Low-cost RPAS navigation and guidance system using Square Root Unscented Kalman Filter

Multi-Sensor Data Fusion (MSDF) techniques involving satellite and inertial-based sensors are widely adopted to improve the navigation solution of a number of mission- and safety-critical tasks. Such integrated Navigation and Guidance Systems (NGS) currently do not meet the required level of performance in all flight phases of small Remotely Piloted Aircraft Systems (RPAS). In this paper an innovative Square Root-Unscented Kalman Filter (SR-UKF) based NGS is presented and compared with a conventional UKF governed design. The presented system architectures adopt state-of-the-art information fusion approach based on a number of low-cost sensors including; Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Navigation (VBN) sensors. Additionally, an Aircraft Dynamics Model (ADM), which is essentially a knowledge based module, is employed to compensate for the MEMS-IMU sensor shortcomings in high-dynamics attitude determination tasks. The ADM acts as a virtual sensor and its measurements are processed with non-linear estimation in order to increase the operational validity time. An improvement in the ADM navigation state vector (i.e., position, velocity and attitude) measurements is obtained, thanks to the accurate modeling of aircraft dynamics and advanced processing techniques. An innovative SR-UKF based VBN-IMU-GNSS-ADM (SR-U-VIGA) architecture design was implemented and compared with a typical UKF design (U-VIGA) in a small RPAS (AEROSONDE) integration arrangement exploring a representative cross-section of the operational flight envelope. The comparison of position and attitude data shows that the SR-U-VIGA and U-VIGA NGS fulfill the relevant RNP criteria, including precision approach tasks

Multi-sensor data fusion techniques for RPAS detect, track and avoid

Accurate and robust tracking of objects is of growing interest amongst the computer vision scientific community. The ability of a multi-sensor system to detect and track objects, and accurately predict their future trajectory is critical in the context of mission- and safety-critical applications. Remotely Piloted Aircraft System (RPAS) are currently not equipped to routinely access all classes of airspace since certified Detect-and-Avoid (DAA) systems are yet to be developed. Such capabilities can be achieved by incorporating both cooperative and non-cooperative DAA functions, as well as providing enhanced communications, navigation and surveillance (CNS) services. DAA is highly dependent on the performance of CNS systems for Detection, Tacking and avoiding (DTA) tasks and maneuvers. In order to perform an effective detection of objects, a number of high performance, reliable and accurate avionics sensors and systems are adopted including non-cooperative sensors (visual and thermal cameras, Laser radar (LIDAR) and acoustic sensors) and cooperative systems (Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS)). In this paper the sensors and system information candidates are fully exploited in a Multi-Sensor Data Fusion (MSDF) architecture. An Unscented Kalman Filter (UKF) and a more advanced Particle Filter (PF) are adopted to estimate the state vector of the objects based for maneuvering and non-maneuvering DTA tasks. Furthermore, an artificial neural network is conceptualised/adopted to exploit the use of statistical learning methods, which acts to combined information obtained from the UKF and PF. After describing the MSDF architecture, the key mathematical models for data fusion are presented. Conceptual studies are carried out on visual and thermal image fusion architectures

5-(carbamoylmethylene)-oxazolidin-2-ones as a promising class of heterocycles inducing apoptosis triggered by increased ROS levels and mitochondrial dysfunction in breast and cervical cancer

Oxazolidinones are antibiotics that inhibit protein synthesis by binding the 50S ribosomal subunit. Recently, numerous worldwide researches focused on their properties and possible involvement in cancer therapy have been conducted. Here, we evaluated in vitro the antiproliferative activity of some 5-(carbamoylmethylene)-oxazolidin-2-ones on MCF-7 and HeLa cells. The tested compounds displayed a wide range of cytotoxicity on these cancer cell lines, measured by MTT assay, exhibiting no cytotoxicity on non-tumorigenic MCF-10A cells. Among the nine tested derivatives, four displayed a good anticancer potential. Remarkably, OI compound showed IC50 values of 17.66 and 31.10 µM for MCF-7 and HeLa cancer cells, respectively. Furthermore, we assessed OI effect on the cell cycle by FACS analysis, highlighting a G1 phase arrest after 72 h, supported by a low expression level of Cyclin D1 protein. Moreover, mitochondrial membrane potential was reduced after OI treatment driven by high levels of ROS. These findings demonstrate that OI treatment can inhibit MCF-7 and HeLa cell proliferation and induce apoptosis by caspase-9 activation and cytochrome c release in the cytosol. Hence, 5-(carbamoylmethylene)-oxazolidin-2-ones have a promising anticancer activity, in particular, OI derivative could represent a good candidate for in vivo further studies and potential clinical use

Low-cost sensors based multi-sensor data fusion techniques for RPAS navigation and guidance

In order for Remotely Piloted Aircraft Systems (RPAS) to coexist seamlessly with manned aircraft in non-segregated airspace, enhanced navigational capabilities are essential to meet the Required Navigational Performance (RNP) levels in all flight phases. A Multi-Sensor Data Fusion (MSDF) framework is adopted to improve the navigation capabilities of an integrated Navigation and Guidance System (NGS) designed for small-sized RPAS. The MSDF architecture includes low-cost and low weight/volume navigation sensors suitable for various classes of RPAS. The selected sensors include Global Navigation Satellite Systems (GNSS), Micro-Electro-Mechanical System (MEMS) based Inertial Measurement Unit (IMU) and Vision Based Sensors (VBS). A loosely integrated navigation architecture is presented where an Unscented Kalman Filter (UKF) is used to combine the navigation sensor measurements. The presented UKF based VBS-INS-GNSS-ADM (U-VIGA) architecture is an evolution of previous research performed on Extended Kalman Filter (EKF) based VBS-INS-GNSS (E-VIGA) systems. An Aircraft Dynamics Model (ADM) is adopted as a virtual sensor and acts as a knowledge-based module providing additional position and attitude information, which is pre-processed by an additional/local UKF. The E-VIGA and U-VIGA performances are evaluated in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) by exploring a representative cross-section of this RPAS operational flight envelope. The position and attitude accuracy comparison shows that the E-VIGA and U-VIGA systems fulfill the relevant RNP criteria, including precision approach in CAT-II. A novel Human Machine Interface (HMI) architecture is also presented, whose design takes into consideration the coordination tasks of multiple human operators. In addition, the interface scheme incorporates the human operator as an integral part of the control loop providing a higher level of situational awareness

An innovative navigation and guidance system for small unmanned aircraft using low-cost sensors

Purpose - The purpose of this paper is to design a compact, light and relatively inexpensive navigation and guidance system capable of providing the required navigation performance (RNP) in all phases of flight of small unmanned aircrafts (UA), with a special focus on precision approach and landing. Design/methodology/approach - Two multi-sensor architectures for navigation and guidance of small UA are proposed and compared in this paper. These architectures are based, respectively, on a standard extended Kalman filter (EKF) approach and a more advanced unscented Kalman filter (UKF) approach for data fusion of global navigation satellite systems (GNSS), micro-electro-mechanical system (MEMS)-based inertial measurement unit (IMU) and vision-based navigation (VBN) sensors. Findings - The EKF-based VBN-IMU-GNSS-aircraft dynamics model (ADM) (VIGA) system and the UKF-based system (VIGA+) performances are compared in a small UA integration scheme (i.e. AEROSONDE UA platform) exploring a representative cross-section of this UA operational flight envelope, including high-dynamics manoeuvres and CAT-I to CAT-III precision approach tasks. The comparison shows that the position and attitude accuracy of the proposed VIGA and VIGA+ systems are compatible with the RNP specified in the various UA flight profiles, including precision approach down to CAT-II. Originality/value - The novelty aspect is the augmentation by ADM in both architectures to compensate for the MEMS-IMU sensor shortcomings in high-dynamics attitude determination tasks. Additionally, the ADM measurements are pre-filtered by an UKF with the purpose of increasing the ADM attitude solution stability time in the UKF-based system

Effect of Aqueous Ozone on the NF-κB System

Ozone has been proposed as an alternative oral antiseptic in dentistry, due to its antimicrobial power reported for gaseous and aqueous forms, the latter showing a high biocompatibility with mammalian cells. New therapeutic strategies for the treatment of periodontal disease and apical periodontitis should consider not only antibacterial effects, but also their influence on the host immune response. Therefore, our aim was to investigate the effect of aqueous ozone on the NF-κB system, a paradigm for inflammationassociated signaling/transcription. We showed that NF-κB activity in oral cells stimulated with TNF, and in periodontal ligament tissue from root surfaces of periodontally damaged teeth, was inhibited following incubation with ozonized medium. Under this treatment, IκBalpah proteolysis, cytokine expression, and κB-dependent transcription were prevented. Specific ozonized amino acids were shown to represent major inhibitory components of ozonized medium. In summary, our study establishes a condition under which aqueous ozone exerts inhibitory effects on the NF-κB system, suggesting that it has an antiinflammatory capacity

Extracts of different polarity of Daphne laureola L. as valuable source of antioxidant and neuroprotective compounds

In this study, Daphne laureola L., an European-Mediterranean species, was investigated for its antioxidant properties using different in vitro bioassays, namely 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), Ferric Reducing Activity Power (FRAP), and β-carotene bleaching assays. The acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory activity using the Ellman’s method was also examined. The aerial parts of D. laureola were subjected to exhaustive and subsequent macerations with solvents at different polarity, such as methanol, dichloromethane, and n-hexane. Dichloromethane extract was the most promising in DPPH and FRAP tests with IC50 values of 32.2 μg/mL and 71.5 μM Fe(II)/g, respectively. Methanol extract showed the most promising inhibition of lipid peroxidation evaluated by β-carotene bleaching test with an IC50 value of 6.6 μg/mL after 30 min of incubation. This extract was the most active against AChE (IC50 value of 56.9 μg/mL). An interesting result was obtained against BChE by the n-hexane extract, with an IC50 value of 49.7 μg/mL. In conclusion, the results suggest that D. laureola may provide a substantial source of phytochemicals, which act as natural antioxidants and acetylcholinesterase inhibitors, and may be beneficial in the treatment of Alzheimer disease

Investigation of the Thermomechanical Response of Cyclically Loaded NiTi Alloys by Means of Temperature Frequency Domain Analyses

Nickel–Titanium (NiTi) shape memory alloys subjected to cyclic loading exhibit reversible temperature changes whose modulation is correlated with the applied load. This reveals the presence of reversible thermomechanical heat sources activated by the applied stresses. One such source is the elastocaloric effect, accounting for the latent heat of Austenite–Martensite phase transformation. It is, however, observed that when the amplitude of cyclic loads is not sufficient to activate or further propagate this phase transformation, the material still exhibits a strong cyclic temperature modulation. The present work investigates the thermomechanical behaviour of NiTi under such low-amplitude cyclic loading. This is carried out by analysing the frequency domain content of temperature sampled over a time window. The amplitude and phase of the most significant harmonics are obtained and compared with the theoretical predictions from the first and second-order theories of the Thermoelastic Effect, this being the typical reversible thermomechanical coupling prevailing under elastic straining. A thin strip of NiTi, exhibiting a fully superelastic behaviour at room temperature, was investigated under low-stress amplitude tensile fatigue cycling. Full-field strain and temperature distributions were obtained by means of Digital Image Correlation and IR Thermography. The work shows that the full field maps of amplitude and phase of the first three significant temperature harmonics carry out many qualitative information about the stress and structural state of the material. It is, though, found that the second-order theory of the Thermoelastic Effect is not fully capable of justifying some of the features of the harmonic response, and further work on the specific nature of thermomechanical heat sources is required for a more quantitative interpretation