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.     

Data-Intensive Cloud Computing: Requirements, Expectations, Challenges, and Solutions

Data-intensive systems encompass terabytes to petabytes of data. Such systems require massive storage and intensive computational power in order to execute complex queries and generate timely results. Further, the rate at which this data is being generated induces extensive challenges of data storage, linking, and processing. A data-intensive cloud provides an abstraction of high availability, usability, and efficiency to users. However, underlying this abstraction, there are stringent requirements and challenges to facilitate scalable and resourceful services through effective physical infrastructure, smart networking solutions, intelligent software tools, and useful software approaches. This paper analyzes the extensive requirements which exist in data-intensive clouds, describes various challenges related to the paradigm, and assess numerous solutions in meeting these requirements and challenges. It provides a detailed study of the solutions and analyzes their capabilities in meeting emerging needs of widespread applications.     

Performance analysis of multimedia based web traffic with QoS constraints

During the recent years, there has been a tremendous growth in the development and deployment of multimedia based networked applications such as video streaming, IP telephony, interactive games, among others. These applications, in contrast to elastic applications such as email and data sharing, are delay and delay jitter sensitive but can tolerate certain level of packet loss. A vital element of end-to-end delay and delay jitter is the random queueing delays in network switches and routers. Analysis of robust mechanisms for buffer management at network routers needs to be carried out in order to reduce end-to-end delay for traffic generated by multimedia applications. In this context, a threshold based buffer management scheme for accommodating multiple class multimedia traffic in network routers has been analysed. This technique effectively controls the allocation of buffer to various traffic classes according to their delay constraints. The forms of the joint state probabilities, as well as basic performance measures such as blocking probabilities are analytically established at equilibrium. Typical numerical experiments are included to illustrate the credibility of the proposed mechanism in the context of different quality of service (QoS) grades for various network traffic classes. This model, therefore, can be used as a powerful tool to provide a required grade of service to a particular class of multimedia based web traffic in any heterogeneous network.     

Potentiometric urea biosensor based on BSA embedded surface modified polypyrrole film

A potentiometric biosensor based on bovine serum albumin (BSA) embedded surface modified polypyrrole has been developed for the quantitative estimation of urea in aqueous solution. The enzyme, urease (Urs), was covalently linked to free amino groups present over the BSA embedded modified surface of the conducting polypyrrole film electrochemically deposited onto an indium–tin-oxide (ITO) coated glass plate. The biosensor has been characterized by UV–visible, infrared spectroscopy and SEM. Potentiometric and spectrophotometric response of the enzyme electrode (Urs/BSA-PPy/ITO) were measured as a function of urea concentration in Tris–HCl buffer (pH 7.0). It has been found that the electrode responds to low urea concentration with wider range of detection. The electrode showed a linear response range of 6.6 × 10⁻⁶ to 7.5 × 10⁻⁴ M urea. The response time is about 70–90 s reaching to a 95% steady-state potential value and 75% of the enzyme activity is retained for about 2 months. These results indicate an efficient covalent linkage of enzyme to free amino groups of the BSA molecules over the surface of polypyrrole film, which leads to high enzyme loading, an increased lifetime stability of the electrode and an improved wide range of detection of low urea concentration in aqueous solution.     

Targeting spam control on middleboxes: Spam detection based on layer-3 e-mail content classification

This paper proposes a spam detection technique, at the packet level (layer 3), based on classification of e-mail contents. Our proposal targets spam control implementations on middleboxes. E-mails are first pre-classified (pre-detected) for spam on a per-packet basis, without the need for reassembly. This, in turn, allows fast e-mail class estimation (spam detection) at receiving e-mail servers to support more effective spam handling on both inbound and outbound (relayed) e-mails. In this paper, the naïve Bayes classification technique is adapted to support both pre-classification and fast e-mail class estimation, on a per-packet basis. We focus on evaluating the accuracy of spam detection at layer 3, considering the constraints on processing byte-streams over the network, including packet re-ordering, fragmentation, overlapped bytes, and different packet sizes. Results show that the proposed layer-3 classification technique gives less than 0.5% false positive, which approximately equals the performance attained at layer 7. This shows that classifying e-mails at the packet level could differentiate non-spam from spam with high confidence for a viable spam control implementation on middleboxes.     

A multi-objective strategy in genetic algorithms for gene selection of gene expression data

A microarray machine offers the capacity to measure the expression levels of thousands of genes simultaneously. It is used to collect information from tissue and cell samples regarding gene expression differences that could be useful for cancer classification. However, the urgent problems in the use of gene expression data are the availability of a huge number of genes relative to the small number of available samples, and the fact that many of the genes are not relevant to the classification. It has been shown that selecting a small subset of genes can lead to improved accuracy in the classification. Hence, this paper proposes a solution to the problems by using a multiobjective strategy in a genetic algorithm. This approach was tried on two benchmark gene expression data sets. It obtained encouraging results on those data sets as compared with an approach that used a single-objective strategy in a genetic algorithm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Performance evaluation of dynamic probabilistic broadcasting for flooding in mobile ad hoc networks

In mobile ad hoc networks (MANETs), flooding is a required message dissemination technique for network-wide broadcast. The conventional blind flooding algorithm causes broadcast storm problem, a high number of unnecessary packet rebroadcasts thus resulting in high contention and packet collisions. This paper proposes a new probabilistic approach that dynamically fine-tunes the rebroadcasting probability of a node for routing request packets (RREQs) according to the number of neighbour nodes. We evaluate the performance of the proposed approach for the ad hoc on demand distance vector (AODV) routing protocol and compared against the blind flooding, fixed probabilistic and adjusted probabilistic flooding [L.M.M.M. Bani-Yassein, M. Ould-Khaoua et al., Performance analysis of adjusted probabilistic broadcasting in mobile ad hoc networks, International Journal of Wireless Information Networks 13(2) (2006) 127–140; M.B. Yassein, M.O. Khaoua et al., Improving route discovery in on-demand routing protocols using local topology information in MANETs, Proceedings of the ACM international workshop on Performance Monitoring, Measurement, and Evaluation of Heterogeneous Wireless and Wired Networks, Terromolinos, Spain, ACM Press, 2006, pp. 95–99.] approaches. The simulation results show that our proposed approach demonstrates better performance than blind flooding, fixed probabilistic and adjusted flooding approaches.     

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.     

DeepClassRooms: a deep learning based digital twin framework for on-campus class rooms

Neural Computing and Applications - A lot of different methods are being opted for improving the educational standards through monitoring of the classrooms. The developed world uses Smart...