257 research outputs found
Frequency-modulated continuous-wave LiDAR compressive depth-mapping
We present an inexpensive architecture for converting a frequency-modulated
continuous-wave LiDAR system into a compressive-sensing based depth-mapping
camera. Instead of raster scanning to obtain depth-maps, compressive sensing is
used to significantly reduce the number of measurements. Ideally, our approach
requires two difference detectors. % but can operate with only one at the cost
of doubling the number of measurments. Due to the large flux entering the
detectors, the signal amplification from heterodyne detection, and the effects
of background subtraction from compressive sensing, the system can obtain
higher signal-to-noise ratios over detector-array based schemes while scanning
a scene faster than is possible through raster-scanning. %Moreover, we show how
a single total-variation minimization and two fast least-squares minimizations,
instead of a single complex nonlinear minimization, can efficiently recover
high-resolution depth-maps with minimal computational overhead. Moreover, by
efficiently storing only data points from measurements of an
pixel scene, we can easily extract depths by solving only two linear equations
with efficient convex-optimization methods
DWT-SMM-based audio steganography with RSA encryption and compressive sampling
Problems related to confidentiality in information exchange are very important in the digital computer era. Audio steganography is a form of a solution that infuses information into digital audio, and utilizes the limitations of the human hearing system in understanding and detecting sound waves. The steganography system applies compressive sampling (CS) to the process of acquisition and compression of bits in binary images. Rivest, Shamir, and Adleman (RSA) algorithms are used as a system for securing binary image information by generating encryption and decryption key pairs before the process is embedded. The insertion method uses statistical mean manipulation (SMM) in the wavelet domain and low frequency sub-band by dividing the audio frequency sub-band using discrete wavelet transform (DWT) first. The optimal results by using our system are the signal-to-noise ratio (SNR) above 45 decibel (dB) and 5.3833 bit per second (bps) of capacity also our system has resistant to attack filtering, noise, resampling and compression attacks
Terahertz Communications and Sensing for 6G and Beyond: A Comprehensive View
The next-generation wireless technologies, commonly referred to as the sixth
generation (6G), are envisioned to support extreme communications capacity and
in particular disruption in the network sensing capabilities. The terahertz
(THz) band is one potential enabler for those due to the enormous unused
frequency bands and the high spatial resolution enabled by both short
wavelengths and bandwidths. Different from earlier surveys, this paper presents
a comprehensive treatment and technology survey on THz communications and
sensing in terms of the advantages, applications, propagation characterization,
channel modeling, measurement campaigns, antennas, transceiver devices,
beamforming, networking, the integration of communications and sensing, and
experimental testbeds. Starting from the motivation and use cases, we survey
the development and historical perspective of THz communications and sensing
with the anticipated 6G requirements. We explore the radio propagation, channel
modeling, and measurements for THz band. The transceiver requirements,
architectures, technological challenges, and approaches together with means to
compensate for the high propagation losses by appropriate antenna and
beamforming solutions. We survey also several system technologies required by
or beneficial for THz systems. The synergistic design of sensing and
communications is explored with depth. Practical trials, demonstrations, and
experiments are also summarized. The paper gives a holistic view of the current
state of the art and highlights the issues and challenges that are open for
further research towards 6G.Comment: 55 pages, 10 figures, 8 tables, submitted to IEEE Communications
Surveys & Tutorial
NEW ALGORITHMS FOR COMPRESSED SENSING OF MRI: WTWTS, DWTS, WDWTS
Magnetic resonance imaging (MRI) is one of the most accurate imaging techniques that can be used to detect several diseases, where other imaging methodologies fail. MRI data takes a longer time to capture. This is a pain taking process for the patients to remain still while the data is being captured. This is also hard for the doctor as well because if the images are not captured correctly then it will lead to wrong diagnoses of illness that might put the patients lives in danger. Since long scanning time is one of most serious drawback of the MRI modality, reducing acquisition time for MRI acquisition is a crucial challenge for many imaging techniques. Compressed Sensing (CS) theory is an appealing framework to address this issue since it provides theoretical guarantees on the reconstruction of sparse signals while projection on a low dimensional linear subspace. Further enhancements have extended the CS framework by performing Variable Density Sampling (VDS) or using wavelet domain as sparsity basis generator. Recent work in this approach considers parent-child relations in the wavelet levels.
This paper further extends the prior approach by utilizing the entire wavelet tree structure as an argument for coefficient correlation and also considers the directionality of wavelet coefficients using Hybrid Directional Wavelets (HDW). Incorporating coefficient thresholding in both wavelet tree structure as well as directional wavelet tree structure, the experiments reveal higher Signal to Noise ratio (SNR), Peak Signal to Noise ratio (PSNR) and lower Mean Square Error (MSE) for the CS based image reconstruction approach. Exploiting the sparsity of wavelet tree using the above-mentioned techniques achieves further lessening for data needed for the reconstruction, while improving the reconstruction result. These techniques are applied on a variety of images including both MRI and non-MRI data. The results show the efficacy of our techniques
Optical Fiber Interferometric Sensors
The contributions presented in this book series portray the advances of the research in the field of interferometric photonic technology and its novel applications. The wide scope explored by the range of different contributions intends to provide a synopsis of the current research trends and the state of the art in this field, covering recent technological improvements, new production methodologies and emerging applications, for researchers coming from different fields of science and industry. The manuscripts published in the Special issue, and re-printed in this book series, report on topics that range from interferometric sensors for thickness and dynamic displacement measurement, up to pulse wave and spirometry applications
Millimetre-Resolution Photonics-Assisted Radar
Radar is essential in applications such as anti-collision systems for driving, airport security screening,
and contactless vital sign detection. The demand for high-resolution and real-time recognition in
radar applications is growing, driving the development of electronic radars with increased bandwidth,
higher frequency, and improved reconfigurability. However, conventional electronic approaches are
challenging due to limitations in synthesising radar signals, limiting performance.
In contrast, microwave photonics-enabled radars have gained interest because they offer numerous
benefits compared to traditional electronic methods. Photonics-assisted techniques provide a broad
fractional bandwidth at the optical carrier frequency and enable spectrum manipulation, producing
wideband and high-resolution radar signals in various formats. However, photonic-based methods
face limitations like low time-frequency linearity due to the inherent nonlinearity of lasers, restricted RF bandwidth, limited stability of the photonic frequency multipliers, and difficulties in achieving
extended sensing with dispersion-based techniques.
In response to these challenges, this thesis presents approaches for generating broadband radar
signals with high time-frequency linearity using recirculated unidirectional optical frequency-shifted
modulation. The photonics-assisted system allows flexible bandwidth tuning from sub-GHz to over 30
GHz and requires only MHz-level electronics. Such a system offers millimetre-level range resolution
and a high imaging refresh rate, detecting fast-moving objects using the ISAR technique. With
millimetre-level resolution and micrometre accuracy, this system supports contactless vital sign
detection, capturing precise respiratory patterns from simulators and a living body using a cane toad.
In the end, we highlight the promise of merging radar and LiDAR, foreshadowing future
advancements in sensor fusion for enhanced sensing performance and resilience
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