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This paper proposes a Full Range Gaussian Markov Random Field (FRGMRF) model for monochrome image compression, where images are assumed to be Gaussian Markov Random Field. The parameters of the model are estimated based on Bayesian approach. The advantage of the proposed model is that it adapts itself according to the nature of the data (image) because it has infinite structure with a finite number of parameters, and so completely avoids the problem of order determination. The proposed model is fitted to reconstruct the image with the use of estimated parameters and seed values. The residual image is computed from the original and the reconstructed images. The proposed FRGMRF model is redefined as an error model to compress the residual image to obtain better quality of the reconstructed image. The parameters of the error model are estimated by employing the Metropolis-Hastings (M-H) algorithm. Then, the error model is fitted to reconstruct the compressed residual image. The Arithmetic coding is employed on seed values, average of the residuals and the model coefficients of both the input and residual images to achieve higher compression ratio. Different types of textured and structured images are considered for experiment to illustrate the efficiency of the proposed model. The results obtained by the FRGMRF model are compared to the JPEG2000. The proposed approach yields higher compression ratio than the JPEG whereas it produces Peak Signal to Noise Ratio (PSNR) with little higher than the JPEG, which is negligible.
The techniques of test
case prioritization schedule the execution order of test cases to attain
respective target, such as enhanced level of forecasting the fault. The
requirement of the prioritization can be viewed as the en-route for deriving an
order of relation on a given set of test cases which results from regression
testing. Alteration of programs between the versions can cause more test cases
which may respond differently to following versions of software. In this, a
fixed approach to prioritizing test cases avoids the preceding drawbacks. The
JUnit test case prioritization techniques operating in the absence of coverage
information, differs from existing dynamic coverage-based test case
prioritization techniques. Further, the prioritization test cases relying on
coverage information were projected from fixed structures relatively other than
gathered instrumentation and execution.
The existing mobile service discovery approaches do not completely address the issues of service selection and the robustness faced to mobility. The infrastructure of mobile service must be QoS-aware plus context-aware (i.e.) aware of the user’s required-QoS and the QoS offered by the other networks in user’s context. In this paper, we propose a cluster based QoS-aware service discovery architecture using swarm intelligence. Initially, in this architecture, the client sends a service request together with its required QoS parameters like power, distance, CPU speed etc. to its source cluster head. Swarm intelligence is used to establish the intra and inter cluster shortest path routing. Each cluster head searches the QoS aware server with matching QoS constraints by means of a service table and a server table. The QoS aware server is selected to process the service request and to send the reply back to the client. By simulation results, we show that the proposed architecture can attain a good success rate with reduced delay and energy consumption, since it satisfies the QoS constraints.
We report the case of a 33 year old female who presented with endometriosis of the anterior abdominal wall following Caesarean Section at the surgical incision site. Abdominal Incisional Site Endometriosis can pose a diagnostic dilema owing to its relative rarity and vagueosity of symptoms, vis-a-vis, cyclical abdominal pain and occasional palpable mass associated with menstruation. A greater index of suspicion should be prompted in such patients especially if symptoms occur following pelvic surgery such as Caesarean Sections, hysterotomy, and myomectomy.