Filter-feature-based rotation-invariant joint Fourier transform correlator

Rotation-invariant target detection using a trained filter-feature-based joint Fourier transform (JFT) correlator is investigated. First, a composite reference image is obtained from a training set of targets. An optimum filter formulation is then applied on this composite image to come up with a new feature that we refer to as a filter feature. This feature is then used in a JFT correlator, which results in a simple and robust rotation-invariant target recognition system.     

Progress on the boundary element method to study the disturbance fields of bodies moving in an unbounded medium

For studying flow problems involved with complex physics it is now common to use numerical field methods for solving Navier-Stokes or Euler equations. However, for a large class of fluid mechanics problems, which can be dealt with linearized potential equations, the boundary element method proves to be quite useful, especially for its easy application and relatively less computational effort compared to the field methods. The boundary element method has undergone some significant advancements in the last decade with respect to the study of steady and unsteady flow problems concerning wing aerodynamics in compressible medium, flow fields of propellers and rotors and acoustical disturbance propagation from moving bodies. In this paper a few recent contributions which evolved in the DLR as research projects and as doctoral and diploma thesis of the Technical University Braunschweig are concisely described.List of symbols a Sound velocity - b Span of a wing - c _p Coefficient of static pressure - c _dp Coefficient of profile drag - c ₁, c _d, c_m Coefficient of lift, drag and moment per unit span width - c _L, c_D, c_M Total lift, drag and moment-coefficients - c _T, c_P Thrust and power-coefficient of a propeller - d Distance - D Doublet strength - e Specific heat energy - E Total energy in a moving medium element - f Frequency - F Field point - g Gravitational acceleration - h Radial distance in cylinder coordinates - I ₁, I ₂ Inducing functions - i, j, k Unit vectors in cartesian coordinates - k Wave number [/a ] - l Local wing-chord - l ₀, l _v Length of singularity element at t _oand t _v - m Notation for Fourier-component - M, M ^* Mach number based on local and critical sound speed - n Number of rotation per second - n Unit normal vector to a surface     

Comparative effects of gamma and microwave irradiation on the quality of black pepper

Powdered black pepper from Egyptian markets, was irradiated with different recommended doses of gamma rays (5.0 and 10.0 kGy) and with microwaves for different periods (20, 40 and 75 s) to improve its hygienic quality. The most common bacterial isolates were of three generaBacillus, Clostridium andMicrococcus (7.5 × 10⁶), whereas the predominant fungi (7.8 × 10⁴) wereAspergillus species,A. glaucus, A. flavus, A. niger andA. ochraceus. Doses of gamma irradiation used (5.0 and 10 kGy) were sufficient to decrease spore-forming bacteria (SFB) and to inhibit the fungal flora and coliforms which contaminated the black pepper powder. Microwave treatments for 40 s and 75 s were of the same effectiveness whereas treatment for 20 s was less so. GLC analysis proved the presence of 31 peaks, only 19 compounds were identified as monoterpene hydrocarbons (56.21%), the major one being -phellandrene and limonene. Sesquiterpenes were also present, mainly -caryollphyllene (3.69%) as well as oxygenated compounds such as terpenol, geraniol, Me-chavicol, eugenol and anisol. Gamma irradiation at 5 kGy and 10 kGy respectively decreased the numbers of identified compounds from 21 (86.58% concentration) in untreated pepper to 16 (59.22% concentration), 15 (54.06% concentration). In comparison, microwave treatments, particularly for 40 s and 75 s, increased the concentration of the same compounds. The results obtained indicate that microwave treatment, under these conditions, is a safe and suitable technique for decontamination of black pepper which does not result in a great loss of flavour compounds, as compared with recommended doses of gamma irradiation.     

Long Duration Persistent Photocurrent in 3 nm Thin Doped Indium Oxide for Integrated Light Sensing and In-Sensor Neuromorphic Computation

Miniaturization and energy consumption by computational systems remain major challenges to address. Optoelectronics based synaptic and light sensing provide an exciting platform for neuromorphic processing and vision applications offering several advantages. It is highly desirable to achieve single-element image sensors that allow reception of information and execution of in-memory computing processes while maintaining memory for much longer durations without the need for frequent electrical or optical rehearsals. In this work, ultra-thin (<3 nm) doped indium oxide (In₂O₃) layers are engineered to demonstrate a monolithic two-terminal ultraviolet (UV) sensing and processing system with long optical state retention operating at 50 mV. This endows features of several conductance states within the persistent photocurrent window that are harnessed to show learning capabilities and significantly reduce the number of rehearsals. The atomically thin sheets are implemented as a focal plane array (FPA) for UV spectrum based proof-of-concept vision system capable of pattern recognition and memorization required for imaging and detection applications. This integrated light sensing and memory system is deployed to illustrate capabilities for real-time, in-sensor memorization, and recognition tasks. This study provides an important template to engineer miniaturized and low operating voltage neuromorphic platforms across the light spectrum based on application demand.     

Design and development of dynamic Agri-ontology for IoT interoperability

In recent years, the usage and applications of Internet of Things (IoT) have increased exponentially. IoT connects multiple heterogeneous devices like sensors, micro controllers, actuators, smart devices like mobiles, watches, etc. IoT contributes the data produced in the context of data collection, including the domains like military, agriculture, healthcare, etc. The diversity of possible applications at the intersection of the IoT and the web semantics has prompted many research teams to work at the interface between these two disciplines. This makes it possible to collect data and control various objects in transparent way. The challenge lies in the use of this data. Ontologies address this challenge to meet specific data needs in the IoT field. This paper presents the implementation of a dynamic agriculture ontology-building tool that parses the ontology files to extract full data and update it based on the user needs. The technology is used to create the angular library for parsing the OWL files. The proposed ontology framework would accept user-defined ontologies and provide an interface for an online updating of the owl files to ensure the interoperability in the agriculture IoT.     

Deterministic Polymorphic Engineering of MoTe2 for Photonic and Optoelectronic Applications

Developing selective and coherent polymorphic crystals at the nanoscale offers a novel strategy for designing integrated architectures for photonic and optoelectronic applications such as metasurfaces, optical gratings, photodetectors, and image sensors. Here, a direct optical writing approach is demonstrated to deterministically create polymorphic 2D materials by locally inducing metallic 1T′-MoTe₂ on the semiconducting 2H-MoTe₂ host layer. In the polymorphic-engineered MoTe₂, 2H- and 1T′- crystalline phases exhibit strong optical contrast from near-infrared to telecom-band ranges (1–1.5 µm), due to the change in the band structure and increase in surface roughness. Sevenfold enhancement of third harmonic generation intensity is realized with conversion efficiency (susceptibility) of ≈1.7 × 10⁻⁷ (1.1 × 10⁻¹⁹ m² V⁻²) and ≈1.7 × 10⁻⁸ (0.3 × 10⁻¹⁹ m² V⁻²) for 1T′ and 2H-MoTe₂, respectively at telecom-band ultrafast pump laser. Lastly, based on polymorphic engineering on MoTe₂, a Schottky photodiode with a high photoresponsivity of 90 AW⁻¹ is demonstrated. This study proposes facile polymorphic engineered structures that will greatly benefit realizing integrated photonics and optoelectronic circuits.     

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

With the increased advancements of smart industries, cybersecurity has become a vital growth factor in the success of industrial transformation. The Industrial Internet of Things (IIoT) or Industry 4.0 has revolutionized the concepts of manufacturing and production altogether. In industry 4.0, powerful Intrusion Detection Systems (IDS) play a significant role in ensuring network security. Though various intrusion detection techniques have been developed so far, it is challenging to protect the intricate data of networks. This is because conventional Machine Learning (ML) approaches are inadequate and insufficient to address the demands of dynamic IIoT networks. Further, the existing Deep Learning (DL) can be employed to identify anonymous intrusions. Therefore, the current study proposes a Hunger Games Search Optimization with Deep Learning-Driven Intrusion Detection (HGSODL-ID) model for the IIoT environment. The presented HGSODL-ID model exploits the linear normalization approach to transform the input data into a useful format. The HGSO algorithm is employed for Feature Selection (HGSO-FS) to reduce the curse of dimensionality. Moreover, Sparrow Search Optimization (SSO) is utilized with a Graph Convolutional Network (GCN) to classify and identify intrusions in the network. Finally, the SSO technique is exploited to fine-tune the hyper-parameters involved in the GCN model. The proposed HGSODL-ID model was experimentally validated using a benchmark dataset, and the results confirmed the superiority of the proposed HGSODL-ID method over recent approaches.     

Observer-based leader-following consensus of one-sided Lipschitz multi-agent systems over input saturation and directed graphs

This paper investigates a local observer-based leader-following consensus control of one-sided Lipschitz (OSL) multi-agent systems (MASs) under input saturation. The proposed consensus control scheme has been formulated by using the OSL property, input saturation, directed graphs, estimated states, and quadratic inner-boundedness condition by attaining the regional stability. It is assumed that the graph always includes a (directed) spanning tree with respect to the leader root to develop matrix inequalities for investigating parameters of the proposed observer and consensus protocols. Further, a new observer-based consensus tracking method for MASs with saturation, concerning independent topologies for communicating outputs and estimates over the network, is explored to deal with a more perplexing and realistic situation. In contrast to the traditional methods, the proposed consensus approach considers output feedback and deals with the input saturation for a generalized class of nonlinear systems. The efficiency of the obtained results is illustrated via application to a group of five moving agents in the Cartesian coordinates.     

Home Automation Using Augmented Reality (HAAR)

Wireless Personal Communications - The development of Smart Home Controllers has seen rapid growth in recent years, especially for smart devices, that can utilize the Internet of Things (IoT)....     

A New Adaptive Beamforming of Multiband Fractal Antenna Array in Strong-Jamming Environment

This paper proposes, for the first time, a new radiation pattern synthesis for fractal antenna array that combines the unique multi-band characteristics of fractal arrays with the adaptive beamforming requirements in wireless environment with high-jamming power. In this work, a new adaptive beamforming method based on discrete cbKalman filter is proposed for linear Cantor fractal array with high performance and low computational requirements. The proposed Kalman filter-based beamformer is compared with the Least Mean Squares (LMS) and the Recursive Least Squares (RLS) techniques under various parameter regimes, and the results reveal the superior performance of the proposed approach in terms of beamforming stability, Half-Power Beam Width (HPBW), maximum Side-Lobe Level (SLL), null depth at the direction of interference signals, and convergence rate for different Signal to Interference (SIR) values. Also, the results demonstrate that the suggested approach not only achieves perfect adaptation of the radiation pattern synthesis at high jamming power, but also keep the same SLL at different operating frequencies. This shows the usefulness of the proposed approach in multi-band smart antenna technology for mobile communications and other wireless systems.