Design of a Novel X-band Slotted Waveguide Antenna Array with TE20 Mode Feeding

The Slotted Waveguide Antenna Array (SWAA) was introduced since the decade of 40s. This type of antenna array is well known by its reliability and ability to handle large amounts of power and high operating frequencies, so that the SWAA is commonly used as a part of radar systems. This research paper studies the design of a novel X‒band Slotted Waveguide Antenna Array (SWAA) which is excited by TE20 mode. The radiation characteristics of TE20 mode excited SWAA are compared with conventionally designed TE10 mode excited SWAA. The CST Microwave Studio (CST MWS) simulation results show that the SWAA with TE20 mode feeding features high bandwidth, narrow beamwidth, and high gain as compared with TE10 mode fed SWAA. Feeding the SWAA by using TE20 mode instead of TE10 mode results in increasing the bandwidth by 5.7 %, decreasing the beamwidth by 30˚, and improving the antenna array gain by 2.2 dBi higher than the obtained gain with TE10 mode feeding

Design and simulation of an analog beamforming phased array antenna

In this paper, a phased array antenna is designed and simulated. The antenna array consists of four circularly polarized slotted waveguide elements. The antenna array is simulated using CST MWS. The simulation results for the proposed antenna array at different values of progressive phase shift demonstrate that the S‒parameters for all four ports are less than ‒10 dB over at least 2% bandwidth, the simulated maximum gain is 13.95 dB, the simulated beamwidth can be 19˚ or narrower based on the value of the progressive phase shift. , the range of frequencies over which the simulated Axial Ratio (AR) is below 3 dB is not fixed and varied according to the selected progressive phase shift. The proposed four-element RF front-end is simulated using Advanced Design System (ADS) at operating frequency of 9.6 GHz. The obtained simulation results by ADS indicate the feasibility of implementing the proposed RF-front end for feeding the antenna array to realize analog beamforming

A Vivaldi Antenna with Improved Bandwidth and Gain

In this paper, the radiation characteristics of the conventional Vivaldi antenna are improved by proposing a novel design of a Vivaldi antenna. This proposed Vivaldi antenna is excited through three slots by using the L-probe microstrip feeder. The novel design can provide higher gain and wider bandwidth compared to that of the conventional Vivaldi antenna of the same size. The CST MWS software is used to simulate the proposed Vivaldi antenna. The measured and the simulated S-parameters were compared so that the feasibility of the proposed Vivaldi antenna was validated. The measured S-parameters show that the impedance bandwidth of the proposed Vivaldi antenna was from 1.976 to 7.728 GHz, while the measured maximum gain is 4.9 dBi at the operating frequency of 3 GHz

Design And Development Of An Indoor Real-Time Locating System

Beberapa teknologi lokasi telah dibangunkan dan secara komersialnya telah digunakan secara berkesan termasuklah Sistem Kedudukan Sejagat (Global Positioning System, GPS) dan sistem khidmat 911 tertingkat wayarles (E-911). Namun demikian, teknologi ini digunakan khusus untuk aplikasi luar dan tidak begitu berkesan jika digunakan di dalam bangunan atau di kawasan Bandar yang padat. Kebanyakan sistem lokasi dalaman komersial yang ada agak lemah prestasinya kerana wujudnya hinggar, gangguan, hilang laluan, pengutuban yang tidak sepadan, pantulan pengutuban dan kesan fenomena pelbagai laluan dalam saluran wayarles. Oleh itu, suatu sistem lokasi yang mempunyai prestasi yang baik untuk persekitaran yang tertutup amat diperlukan. Several locating technologies have been developed and commercially deployed including the Global Positioning System (GPS) and the wireless enhanced 911 service system (E-911), but these locating technologies are used mainly for outdoor locationbased applications and can not be used effectively inside buildings and in dense urban areas. Most of the commercial indoor localization systems provide a poor performance due to the presence of noise, interference, path loss, polarization mismatch, polarization reflection and the effect of the multi-path phenomenon in the wireless channel. Therefore, there is a need for a locating system which has a good performance in indoor environments

A review on Precoding Techniques For mm-Wave Massive MIMO Wireless Systems

The growing demands for high data rate wireless connectivity shed lights on the fact that appropriate spectrum regions need to be investigated so that the expected future needs will be satisfied. With this in mind, the research community has shown considerable interest in millimeter-wave (mm-wave) communication. Generally, hybrid transceivers combining the analog phase shifter and the RF chains with digital signal processing (DSP) systems are used for MIMO communication in the fifth generation (5G) wireless networks. This paper presents a survey for different precoding or beamforming techniques that have been proposed in the literature. These beamforming techniques are mainly classified based on their hardware structure into analog and digital beamforming. To reduce the hardware complexity and power consumption, the hybrid precoding techniques that combine analog and digital beamforming can be implemented for mm-wave massive MIMO wireless systems. The performance of the most common hybrid precoding algorithms has been investigated in this paper

Nonlinear dynamics of the interface of dielectric liquids in a strong electric field: Reduced equations of motion

The evolution of the interface between two ideal dielectric liquids in a strong vertical electric field is studied. It is found that a particular flow regime, for which the velocity potential and the electric field potential are linearly dependent functions, is possible if the ratio of the permittivities of liquids is inversely proportional to the ratio of their densities. The corresponding reduced equations for interface motion are derived. In the limit of small density ratio, these equations coincide with the well-known equations describing the Laplacian growth.Comment: 10 page

Polygon Generation & Deep Learning: Towards Industrial Data Fusion for Optimizing Engineering Systems’ Performance

RÉSUMÉ: L'analyse des données industrielles pose plusieurs défis. Ces données sont collectées à partir de sources hétérogènes et stockées dans des silos isolés. Cela complique leur accessibilité et empêche leur pleine exploitation dans la prise de décision. Par conséquent, il existe un besoin urgent et prioritaire de fusionner ces silos déconnectés pour profiter au maximum de ces données. Toutefois, la fusion de données de sources multiples présente deux principaux défis : la représentation efficace des données afin de permettre une meilleure fusion des données, et la sélection des méthodes de modélisation les plus appropriées en fonction du type et de la quantité des données ainsi que la nature du problème à résoudre. Relever ces défis contribuera à ce que les systèmes industriels complexes maintiennent un fonctionnement économe en énergie, à réduire leur empreinte environnementale et ainsi à atteindre l'excellence opérationnelle. Cette thèse apporte trois contributions dans résolution de la problématique de la fusion de données issues de l’exploitation de systèmes industriels. L'objectif des trois contributions proposées est d'obtenir une représentation des données efficace et utile qui permet de maximiser la valeur globale des données industrielles disponibles pour faciliter le processus de fusion à différents niveaux sémantiques (brut, information et connaissance). Les contributions utilisent les performances de l’apprentissage profond (DL) dans la modélisation prédictive et générative. Elles permettent de construire des modèles précis et robustes utilisables à plusieurs finalités dans l'industrie. Ces modèles peuvent diagnostiquer avec précision divers systèmes industriels, prédire leurs indicateurs clés de performance et capturer la vraie distribution des systèmes industriels complexes hautement non linéaires pour fournir à l'expert des connaissances précieuses pour prescrire les bonnes actions au bon moment. Toutes les méthodes proposées dans cette recherche doctorale ont été validées à l'aide d'ensembles de données complexes provenant de différents équipements complexes des procédés industriels : Rebouilleur dans une usine de pâte thermomécanique (TMP), chaudière de récupération de liqueur noire (BLRB) et concentrateur de liqueur noire dans une usine de pâte Kraft. Les méthodes proposées ont été comparées à différentes méthodes d’apprentissage automatique (ML) et d’apprentissage profond largement utilisées dans la littérature. Les résultats indiquent que nos méthodes surpassent les méthodes comparables en termes de précision de prédiction. Les résultats obtenus montrent que notre approche conduit à des prédictions précises et arrive à capturer la non-linéarité et le comportement dynamique de systèmes industriels complexes avec des distributions mal définies. Cette recherche doctorale ouvre la porte à la fusion de données hétérogènes industrielles incluant des données sensorielles, images, et vidéos.----------ABSTRACT: Analysis of industrial data imposes several challenges. This type of data is collected from heterogeneous sources and stored in isolated silos. This hinders the accessibility of this asset and its proper exploitation in the decision making process. Therefore, there is an urgent and prioritized need to merge these disconnected silos to maximize the benefits from this asset. However, merging data from multiple sources has two main challenges: efficient data representation for better data fusion, and selecting the most appropriate modeling technique based on the type and quantity of data as well as the nature of the problem to be solved. Solving these challenges will help the complex industrial systems maintain energy-efficient operations, reduce their environmental footprint, and thus achieve operational excellence. This thesis makes three contributions as a step for solving the problems of the data fusion resulting from the operation of industrial systems. The objective of the three proposed contributions is to obtain an efficient and useful data representation that helps maximize the global value of the available industrial data to facilitate the fusion process at different semantic levels (raw, information and knowledge levels). Those contributions make use of the performance of deep learning (DL) in predictive and generative modeling. They allow for building accurate and robust models to be used for several purposes in industry. These models can accurately diagnose various industrial systems, predict their key performance indicators (KPIs) and capture the true distribution of highly non-linear complex industrial systems to provide the expert with valuable knowledge to prescribe the right actions at the right time. All the proposed methods in this doctoral research are validated using challenging datasets collected from different complex equipment in process industries: reboiler system in a theromomechancal pulp mill (TMP), black liquor recovery boiler (BLRB) and concentrator of black liquor in a Kraft pulp mill. The methods were compared to different machine learning and deep learning techniques extensively used in the literature. Our results indicate that our methods outperform the comparable methods in terms of prediction accuracy. The results obtained show that our proposed approach leads to accurate predictions and successfully captures the non-linearity and dynamic behavior of complex industrial systems with ill-defined distributions. This doctoral research opens the door to the industrial heterogeneous data fusion including sensory data, images, and videos

Activitat herbicida de l'oli essencial d'Eucalyptus camaldulensis contra les males herbes problemàtiques d'estiu en cultius mediterranis.

[ES] Las malas hierbas y los herbicidas son factores de estrés importantes para los cultivos. Las malas hierbas son responsables de grandes pérdidas en los rendimientos de los cultivos; más del 50% en algunos cultivos si no se controlan. Los herbicidas se han utilizado como el método principal para el control de malas hierbas desde su desarrollo. Sin embargo, su uso generalizado ha dado lugar a diversos efectos toxicológicos sobre el medio ambiente y la salud humana, además de dar lugar a la aparición de biotipos de malas hierbas resistentes a los herbicidas. Para superar estos problemas, existe una necesidad urgente de buscar nuevos compuestos, particularmente productos vegetales naturales, con potencial actividad herbicida. La composición de aceites esenciales (EO) de fue analizada por GC. Los componentes principales fueron Spathulenol (31,29%), ρ-Cimene (20,38%) y Cryptone (17,00%). Se estudió el potencial herbicida del aceite esencial de Eucalyptus camaldulensis, por ejemplo, eficacia y nivel de daño en el invernadero contra dos especies de malas hierbas: Echinochloa crus-galli y Amaranthus retroflexus en ensayos de pre y postemergencia. Para ello, se aplicó la OE con espathulenol como componente principal en diferentes concentraciones mediante pulverización y riego con ácido pelargonico y glifosato como referencia. Los resultados mostraron que el aceite esencial fue efectivo en cada dosis para la inhibición de la germinación en ensayos de preemergencia. En los ensayos posteriores a la emergencia, la dosis 32 fue efectiva para matar las plantas de Amaranthus con una eficacia del 60% por pulverización. Concluimos que el aceite fue más efectivo cuando se aplicó por pulverización y que la especie Amaranthus era más sensible que Echinochloa. Los resultados sugieren el posible uso del aceite como herbicida natural. Se necesita más investigación para determinar las dosis efectivas para diferentes especies.[EN] Weeds and herbicides are important stress factors for crops. Weeds are responsible for great losses in crop yields, more than 50% in some crops if left uncontrolled. Herbicides have been used as the main method for weed control since their development. However, their widespread use has resulted in various toxicological effects on the environment and human health, besides resulting in the emergence of herbicide-resistant weed biotypes. To overcome these problems, there is an urgent need to search for novel compounds, particularly natural plant products, with potential herbicidal activity. The essential oil composition (EO) of was analyzed by GC. The main components were Spathulenol (31.29%), ρ-Cymene (20.38%) and Cryptone (17.00%). The herbicidal potential of the essential oil of Eucalyptus camaldulensis e.g., efficacy and damage level in the greenhouse against two weeds: Echinochloa crus-galli and Amaranthus retroflexus in pre- and post-emergence trials. For this purpose, the EO with spathulenol as the main component was applied in different concentrations by spraying and irrigation with pelargonic acid and glyphosate as reference. The results showed that the essential oil was effective in every dose for inhibition of germination in pre- emergence assays. In post emergence assays the dose 32 was effective in killing the Amaranthus plants with 60% efficacy by spraying. We conclude that the oil was more effective when applied by spraying and that Amaranthus species was more sensitive than Echinochloa. The results suggest the possible use of the oil as natural herbicide. Further research is needed to determine the effective doses for different species.Elhefnawy, MH. (2022). Herbicidal activity of Eucalyptus camaldulensis essential oil against problematic summer weeds in Mediterranean crops. Universitat Politècnica de València. http://hdl.handle.net/10251/186305TFG