Three-qubit Grover’s algorithm using superconducting quantum interference devices in cavity-QED

We present a scheme for the implementation of three-qubit Grover’s algorithm using four-level superconducting quantum interference devices (SQUIDs) coupled to a superconducting resonator. The scheme is based on resonant, off-resonant interaction of the cavity field with SQUIDs and application of classical microwave pulses. We show that adjustment of SQUID level spacings during the gate operations, adiabatic passage, and second-order detuning are not required that leads to faster implementation. We also show that the marked state can be searched with high fidelity even in the presence of unwanted off-resonant interactions, level decay, and cavity dissipation.     

Amplitudes distribution over two copies of small quantum subsystems

We propose a new formulation of quantum algorithm which allows to distribute amplitudes over two copies of small quantum subsystems. The new method gives a fixed number of copies and applied to the control of multi-qubit system. The analysis for the amount of error due to the distribution process has been presented for a system of 10 qubits with a small quantum subsystems to be copied. The present scheme provides a new way to distribute amplitudes over small quantum subsystems.     

Set-Dueling-Controlled Adaptive Insertion for High-Performance Caching

The commonly used LRU replacement policy causes thrashing for memory- intensive workloads. A simple mechanism that dynamically changes the insertion policy used by LRU replacement reduces cache misses by 21 percent and requires a total storage overhead of less than 2 bytes.     

Optisim: A System Simulation Methodology for Optically Interconnected HPC Systems

Although CAD tools have significantly assisted electronic system simulation, the system-level optoelectronics modeling field has lagged behind due to a lack of simulation methodologies and tools. Optisim, a system-level modeling and simulation methodology of optical interconnects for HPC systems, can provide computer architects, designers, and researchers with a highly optimized, efficient, and accurate discrete-event environment to test various HPC systems.     

Data aggregation and routing in Wireless Sensor Networks: Optimal and heuristic algorithms

A fundamental challenge in the design of Wireless Sensor Networks (WSNs) is to maximize their lifetimes especially when they have a limited and non-replenishable energy supply. To extend the network lifetime, power management and energy-efficient communication techniques at all layers become necessary. In this paper, we present solutions for the data gathering and routing problem with in-network aggregation in WSNs. Our objective is to maximize the network lifetime by utilizing data aggregation and in-network processing techniques. We particularly focus on the joint problem of optimal data routing with data aggregation en route such that the above mentioned objective is achieved. We present Grid-based Routing and Aggregator Selection Scheme (GRASS), a scheme for WSNs that can achieve low energy dissipation and low latency without sacrificing quality. GRASS embodies optimal (exact) as well as heuristic approaches to find the minimum number of aggregation points while routing data to the Base-Station (BS) such that the network lifetime is maximized. Our results show that, when compared to other schemes, GRASS improves system lifetime with acceptable levels of latency in data aggregation and without sacrificing data quality.     

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     

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.     

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.