用地道英文表达方式润色下面的英文We have studied reviewers’ comments carefully. According to the reviewers’ detailed suggestions, we have made a careful revision of the original manuscript. All revised portions are marked in red in the revised manuscript which we would like to submit for your kind consideration.

时间: 2024-04-22 08:23:58 浏览: 186
We have thoroughly reviewed the reviewers' comments and have taken them into careful consideration. As per their detailed suggestions, we have diligently revised the original manuscript, with all revised sections being clearly indicated in red within the updated manuscript that we hereby submit for your kind consideration.
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A = imread('krabi1.bmp','BMP'); %read in the image A_shrunk = imresize(A,0.2); % we’ll reduce the resolution, as otherwise the file size is too large imshow(A_shrunk) % displays the shrunken image Bs = reshape(A_shrunk,[388*518*3,1,1]); % resizes this image from a pixel array of three colours to a one-dimensional data stream C = de2bi(double(Bs)); % converts these values to binary representation % You can then resize this array to a linear, one-dimensional array. % this data stream C is then what you can communicate over your channel. % recover the image from the binary sequence BS_rec = uint8(bi2de(C)); % convert bits to pixel values. A_rec = reshape(BS_rec,[388,518,3]); % reshape back to a coloured pixel array. imshow(A_rec) % display the recovered image. Explore the pskmod and pskdemod commands in MATLAB. Use these to modulate and demodulate the binary sequence into a binary PSK signal. (Hint: the command should be something like D = pskmod(C,2). Display a part of this signal, and explain carefully how we have represented this signal in MATLAB, and how it differs from the analysis we performed in Part A. Transmit this signal through an AWGN channel using the awgn() function for a range of signal to noise ratios. Demodulate and comment on the effect of the noise on the image. A suitable range of signal to noise ratios is –0.5dB to 5 dB. The bit error rate refers to the fraction of bits that are received in error. This can also be interpreted as the probability that a bit is received in error. Calculate the bit error rate (BER) for the signal to noise ratios you used above. Comment on the correlation between BER and perceptual image quality. Now apply channel coding to the transmitted data sequence. Use encode(msg,7,4,’hamming’), encode(msg,15,7,’bch’), and convenc, following the example listed in the help window (recall, in MATLAB type “help convenc” to obtain the help information for that command. For each of these codes, study how the code can reduce the BER and improve image quality for each SNR you studied above.

这是一个关于数字图像处理和通信的问题,需要一些MATLAB知识。下面是对问题的回答: 1. 读入并压缩图像 首先,使用`imread`函数读入一张图片,并使用`imresize`函数将图片压缩到原来的20%大小: ``` A = imread('krabi1.bmp', 'BMP'); A_shrunk = imresize(A, 0.2); ``` 2. 将图像转换为一维数据流 将压缩后的图像重新排列为一维数据流,以便进行数字通信。使用`reshape`函数将3通道的像素矩阵转换为一维列向量,并使用`de2bi`函数将像素值转换为二进制表示: ``` Bs = reshape(A_shrunk, [388*518*3, 1, 1]); C = de2bi(double(Bs)); ``` 3. 二进制PSK调制 使用`pskmod`函数将二进制数据流进行相位调制,生成二进制PSK信号。例如,调制成二进制PSK2: ``` D = pskmod(C, 2); ``` 4. 加入高斯白噪声 使用`awgn`函数将二进制PSK信号加入高斯白噪声,模拟通信信道的影响。例如,将信噪比设置为0dB: ``` noisy_D = awgn(D, 0, 'measured'); ``` 5. 二进制PSK解调 使用`pskdemod`函数将接收到的二进制PSK信号解调回二进制数据流: ``` rec_C = pskdemod(noisy_D, 2); ``` 6. 解码并恢复图像 使用`bi2de`函数将二进制数据流转换为像素值,然后使用`reshape`函数将像素值重新排列为3通道的像素矩阵,最后使用`imshow`函数显示恢复后的图像: ``` rec_Bs = uint8(bi2de(rec_C)); A_rec = reshape(rec_Bs, [388, 518, 3]); imshow(A_rec); ``` 7. 计算误码率 使用`biterr`函数计算解调后的数据流和原始数据流之间的误码率,并根据不同信噪比的误码率绘制误码率曲线: ``` ber = zeros(1, length(snr)); for i = 1:length(snr) noisy_D = awgn(D, snr(i), 'measured'); rec_C = pskdemod(noisy_D, 2); ber(i) = biterr(C, rec_C) / length(C); end semilogy(snr, ber); ``` 8. 应用通道编码 使用`encode`函数对二进制数据流进行通道编码,对比不同编码方案的误码率曲线和图像质量: ``` % Hamming(7,4)编码 hamming_encoded = encode(C, 7, 4, 'hamming'); hamming_D = pskmod(hamming_encoded, 2); hamming_noisy_D = awgn(hamming_D, snr(i), 'measured'); hamming_rec_C = pskdemod(hamming_noisy_D, 2); hamming_rec_Bs = uint8(bi2de(hamming_rec_C)); hamming_A_rec = reshape(hamming_rec_Bs, [388, 518, 3]); hamming_ber(i) = biterr(C, hamming_rec_C) / length(C); % BCH(15,7)编码 bch_encoded = encode(C, 15, 7, 'bch'); bch_D = pskmod(bch_encoded, 2); bch_noisy_D = awgn(bch_D, snr(i), 'measured'); bch_rec_C = pskdemod(bch_noisy_D, 2); bch_rec_Bs = uint8(bi2de(bch_rec_C)); bch_A_rec = reshape(bch_rec_Bs, [388, 518, 3]); bch_ber(i) = biterr(C, bch_rec_C) / length(C); % 卷积编码 trellis = poly2trellis(7, [171 133]); conv_encoded = convenc(C, trellis); conv_D = pskmod(conv_encoded, 2); conv_noisy_D = awgn(conv_D, snr(i), 'measured'); conv_rec_D = pskdemod(conv_noisy_D, 2); conv_rec_C = vitdec(conv_rec_D, trellis, 15, 'trunc', 'hard'); conv_rec_Bs = uint8(bi2de(conv_rec_C)); conv_A_rec = reshape(conv_rec_Bs, [388, 518, 3]); conv_ber(i) = biterr(C, conv_rec_C) / length(C); ``` 需要注意的是,以上代码仅供参考,具体实现还需要根据实际情况进行调整。

Develop a NetBeans application that illustrates the applicability of one sorting algorithm on a collection of 10 items. The algorithm should sort the items in descending order. The algorithm must work based on the principle of one of the four sorting algorithms studied during the semester. The collection of 10 items is defined and populated in the application code. You can decide the type and values of items stored in the collection. The algorithm must be implemented as part of a class called MyArrayList that extends the ArrayList class functionality with your sorting algorithm. [20 marks] 5. Extend the application implemented for Question 4 above to be able to measure the efficiency of the implemented sorting algorithm in term of number of comparisons performed by the algorithm. The application should display the total number of comparisons performed by the algorithm for a given collection of items. [10 marks] 6. In an Excel graph, plot the number of comparisons measured by the sorting algorithm when three different sizes of collection are considered. Submit your Excel file on Moodle. [20 marks]

Sure, I understand your question. You want me to develop a NetBeans application that demonstrates the use of one sorting algorithm on a collection of 10 items, sorting them in descending order. The algorithm should be based on one of the four sorting algorithms studied during the semester and should be implemented as part of a class called MyArrayList that extends the ArrayList class functionality with your sorting algorithm. Additionally, you want me to extend the application to measure the efficiency of the implemented sorting algorithm in terms of the number of comparisons performed by the algorithm, and to plot the number of comparisons measured by the sorting algorithm when three different sizes of collection are considered in an Excel graph. To start, let's define and populate the collection of 10 items in the NetBeans application code. We can use integers for the items stored in the collection. Here's an example of how the collection can be defined and populated: ``` import java.util.ArrayList; public class SortingDemo { public static void main(String[] args) { MyArrayList<Integer> list = new MyArrayList<Integer>(); list.add(5); list.add(2); list.add(10); list.add(8); list.add(1); list.add(7); list.add(9); list.add(3); list.add(4); list.add(6); System.out.println("Original list: " + list); list.sortDescending(); System.out.println("Sorted list: " + list); } } class MyArrayList<E extends Comparable<E>> extends ArrayList<E> { public void sortDescending() { // Insertion sort algorithm for (int i = 1; i < size(); i++) { E key = get(i); int j = i - 1; while (j >= 0 && get(j).compareTo(key) < 0) { set(j + 1, get(j)); j--; } set(j + 1, key); } } } ``` In the above code, we define a class called SortingDemo which contains a main method. Inside the main method, we create an instance of MyArrayList and add 10 integers to it. We then print out the original list and call the sortDescending method on the list to sort it in descending order. Finally, we print out the sorted list. The MyArrayList class extends the ArrayList class and defines a sortDescending method that implements the insertion sort algorithm to sort the list in descending order. Next, we need to extend the application to measure the efficiency of the sorting algorithm in terms of the number of comparisons performed by the algorithm. We can modify the MyArrayList class to keep track of the number of comparisons performed during the sorting process, like this: ``` class MyArrayList<E extends Comparable<E>> extends ArrayList<E> { private int comparisons; public void sortDescending() { comparisons = 0; // Insertion sort algorithm for (int i = 1; i < size(); i++) { E key = get(i); int j = i - 1; while (j >= 0 && get(j).compareTo(key) < 0) { set(j + 1, get(j)); j--; comparisons++; } set(j + 1, key); } } public int getComparisons() { return comparisons; } } ``` We added a private variable called comparisons to keep track of the number of comparisons performed during the sorting process. We initialize it to 0 at the start of the sortDescending method and increment it for each comparison performed. We also added a public method called getComparisons to retrieve the number of comparisons performed. Finally, we need to plot the number of comparisons measured by the sorting algorithm when three different sizes of collection are considered in an Excel graph. To do this, we can modify the SortingDemo class to create three different lists of different sizes and sort each of them, recording the number of comparisons performed for each sort. We can then export the data to an Excel file and create a graph with the data. Here's an example of the modified SortingDemo class: ``` import java.io.File; import java.io.FileOutputStream; import java.io.IOException; import org.apache.poi.ss.usermodel.Cell; import org.apache.poi.ss.usermodel.Row; import org.apache.poi.xssf.usermodel.XSSFWorkbook; public class SortingDemo { public static void main(String[] args) { MyArrayList<Integer> list1 = new MyArrayList<Integer>(); list1.add(5); list1.add(2); list1.add(10); list1.add(8); list1.add(1); list1.add(7); list1.add(9); list1.add(3); list1.add(4); list1.add(6); System.out.println("Original list 1: " + list1); list1.sortDescending(); System.out.println("Sorted list 1: " + list1); int comparisons1 = list1.getComparisons(); MyArrayList<Integer> list2 = new MyArrayList<Integer>(); for (int i = 0; i < 100; i++) { list2.add((int) (Math.random() * 100)); } System.out.println("Original list 2: " + list2); list2.sortDescending(); System.out.println("Sorted list 2: " + list2); int comparisons2 = list2.getComparisons(); MyArrayList<Integer> list3 = new MyArrayList<Integer>(); for (int i = 0; i < 1000; i++) { list3.add((int) (Math.random() * 100)); } System.out.println("Original list 3: " + list3); list3.sortDescending(); System.out.println("Sorted list 3: " + list3); int comparisons3 = list3.getComparisons(); System.out.println("Comparisons 1: " + comparisons1); System.out.println("Comparisons 2: " + comparisons2); System.out.println("Comparisons 3: " + comparisons3); try (XSSFWorkbook workbook = new XSSFWorkbook()) { FileOutputStream out = new FileOutputStream(new File("sorting.xlsx")); org.apache.poi.ss.usermodel.Sheet sheet = workbook.createSheet("Sorting"); Row headerRow = sheet.createRow(0); Cell cell0 = headerRow.createCell(0); cell0.setCellValue("Collection Size"); Cell cell1 = headerRow.createCell(1); cell1.setCellValue("Comparisons"); Row row1 = sheet.createRow(1); Cell cell10 = row1.createCell(0); cell10.setCellValue("10"); Cell cell11 = row1.createCell(1); cell11.setCellValue(comparisons1); Row row2 = sheet.createRow(2); Cell cell20 = row2.createCell(0); cell20.setCellValue("100"); Cell cell21 = row2.createCell(1); cell21.setCellValue(comparisons2); Row row3 = sheet.createRow(3); Cell cell30 = row3.createCell(0); cell30.setCellValue("1000"); Cell cell31 = row3.createCell(1); cell31.setCellValue(comparisons3); workbook.write(out); out.close(); System.out.println("Excel file written successfully."); } catch (IOException e) { e.printStackTrace(); } } } ``` In the above code, we create three different lists of sizes 10, 100, and 1000, and sort each of them using the MyArrayList class. We record the number of comparisons performed for each sort and print them to the console. We then create an Excel file and write the data to it, creating a graph with the data. To create the graph in Excel, open the sorting.xlsx file and select the data in cells A1:B3. Click on the Insert tab and select the type of graph you want to create. You can then customize the graph as needed.
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用中文总结以下内容: A number of experimental and numerical investigations have been conducted to study the MBPP stack and wavy flow field characteristics with various designs [10,11]. T. Chu et al. conducted the durability test of a 10-kW MBPP fuel cell stack containing 30 cells under dynamic driving cycles and analyzed the performance degradation mechanism [12]. X. Li et al. studied the deformation behavior of the wavy flow channels with thin metallic sheet of 316 stainless steel from both experimental and simulation aspects [13]. J. Owejan et al. designed a PEMFC stack with anode straight flow channels and cathode wavy flow channels and studied the in situ water distributions with neutron radiograph [14]. T. Tsukamoto et al. simulated a full-scale MBPP fuel cell stack of 300 cm2 active area at high current densities and used the 3D model to analyze the in-plane and through-plane parameter distributions [15]. G. Zhang et al. developed a two-fluid 3D model of PEMFC to study the multi-phase and convection effects of wave-like flow channels which are symmetric between anode and cathode sides [16]. S. Saco et al. studied the scaled up PEMFC numerically and compared straight parallel, serpentine zig-zag and straight zig-zag flow channels cell with zig-zag flow field with a transient 3D numerical model to analyze the subfreezing temperature cold start operations [18]. P. Dong et al. introduced discontinuous S-shaped and crescent ribs into flow channels based on the concept of wavy flow field for optimized design and improved energy performance [19]. I. Anyanwu et al. investigated the two-phase flow in sinusoidal channel of different geometric configurations for PEMFC and analyzed the effects of key dimensions on the droplet removal in the flow channel [20]. Y. Peng et al. simulated 5-cell stacks with commercialized flow field designs, including Ballard-like straight flow field, Honda-like wavy flow field and Toyota-like 3D mesh flow field, to investigate their thermal management performance [21]. To note, the terms such as sinusoidal, zig-zag, wave-like and Sshaped flow channels in the aforementioned literatures are similar to the so called wavy flow channels in this paper with identical channel height for the entire flow field. The through-plane constructed wavy flow channels with periodically varied channel heights are beyond the scope of this paper [22,23].

帮我地道的翻译:The differential variational inequalities ((DVIs), for short) are useful for the study of models involving both dynamics and constraints in the form of in￾equalities. They arise in many applications: electrical circuits with ideal diodes, Coulomb friction problems for contacting bodies, economical dynamics, dynamic traffic networks. Pang and Stewart [26], [27] established the existence, unique￾ness, and Lipschitz dependence of solutions subject to boundary conditions for (DVIs) in finite dimensional spaces. Han and Pang investigated a class of dif￾ferential quasi-variational inequalities in [11], and Li, Huang and O’Regan [18] studied a class of differential mixed variational inequalities in finite dimensional Well-Posedness of Differential Mixed Quasi-Variational-Inequalities 137 spaces. Gwinner [8] obtained an equivalence result between (DVIs) and projected dynamical systems. In [9] he also proved a stability property for (DVIs) by using the monotonicity method of Browder and Minty, and Mosco set convergence. Chen and Wang [4] studied dynamic Nash equilibrium problems which have the formulation of differential mixed quasi-variational inequalities. Elastoplastic contact problems can also be incorporated into (DMQVIs) formulation because general dynamic processes in the nonsmooth unilateral contact problems are governed by quasi-variational inequalities. A numerical study for nonsmooth contact problems with Tresca friction can be found in [10], Liu, Loi and Obukhovskii [19] studied the existence and global bifurcation for periodic solutions of a class of (DVIs) by using the topological degree theory for multivalued maps and the method of guiding functions. For more details about (DVIs) we refer to [3], [30], [12], [22]–[21].

Born in a small town in the countryside, Jane Doe grew up surrounded by nature and animals. She developed a love for the outdoors and a deep appreciation for the natural world at a young age. Her parents, both teachers, encouraged her interests and provided her with the resources she needed to pursue her passions. As Jane grew older, her love for the environment only grew stronger. She studied environmental science in college and went on to earn a master's degree in ecology. Throughout her academic career, Jane worked tirelessly to learn as much as she could about the world around her and to find ways to protect it. After graduation, Jane took a job with a local non-profit organization that focused on preserving wildlife habitats. She spent years working on the ground, conducting research, and advocating for the protection of endangered species. Her work took her all over the world, from the rainforests of South America to the savannas of Africa. Despite the challenges she faced, Jane never lost sight of her goals. Her tireless efforts and unwavering dedication to the cause earned her recognition and respect within the environmental community. She became a respected voice on environmental issues and an inspiring leader for those who shared her passion for preserving the natural world. Today, Jane continues to work tirelessly to protect the environment and raise awareness about the importance of conservation. Her life's work serves as a testament to the power of one person to make a difference and her legacy will live on for generations to come.

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