5,448 research outputs found
LiDAR Meta Depth Completion
Depth estimation is one of the essential tasks to be addressed when creating
mobile autonomous systems. While monocular depth estimation methods have
improved in recent times, depth completion provides more accurate and reliable
depth maps by additionally using sparse depth information from other sensors
such as LiDAR. However, current methods are specifically trained for a single
LiDAR sensor. As the scanning pattern differs between sensors, every new sensor
would require re-training a specialized depth completion model, which is
computationally inefficient and not flexible. Therefore, we propose to
dynamically adapt the depth completion model to the used sensor type enabling
LiDAR adaptive depth completion. Specifically, we propose a meta depth
completion network that uses data patterns derived from the data to learn a
task network to alter weights of the main depth completion network to solve a
given depth completion task effectively. The method demonstrates a strong
capability to work on multiple LiDAR scanning patterns and can also generalize
to scanning patterns that are unseen during training. While using a single
model, our method yields significantly better results than a non-adaptive
baseline trained on different LiDAR patterns. It outperforms LiDAR-specific
expert models for very sparse cases. These advantages allow flexible deployment
of a single depth completion model on different sensors, which could also prove
valuable to process the input of nascent LiDAR technology with adaptive instead
of fixed scanning patterns.Comment: Accepted at IROS 202
ML-based Secure Low-Power Communication in Adversarial Contexts
As wireless network technology becomes more and more popular, mutual
interference between various signals has become more and more severe and
common. Therefore, there is often a situation in which the transmission of its
own signal is interfered with by occupying the channel. Especially in a
confrontational environment, Jamming has caused great harm to the security of
information transmission. So I propose ML-based secure ultra-low power
communication, which is an approach to use machine learning to predict future
wireless traffic by capturing patterns of past wireless traffic to ensure
ultra-low-power transmission of signals via backscatters. In order to be more
suitable for the adversarial environment, we use backscatter to achieve
ultra-low power signal transmission, and use frequency-hopping technology to
achieve successful confrontation with Jamming information. In the end, we
achieved a prediction success rate of 96.19%
Multispectral Stereo-Image Fusion for 3D Hyperspectral Scene Reconstruction
Spectral imaging enables the analysis of optical material properties that are
invisible to the human eye. Different spectral capturing setups, e.g., based on
filter-wheel, push-broom, line-scanning, or mosaic cameras, have been
introduced in the last years to support a wide range of applications in
agriculture, medicine, and industrial surveillance. However, these systems
often suffer from different disadvantages, such as lack of real-time
capability, limited spectral coverage or low spatial resolution. To address
these drawbacks, we present a novel approach combining two calibrated
multispectral real-time capable snapshot cameras, covering different spectral
ranges, into a stereo-system. Therefore, a hyperspectral data-cube can be
continuously captured. The combined use of different multispectral snapshot
cameras enables both 3D reconstruction and spectral analysis. Both captured
images are demosaicked avoiding spatial resolution loss. We fuse the spectral
data from one camera into the other to receive a spatially and spectrally high
resolution video stream. Experiments demonstrate the feasibility of this
approach and the system is investigated with regard to its applicability for
surgical assistance monitoring.Comment: VISAPP 2024 - 19th International Conference on Computer Vision Theory
and Application
Measuring Blood Glucose Concentrations in Photometric Glucometers Requiring Very Small Sample Volumes
Glucometers present an important self-monitoring tool for diabetes patients
and therefore must exhibit high accu- racy as well as good usability features.
Based on an invasive, photometric measurement principle that drastically
reduces the volume of the blood sample needed from the patient, we present a
framework that is capable of dealing with small blood samples, while
maintaining the required accuracy. The framework consists of two major parts:
1) image segmentation; and 2) convergence detection. Step 1) is based on
iterative mode-seeking methods to estimate the intensity value of the region of
interest. We present several variations of these methods and give theoretical
proofs of their convergence. Our approach is able to deal with changes in the
number and position of clusters without any prior knowledge. Furthermore, we
propose a method based on sparse approximation to decrease the computational
load, while maintaining accuracy. Step 2) is achieved by employing temporal
tracking and prediction, herewith decreasing the measurement time, and, thus,
improving usability. Our framework is validated on several real data sets with
different characteristics. We show that we are able to estimate the underlying
glucose concentration from much smaller blood samples than is currently
state-of-the- art with sufficient accuracy according to the most recent ISO
standards and reduce measurement time significantly compared to
state-of-the-art methods
What a MESS: Multi-Domain Evaluation of Zero-Shot Semantic Segmentation
While semantic segmentation has seen tremendous improvements in the past,
there is still significant labeling efforts necessary and the problem of
limited generalization to classes that have not been present during training.
To address this problem, zero-shot semantic segmentation makes use of large
self-supervised vision-language models, allowing zero-shot transfer to unseen
classes. In this work, we build a benchmark for Multi-domain Evaluation of
Semantic Segmentation (MESS), which allows a holistic analysis of performance
across a wide range of domain-specific datasets such as medicine, engineering,
earth monitoring, biology, and agriculture. To do this, we reviewed 120
datasets, developed a taxonomy, and classified the datasets according to the
developed taxonomy. We select a representative subset consisting of 22 datasets
and propose it as the MESS benchmark. We evaluate eight recently published
models on the proposed MESS benchmark and analyze characteristics for the
performance of zero-shot transfer models. The toolkit is available at
https://github.com/blumenstiel/MESS
Acoustic-channel attack and defence methods for personal voice assistants
Personal Voice Assistants (PVAs) are increasingly used as interface to digital environments. Voice commands are used to interact with phones, smart homes or cars. In the US alone the number of smart speakers such as Amazon’s Echo and Google Home has grown by 78% to 118.5 million and 21% of the US population own at least one device. Given the increasing dependency of society on PVAs, security and privacy of these has become a major concern of users, manufacturers and policy makers. Consequently, a steep increase in research efforts addressing security and privacy of PVAs can be observed in recent years. While some security and privacy research applicable to the PVA domain predates their recent increase in popularity and many new research strands have emerged, there lacks research dedicated to PVA security and privacy. The most important interaction interface between users and a PVA is the acoustic channel and acoustic channel related security and privacy studies are desirable and required. The aim of the work presented in this thesis is to enhance the cognition of security and privacy issues of PVA usage related to the acoustic channel, to propose principles and solutions to key usage scenarios to mitigate potential security threats, and to present a novel type of dangerous attack which can be launched only by using a PVA alone. The five core contributions of this thesis are: (i) a taxonomy is built for the research domain of PVA security and privacy issues related to acoustic channel. An extensive research overview on the state of the art is provided, describing a comprehensive research map for PVA security and privacy. It is also shown in this taxonomy where the contributions of this thesis lie; (ii) Work has emerged aiming to generate adversarial audio inputs which sound harmless to humans but can trick a PVA to recognise harmful commands. The majority of work has been focused on the attack side, but there rarely exists work on how to defend against this type of attack. A defence method against white-box adversarial commands is proposed and implemented as a prototype. It is shown that a defence Automatic Speech Recognition (ASR) can work in parallel with the PVA’s main one, and adversarial audio input is detected if the difference in the speech decoding results between both ASR surpasses a threshold. It is demonstrated that an ASR that differs in architecture and/or training data from the the PVA’s main ASR is usable as protection ASR; (iii) PVAs continuously monitor conversations which may be transported to a cloud back end where they are stored, processed and maybe even passed on to other service providers. A user has limited control over this process when a PVA is triggered without user’s intent or a PVA belongs to others. A user is unable to control the recording behaviour of surrounding PVAs, unable to signal privacy requirements and unable to track conversation recordings. An acoustic tagging solution is proposed aiming to embed additional information into acoustic signals processed by PVAs. A user employs a tagging device which emits an acoustic signal when PVA activity is assumed. Any active PVA will embed this tag into their recorded audio stream. The tag may signal a cooperating PVA or back-end system that a user has not given a recording consent. The tag may also be used to trace when and where a recording was taken if necessary. A prototype tagging device based on PocketSphinx is implemented. Using Google Home Mini as the PVA, it is demonstrated that the device can tag conversations and the tagging signal can be retrieved from conversations stored in the Google back-end system; (iv) Acoustic tagging provides users the capability to signal their permission to the back-end PVA service, and another solution inspired by Denial of Service (DoS) is proposed as well for protecting user privacy. Although PVAs are very helpful, they are also continuously monitoring conversations. When a PVA detects a wake word, the immediately following conversation is recorded and transported to a cloud system for further analysis. An active protection mechanism is proposed: reactive jamming. A Protection Jamming Device (PJD) is employed to observe conversations. Upon detection of a PVA wake word the PJD emits an acoustic jamming signal. The PJD must detect the wake word faster than the PVA such that the jamming signal still prevents wake word detection by the PVA. An evaluation of the effectiveness of different jamming signals and overlap between wake words and the jamming signals is carried out. 100% jamming success can be achieved with an overlap of at least 60% with a negligible false positive rate; (v) Acoustic components (speakers and microphones) on a PVA can potentially be re-purposed to achieve acoustic sensing. This has great security and privacy implication due to the key role of PVAs in digital environments. The first active acoustic side-channel attack is proposed. Speakers are used to emit human inaudible acoustic signals and the echo is recorded via microphones, turning the acoustic system of a smartphone into a sonar system. The echo signal can be used to profile user interaction with the device. For example, a victim’s finger movement can be monitored to steal Android unlock patterns. The number of candidate unlock patterns that an attacker must try to authenticate herself to a Samsung S4 phone can be reduced by up to 70% using this novel unnoticeable acoustic side-channel
RF Fingerprinting Needs Attention: Multi-task Approach for Real-World WiFi and Bluetooth
A novel cross-domain attentional multi-task architecture - xDom - for robust
real-world wireless radio frequency (RF) fingerprinting is presented in this
work. To the best of our knowledge, this is the first time such comprehensive
attention mechanism is applied to solve RF fingerprinting problem. In this
paper, we resort to real-world IoT WiFi and Bluetooth (BT) emissions (instead
of synthetic waveform generation) in a rich multipath and unavoidable
interference environment in an indoor experimental testbed. We show the impact
of the time-frame of capture by including waveforms collected over a span of
months and demonstrate the same time-frame and multiple time-frame
fingerprinting evaluations. The effectiveness of resorting to a multi-task
architecture is also experimentally proven by conducting single-task and
multi-task model analyses. Finally, we demonstrate the significant gain in
performance achieved with the proposed xDom architecture by benchmarking
against a well-known state-of-the-art model for fingerprinting. Specifically,
we report performance improvements by up to 59.3% and 4.91x under single-task
WiFi and BT fingerprinting respectively, and up to 50.5% increase in
fingerprinting accuracy under the multi-task setting.Comment: Accepted to IEEE GLOBECOM 202
Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations
Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions
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