Performance of Hybrid Sensing Method in Environment with Noise Uncertainty, Journal of Telecommunications and Information Technology, 2018, nr 1

The paper presents a novel hybrid spectrum sensing method used in cognitive radio and presents a hybrid detector (HD) which improves the sensing performance. The proposed HD takes advantage of the energy detection (ED) principle and a method based on Covariance Absolute Value (CAV), as well as on Cyclic Autocorrelation Function (CAF). The paper shows the limitations of using ED, resulting from the uncertainty of spectral density of noise power estimation, known as the SNR wall. The paper describes a system model and presents simulation results for the OFDM signal of a WiMAX-based communications system. The simulation results refer to an ideal environment with well-known parameters, and to an environment with uncertain spectral density of noise power estimation

Evanescent single-molecule biosensing with quantum limited precision

Sensors that are able to detect and track single unlabelled biomolecules are an important tool both to understand biomolecular dynamics and interactions at nanoscale, and for medical diagnostics operating at their ultimate detection limits. Recently, exceptional sensitivity has been achieved using the strongly enhanced evanescent fields provided by optical microcavities and nano-sized plasmonic resonators. However, at high field intensities photodamage to the biological specimen becomes increasingly problematic. Here, we introduce an optical nanofibre based evanescent biosensor that operates at the fundamental precision limit introduced by quantisation of light. This allows a four order-of-magnitude reduction in optical intensity whilst maintaining state-of-the-art sensitivity. It enable quantum noise limited tracking of single biomolecules as small as 3.5 nm, and surface-molecule interactions to be monitored over extended periods. By achieving quantum noise limited precision, our approach provides a pathway towards quantum-enhanced single-molecule biosensors.Comment: 17 pages, 4 figures, supplementary informatio

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Radar and RGB-depth sensors for fall detection: a review

This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing

Statistical Properties and Applications of Empirical Mode Decomposition

Signal analysis is key to extracting information buried in noise. The decomposition of signal is a data analysis tool for determining the underlying physical components of a processed data set. However, conventional signal decomposition approaches such as wavelet analysis, Wagner-Ville, and various short-time Fourier spectrograms are inadequate to process real world signals. Moreover, most of the given techniques require \emph{a prior} knowledge of the processed signal, to select the proper decomposition basis, which makes them improper for a wide range of practical applications. Empirical Mode Decomposition (EMD) is a non-parametric and adaptive basis driver that is capable of breaking-down non-linear, non-stationary signals into an intrinsic and finite components called Intrinsic Mode Functions (IMF). In addition, EMD approximates a dyadic filter that isolates high frequency components, e.g. noise, in higher index IMFs. Despite of being widely used in different applications, EMD is an ad hoc solution. The adaptive performance of EMD comes at the expense of formulating a theoretical base. Therefore, numerical analysis is usually adopted in literature to interpret the behavior. This dissertation involves investigating statistical properties of EMD and utilizing the outcome to enhance the performance of signal de-noising and spectrum sensing systems. The novel contributions can be broadly summarized in three categories: a statistical analysis of the probability distributions of the IMFs and a suggestion of Generalized Gaussian distribution (GGD) as a best fit distribution; a de-noising scheme based on a null-hypothesis of IMFs utilizing the unique filter behavior of EMD; and a novel noise estimation approach that is used to shift semi-blind spectrum sensing techniques into fully-blind ones based on the first IMF. These contributions are justified statistically and analytically and include comparison with other state of art techniques

Study and manufacturing of biosensors based on plasmonic effects and built on silicon

Abstract: Lab-on-a-chip (or LOC) devices scale down the laboratory processes for detecting illnesses and monitoring sick patients without the need of medical laboratories. Well-known examples of LOC are pregnancy test kits or portable HIV sensors. To be useful, LOC devices must be sensitive, specific, compact, and affordable. These criteria are made possible with a transducer that can convert the biological presence of the target molecule into electrical information. Since the early 2000s, integrated photonics have offered a possible solution for a transducer compatible with LOC needs. In particular, silicon micro-ring resonators represent a compact and sensitive choice to use as a transducer in LOC devices. In agreement with the requirements of LOC devices, the objective of this project is to design and assess the performance of a compact photonic biosensor. The system will be based on integrated photonic transduction. The requirements are that it is compatible with an industrial fabrication platform and fluidic systems, with a sensitivity equal to or higher than the state-of-the-art and simple to functionalize in order to localize the target molecules in the sensitive regions of the sensor. This project details the design, fabrication, and characterization of such a biosensor. We found that ring resonators with a Hybrid Plasmonic Waveguide (HPWG) cross-section fulfill the LOC requirements and outperform the state-of-the-art biosensor. Furthermore, based on a principle called mode lift, we patented new geometry of HPWG, which will be the object of an article. We simulated the HPWG structure to understand the coupling mechanisms of the modes inside the structure (more specifically, the plasmonic and the ridge dielectric modes). The fabrication was possible thanks to the collaboration of the industrial and university cleanrooms. An advantage of industrial production is that we can reproducibly create the geometric components necessary for the LOC in a high-throughput manner, thus lowering the cost per unit cell. Once the 300 mm Si wafers were patterned, the university cleanroom fabrication process adds the metallic waveguides. The Au nanopatterning on the devices characterized in this project was created using the lift-off method. The preliminary measures define the optimal testing liquid (glucose monohydrate) and the uncertainty of the measures. The HPWG samples showed an experimental sensitivity lower than the simulations. After adjusting the fabrication parameters (mainly Au and Cr deposition rates and thicknesses), the second-generation HPWG devices suggest that the mode lift improves the sensitivity for waveguides below cutoff (the sensitivity increases from 210 nm/RIU to 320 nm/RIU when only 10% of the ring resonator has an HPWG section and the rest is a ridge waveguide). Even in the case where ridge waveguides are above the cutoff, the sensitivity increases by 40 nm/RIU when using mode lift. We also showed the compatibility of the fabricated devices’ surface with differential functionalization, by means of fluorescent nanoparticles. Due to time limitations, the presence of the nanoparticles will be measured with the fabricated devices in future experiments.Les dispositifs laboratoire sur puce (ou Lab-on-a-chip ou LOC) visent à miniaturiser les procédés de laboratoires pour la détection des maladies et la surveillance des patients malades, sans avoir besoin de laboratoires médicaux. Deux exemples bien connus de LOC sont les kits de test de grossesse ou les capteurs portables du VIH. Pour être efficaces, les appareils LOC doivent être sensibles, spécifiques à l’analyte concerné, compacts et abordables. Ces critères sont possibles grâce à un transducteur, qui peut convertir la présence biologique de la molécule cible en informations électriques. Depuis le début des années 2000, la photonique intégrée a offert une solution pour un système de transduction compatible avec les besoins du LOC. En particulier, les micro-résonateurs à anneaux en silicium représentent un transducteur compact et sensible adapté aux appareils LOC. En accord avec les exigences des dispositifs LOC, l’objectif de ce projet est de concevoir et d’évaluer les performances d’un biocapteur photonique compact. Le système sera basé sur une transduction photonique intégrée. Les exigences sont : une simple fonctionnalisation, la compatibilité avec une plateforme de fabrication industrielle et des systèmes fluidiques, avec une sensibilité égale ou supérieure à l’état de l’art. Ce projet détaille la conception, la fabrication et la caractérisation d’un tel biocapteur. Nous avons constaté que les résonateurs en anneau avec une section transversale de guide d’ondes hybrides plasmoniques (HPWG) remplissent les exigences LOC et sont compétitifs en comparaison avec l’état de l’art des biocapteurs photoniques. Par ailleurs, basée sur un principe appelé mode lift, une nouvelle géométrie de HPWG a été brevetée et fera l’objet d’un article. Nous avons simulé la structure HPWG pour comprendre les mécanismes de couplage des modes photoniques à l’intérieur de la structure (plus précisément les modes plasmoniques et les modes diélectriques du guide d’onde à ruban). La fabrication a été possible grâce à la collaboration de la salle blanche industrielle de STMicroelectronics et des salles blanches universitaires de l’université de Sherbrooke et de l’Institut de Nanotechnologies de Lyon. Un avantage de la production industrielle est que nous pouvons créer de manière reproductible la géométrie des composants nécessaires pour le LOC à haut débit, réduisant ainsi le coût par unité. Une fois que les wafers de 300 mm ont été structurés, le processus de fabrication en salle blanche universitaire permet d’ajouter le métal des guides d’ondes plasmoniques. La méthode du lift-off a été utilisée pour la nanostructuration Au sur les dispositifs caractérisés dans ce projet. Des mesures préliminaires ont permis de définir le liquide d’essai optimal (glucose monohydrate) ainsi que l’incertitude des mesures. Les échantillons HPWG ont montré une sensibilité expérimentale inférieure aux simulations. Après avoir ajusté les paramètres de fabrication (principalement les taux et les épaisseurs de dépôt d’Au et de Cr), les dispositifs HPWG de deuxième génération suggèrent que le mode lift améliore la sensibilité des guides d’ondes en dessous de la coupure (la sensibilité augmente de 210 nm/RIU à 320 nm/RIU lorsque seulement 10 % du résonateur en anneau a une section HPWG). Même par rapport aux guides d’ondes au-dessus de la coupure, la sensibilité augmente de 40 nm/RIU lors de l’utilisation du mode lift. Nous avons également montré la compatibilité de la surface des appareils fabriqués avec la fonctionnalisation différentielle en utilisant des nanoparticules fluorescentes. Pour des contraintes de temps, la présence des nanoparticules ne sera mesurée que dans des futures expériences

Cognitive radio networks : quality of service considerations and enhancements

The explosive growth of wireless and mobile networks, such as the Internet of Things and 5G, has led to a massive number of devices that primarily use wireless channels within a limited range of the radio frequency spectrum (RFS). The use of RFS is heavily regulated, both nationally and internationally, and is divided into licensed and unlicensed bands. While many of the licensed wireless bands are underutilised, useable unlicensed bands are usually overcrowded, making the efficient use of RFS one of the critical challenges faced by future wireless communication technologies. The cognitive radio (CR) concept is proposed as a promising solution for the underutilisation of useful RFS bands. Fundamentally, CR technology is based on determining the unoccupied licensed RFS bands, called spectrum white spaces or holes, and accessing them to achieve better RFS utilisation and transmission propagation. The holes are the frequencies unused by the licensed user, or primary user (PU). Based on spectrum sensing, a CR node, or secondary user (SU), senses the surrounding spectrum periodically to detect any potential PU transmission in the current channel and to identify the available spectrum holes. Under current RFS regulations, SUs may use spectrum holes as long as their transmissions do not interfere with those of the PU. However, effective spectrum sensing can introduce overheads to a CR node operation. Such overheads affect the quality of service (QoS) of the running applications. Reducing the sensing impact on the QoS is one of the key challenges to adopting CR technology, and more studies of QoS issues related to implementing CR features are needed. This thesis aims to address these QoS issues in CR while considered the enhancement of RFS utilisation. This study concentrates on the spectrum sensing function, among other CR functions, because of its major impact on QoS and spectrum utilisation. Several spectrum sensing methods are reviewed to identify potential research gaps in analysing and addressing related QoS implications. It has been found that none of the well-known sensing techniques is suitable for all the diverse QoS requirements and RFS conditions: in fact, higher accuracy sensing methods cause a significant QoS degradation, as illustrated by several simulations in this work. For instance, QoS degradation caused by high-accuracy sensing has not yet been addressed in the IEEE 802.11e QoS mechanism used in the proposed CR standard, IEEE 802.11af (or White-Fi). This study finds that most of the strategies proposed to conduct sensing are based on a fixed sensing method that is not adaptable to the changeable nature of QoS requirements. In contrast, this work confirms the necessity of using various sensing techniques and parameters during a CR node operation for better performance