Forecast-Driven Enhancement of Received Signal Strength (RSS)-Based Localization Systems

Real-time user localization in indoor environments is an important issue in ambient assisted living (AAL). In this context, localization based on received signal strength (RSS) has received considerable interest in the recent literature, due to its low cost and energy consumption and to its availability on all wireless communication hardware. On the other hand, the RSS-based localization is characterized by a greater error with respect to other technologies. Restricting the problem to localization of AAL users in indoor environments, we demonstrate that forecasting with a little user movement advance (for example, when the user is about to leave a room) provides significant benefits to the accuracy of RSS-based localization systems. Specifically, we exploit echo state networks (ESNs) fed with RSS measurements and trained to recognize patterns of user’s movements to feed back to the RSS-based localization syste

Neural-network-aided automatic modulation classification

Automatic modulation classification (AMC) is a pattern matching problem which significantly impacts divers telecommunication systems, with significant applications in military and civilian contexts alike. Although its appearance in the literature is far from novel, recent developments in machine learning technologies have triggered an increased interest in this area of research. In the first part of this thesis, an AMC system is studied where, in addition to the typical point-to-point setup of one receiver and one transmitter, a second transmitter is also present, which is considered an interfering device. A convolutional neural network (CNN) is used for classification. In addition to studying the effect of interference strength, we propose a modification attempting to leverage some of the debilitating results of interference, and also study the effect of signal quantisation upon classification performance. Consequently, we assess a cooperative setting of AMC, namely one where the receiver features multiple antennas, and receives different versions of the same signal from the single-antenna transmitter. Through the combination of data from different antennas, it is evidenced that this cooperative approach leads to notable performance improvements over the established baseline. Finally, the cooperative scenario is expanded to a more complicated setting, where a realistic geographic distribution of four receiving nodes is modelled, and furthermore, the decision-making mechanism with regard to the identity of a signal resides in a fusion centre independent of the receivers, connected to them over finite-bandwidth backhaul links. In addition to the common concerns over classification accuracy and inference time, data reduction methods of various types (including “trained” lossy compression) are implemented with the objective of minimising the data load placed upon the backhaul links.Open Acces

POTENTIAL OF TERAHERTZ PULSED REFLECTOMETRY AND IMAGING FOR THE EARLY DIAGNOSIS OF CUTANEOUS MELANOMA

In the last two decades the incidence rate of cutaneous malignant melanoma have been risen faster than any other form of cancer worldwide in the white-Caucasian population. The mortality rates over time show that an early diagnosis is the key point for quick treatment, which increase survival rates. As a standard procedure dermatologists use a dermascope or the naked eye for evaluation of possible lesions, where experts have a higher chance of spotting infiltrated tissue than untrained persons. Multiple investigations on diagnostic imaging for the detection of melanoma have been conducted in the past, like Ultrasound, Near-Infrared spectroscopy or Optical Coherence Tomography, with mixed, but not sufficient results to date. In recent years terahertz radiation has shown to be a promising technology for the early detection of various types of cancers, i.e., colon ex-vivo}, breast ex-vivo and non-melanoma skin cancers ex-vivo andmin-vivo as terahertz radiation is able to penetrate slightly into the bio-tissue but also deemed to be a non-ionising and therefore safe method for diagnosis of lesions in-vivo. Investigations into the practicality and benefits of using terahertz reflectometry for the early diagnosis of melanoma has never been performed. Therefore, as a pilot study, an investigation into the modalities of utilising terahertz technology on freshly excised human cutaneous melanoma is anticipated, which includes a comparison of the collected 3D terahertz images with visuals, comparison of histopathologists findings but also investigations about modelling skin and abnormalities of the skin using terahertz radiation. Diverse and manifold results can be reported based on the study conducted, which show that there is a good potential of terahertz detecting abnormalities on a per patient basis of up to 78% sensitivity and 95% specificity respectively. However, skin is a very diverse medium and results of the modelling approach have to be seen very critically. As modality for a diagnostic tool, this investigation suggests that there is potential in detecting margins and active regions of cancerous region spreading, which may help to support the dermatologists to determine better margins for the excision of the lesion.This work has been sponsored by the Hope Againast Cancer Foundation, Leiceste

Machine Learning based RF Transmitter Characterization in the Presence of Adversaries

The advances in wireless technologies have led to autonomous deployments of various wireless networks. As these networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can learn and adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become popular as they can learn, analyze and even predict the RF signals and associated parameters that characterize the RF environment. In this dissertation, we address some of the fundamental challenges on how to effectively apply different learning techniques in the RF domain. In the presence of adversaries, malicious activities such as jamming, and spoofing are inevitable which render most machine learning techniques ineffective. To facilitate learning in such settings, we propose an adversarial learning-based approach to detect unauthorized exploitation of RF spectrum. First, we show the applicability of existing machine learning algorithms in the RF domain. We design and implement three recurrent neural networks using different types of cell models for fingerprinting RF transmitters. Next, we focus on securing transmissions on dynamic spectrum access network where primary user emulation (PUE) attacks can pose a significant threat. We present a generative adversarial net (GAN) based solution to counter such PUE attacks. Ultimately, we propose recurrent neural network models which are able to accurately predict the primary users\u27 activities in DSA networks so that the secondary users can opportunistically access the shared spectrum. We implement the proposed learning models on testbeds consisting of Universal Software Radio Peripherals (USRPs) working as Software Defined Radios (SDRs). Results reveal significant accuracy gains in accurately characterizing RF transmitters- thereby demonstrating the potential of our models for real world deployments

Learning with Privileged Information using Multimodal Data

Computer vision is the science related to teaching machines to see and understand digital images or videos. During the last decade, computer vision has seen tremendous progress on perception tasks such as object detection, semantic segmentation, and video action recognition, which lead to the development and improvements of important industrial applications such as self-driving cars and medical image analysis. These advances are mainly due to fast computation offered by GPUs, the development of high capacity models such as deep neural networks, and the availability of large datasets, often composed by a variety of modalities. In this thesis, we explore how multimodal data can be used to train deep convolutional neural networks. Humans perceive the world through multiple senses, and reason over the multimodal space of stimuli to act and understand the environment. One way to improve the perception capabilities of deep learning methods is to use different modalities as input, as it offers different and complementary information about the scene. Recent multimodal datasets for computer vision tasks include modalities such as depth maps, infrared, skeleton coordinates, and others, besides the traditional RGB. This thesis investigates deep learning systems that learn from multiple visual modalities. In particular, we are interested in a very practical scenario in which an input modality is missing at test time. The question we address is the following: how can we take advantage of multimodal datasets for training our model, knowing that, at test time, a modality might be missing or too noisy? The case of having access to more information at training time than at test time is referred to as learning using privileged information. In this work, we develop methods to address this challenge, with special focus on the tasks of action and object recognition, and on the modalities of depth, optical flow, and RGB, that we use for inference at test time. This thesis advances the art of multimodal learning in three different ways. First, we develop a deep learning method for video classification that is trained on RGB and depth data, and is able to hallucinate depth features and predictions at test time. Second, we build on this method and propose a more generic mechanism based on adversarial learning to learn to mimic the predictions originated by the depth modality, and is able to automatically switch from true depth features to generated depth features in case of a noisy sensor. Third, we develop a method that learns a single network trained on RGB data, that is enriched with additional supervision information from other modalities such as depth and optical flow at training time, and that outperforms an ensemble of networks trained independently on these modalities

One-stage blind source separation via a sparse autoencoder framework

Blind source separation (BSS) is the process of recovering individual source transmissions from a received mixture of co-channel signals without a priori knowledge of the channel mixing matrix or transmitted source signals. The received co-channel composite signal is considered to be captured across an antenna array or sensor network and is assumed to contain sparse transmissions, as users are active and inactive aperiodically over time. An unsupervised machine learning approach using an artificial feedforward neural network sparse autoencoder with one hidden layer is formulated for blindly recovering the channel matrix and source activity of co-channel transmissions. The BSS sparse autoencoder provides one-stage learning using the receive signal data only, which solves for the channel matrix and signal sources simultaneously. The recovered co-channel source signals are produced at the encoded output of the sparse autoencoder hidden layer. A complex-valued soft-threshold operator is used as the activation function at the hidden layer to preserve the ordered pairs of real and imaginary components. Once the weights of the sparse autoencoder are learned, the latent signals are recovered at the hidden layer without requiring any additional optimization steps. The generalization performance on future received data demonstrates the ability to recover signal transmissions on untrained data and outperform the two-stage BSS process

Modelling reindeer rut activity using on-animal acoustic recorders and machine learning

Researchers have been using sound to study the biology of wildlife to understand their ecology and behaviour for decades. By gathering audio from free-ranging species using on-animal recorders, their vocalizations can be used to describe their behaviour and ecology through signal processing. Unfortunately, processing hours of recordings is incredibly time-consuming. By applying machine learning to audio recordings, researchers have used neural networks to decrease the processing time of acoustic data. However, until now, most of this research has focused on analyzing the data of stationary recorders. To show the utility of on-animal recorders in combination with machine learning, we recorded the vocalizations of reindeer (Rangifer tarandus) during their rut at the Kutuharju research station in Kaamanen, Finland. We used vocalizations as an activity index to describe the rut activity of male reindeer. In 2019 and 2020, we placed recorders around the necks of seven reindeer during their rut. We trained convolutional neural networks to identify reindeer grunts, which were then used to classify their vocalizations. Of the networks’ vocalization classifications, around 95% of them were correct. With such high metrics, we could reliably explore the males' activity patterns using a neural network. We then analyzed the reindeers’ vocalization using generalized additive models. The patterns suggested heavier, older males vocalized more than lighter, younger males and, overall, were more active during the day than night. Overall, on-animal acoustic recorders, in tandem with machine learning, proved to be effective tools, and with more attention, they could prove valuable tools for other researchers