401,028 research outputs found
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
The unprecedented increase in the usage of computer vision technology in
society goes hand in hand with an increased concern in data privacy. In many
real-world scenarios like people tracking or action recognition, it is
important to be able to process the data while taking careful consideration in
protecting people's identity. We propose and develop CIAGAN, a model for image
and video anonymization based on conditional generative adversarial networks.
Our model is able to remove the identifying characteristics of faces and bodies
while producing high-quality images and videos that can be used for any
computer vision task, such as detection or tracking. Unlike previous methods,
we have full control over the de-identification (anonymization) procedure,
ensuring both anonymization as well as diversity. We compare our method to
several baselines and achieve state-of-the-art results.Comment: CVPR 202
An OFDM Signal Identification Method for Wireless Communications Systems
Distinction of OFDM signals from single carrier signals is highly important
for adaptive receiver algorithms and signal identification applications. OFDM
signals exhibit Gaussian characteristics in time domain and fourth order
cumulants of Gaussian distributed signals vanish in contrary to the cumulants
of other signals. Thus fourth order cumulants can be utilized for OFDM signal
identification. In this paper, first, formulations of the estimates of the
fourth order cumulants for OFDM signals are provided. Then it is shown these
estimates are affected significantly from the wireless channel impairments,
frequency offset, phase offset and sampling mismatch. To overcome these
problems, a general chi-square constant false alarm rate Gaussianity test which
employs estimates of cumulants and their covariances is adapted to the specific
case of wireless OFDM signals. Estimation of the covariance matrix of the
fourth order cumulants are greatly simplified peculiar to the OFDM signals. A
measurement setup is developed to analyze the performance of the identification
method and for comparison purposes. A parametric measurement analysis is
provided depending on modulation order, signal to noise ratio, number of
symbols, and degree of freedom of the underlying test. The proposed method
outperforms statistical tests which are based on fixed thresholds or empirical
values, while a priori information requirement and complexity of the proposed
method are lower than the coherent identification techniques
Distributed and adaptive location identification system for mobile devices
Indoor location identification and navigation need to be as simple, seamless,
and ubiquitous as its outdoor GPS-based counterpart is. It would be of great
convenience to the mobile user to be able to continue navigating seamlessly as
he or she moves from a GPS-clear outdoor environment into an indoor environment
or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing
infrastructure-based indoor localization systems lack such capability, on top
of potentially facing several critical technical challenges such as increased
cost of installation, centralization, lack of reliability, poor localization
accuracy, poor adaptation to the dynamics of the surrounding environment,
latency, system-level and computational complexities, repetitive
labor-intensive parameter tuning, and user privacy. To this end, this paper
presents a novel mechanism with the potential to overcome most (if not all) of
the abovementioned challenges. The proposed mechanism is simple, distributed,
adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a
mobile blind device can potentially utilize, as GPS-like reference nodes,
either in-range location-aware compatible mobile devices or preinstalled
low-cost infrastructure-less location-aware beacon nodes. The proposed approach
is model-based and calibration-free that uses the received signal strength to
periodically and collaboratively measure and update the radio frequency
characteristics of the operating environment to estimate the distances to the
reference nodes. Trilateration is then used by the blind device to identify its
own location, similar to that used in the GPS-based system. Simulation and
empirical testing ascertained that the proposed approach can potentially be the
core of future indoor and GPS-obstructed environments
Characterizing the Dynamic Response of a Chassis Frame in a Heavy-Duty Dump Vehicle based on an Improved Stochastic System Identification
This paper presents an online method for the assessment of the dynamic performance of the chassis frame in a heavy-duty dump truck based on a novel stochastic subspace identification (SSI) method. It introduces the use of an average correlation signal as the input data to conventional SSI methods in order to reduce the noisy and nonstationary contents in the vibration signals from the frame, allowing accurate modal properties to be attained for realistically assessing the dynamic behaviour of the frame when the vehicle travels on both bumped and unpaved roads under different operating conditions. The modal results show that the modal properties obtained online are significantly different from the offline ones in that the identifiable modes are less because of the integration of different vehicle systems onto the frame. Moreover, the modal shapes between 7Hz and 40Hz clearly indicate the weak section of the structure where earlier fatigues and unsafe operations may occur due to the high relative changes in the modal shapes. In addition, the loaded operations show more modes which cause high deformation on the weak section. These results have verified the performance of the proposed SSI method and provide reliable references for optimizing the construction of the frame
Development and Validation of a Rule-based Time Series Complexity Scoring Technique to Support Design of Adaptive Forecasting DSS
Evidence from forecasting research gives reason to believe that understanding time series complexity can enable design of adaptive forecasting decision support systems (FDSSs) to positively support forecasting behaviors and accuracy of outcomes. Yet, such FDSS design capabilities have not been formally explored because there exists no systematic approach to identifying series complexity. This study describes the development and validation of a rule-based complexity scoring technique (CST) that generates a complexity score for time series using 12 rules that rely on 14 features of series. The rule-based schema was developed on 74 series and validated on 52 holdback series using well-accepted forecasting methods as benchmarks. A supporting experimental validation was conducted with 14 participants who generated 336 structured judgmental forecasts for sets of series classified as simple or complex by the CST. Benchmark comparisons validated the CST by confirming, as hypothesized, that forecasting accuracy was lower for series scored by the technique as complex when compared to the accuracy of those scored as simple. The study concludes with a comprehensive framework for design of FDSS that can integrate the CST to adaptively support forecasters under varied conditions of series complexity. The framework is founded on the concepts of restrictiveness and guidance and offers specific recommendations on how these elements can be built in FDSS to support complexity
- …