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In the provision of massive open online courses (MOOCs), cloud computing services enable students to synchronize their study materials anywhere, anytime, and using any device, which can improve learning performance and strengthen the teacher–student relationship via knowledge sharing. This study builds on the technological–organizational–environmental (TOE) framework and aims to identify the influencing factors of cloud computing adoption in educational settings for the provision of MOOCs. Another aim is to determine how intrinsic motivation moderates individual intention. Therefore, our study conceptualized a model that is supported by an empirical analysis of 232 respondents and takes into account the technological, organizational, and environmental impacts on individual attitudes toward adopting cloud computing in education. We evaluate the study hypotheses using structural equation modeling. The results demonstrate significant relationships between the technological and organizational constructs and attitudes toward the use of cloud computing. Meanwhile, competitive pressure from the environment has not been identified in any relationship with individual attitudes in government universities. The results provide new directions for policymakers to consider in the implementation of CC systems for the provision of MOOCs in developing countries. We also discuss potential implications, contributions, and suggestions for future research.

Currently most web services obtain REST architectural styles, which were originally founded by Roy Fielding in the year 2000. Alongside the styles, constraints and techniques were also discussed in Roys famous dissertation. In this research paper we will take a look at understanding the techniques used in the REST architecture style, covering the six constraints. Evaluating the testing techniques of REST and finally comparing REST with the SOAP standard. In order to remove the high latency, reduce network traffic and processing delays, which was being caused by SOAP, REST was introduced to overcome all of these issues. Furthermore, 70% of websites use REST architecture as many have found SOAP to be outdated.

This letter presents a novel technique for optimizing the performance of a phased-multi-in multi-out (MIMO) radar in the presence of strong jamming effects. The proposed technique not only overcomes the noise jamming but also counters the effects of deception jamming. Two orthogonal waveforms beam-formed by coherent operation of two sub-arrays are used to leverage the advantages of both the MIMO and phased-radar simultaneously. The heart of the idea lies with its coherent receive operation where matched filtering and Karhunen–Loeve transform is applied for separating the signals. We make use of the MUltiple SIgnal Classification (MUSIC) algorithm for angle estimation; thereafter the signal-to-noise-plus-jamming ratio is maximized by solving a convex optimization problem. The output shows remarkable results with an excellent peak to sidelobe ratio of more than 40 dB.

Intentional sidelobe jamming is a serious concern to the effectiveness of modern day phased array systems that beamforms in the direction of interest. The approach suggested in this paper uses a linear antenna array that adapts its beam pattern so as to emphasize the signals of interest and to minimize interfering signals. We show that a combination of adaptive beamforming, sidelobe blanking and optimization of weights according to the jamming environment overcomes all forms of noise jamming. In order to achieve this objective, convex optimization problems are formulated to restrict the sidelobes under a certain level, identify and nullform the jamming direction and maximize gain in the target's direction.

This paper is built upon our earlier work on using a Markovian approach to design demodulators for diffusion-based molecular communications. In this earlier work, we show that demodulation can be performed by using a bank of analog filters, which are modelled by ordinary differential equations, to compute the log-posteriori probability that a transmission symbol is transmitted given the observations available to the demodulator. This earlier work was recently extended in two different ways. First, the earlier work assumes that the receiver is limited to a small volume called a voxel. We recently extended the work to the case where a receiver is modelled by a volume consisting of multiple voxels. In particular, we show that we can reduce the bit error rate by using receivers that are spatially partitioned. Second, the earlier work assumes that the computation of the log-posteriori probability is carried out in silico. We recently showed how the computation can be approximately carried out by a molecular circuit, i.e. a set of reactions. This paper builds on these two recent extensions. We make the following contributions. First, we derive molecular circuits to approximately compute the log-posteriori probability for partitioned receivers. We show how this is done for two different types of concentration modulation schemes. Second, the earlier work considered the demodulation of only one symbol. We extend the work so that the demodulator can decode a sequence of symbols by introducing a reset mechanism in the demodulator.

This work builds on our earlier work on designing demodulators for diffusion-based molecular communications using a Markovian approach. The demodulation filters take the form of an ordinary differential equation (ODE) which computes the log-posteriori probability of observing a transmission symbol given the continuous history of receptor activities. A limitation of our earlier work is that the receiver is assumed to be a small cubic volume called a voxel. In this work, we extend the maximum a-posteriori demodulation to the case where the receiver may consist of multiple voxels and derive the ODE for log-posteriori probability calculation. This extension allows us to study receiver behaviour of different volumes and shapes. In particular, it also allows us to consider spatially partitioned receivers where the chemicals in the receiver are not allowed to mix. The key result of this paper is that spatial partitioning can be used to reduce bit-error rate in diffusion-based molecular communications., 39 pages, 20 figures, submitted for possible publication in IEEE

This paper considers a diffusion-based molecular communication link assuming the receiver uses chemical reactions. When the signalling molecules reach the receiver, they react with the chemicals at the receiver to produce output molecules. We consider the number of output molecules over time as the output signal of the receiver. This output signal is stochastic because both diffusion and reactions are stochastic processes. If this output signal has high variance, then it can lead to degradation in communication performance. Inspired by the spatial partitioning of receptors in cell membrane, this paper investigates the effect of spatial partitioning on the variance of the output signal. By modelling the diffusion and reaction using linear noise approximation, we show that a spatially partitioned system gives a lower total output signal variance than one that does not use partitioning, especially in steady state.

Multi-static and MIMO radars have opened up new horizons of research as they offer diversified degrees of freedom and arbitrary geometrical patterns. The biggest hurdle that such a system can face at implementation level is the synchronization of this distributed system. Use of GPS atomic clock, although quite accurate, is considered to be an expensive option for low cost distributed radar solutions. In this paper, we propose a simple, cost effective yet accurate scheme for synchronizing a long-range, low to medium PRF bi-static radar system using GSM technology. The concept can easily be extended to multi-static radar networks. The proposed system achieves a temporal resolution of tens of micro seconds and thus can be used in a large number of radar applications.

Youtube is one of the most popular video sharing online resource that has millions of users around the world. The huge bulk of videos, which are growing at a high rate is posing problems for users to traverse through to relevant content. Users are facilitated with recommended videos that appeal to there interests. Following a hybrid recommendation approach, videos are recommended based on both collaborative recommendation and content-based recommendation. A limitation associated to this approach is that the videos recommended may not necessarily be appropriate to the current context that the user is in. Its very common for a single user to follow different interests depending of on the context they are in. A context-aware recommender system is proposed for Youtube that keeps track of multiple interests of a user and recommends videos based on their current context only. It serves a user better in finding relevant videos and has higher relevance to human judgment.

MIMO radar technology has recently attracted attention of large number of research scientists and engineers. Over the last decade, phased array radars have been quite popular because of their beam forming capabilities. In this paper, we propose a novel scheme for an improved performance of radar systems by transmitting orthogonal waveforms from different antennas. We first investigate MIMO radar systems, with orthogonal transmitted waveforms, with regards to their utility for low SNR fluctuating targets. The results show that the conventional mono-static radars are incapable of dealing with low observable fluctuating targets. The highly sophisticated vehicular targets pose great challenge to the ground based radar systems. Thereafter, we propose an algorithm that exploits the time diversity in addition to inherent spatial diversity order of MIMO systems, thereby exploiting the target scintillations and improving the detection performance. The proposed scheme transmits waveforms separated in time such that they experience un-correlated channels. The proposed scheme outperforms mono-static radar and gives a gain of 12 dB with a 2 X 2 MIMO radar system.

Persistently saturated links are abnormal conditions that indicate bottlenecks in Internet traffic. Network operators are interested in detecting such links for troubleshooting, to improve capacity planning and traffic estimation, and to detect denial-of-service attacks. Currently bottleneck links can be detected either locally, through SNMP information, or remotely, through active probing or passive flow-based analysis. However, local SNMP information may not be available due to administrative restrictions, and existing remote approaches are not used systematically because of their network or computation overhead. This paper proposes a new approach to remotely detect the presence of bottleneck links using spectral and statistical analysis of traffic. Our approach is passive, operates on aggregate traffic without flow separation, and supports remote detection of bottlenecks, addressing some of the major limitations of existing approaches. Our technique assumes that traffic through the bottleneck is dominated by packets with a common size (typically the maximum transfer unit, for reasons discussed in Section 5.1). With this assumption, we observe that bottlenecks imprint periodicities on packet transmissions based on the packet size and link bandwidth. Such periodicities manifest themselves as strong frequencies in the spectral representation of the aggregate traffic observed at a downstream monitoring point. We propose a detection algorithm based on rigorous statistical methods to detect the presence of bottleneck links by examining strong frequencies in aggregate traffic. We use data from live Internet traces to evaluate the performance of our algorithm under various network conditions. Results show that with proper parameters our algorithm can provide excellent accuracy (up to 95%) even if the traffic through the bottleneck link accounts for less than 10% of the aggregate traffic.

Denoising of real world images that are degraded by Gaussian noise is a long established problem in statistical signal processing. The existing models in time-frequency domain typically model the wavelet coefficients as either independent or jointly Gaussian. However, in the compression arena, techniques like denoising and detection, states the need for models to be non-Gaussian in nature. Probabilistic Graphical Models designed in time-frequency domain, serves the purpose for achieving denoising and compression with an improved performance. In this work, Hidden Markov Model (HMM) designed with 2D Discrete Wavelet Transform (DWT) is proposed. A comparative analysis of proposed method with different existing techniques: Wavelet based and curvelet based methods in Bayesian Network domain and Empirical Bayesian Approach using Hidden Markov Tree model for denoising has been presented. Results are compared in terms of PSNR and visual quality.

Statistical signal modeling using hidden Markov model is one of the techniques used for image compression. Wavelet based statistical signal models are impractical for most of the real time processing because they usually represent the wavelet coefficients as jointly Gaussian or independent to each other. In this paper, we build up an algorithm that succinctly characterizes the interdependencies of wavelet coefficients and their Non-Gaussian behavior especially for image compression. This is done by extracting the combine feature of hidden Markov model and Wavelet transformation that gives us comparatively better results. To estimate the parameter of wavelet based Hidden Markov model, an efficient expectation maximization algorithm is developed.