David J. Malan

Sensor networks for emergency response: challenges and opportunities

Sensor networks, a new class of devices has the potential to revolutionize the capture, processing, and communication of critical data for use by first responders. CodeBlue integrates sensor nodes and other wireless devices into a disaster response setting and provides facilities for ad hoc network formation, resource naming and discovery, security, and in-network aggregation of sensor-produced data. We designed CodeBlue for rapidly changing, critical care environments. To test it, we developed two wireless vital sign monitors and a PDA-based triage application for first responders. Additionally, we developed MoteTrack, a robust radio frequency (RF)-based localization system, which lets rescuers determine their location within a building and track patients. Although much of our work on CodeBlue is preliminary, our initial experience with medical care sensor networks raised many exciting opportunities and challenges.

Starting with scratch in CS 1

1. Summary Scratch [4] is both a social computing environment and a rich programming language with a highly supportive interface. Although originally intended for an audience younger than college freshman, there is growing interest in using Scratch at the undergraduate level as a gentle introduction to hard concepts. David Malan and Henry Leitner first presented their experience teaching Harvard introductory CS courses with Scratch at SIGCSE two years ago [3]. At SIGCSE 08 John Maloney and Ursula Wolz provided the SIGCSE community with an overview of the language and the social computing website. John also presented research results at SIGCSE 08 on the popularity of programming among young people in a community clubhouse setting [4]. The kids preferred to program rather than play computer games. At both Harvard and The College of New Jersey (TCNJ) we have seen similar phenomena where the flexibility, simplicity and ease with which students can make their programming experience highly personal in a supportive community, actively engages them in the process of learning to program regardless of ethnicity or gender.

One big file is not enough: a critical evaluation of the dominant free-space sanitization technique

Many of todays privacy-preserving tools create a big file that fills up a hard drive or USB storage device in an effort to overwrite all of the “deleted files” that the media contain. But while this technique is widespread, it is largely unvalidated. We evaluate the effectiveness of the “big file technique” using sector-by-sector disk imaging on file systems running under Windows, Mac OS, Linux, and FreeBSD. We find the big file is effective in overwriting file data on FAT32, NTFS, and HFS, but not on Ext2fs, Ext3fs, or Reiserfs. In one case, a total of 248 individual files consisting of 1.75MB of disk space could be recovered in their entirety. Also, file metadata such as filenames are rarely overwritten. We present a theoretical analysis of the file sanitization problem and evaluate the effectiveness of a commercial implementation that implements an improved strategy.

Advanced Forensic Format: an Open Extensible Format for Disk Imaging

This paper describes the Advanced Forensic Format (AFF), which is designed as an alternative to current proprietary disk image formats. AFF offers two significant benefits. First, it is more flexible because it allows extensive metadata to be stored with images. Second, AFF images consume less disk space than images in other formats (e.g., EnCase images). This paper also describes the Advanced Disk Imager, a new program for acquiring disk images that compares favorably with existing alternatives.

Swarm Filtering Procedure and Application to MRI Mammography

Research on swarming has primarily focused on applying swarming behavior with physics–derived or ad–hoc models to tasks requiring collective intelligence in robotics and optimization. In contrast, applications in signal processing are still lacking. The purpose of this paper is to investigate the use of biologically–inspired swarm methods for signal filtering. The signal, in the case of images the grayscale value of the pixels along a line in the image, is modeled by the trajectory of an agent playing the role of the prey for a swarm of hunting agents. The swarm hunting the prey is the system performing the signal processing. The movement of the center of mass of the swarm represents the filtered signal. The position of the center of mass of the swarm during the virtual hunt is reverted into grayscale values and represents the output signal. We show results of applying the swarm–based signal processing method to MRI mammographies