74 research outputs found
An adaptable fuzzy-based model for predicting link quality in robot networks.
It is often essential for robots to maintain wireless connectivity with other systems so that commands, sensor data, and other situational information can be exchanged. Unfortunately, maintaining sufficient connection quality between these systems can be problematic. Robot mobility, combined with the attenuation and rapid dynamics associated with radio wave propagation, can cause frequent link quality (LQ) issues such as degraded throughput, temporary disconnects, or even link failure. In order to proactively mitigate such problems, robots must possess the capability, at the application layer, to gauge the quality of their wireless connections. However, many of the existing approaches lack adaptability or the framework necessary to rapidly build and sustain an accurate LQ prediction model. The primary contribution of this dissertation is the introduction of a novel way of blending machine learning with fuzzy logic so that an adaptable, yet intuitive LQ prediction model can be formed. Another significant contribution includes the evaluation of a unique active and incremental learning framework for quickly constructing and maintaining prediction models in robot networks with minimal sampling overhead
Effects of YouTube Video as Pre-Lecture Preparation
Classroom lectures convey course concepts more effectively when students have prepared in advance. Traditionally, students prepare for lectures by reading the course textbook. Textbooks are the default study material for most educational courses; however, some technical subjects are better conveyed in video format. Therefore, in this study, we encouraged students to supplement their learning resources with web-based video tutorials that provide detailed demonstrations with the respect to technical network configuration and management. YouTube is a video sharing website that can provide free educational tutorials and instructions on technical subject matter, where students can observe practical human-machine interaction to prepare for lectures and increase overall course performance on exams, assignments, and laboratory projects. Our goal was to compare the overall performance as well as the level of active class participation between two groups of the same computer networking course. We found that the group that used YouTube videos for pre-lecture preparation scored approximately 3% higher on exams but 5% lower on homework assignments than the control group (textbook only). There was no statistical significance between the two groups with respect to overall course grades. Study habits and degree of class participation of each student correlated more strongly with overall course performance than whether the student viewed the videos
Cascaded Neural Networks for Identification and Posture-Based Threat Assessment of Armed People
This paper presents a near real-time, multi-stage classifier which identifies people and handguns in images, and then further assesses the threat-level that a person poses based on their body posture. The first stage consists of a convolutional neural network (CNN) that determines whether a person and a handgun are present in an image. If so, a second stage CNN is then used to estimate the pose of the person detected to have a handgun. Lastly, a feed-forward neural network (NN) makes the final threat assessment based on the joint positions of the person’s skeletal pose estimate from the previous stage. On average, this entire pipeline requires less than 1 second of processing time on a desktop computer. The model was trained using approximately 2,000 images and achieved a pistol and person detection rate of 22% and 55%, respectively. The final stage NN correctly identified the severity of the threat with 84% accuracy. The images used to train each stage of our multi-classifier model are available online. With an expanded dataset the accuracy of detecting people and pistols can likely be improved in the future
The contemporary role of renal mass biopsy in the management of small renal tumors
The selective use of percutaneous biopsy for diagnosis in renal masses is a relatively uncommon approach when compared to the management of other solid neoplasms. With recent advancements in imaging techniques and their widespread use, the incidental discovery of asymptomatic, small renal masses (SRM) is on the rise and a substantial percentage of these SRM are benign. Recent advances in diagnostics have significantly improved accuracy rates of renal mass biopsy (RMB), making it a potentially powerful tool in the management of SRM. In this review, we will discuss the current management of SRM, problems with the traditional view of RMB, improvements in the diagnostic power of RMB, cost-effectiveness of RMB, and risks associated with RMB. RMB may offer important information enabling treating clinicians to better risk-stratify patients and ultimately provide a more personalized treatment approach for SRM
Trusting AI: Integrating Artificial Intelligence into the Army’s Professional Expert Knowledge
Integrating artificially intelligent technologies for military purposes poses a special challenge. In previous arms races, such as the race to atomic bomb technology during World War II, expertise resided within the Department of Defense. But in the artificial intelligence (AI) arms race, expertise dwells mostly within industry and academia. Also, unlike the development of the bomb, effective employment of AI technology cannot be relegated to a few specialists; almost everyone will have to develop some level of AI and data literacy. Complicating matters is AI-driven systems can be a “black box” in that humans may not be able to explain some output, much less be held accountable for its consequences. This inability to explain coupled with the cession to a machine of some functions normally performed by humans risks the relinquishment of some jurisdiction and, consequently, autonomy to those outside the profession. Ceding jurisdiction could impact the American people’s trust in their military and, thus, its professional standing. To avoid these outcomes, creating and maintaining trust requires integrating knowledge of AI and data science into the military’s professional expertise. This knowledge covers both AI technology and how its use impacts command responsibility; talent management; governance; and the military’s relationship with the US government, the private sector, and society.https://press.armywarcollege.edu/monographs/1955/thumbnail.jp
Discovery of a Very Young Field L Dwarf, 2MASS J01415823-4633574
While following up L dwarf candidates selected photometrically from the Two
Micron All Sky Survey, we uncovered an unusual object designated 2MASS
J01415823-4633574. Its optical spectrum exhibits very strong bands of vanadium
oxide but abnormally weak absorptions by titanium oxide, potassium, and sodium.
Morphologically such spectroscopic characteristics fall intermediate between
old, field early-L dwarfs (log(g)~5) and very late M giants (log(g)~0), leading
us to favor low gravity as the explanation for the unique spectral signatures
of this L dwarf. Such a low gravity can be explained only if this L dwarf is
much lower in mass than a typical old field L dwarf of similar temperature and
is still contracting to its final radius. These conditions imply a very young
age. Further evidence of youth is found in the near-infrared spectrum,
including a triangular-shaped H-band continuum reminiscent of young brown dwarf
candidates discovered in the Orion Nebula Cluster. Using the above information
along with comparisons to brown dwarf atmospheric and interior models, our
current best estimate is that this L dwarf has an age of 1-50 Myr and a mass of
6-25 M_Jupiter. The location of 2MASS 0141-4633 on the sky coupled with a
distance estimate of ~35 pc and the above age estimate suggests that this
object may be a brown dwarf member of either the 30-Myr-old Tucana/Horologium
Association or the ~12-Myr-old beta Pic Moving Group.Comment: Accepted for publication in the 10 March 2006 issue (volume 639) of
the Astrophysical Journa
A Study of the Diverse T Dwarf Population Revealed by WISE
We report the discovery of 87 new T dwarfs uncovered with the Wide-field Infrared Survey Explorer (WISE) and 3 brown dwarfs with extremely red near-infrared colors that exhibit characteristics of both L and T dwarfs. Two of the new T dwarfs are likely binaries with L7 ± 1 primaries and mid-type T secondaries. In addition, our follow-up program has confirmed 10 previously identified T dwarfs and 4 photometrically selected L and T dwarf candidates in the literature. This sample, along with the previous WISE discoveries, triples the number of known brown dwarfs with spectral types later than T5. Using the WISE All-Sky Source Catalog we present updated color-color and color-type diagrams for all the WISE-discovered T and Y dwarfs. Near-infrared spectra of the new discoveries are presented along with spectral classifications. To accommodate later T dwarfs we have modified the integrated flux method of determining spectral indices to instead use the median flux. Furthermore, a newly defined J-narrow index differentiates the early-type Y dwarfs from late-type T dwarfs based on the J-band continuum slope. The K/J indices for this expanded sample show that 32% of late-type T dwarfs have suppressed K-band flux and are blue relative to the spectral standards, while only 11% are redder than the standards. Comparison of the Y/J and K/J index to models suggests diverse atmospheric conditions and supports the possible re-emergence of clouds after the L/T transition. We also discuss peculiar brown dwarfs and candidates that were found not to be substellar, including two young stellar objects and two active galactic nuclei. The substantial increase in the number of known late-type T dwarfs provides a population that will be used to test models of cold atmospheres and star formation. The coolest WISE-discovered brown dwarfs are the closest of their type and will remain the only sample of their kind for many years to come
A Sample of Very Young Field L Dwarfs and Implications for the Brown Dwarf "Lithium Test" at Early Ages
Using a large sample of optical spectra of late-type dwarfs, we identify a
subset of late-M through L field dwarfs that, because of the presence of
low-gravity features in their spectra, are believed to be unusually young. From
a combined sample of 303 field L dwarfs, we find observationally that
7.6+/-1.6% are younger than 100 Myr. This percentage is in agreement with
theoretical predictions once observing biases are taken into account. We find
that these young L dwarfs tend to fall in the southern hemisphere (Dec < 0 deg)
and may be previously unrecognized, low-mass members of nearby, young
associations like Tucana-Horologium, TW Hydrae, beta Pictoris, and AB Doradus.
We use a homogeneously observed sample of roughly one hundred and fifty
6300-10000 Angstrom spectra of L and T dwarfs taken with the Low-Resolution
Imaging Spectrometer at the W. M. Keck Observatory to examine the strength of
the 6708-A Li I line as a function of spectral type and further corroborate the
trends noted by Kirkpatrick et al. (2000). We use our low-gravity spectra to
investigate the strength of the Li I line as a function of age. The data weakly
suggest that for early- to mid-L dwarfs the line strength reaches a maximum for
a few 100 Myr, whereas for much older (few Gyr) and much younger (<100 Myr) L
dwarfs the line is weaker or undetectable. We show that a weakening of lithium
at lower gravities is predicted by model atmosphere calculations, an effect
partially corroborated by existing observational data. Larger samples
containing L dwarfs of well determined ages are needed to further test this
empirically. If verified, this result would reinforce the caveat first cited in
Kirkpatrick et al. (2006) that the lithium test should be used with caution
when attempting to confirm the substellar nature of the youngest brown dwarfs.Comment: 73 pages with 22 figures; to appear in ApJ (Dec 20, 2008, v689n2
issue
A Study of the Diverse T Dwarf Population Revealed by WISE
We report the discovery of 87 new T dwarfs uncovered with the Wide-field
Infrared Survey Explorer (WISE) and three brown dwarfs with extremely red
near-infrared colors that exhibit characteristics of both L and T dwarfs. Two
of the new T dwarfs are likely binaries with L7+/-1 primaries and mid-type T
secondaries. In addition, our follow-up program has confirmed 10 previously
identified T dwarfs and four photometrically-selected L and T dwarf candidates
in the literature. This sample, along with the previous WISE discoveries,
triples the number of known brown dwarfs with spectral types later than T5.
Using the WISE All-Sky Source Catalog we present updated color-color and
color-type diagrams for all the WISE-discovered T and Y dwarfs. Near-infrared
spectra of the new discoveries are presented, along with spectral
classifications. To accommodate later T dwarfs we have modified the integrated
flux method of determining spectral indices to instead use the median flux.
Furthermore, a newly defined J-narrow index differentiates the early-type Y
dwarfs from late-type T dwarfs based on the J-band continuum slope. The K/J
indices for this expanded sample show that 32% of late-type T dwarfs have
suppressed K-band flux and are blue relative to the spectral standards, while
only 11% are redder than the standards. Comparison of the Y/J and K/J index to
models suggests diverse atmospheric conditions and supports the possible
re-emergence of clouds after the L/T transition. We also discuss peculiar brown
dwarfs and candidates that were found not to be substellar, including two Young
Stellar Objects and two Active Galactic Nuclei. The coolest WISE-discovered
brown dwarfs are the closest of their type and will remain the only sample of
their kind for many years to come.Comment: Accepted to ApJS on 15 January 2013; 99 pages in preprint format, 30
figures, 12 table
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