137 research outputs found
Synchronized chaotic phase masks for encrypting and decrypting images
ABSTRACT: This paper presents an alternative to secure exchange of encrypted information through public open channels. Chaotic encryption introduces a security improvement by an efficient masking of the message with a chaotic signal. Message extraction by an authorized end user is done using a synchronization procedure, thus allowing a continuous change of the encrypting and decrypting keys. And optical implementation with a 4f optical encrypting architecture is suggested. Digital simulations, including the effects of missing data, corrupted data and noise addition are shown. These results proof the consistency of the proposal, and demonstrate a practical way to operate with it. Keywords: Optical encryptionChaotic phase generationSynchronization syste
Probabilistic modeling for single-photon lidar
Lidar is an increasingly prevalent technology for depth sensing, with applications including scientific measurement and autonomous navigation systems. While conventional systems require hundreds or thousands of photon detections per pixel to form accurate depth and reflectivity images, recent results for single-photon lidar (SPL) systems using single-photon avalanche diode (SPAD) detectors have shown accurate images formed from as little as one photon detection per pixel, even when half of those detections are due to uninformative ambient light. The keys to such photon-efficient image formation are two-fold: (i) a precise model of the probability distribution of photon detection times, and (ii) prior beliefs about the structure of natural scenes. Reducing the number of photons needed for accurate image formation enables faster, farther, and safer acquisition. Still, such photon-efficient systems are often limited to laboratory conditions more favorable than the real-world settings in which they would be deployed.
This thesis focuses on expanding the photon detection time models to address challenging imaging scenarios and the effects of non-ideal acquisition equipment. The processing derived from these enhanced models, sometimes modified jointly with the acquisition hardware, surpasses the performance of state-of-the-art photon counting systems.
We first address the problem of high levels of ambient light, which causes traditional depth and reflectivity estimators to fail. We achieve robustness to strong ambient light through a rigorously derived window-based censoring method that separates signal and background light detections. Spatial correlations both within and between depth and reflectivity images are encoded in superpixel constructions, which fill in holes caused by the censoring. Accurate depth and reflectivity images can then be formed with an average of 2 signal photons and 50 background photons per pixel, outperforming methods previously demonstrated at a signal-to-background ratio of 1.
We next approach the problem of coarse temporal resolution for photon detection time measurements, which limits the precision of depth estimates. To achieve sub-bin depth precision, we propose a subtractively-dithered lidar implementation, which uses changing synchronization delays to shift the time-quantization bin edges. We examine the generic noise model resulting from dithering Gaussian-distributed signals and introduce a generalized Gaussian approximation to the noise distribution and simple order statistics-based depth estimators that take advantage of this model. Additional analysis of the generalized Gaussian approximation yields rules of thumb for determining when and how to apply dither to quantized measurements. We implement a dithered SPL system and propose a modification for non-Gaussian pulse shapes that outperforms the Gaussian assumption in practical experiments. The resulting dithered-lidar architecture could be used to design SPAD array detectors that can form precise depth estimates despite relaxed temporal quantization constraints.
Finally, SPAD dead time effects have been considered a major limitation for fast data acquisition in SPL, since a commonly adopted approach for dead time mitigation is to operate in the low-flux regime where dead time effects can be ignored. We show that the empirical distribution of detection times converges to the stationary distribution of a Markov chain and demonstrate improvements in depth estimation and histogram correction using our Markov chain model. An example simulation shows that correctly compensating for dead times in a high-flux measurement can yield a 20-times speed up of data acquisition. The resulting accuracy at high photon flux could enable real-time applications such as autonomous navigation
Synchronized chaotic phase masks for encrypting and decrypting images
This paper presents an alternative to secure exchange of encrypted information through public open channels. Chaotic encryption introduces a security improvement by an efficient masking of the message with a chaotic signal. Message extraction by an authorized end user is done using a synchronization procedure, thus allowing a continuous change of the encrypting and decrypting keys.
And optical implementation with a 4f optical encrypting architecture is suggested. Digital simulations, including the effects of missing data, corrupted data and noise addition are shown. These results proof the consistency of the proposal, and demonstrate a practical way to operate with it.Facultad de IngenieríaCentro de Investigaciones ÓpticasUID en Óptica, Procesamiento de Imágenes y Metrología Óptic
Reservoir computing based on delay-dynamical systems
Today, except for mathematical operations, our brain functions much faster and more efficient than any supercomputer. It is precisely this form of information processing in neural networks that inspires researchers to create systems that mimic the brain’s information processing capabilities. In this thesis we propose a novel approach to implement these alternative computer architectures, based on delayed feedback. We show that one single nonlinear node with delayed feedback can replace a large network of nonlinear nodes. First we numerically investigate the architecture and performance of delayed feedback systems as information processing units. Then we elaborate on electronic and opto-electronic implementations of the concept. Next to evaluating their performance for standard benchmarks, we also study task independent properties of the system, extracting information on how to further improve the initial scheme. Finally, some simple modifications are suggested, yielding improvements in terms of speed or performanc
Orientation-Aware 3D SLAM in Alternating Magnetic Field from Powerlines
Identifying new sensing modalities for indoor localization is an interest of research. This paper studies powerline-induced alternating magnetic field (AMF) that fills the indoor space for the orientation-aware three-dimensional (3D) simultaneous localization and mapping (SLAM). While an existing study has adopted a uniaxial AMF sensor for SLAM in a plane surface, the design falls short of addressing the vector field nature of AMF and is therefore susceptible to sensor orientation variations. Moreover, although the higher spatial variability of AMF in comparison with indoor geomagnetism promotes location sensing resolution, extra SLAM algorithm designs are needed to achieve robustness to trajectory deviations from the constructed map. To address the above issues, we design a new triaxial AMF sensor and a new SLAM algorithm that constructs a 3D AMF intensity map regularized and augmented by a Gaussian process. The triaxial sensor’s orientation estimation is free of the error accumulation problem faced by inertial sensing. From extensive evaluation in eight indoor environments, our AMF-based 3D SLAM achieves sub-1m to 3m median localization errors in spaces of up to 500 m2 , sub-2° mean error in orientation sensing, and outperforms the SLAM systems based on Wi-Fi, geomagnetism, and uniaxial AMF by more than 30%
A Novel SNR Estimation Technique for OFDM Systems
Orthogonal Frequency Division Multiplexing (OFDM) systems have received a lot of
attention because of their robust performance in frequency dispersive channels. Further
performance improvement is achieved by employing more sophisticated receiver
techniques that often require the knowledge of signal-to-noise ratio (SNR) - broadly
defined as the ratio of the desired signal power to the unwanted noise power. For
example, noise variance and, hence, signal to noise ratio (SNR) estimates of the received
signal are very important for the channel quality control in communication systems.
Similarly, in advanced communication systems, SNR estimation is used for adaptive
algorithms for modulation, power control and coding.
The objective of the work undertaken in this thesis is to design a front-end noise power
estimator and, thence, SNR estimator. The proposed SNR estimator utilizes the OFDM
preamble signal - the preamble used for synchronization. The estimation is achieved by
auto correlating the preamble and it is deployed right at the front-end of the receiver.
Noise power and, hence, signal power is estimated from the correlation results. The
technique is also extended to obtaining noise power estimates of colored noise using
wavelet-packet based filter bank analysis of the noise.
In order to benchmark the proposed noise power and SNR estimation technique, a
complete end-to-end fixed-broadband-wireless-access-system (IEEE 802.16d) simulation
has been developed and the results are compared with other works reported in the
literature. The simulations are conducted in both frequency non-dispersive and dispersive
channels with real additive white Gaussian noise (A WGN) and also colored noise. It is
observed that the proposed estimator gives better SNR estimates. The proposed estimator
is also checked with WiMAX systems (IEEE802.\6d, 2004) using SUI multipath
channels and with Wi-Fi systems (IEEE802.11 a) with indoor channel models. The
estimator performs SNR estimation at front-end of the receiver unlike all other estimators
which perform SNR estimation at back-end of the receiver. Furthermore, the proposed
estimator has relatively low computational complexity; for it makes use of only one OFDM preamble signal to find the SNR estimates. The criteria of good SNR estimator
are accuracy of estimates, low complexity and easy to implement. The results show that
the proposed estimator fulfills these criteria successfully
Radar Imaging Based on IEEE 802.11ad Waveform in V2I Communications
Since most of vehicular radar systems are already exploiting millimeter-wave
(mmWave) spectra, it would become much more feasible to implement a joint radar
and communication system by extending communication frequencies into the mmWave
band. In this paper, an IEEE 802.11ad waveform-based radar imaging technique is
proposed for vehicular settings. A roadside unit (RSU) transmits the IEEE
802.11ad waveform to a vehicle for communications while the RSU also listens to
the echoes of transmitted waveform to perform inverse synthetic aperture radar
(ISAR) imaging. To obtain high-resolution images of the vehicle, the RSU needs
to accurately estimate round-trip delays, Doppler shifts, and velocity of
vehicle. The proposed ISAR imaging first estimates the round-trip delays using
a good correlation property of Golay complementary sequences in the IEEE
802.11ad preamble. The Doppler shifts are then obtained using least square
estimation from the echo signals and refined to compensate phase wrapping
caused by phase rotation. The velocity of vehicle is determined using an
equation of motion and the estimated Doppler shifts. Simulation results verify
that the proposed technique is able to form high-resolution ISAR images from
point scatterer models of realistic vehicular settings with different
viewpoints. The proposed ISAR imaging technique can be used for various
vehicular applications, e.g., traffic condition analyses or advanced collision
warning systems
Grid-Free MIMO Beam Alignment through Site-Specific Deep Learning
Beam alignment is a critical bottleneck in millimeter wave (mmWave)
communication. An ideal beam alignment technique should achieve high
beamforming (BF) gain with low latency, scale well to systems with higher
carrier frequencies, larger antenna arrays and multiple user equipments (UEs),
and not require hard-to-obtain context information (CI). These qualities are
collectively lacking in existing methods. We depart from the conventional
codebook-based (CB) approach where the optimal beam is chosen from quantized
codebooks and instead propose a grid-free (GF) beam alignment method that
directly synthesizes the transmit (Tx) and receive (Rx) beams from the
continuous search space using measurements from a few site-specific probing
beams that are found via a deep learning (DL) pipeline. In realistic settings,
the proposed method achieves a far superior signal-to-noise ratio (SNR)-latency
trade-off compared to the CB baselines: it aligns near-optimal beams 100x
faster or equivalently finds beams with 10-15 dB better average SNR in the same
number of searches, relative to an exhaustive search over a conventional
codebook
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