Recap from Fall 2018
Northeastern Undergraduate Engineering Review
Dalton Cox, Student, Katherine Ziemer, Professor
A Ti-Ball titanium sublimation source was calibrated to atomic flux for use in molecular beam epitaxy (MBE) growth of thin films. Precise calculations of atomic flux are necessary for growth of crystalline barium titanate (BTO) films as well as measurements of the sticking coefficients (σ) to better understand the mechanism of crystal growth. Two sources used for flux calculations, film growth rate and manufacturer supplied total sublimation rate, disagreed by an order of magnitude, requiring additional inquiry into the source of error; however, both calculations agreed that σTi >> σBa in growth of BTO.
Maria Jennings, Undergraduate Student, Ian Kendrick, Post-Doctoral Research Associate, Clive Green, Graduate Student, Steve Lustig, Associate Professor
Microbial fuel cells (MFCs) intended for at-risk communities lacking sources of clean water and electricity could be more economically produced by the implementation of biomolecular air cathode technology: an encapsulated enzyme within an electrically conductive nonwoven spunbound polymer such as PEDOT:PSS. While PEDOT:PSS may be spunbound and boasts high conductivity, it is prone to delamination and redispersion due to its water solubility. We report the analysis of crosslinking reactions with divinyl sulfone (DVS) in order to improve the durability and insolubility of conductive PEDOT:PSS fibers. Analytical characterization of crosslinking PEDOT:PSS with DVS using time-lapsed ATR-FTIR spectroscopy in concentrated solutions permits a clear characterization of PEDOT:PSS-DVS crosslinking kinetics and structure.
Andrew Fish, Student, Yashar A. Aval, Researcher, Stefano Basagni, Professor
The purpose of this research is to explore mobilizing the control of SmartBuoyDuo devices that provide access to the nodes of an underwater acoustic network. The research entails the creation of a smartphone-based ultra-portable system to control basic functionalities of SmartBuoyDuos including their relays, sensor readings, and sleep cycles. The Teensy platform pair with a Bluetooth LE module and an XBee S3B are used to create a remote control gateway device capable of sending commands to, and receiving responses from, SmartBuoyDuos. This system is paired with an iOS application developed in the Swift 3 language using Apple’s CoreBluetooth framework. Prototyped on a breadboard, then finalized on a soldered protoboard, the remote control gateway also integrates an OLED display and a LiPo battery with charge monitoring.
Alexander M. Interrante-Grant, Student, David Kaeli, Professor
As the number of unique malware samples grows at a rapidly increasing rate, analysts are having trouble tracking the evolution of existing malware and identifying new malware in an ever-changing threat landscape. It can take hours for a malware analyst to evaluate a single sample, so they are increasingly turning to methods of fast, automated malware analysis to identify trends across and attributes of newly observed malware. One goal is to identify the author of a new piece of malware. Various approaches have been proposed, applying machine learning to various concise program representations. Because ground truth labels for malware samples are notoriously difficult to find, most machine learning approaches rely on unsupervised learning (i.e. clustering) methods.
In this paper, a number of recently proposed clustering approaches using co-occurrence matrices of system calls are evaluated. In addition to applying previously-proposed clustering algorithms to this program representation, this work applies Gaussian mixture model (GMM) clustering – an approach that had not been evaluated by the research community for dynamic malware clustering. Our results show that GMM clustering outperforms other clustering approaches, achieving a Fowlkes-Mallows score two times better than the state-of-the-art on a dataset of real-world malware curated from the VirusShare malware corpus.
Benjamin Trapani, Student, Julian Gutierrez, Student, David Kaeli, Professor
Image segmentation is one of the key analysis tools in biomedical imaging applications. Although level set segmentation algorithms have been explored thoroughly in the past, these approaches are non-scalable due to their inherent data dependencies. Algorithms with large corresponding data dependency graphs that contain many small cycles are difficult to parallelize, prohibiting these algorithms from effectively leveraging modern highly parallel compute devices. Given that the resolution of medical imaging hardware has continued to increase each year, and CPU performance has not kept pace, there is a need to explore parallel solutions for processing medical images. Prior work described an efficient level set segmentation algorithm designed for parallel architectures for segmenting 2D images. The algorithm segments an input image into four components based on an initial curve. The prior 2-D level set segmentation algorithm is extended, providing a solution for 3-D images. The algorithm is improved by examining adjacent voxels at each step, versus visiting adjacent pixels instead. The initial curve is a user-provided sphere that is defined parametrically to reduce copy overhead to the compute device. The efficiency of the 2D algorithm is preserved in the conversion, enabling the resulting algorithm to perform on the order of ten times faster than existing GPU-accelerated 3-D level set segmentation implementations. The implementation presented in this work supports real-time segmentation of 7T MRI images, leveraging the computational power of a NVIDIA Tesla K20 GPU to reduce execution time. This image segmentation algorithm supports identification of tumors, tissue volume measurements, and surgery planning at the rate required by radiologists today.
Recap from Fall 2016
Northeastern Undergraduate Engineering Review
Digitally Tunable Lowpass-Notch Filter Design for Analog Front-Ends in Brain Signal Measurement Applications
Kaidi Du, Student, Marvin Onabajo, Professor
A digitally tunable Transconductance-Capacitor Low-pass Notch Filter (LPNF) for Electroencephalography (EEG) application is presented in this research report. Since EEG signals fall into four basic frequency bands, δ (1-4Hz), θ (4-8Hz), α (8-13Hz), and β (13-40Hz), but the power line interference at 60Hz, created by electrode cable and circuitry, has much higher power than the brain signals, the power line interference negatively affects the accuracy of the EEG system. Therefore, a combination of a notch filter and a high-order low-pass filter is employed in this work. With the development of microcontrollers, digital control methods are becoming more frequent in integrated circuit (IC) implementations. Hence, a digital tuning method for this LPNF is in high demand. Due to the digital tuning approach, an automatic calibration of this Gm-C LPNF through a microcontroller can be realized in the future.
Craig W. Martland, Student, David P. Marchessault, Student, Andrew McGarey, Student, Diego Rivas, Student, Kevin W. Stanley, Student, and Yiannis Levendis, Professor
In recent years forest fires have become increasingly frequent, increasingly large and, hence, increasingly catastrophic. As these fires burn unchecked, firefighters strive to extinguish them by dropping water onto affected areas with aerial delivery methods, such as planes and helicopters. Past research at Northeastern University, showed that direct application of liquid nitrogen is very effective at extinguishing fuel pool fires and, thus, research was initiated to explore the application of liquid nitrogen to forest fires. It is hypothesized that liquid nitrogen would be effective at suppressing forest fires, most likely as a two- part approach. Initial application of liquid nitrogen can suppress the flames and subsequent application of water can extinguish deep-seated fires in the pores of the wood. Herein, as an initial step to realize this approach, a capsule was designed to deliver liquid nitrogen to forest fires. This capsule is designed to insulate the liquid nitrogen and minimize in-transit vaporization, whereas incorporation of exterior fins is expected to impart a controlled spin as the capsule falls from the helicopter. This spin will eject liquid nitrogen, which can create a sprinkling effect as it reaches a crown fire whereas any liquid nitrogen remaining in the capsule will be ejected upon impact and will affect the bottom fire. The capsule is made of a single injection molded piece to be cost-effective. Initial tests proved the insulating, spinning and spilling capabilities of the capsule. No fire tests have been conducted yet.
Matthew T. Tivnan, Student, Carey M. Rappaport, Professor
Data fusion is the process by which measurements collected by two or more sensors are combined to produce a better result than could have been produced by any of the sensors acting individually. X-ray transmission and Microwave Tomography (MWT) are good candidates for data fusion because of their complementary strengths. For example, X-Ray is known for high spatial resolution structural imaging and MWT provides higher contrast in the physical properties for certain applications. In this work, a simple image reconstruction algorithm is presented which utilizes data fusion between X-Ray and MWT measurements. One possible application in neuroimaging is then simulated in a numerical experiment. The final results show that data fusion has significant advantages over conventional approaches.
Andrew Tu, Student Member, IEEE, Brian Wilcox, Student Member, IEEE, Mark German, Yashar M. Aval, Member, IEEE, and Stefano Basagni, Senior Member, IEEE
Underwater acoustic communication and networks have attracted significant attention in recent years, with applications ranging from ocean monitoring to off-shore sensor control, and port surveillance. Experimental data are required to test and develop effective underwater networking protocols before underwater networks can be successfully deployed for real world applications. Unfortunately, there are very few permanent underwater acoustic testbeds currently in operation, making it difficult for full scale tests to be conducted. To meet the demands for experimental data, we are working to deploy a permanent underwater acoustic network at the Northeastern University Marine Science Center in Nahant, MA. At the final stage, the network will consist of at least five SM 975 Teledyne Benthos acoustic smart modems, with one wirelessly connected to the shore through a smart buoy of our design. This paper describes the interface for programming these modems and how we used it to implement a fundamental protocol to be used as performance benchmark for more advanced underwater solutions.
Gianmarco Vella, Student at Advanced Materials Processing Lab (AMPL)
The need for heating at nanoscale has pushed researchers in the study of reactive, nanostructured composites known as nanoheaters. Major topics of interest are the best conditions for consolidation, composition, and ignition of these innovative heat sources. This work presents a new method of ignition for nanoheaters, known as non contact microwave ignition, distinguishing itself from previously developed direct heat application methods. Al-Ni nanoheaters were fabricated through ultrasonic powder consolidation (UPC) with embedded aluminum and copper wires. The conductive properties of the embedded wires, acting as susceptors when exposed to electromagnetic radiation in the microwave range, were found to induce enough heat to Al-Ni nanoheaters to facilitate ignition. This nullifies the requirement of direct heat application to the fabricated nanoheaters to produce ignition. In addition to testing in gaseous environment, this new method of ignition for nanostructured, reactive composites was also tested in vacuum, verifying its effectiveness in a non-gaseous environment.