96 research outputs found
Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications
This work takes a critical look at the application of conventional machine
learning methods to wireless communication problems through the lens of
reliability and robustness. Deep learning techniques adopt a frequentist
framework, and are known to provide poorly calibrated decisions that do not
reproduce the true uncertainty caused by limitations in the size of the
training data. Bayesian learning, while in principle capable of addressing this
shortcoming, is in practice impaired by model misspecification and by the
presence of outliers. Both problems are pervasive in wireless communication
settings, in which the capacity of machine learning models is subject to
resource constraints and training data is affected by noise and interference.
In this context, we explore the application of the framework of robust Bayesian
learning. After a tutorial-style introduction to robust Bayesian learning, we
showcase the merits of robust Bayesian learning on several important wireless
communication problems in terms of accuracy, calibration, and robustness to
outliers and misspecification.Comment: Submitted for publicatio
Walking Speed Detection from 5G prototype System
While most RF-sensing approaches proposed in the literature rely on short-distance indoor point-to-point instrumentation, actual large-scale installation of RF sensing suggests the use of ubiquitously available cellular systems. In particular, the 5th generation of the wireless communication standard (5G) is envisioned as a universal communication means also for Internet of Things devices.
This thesis presents an investigation of device-free environmental perception capabilities in a 5G prototype system in two cases; walking speed and human presence detection, and elaborate a comparison with the former case and acceleration sensing analysis. This thesis attempts to analyze the perception capabilities of 5G system in order to recognize human mostly common activities and presence detection near transceiver devices which the instrumentation exploits a device-free system capable of detect activities without carrying devices capitalizing on environmental RF-noise. This is done via the study of existing and related literature. After that, the implementation and evaluation of walking speed and presence detection is described in details. In addition, evaluation consists of utilizing a prototypical 5G system with 52 OFDM carriers over 12.48 MHz bandwidth at 3.45 GHz, which we consider the impact of the number and choice of channels and compare the recognition performance with acceleration-based sensing. It was concluded that in realistic settings with five subjects, accurate recognition of activities and environmental situations can be a reliable implicit service of future 5G installations
Multi-Sensor Methods for Mobile Radar Motion Capture and Compensation.
Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017
Edge Artificial Intelligence for Real-Time Target Monitoring
The key enabling technology for the exponentially growing cellular communications sector is location-based services. The need for location-aware services has increased along with the number of wireless and mobile devices. Estimation problems, and particularly parameter estimation, have drawn a lot of interest because of its relevance and engineers' ongoing need for higher performance. As applications expanded, a lot of interest was generated in the accurate assessment of temporal and spatial properties.
In the thesis, two different approaches to subject monitoring are thoroughly addressed. For military applications, medical tracking, industrial workers, and providing location-based services to the mobile user community, which is always growing, this kind of activity is crucial.
In-depth consideration is given to the viability of applying the Angle of Arrival (AoA) and Receiver Signal Strength Indication (RSSI) localization algorithms in real-world situations. We presented two prospective systems, discussed them, and presented specific assessments and tests. These systems were put to the test in diverse contexts (e.g., indoor, outdoor, in water...). The findings showed the localization capability, but because of the low-cost antenna we employed, this method is only practical up to a distance of roughly 150 meters. Consequently, depending on the use-case, this method may or may not be advantageous. An estimation algorithm that enhances the performance of the AoA technique was implemented on an edge device.
Another approach was also considered. Radar sensors have shown to be durable in inclement weather and bad lighting conditions. Frequency Modulated Continuous Wave (FMCW) radars are the most frequently employed among the several sorts of radar technologies for these kinds of applications. Actually, this is because they are low-cost and can simultaneously provide range and Doppler data. In comparison to pulse and Ultra Wide Band (UWB) radar sensors, they also need a lower sample rate and a lower peak to average ratio. The system employs a cutting-edge surveillance method based on widely available FMCW radar technology. The data processing approach is built on an ad hoc-chain of different blocks that transforms data, extract features, and make a classification decision before cancelling clutters and leakage using a frame subtraction technique, applying DL algorithms to Range-Doppler (RD) maps, and adding a peak to cluster assignment step before tracking targets. In conclusion, the FMCW radar and DL technique for the RD maps performed well together for indoor use-cases. The aforementioned tests used an edge device and Infineon Technologies' Position2Go FMCW radar tool-set
RF signal sensing and source localisation systems using Software Defined Radios
Radio frequency (RF) source localisation is a critical technology
in numerous location-based military and civilian applications. In
this thesis, the problem of RF source localisation has been
studied from the perspective of the system implementation for
real-world applications. Commercial off-the-shelf Software
Defined Radio (SDR) devices are used to demonstrate the practical
RF source localisation systems. Compared to the conventional
localisation systems, which rely on dedicated hardware, the
SDR-based system is developed using general-purpose hardware and
software-defined components, offering great flexibility and cost
efficiency in system design and implementation.
In this thesis, the theoretical results of source localisation
are evaluated and put into practice. To be specific, the
practical localisation systems using different measurement
techniques, including received-signal-strength-indication (RSSI)
measurements, time-difference-of-arrival (TDOA) measurements and
joint TDOA and frequency-difference-of-arrival (FDOA)
measurements, are demonstrated to localise the stationary RF
signal sources using the SDRs. The RSSI-based localisation system
is demonstrated in small indoor and outdoor areas with a range of
several metres using the SDR-based transceivers. Furthermore,
interests from the defence area motivated us to implement the
time-based localisation systems. The TDOA-based source
localisation system is implemented using multiple spatially
distributed SDRs in a large outdoor area with the sensor-target
range of several kilometres. Moreover, they are implemented in a
fully passive way without prior knowledge of the signal emitter,
so the solutions can be applied in the localisation of
non-cooperative signal sources provided that emitters are
distant. To further reduce the system cost, and more importantly,
to deal with the situation when the deployment of multiple SDRs,
due to geographical restrictions, is not feasible, a joint TDOA
and FDOA-based localisation system is also demonstrated using
only one stationary SDR and one mobile SDR.
To improve the localisation accuracy, the methods that can reduce
measurement error and obtain accurate location estimates are
studied. Firstly, to obtain a better understanding of the
measurement error, the error sources that affect the measurement
accuracy are systematically analysed from three aspects: the
hardware precision, the accuracy of signal processing methods,
and the environmental impact. Furthermore, the approaches to
reduce the measurement error are proposed and verified in the
experiments. Secondly, during the process of the location
estimation, the theoretical results on the pre-existing
localisation algorithms which can achieve a good trade-off
between the accuracy of location estimation and the computational
cost are evaluated, including the weight least-squares
(WLS)-based solution and the Extended Kalman Filter (EKF)-based
solution. In order to use the pre-existing algorithms in the
practical source localisation, the proper adjustments are
implemented.
Overall, the SDR-based platforms are able to achieve low-cost and
universal localisation solutions in the real-world environment.
The RSSI-based localisation system shows tens of centimetres of
accuracy in a range of several metres, which provides a useful
tool for the verification of the range-based localisation
algorithms. The localisation accuracy of the TDOA-based
localisation system and the joint TDOA and FDOA-based
localisation system is several tens of metres in a range of
several kilometres, which offers potential in the low-cost
localisation solutions in the defence area
Sensors and Systems for Indoor Positioning
This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications
Wireless sensor network as a distribute database
Wireless sensor networks (WSN) have played a role in various fields. In-network data processing is one of the most important and challenging techniques as it affects the key features of WSNs, which are energy consumption, nodes life circles and network performance. In the form of in-network processing, an intermediate node or aggregator will fuse or aggregate sensor data, which are collected from a group of sensors before transferring to the base station. The advantage of this approach is to minimize the amount of information transferred due to lack of computational resources.
This thesis introduces the development of a hybrid in-network data processing for WSNs to fulfil the WSNs constraints. An architecture for in-network data processing were proposed in clustering level, data compression level and data mining level. The Neighbour-aware Multipath Cluster Aggregation (NMCA) is designed in the clustering level, which combines cluster-based and multipath approaches to process different packet loss rates. The data compression schemes and Optimal Dynamic Huffman (ODH) algorithm compressed data in the cluster head for the compressed level. A semantic data mining for fire detection was designed for extracting information from the raw data by the semantic data-mining model is developed to improve data accuracy and extract the fire event in the simulation. A demo in-door location system with in-network data processing approach is built to test the performance of the energy reduction of our designed strategy. In conclusion, the added benefits that the technical work can provide for in-network data processing is discussed and specific contributions and future work are highlighted
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