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NET Framework 3. We highly recommend that you run the bit drvupdate-amd You may sometimes need to re-apply and Start these again when re-connecting device or after performing a Restart on the PC. Even if you are not able to get WMDC to show it is connected, you may still be able to browse the contents of the device through File Explorer which will appear as a drive under \”This PC\” or synchronize anyway. This is as shown below:.

Also, tests so far have shown that Windows Mobile Device Center seems to work as well in Windows 10 as it did in Windows 8, 7, and Vista.

Some known issues may still apply. If you have been forced to download and re-install Windows Mobile Device Center in Windows 10, it is recommended to use the bit or bit links at the following web page.

The actual artificial cell division approach [46] is slightly modified to design the optimization procedure. The incorporated modifications are listed below [47] :. The hierarchical tree structure is formed throughout the generations due to the artificial cell division process. Swarms of artificial cells are considered in the optimization process to take part in the artificial cell division process.

No communication is allowed between any pair of artificial cells. Lifespan of the k th artificial cell at a certain timestamp t s is an important parameter and it is directly dependent on the fitness value f i t n e s s k as given in Eq. A huge number of swarms can significantly increase the fitness evaluations and a small number of swarms can increase time to converge and therefore, is essential to decide the swarm count moderately.

In this work, the swarm count is considered is a fixed parameter. One artificial cell can produce some new cells and the production of new cells occur at a certain distance which is inversely dependent on the fitness of the producer cell as expressed in Eq.

The distance between the k th cell and any of the l th cell, which are produced from the same parent cell, must be same. Therefore, if a cell is near to global optima, then it can generate some other cells at a smaller distance and vice-versa. Smaller steps help to search the nearest portions of the global optima cautiously so that the global optima may not be missed accidentally.

A cell does not have any effect on the population once its lifespan is over. This property helps to maintain the size of the population and prevents getting overpopulated. The successor cells of a cell can produce some other cells by the cell division process to maintain the population. The life span of a cell can belong it belongs to the near-optimal area. The quality of a population is evaluated using the lambda function which is given in Eq. Algorithm 1 illustrates the artificial cell swarm optimization approach in brief [47].

The proposed approach adopts the type 2 fuzzy logic-based clustering approach to effectively model and handle the random uncertainties. In most real-life applications, the uncertainty cannot be predicted in advance. A wide range of input types can produce random uncertainties. Hence, it is essential to cope up with the random uncertainties in real-life scenarios. The fuzzy C-means clustering approach is one of the widely used clustering approaches which is suitable to various problems of different domains [48] , [49] , [50] , [51].

The main reason behind the increasing popularity of fuzzy systems is the suitability of this approach in different scenarios where the crisp clustering approaches do not perform well.

A single point can be a member of more than one cluster at the same time with some membership values. The total sum of all membership values for a certain point must be one. So, the value of the membership can be anything between 0 and 1. The dissimilarity function which is optimized by the fuzzy C-means clustering approach is given in Eq.

The cluster centers can be updated using Eq. The type 2 fuzzy logic systems use separate sets of membership values that are also fuzzy in nature. This approach allows efficient modeling of dynamic input uncertainties by providing additional degrees of freedom. In this work, the type 2 fuzzy logic-based clustering approach is adopted to overcome some of the common problems of type 1 fuzzy systems like noise sensitivity, relative membership values, etc.

It is essential to improve the outcome of the segmentation process. The uncertainty of a point must be decided depending on the membership value i. So, a lower membership value indicates higher uncertainty and vice-versa.

Some of the basic reason behind the adoption of type 2 fuzzy system in this work is listed below [53] :. The membership value in type 2 fuzzy systems can be calculated using Eq. The proposed approach does not require Eq. The artificial cell swarm optimization process will guide the proposed approach to determine the optimal cluster centers. The type 2 fuzzy clustering system can be easily understood from algorithm 2 and the schematic diagram of the type 2 fuzzy system can be visualized from Fig.

The ever-growing technology allows us to increase the quality of the image acquisition hardware. High-quality biomedical images can be acquired from various biomedical image acquisition devices and it is helpful in a precise analysis of the biomedical images. Automated biomedical image analysis devices are facing some challenges due to the increasing quality of biomedical images. A high amount of spatial information creates severe problems for automated and computer-aided diagnostic systems because medical diagnostic systems demand quick and accurate results.

Image segmentation plays a vital role in many automated computer-aided image analysis systems. It is essential to generate precise reports within the stipulated amount of time to provide accurate treatment to the patients. To handle this situation effectively and to accelerate the screening process of the COVID infection, a superpixel-based novel approach is proposed in this work to segment the CT scan images.

Superpixels are useful to represent a set of pixels in a computation-friendly manner. Different approaches can be found in the literature to find the superpixel image from an input image [54] , [55] , [56]. Some superpixel computation methods like mean shift [54] and watershed [56] produce irregular superpixels and some methods like SLIC [55] generate regular superpixels. Meanshift and watershed approaches are more useful due to the capability to generate irregular superpixels.

The watershed approach is simpler to implement compared to the mean-shift approach but it is sensitive to the noise which is not at all desirable for the image segmentation approaches. In this work, the noise sensitivity of the watershed approach is removed with the help of the gradient image, which is generated using the approach, proposed in [57].

The obtained gradient image is processed using the morphological erosion and dilation-based reconstruction operations, which are given in Eqs.

Here, s e is the structuring element and it is an important parameter that controls the segmented outcome. The size of the structuring element is subjective and depends on the image under consideration.

Practically, it is not possible to determine different structuring elements which are of various sizes, depending on the image. Therefore, the pointwise maximum value is computed using Eq. The number of superpixels is inversely dependent on the size of the structuring elements.

It can be easily understood in Fig. The image considered in these two figures is the T e s t 01 image [58] please refer to Table 3. Dependency of the number of superpixels on the size of the disk structuring element a — h superpixel images obtained using the disk structuring element of size 3 to 10 respectively, i Size of the structuring element vs. Dependency of the number of superpixels on the size of the square structuring element a — h superpixel images obtained using the square structuring element of size 3 to 10 respectively, i Size of the structuring element vs.

A very small lower bound is not desirable because it will produce very small regions and some essential edge information can be lost. So, the threshold value can be adjusted as per the requirement and depending on the available resources. The conventional fuzzy C-means clustering approach often overlooks some important spatial information that can be costly in terms of the segmentation performance.

Some approaches try to solve this problem by considering and blending some local spatial information in the objective function but it increases the computational cost and therefore not suitable on many occasions. Superpixels can help in this context by over-segmenting an image in many small, perceptually uniform, and homogeneous regions.

In this work, the CT images are first processed to determine the superpixels using the proposed approach and then the fuzzy artificial cell swarm optimization approach is used to determine the segmented image by finding the optimal clusters. As discussed earlier, the type 2 fuzzy system is used to perform the segmentation. The fuzzy objective function which is given in Eq. To incorporate the advantages of the superpixel, it is necessary to modify the fuzzy objective function.

The representative value is used in the objective function, and the modified objective function is given in Eq. The cluster centers can be updated and guided by the artificial cell swarm optimization and therefore, no equation is required to compute the updated positions of the cluster center. This approach is not dependent on the selection of the initial cluster centers.

The proposed procedure is given in algorithm 3 and the schematic flow diagram is given in Fig. As discussed earlier, the properly annotated ground truth segmented images may not be available always, and therefore, some standard intrinsic cluster evaluation methods are used here to evaluate the proposed approach quantitatively.

The proposed methods are applied to the images and the test results are demonstrated with the 10 CT scan images that are randomly selected which are obtained from different countries of the world.

Table 3 gives a brief overview of the test images and the test images along with their histograms are given in Fig. The experiments are performed in the MatLab Ra on a computer that is equipped with an Intel i3 processor and 4 GB main memory. The proposed method is compared with some metaheuristic optimization-based image segmentation approaches like modified genetic algorithm [67] , modified PSO [68] , improved bat algorithm [69] and modified cuckoo search method [70] in both qualitative and quantitative manner.

The visual comparison is presented in Fig. The acceptable values are highlighted in boldface. The comparisons and evaluations are performed for different numbers of clusters.

A comparative study of different approaches using T e s t 01 for different number of clusters. Performance evaluation of different approaches using Davies—Bouldin index The highlighted values indicates acceptable values.

Performance evaluation of different approaches using Xie—Beni index The highlighted values indicates acceptable values. Performance evaluation of different approaches using Dunn index The highlighted values indicates acceptable values.

From the qualitative and quantitative results, it can be observed that the proposed SUFACSO approach outperforms some state-of-the-art works and can produce realistic outputs that are certainly helpful for the interpretation of the real-life CT scan images and therefore, this approach can be helpful for the early screening purposes.

At the end of each table, the average performance of the five approaches is reported which is beneficial to understand the overall performance of these methods for the different number of clusters and different cluster validity indices. In the case of average, the column-wise optimal values are highlighted instead of highlighting the row-wise optimal values.

The row-wise highlighted values talk about the performance of the individual algorithm for the different number of clusters whereas the column-wise highlighted values help to understand the performance of the individual algorithms. It can be observed that the proposed approach outperforms other approaches for most of the number of clusters as well as for most of the validity indices.

For example, on a total of 16 occasions i. These comparative results are graphically presented in Fig. In X -axis the number of clusters and in the Y -axis, the values of the corresponding validity index are plotted.

The experiments are carried out for the different numbers of clusters. A particular approach may perform well for a particular cluster count. That is why the average values of all experiments are reported at the end of each table for better interpretation.

It can be observed that the proposed approach can optimize different objective functions effectively. Actually, the experiments are carried out on CT images in the first phase and CT images in the second phase.

It is already mentioned in Section 5. Results that are obtained from all images are not possible to report in this stipulated amount of space. Therefore, only some results that are obtained from some selected images are reported.

Apart from these tests, the proposed approach is also compared with some of the active contour models based on some standard parameters like accuracy, precision, and recall. This comparison is performed by using the database that is available at [71]. This dataset is created by collecting sample images from 49 patients with age range 32—86 years.

The obtained average results are reported in Table 8. The rate of convergence is an important parameter to be studied. The performance evaluation remains incomplete without studying and comparing the convergence of different algorithms. The convergence analysis gives a clear view of the comparative performance of different algorithms for the different numbers of clusters.

The graphical analysis of the convergence is presented in this subsection using the image T e s t 01 for the Dunn index. In Fig. In a single plot, four separate curves are indicating four different clusters. These curves show that the proposed approach can efficiently segment the images for a higher number of clusters.

Moreover, the proposed approach also outperforms some other methods in terms of convergence besides quantitative and qualitative performance. The time complexity is an important aspect that is to be analyzed.

Retrieved April 22, Retrieved April 7, Retrieved September 30, The Start menu is coming back to Windows\”. Archived from the original on February 3, Retrieved March 31, The Slate Group. Seattle Times. Seattle Times Network. Archived from the original on September 30, Retrieved November 5, Ziff Davis. February 2, September 30, The Guardian. Thomson Reuters. Business Insider. January 21, Retrieved January 24, PC Magazine. Ziff Davis Media.

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June 22, Retrieved June 23, Retrieved June 21, Purch, Inc. Retrieved July 27, July 15, Retrieved July 15, Retrieved August 4, November 30, Redmond Magazine. Retrieved August 6, Retrieved July 31, Supersite for Windows. Archived from the original on August 1, March 18, May 19, Retrieved May 21, Retrieved July 14, Retrieved May 18, Windows Help. Archived from the original on May 1, Retrieved February 1, May 16, While our free offer to upgrade to Windows 10 will not apply to Non-Genuine Windows devices.

March 19, Retrieved March 19, Retrieved August 13, July 31, Windows 10 blog. Microsoft Corporation. October 12, Retrieved October 12, Retrieved July 4, Network World. July 28, Retrieved July 29, Retrieved August 1, May 9, Retrieved November 3, Retrieved November 14, July 27, Retrieved April 4, Microsoft Tech Community. August 12, July 17, July 20, Microsoft Edge stands ready to be your new everyday browser for work, life and everything in between. With IE mode.

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An updated version of Microsoft\’s Windows Phone operating system for smartphones, and also tablets, was branded as Windows 10 Mobile. On May 2,Microsoft unveiled Windows 10 S referred to in leaks as Windows 10 Clouda feature-limited edition of Windows 10 which was designed primarily for devices in the education market competing, in particular, with ChromeOS netbookssuch as the Surface Laptop that Microsoft also unveiled at this time.

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