Feb 22
The IUP Journal of Telecommunications
Focus
Obviously, these capabilities must need a very robust and noise-free channel in the network to minimize the decision errors and an efficient data management strategy to exploit the channel capacity effectively by adopting suitable data compression algorithms. This issue contains four papers. The first paper presents a study on white space spectrum to characterize the channel model and its related power levels to take decisions in the network installation and design. The next three papers illustrate and emphasize the image compression and processing techniques using deep learning.
The UHF spectrum band used in TV has good propagation characteristics, and sometimes or at some places, these are not well utilized and therefore opens scope for research to look into this, to use them effectively for communication purposes. The first paper, "Analysis of TV White Space in Southeast Nigeria Using Pollution Technique", by Atuanya U O, Azubogu A C O and Nwalozie G C, attempts to study the television spectrum white space in the UHF TV band in Nigeria by proposing a quantification assessment of the band. The average available band space is calculated using secondary user point of view, known as pollution viewpoint, and thus characterizes a suitable white space.
The second paper, "Various Image Compression Techniques: A Review", by Garima Garg and Raman Kumar, presents a review outlining some suitable image compression algorithms for both the lossy and lossless images, emphasizing their important characteristics and future research opportunities. The paper observes a better performance of lossy compression algorithms.
Nowadays, telecommunication networks are extensively used in agriculture and fruit-health management systems. These images are suitably captured, processed and transmitted at control processing centers to make decisions about the health of the object under study. The third paper, "Advanced Image Processing Algorithms for Categorizing and Evaluating Plant Diseases: A Study", by G Mahalaxmi, T Tirupal and T Aditya Sai Srinivas, presents some approaches used in detecting, evaluating and categorizing plant diseases from digital images in the visible spectrum. The paper examines and discusses image processing strategies for identifying plant diseases in a variety of plant species using BPNN, SVM, K-means clustering and SGDM algorithms.
The last paper, "Categorization of Leaf Ailments Using Deep Learning Techniques: A Review", by Golla Mahalaxmi, T Tirupal, T Aditya Sai Srinivas and Dudekula Razia, compares the results and image detection schemes using algorithms like Support Vector Machines (SVM) and Deep Learning Neural Networks (DLNN). The paper discusses the deep learning techniques for leaf disease detection and classification for better agriculture yield and crop cultivation.
Consulting Editor
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| Article | Price (₹) | ||
| Analysis of Tv White Space in Southeast Nigeria Using Pollution Technique |
100
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| Various Image Compression Techniques: A Review |
100
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| Advanced Image Processing Algorithms for Categorizing and Evaluating Plant Diseases: A Study |
100
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| Categorization of Leaf Ailments Using Deep Learning Techniques: A Review |
100
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Analysis of Tv White Space in Southeast Nigeria Using Pollution Technique
Portions of the Ultra High Frequency (UHF) radio spectrum which are not assigned to licensed operators (often termed as primary user) or licensed but unutilized at all times or in all locations are referred to as TV White Space (TVWS). The UHF TV band spectrum has very good wireless radio propagation characteristics. The paper attempts to study the amount of TVWS in the UHF TV band in Nigeria. Quantification assessment and estimates for the TVWS in the 470-590 MHz band for Awka, Anambra state, southeast Nigeria are presented. The average available TVWS in this area is calculated using secondary (unlicensed) user point of view known as pollution viewpoint. By this method, characterization using the developed pollution radius and tolerable interference of 5 dB is found to be 83% of the area around Awka available as white space, indicating the spectrum utilization opportunity within this area for secondary use. The characterization of the environment showed that it is an excellent environment for deployment of TVWS antennas for optimal RF utilization for the provision of broadband Internet services.
Various Image Compression Techniques: A Review
The use of images in a wide variety of applications has expanded rapidly due to technological developments that have influenced the operations and availability of advanced image modification management software. Despite technological breakthroughs in storage and transmission, demand for storage capacity and communication bandwidth exceeds available capacity. As a result, image compression has proven to be a helpful technique. When it comes to image compression, we do not just focus on lowering size but also focus on not sacrificing image quality or information. This review outlines the primary image compression algorithms, both lossy and lossless, their benefits, drawbacks, and research opportunities. The examination of several compression techniques aids in the identification of advantageous qualities and selection of proper compression method. Some general criteria for choosing the optimum compression algorithm for an image have been suggested based on the review.
Advanced Image Processing Algorithms for Categorizing and Evaluating Plant Diseases: A Study
The paper studies the approaches to detecting, evaluating and categorizing plant diseases from digital images in the visible spectrum using appropriate processing techniques. Despite the fact that disease symptoms might appear anywhere on the plant, only approaches that looked at obvious symptoms in leaves and stems were examined. This was designed for various reasons: to keep the report short and because methods dealing with roots, seeds, and fruits have some unique characteristics that would necessitate a separate survey. The concepts chosen are organized into three categories based on their goal: detection, severity quantification and categorization. Each classification is further categorized based on the algorithm's primary technical solution. The paper also examines and contrasts the benefits and drawbacks of different prospective strategies. Image acquisition, image preprocessing, feature extraction and neural network-based categorization are a few of the techniques included. Researchers working on both vegetable pathology and pattern recognition can benefit from this study, which provides a detailed and accessible summary of this vital field of research.
Categorization of Leaf Ailments Using Deep Learning Techniques: A Review
Computerized image processing techniques are extremely useful in agriculture. The technology can help detect plant diseases and improve cultivation quality. The study examines the advantages and disadvantages of previous research on the subject. To find the most effective image processing methods for diagnosing plant diseases, cutting-edge techniques are examined. To find plant pathogens, many computerized image processing methods are used. This review compares the results and many different approaches to develop algorithms such as Support Vector Machines (SVM) and Deep Learning Neural Networks (DLNN), which are important in the detection and classification of leaf diseases.