24 research outputs found
Supervised Machine Learning Techniques for Trojan Detection with Ring Oscillator Network
With the globalization of the semiconductor manufacturing process, electronic
devices are powerless against malicious modification of hardware in the supply
chain. The ever-increasing threat of hardware Trojan attacks against integrated
circuits has spurred a need for accurate and efficient detection methods. Ring
oscillator network (RON) is used to detect the Trojan by capturing the
difference in power consumption; the power consumption of a Trojan-free circuit
is different from the Trojan-inserted circuit. However, the process variation
and measurement noise are the major obstacles to detect hardware Trojan with
high accuracy. In this paper, we quantitatively compare four supervised machine
learning algorithms and classifier optimization strategies for maximizing
accuracy and minimizing the false positive rate (FPR). These supervised
learning techniques show an improved false positive rate compared to principal
component analysis (PCA) and convex hull classification by nearly 40% while
maintaining > 90\% binary classification accuracy
A Non-invasive Technique to Detect Authentic/Counterfeit SRAM Chips
Many commercially available memory chips are fabricated worldwide in
untrusted facilities. Therefore, a counterfeit memory chip can easily enter
into the supply chain in different formats. Deploying these counterfeit memory
chips into an electronic system can severely affect security and reliability
domains because of their sub-standard quality, poor performance, and shorter
lifespan. Therefore, a proper solution is required to identify counterfeit
memory chips before deploying them in mission-, safety-, and security-critical
systems. However, a single solution to prevent counterfeiting is challenging
due to the diversity of counterfeit types, sources, and refinement techniques.
Besides, the chips can pass initial testing and still fail while being used in
the system. Furthermore, existing solutions focus on detecting a single
counterfeit type (e.g., detecting recycled memory chips). This work proposes a
framework that detects major counterfeit static random-access memory (SRAM)
types by attesting/identifying the origin of the manufacturer. The proposed
technique generates a single signature for a manufacturer and does not require
any exhaustive registration/authentication process. We validate our proposed
technique using 345 SRAM chips produced by major manufacturers. The silicon
results show that the test scores ( score) of our proposed technique of
identifying memory manufacturer and part-number are 93% and 71%, respectively.Comment: This manuscript has been submitted for possible publication.
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Respiratory Rate Monitoring in Clinical Environments with a Contactless Ultra-Wideband Impulse Radar-based Sensor System
Respiratory rate is an extremely important but poorly monitored vital sign for medical conditions. Current modalities for respiratory monitoring are suboptimal. This paper presents a proof of concept of a new algorithm using a contactless ultra-wideband (UWB) impulse radar-based sensor to detect respiratory rate in both a laboratory setting and in a two-subject case study in the Emergency Department. This novel approach has shown correlation with manual respiratory rate in the laboratory setting and shows promise in Emergency Department subjects. In order to improve respiratory rate monitoring, the UWB technology is also able to localize subject movement throughout the room. This technology has potential for utilization both in and out of the hospital environments to improve monitoring and to prevent morbidity and mortality from a variety of medical conditions associated with changes in respiratory rate
Towards the Avoidance of Counterfeit Memory: Identifying the DRAM Origin
Due to the globalization in the semiconductor supply chain, counterfeit
dynamic random-access memory (DRAM) chips/modules have been spreading worldwide
at an alarming rate. Deploying counterfeit DRAM modules into an electronic
system can have severe consequences on security and reliability domains because
of their sub-standard quality, poor performance, and shorter life span.
Besides, studies suggest that a counterfeit DRAM can be more vulnerable to
sophisticated attacks. However, detecting counterfeit DRAMs is very challenging
because of their nature and ability to pass the initial testing. In this paper,
we propose a technique to identify the DRAM origin (i.e., the origin of the
manufacturer and the specification of individual DRAM) to detect and prevent
counterfeit DRAM modules. A silicon evaluation shows that the proposed method
reliably identifies off-the-shelf DRAM modules from three major manufacturers
Eulerian Phase-based Motion Magnification for High-Fidelity Vital Sign Estimation with Radar in Clinical Settings
Efficient and accurate detection of subtle motion generated from small
objects in noisy environments, as needed for vital sign monitoring, is
challenging, but can be substantially improved with magnification. We developed
a complex Gabor filter-based decomposition method to amplify phases at
different spatial wavelength levels to magnify motion and extract 1D motion
signals for fundamental frequency estimation. The phase-based complex Gabor
filter outputs are processed and then used to train machine learning models
that predict respiration and heart rate with greater accuracy. We show that our
proposed technique performs better than the conventional temporal FFT-based
method in clinical settings, such as sleep laboratories and emergency
departments, as well for a variety of human postures.Comment: Accepted in IEEE Sensors 202
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Contactless Monitoring System Versus Gold Standard for Respiratory Rate Monitoring in Emergency Department Patients: Pilot Comparison Study
Background: Respiratory rate is a crucial indicator of disease severity yet is the most neglected vital sign. Subtle changes in respiratory rate may be the first sign of clinical deterioration in a variety of disease states. Current methods of respiratory rate monitoring are labor-intensive and sensitive to motion artifacts, which often leads to inaccurate readings or underreporting; therefore, new methods of respiratory monitoring are needed. The PulsON 440 (P440; TSDR Ultra Wideband Radios and Radars) radar module is a contactless sensor that uses an ultrawideband impulse radar to detect respiratory rate. It has previously demonstrated accuracy in a laboratory setting and may be a useful alternative for contactless respiratory monitoring in clinical settings; however, it has not yet been validated in a clinical setting.
Objective: The goal of this study was to (1) compare the P440 radar module to gold standard manual respiratory rate monitoring and standard of care telemetry respiratory monitoring through transthoracic impedance plethysmography and (2) compare the P440 radar to gold standard measurements of respiratory rate in subgroups based on sex and disease state.
Methods: This was a pilot study of adults aged 18 years or older being monitored in the emergency department. Participants were monitored with the P440 radar module for 2 hours and had gold standard (manual respiratory counting) and standard of care (telemetry) respiratory rates recorded at 15-minute intervals during that time. Respiratory rates between the P440, gold standard, and standard telemetry were compared using Bland-Altman plots and intraclass correlation coefficients.
Results: A total of 14 participants were enrolled in the study. The P440 and gold standard Bland-Altman analysis showed a bias of –0.76 (–11.16 to 9.65) and an intraclass correlation coefficient of 0.38 (95% CI 0.06-0.60). The P440 and gold standard had the best agreement at normal physiologic respiratory rates. There was no change in agreement between the P440 and the gold standard when grouped by admitting diagnosis or sex.
Conclusions: Although the P440 did not have statistically significant agreement with gold standard respiratory rate monitoring, it did show a trend of increased agreement in the normal physiologic range, overestimating at low respiratory rates, and underestimating at high respiratory rates. This trend is important for adjusting future models to be able to accurately detect respiratory rates. Once validated, the contactless respiratory monitor provides a unique solution for monitoring patients in a variety of settings
Anti-MRSA Activity of Oxysporone and Xylitol from the Endophytic Fungus Pestalotia sp. Growing on the Sundarbans Mangrove Plant Heritiera fomes
Heritiera fomes Buch.-Ham., a mangrove plant from the Sundarbans, has adapted to a unique habitat, muddy saline water, anaerobic soil, brackish tidal activities and high microbial competition. Endophytic fungal association protects this plant from adverse environmental conditions. This plant is used in Bangladeshi folk medicine, but it has not been extensively studied phytochemically, and there is hardly any report on investigation on endophytic fungi growing on this plant. In this study, endophytic fungi were isolated from the surface sterilized cladodes and leaves of H. fomes. The antimicrobial activities were evaluated against two Gram-positive and two Gram-negative bacteria and the fungal strain, Candida albicans. Extracts of Pestalotia sp. showed activities against all test bacterial strains, except that the EtOAc extract was inactive against E. coli. The structures of the purified compounds, oxysporone and xylitol, were elucidated by spectroscopic means. The anti-MRSA potential of the isolated compounds were determined against various MRSA strains, i.e., ATCC 25923, SA-1199B, RN4220, XU212, EMRSA-15 and EMRSA-16, with MIC values ranging from 32-128 g/mL. This paper, for the first time, reports on the anti-MRSA property of oxysporone and xylitol, isolation of the endophyte Pestalotia sp. from H. fomes, and isolation of xylitol from a Pestalotia sp