering students the touch sensing technology. This paper presents our experience of teaching touch sensing technology in our second microprocessor course. This course taught the touch-sensing technology in slightly over 5 weeks. As the start of the course, we introduced the working principles of each touch technology to students. We then conducted a comparison among these technologies and their applications in real-world electronic devices. Students were taught to program a touchscreen through a series of lab exercises. The Atmel SAM 4S-EK board was the main development board employed in the course for practicing touchscreen programming. This board includes four QTouch buttons and slides which utilize capacitive sensing technology, and a resistive touch panel on a color LCD display. Atmel provides a royalty free software library for developing touch applications in C. Students learned to link the library into their applications so as to provide touch sensing capability in their projects. During the course our students have shown great interests in touch sensing technologies and are capable of incorporating touch devices to improve the human machine interface of their capstone projects

The Use of BeagleBone Black Board in Engineering Design and Development

The BeagleBone Black (BBB) board is a low cost, powerful expandable computer launched by a community of developers sponsored by Texas Instruments in the early 2013. It is the newest product in the Beagle family. This board features a powerful TI Sitara™ ARM Cortex™-A8 processor which runs at 1 GHz. And a 2 GB on-board flash memory acts as the “hard drive” for the board to host a Linux operating system and other software development tools. The size of the board is small enough to fit in a mint tin box. It can be used for a variety of projects from high school fair projects to prototypes of very complex embedded systems. With a user-friendly, browser-based Bonescript programming environment called Cloud9, a learner can easily program the BBB board to rapidly prototype electronic systems that interface with real-world applications. Afterwards, as the knowledge of users develops, the board provides more complicated interfaces including C/C++ functions to access digital and analog pins aboard the ARM Cortex A8 microprocessor. The full power and capability of the BBB board may be programmed in the underlying onboard Linux operating system, such as Angstrom or Ubuntu. Moreover, the Beagle community provides a useful repository of example projects, forums and hardware/software documentation. This paper presents our work of employing the BBB board in designing engineering senior projects, and uses a case study of robot car with voice recognition senior project to compares it with Raspberry Pi and Arduino in educating engineering students to construct embedded systems. Our primary experiences demonstrate that the BBB board is an easy-to-use and cost-effective development kit which can be employed by college-level engineering students for their capstone design projects

Strength-Adaptive Adversarial Training

Adversarial training (AT) is proved to reliably improve network's robustness against adversarial data. However, current AT with a pre-specified perturbation budget has limitations in learning a robust network. Firstly, applying a pre-specified perturbation budget on networks of various model capacities will yield divergent degree of robustness disparity between natural and robust accuracies, which deviates from robust network's desideratum. Secondly, the attack strength of adversarial training data constrained by the pre-specified perturbation budget fails to upgrade as the growth of network robustness, which leads to robust overfitting and further degrades the adversarial robustness. To overcome these limitations, we propose \emph{Strength-Adaptive Adversarial Training} (SAAT). Specifically, the adversary employs an adversarial loss constraint to generate adversarial training data. Under this constraint, the perturbation budget will be adaptively adjusted according to the training state of adversarial data, which can effectively avoid robust overfitting. Besides, SAAT explicitly constrains the attack strength of training data through the adversarial loss, which manipulates model capacity scheduling during training, and thereby can flexibly control the degree of robustness disparity and adjust the tradeoff between natural accuracy and robustness. Extensive experiments show that our proposal boosts the robustness of adversarial training