A new lossless electroencephalogram compression technique for fog computing‐based IoHT networks

Density-Based Spatial Clustering of Application with Noise (DBSCAN) is a well-known, unsupervised machine learning tool for clustering. DBSCAN creates clusters based on dense regions while marking points that lie alone in low-density regions as outliers. In DBSCAN, the density is detected via core points which are quite sensitive to input parameters: ϵ is radius of the neighborhood and MinPts is minimum number of points constraint within ϵ radius. In contrast to crisp core point definition, intuitionistic fuzzy core point definition makes DBSCAN algorithm capable to detect different patterns of density by two different combinations of input parameters. In this study, a DBSCAN extension is proposed based on this idea: IFDBSCAN. The proposed extension is then illustrated by a computational experiment on a synthetic dataset. Results show that, IFDBSCAN can establish fine-tuned clusters relatively to classical DBSCAN and provide users more insight on the estimation of input parameters.

This research introduces an enhanced stratified sampling-density-based spatial clustering of applications with noise (SS-DBSCAN), a scalable and robust density-based clustering algorithm designed to tackle challenges in high-dimensional and complex data analysis. The algorithm integrates advanced parameter optimization techniques to improve clustering accuracy and interpretability. Key innovations include a fast grid search method for optimizing the search of optimal minimum points (MinPts) by keeping the ϵ parameter obtained constant. Notably, this study emphasizes the often-overlooked MinPts parameter, introducing a dynamic approach that initiates by calculating density metrics within a specified ϵ distance and adjusting the MinPts range based on the standard deviation of these metrics. This approach identifies optimal MinPts values based on the maximum allowed range. Comprehensive experiments on five real-world datasets demonstrate SS-DBSCAN’s superior performance compared to density-based spatial clustering of applications with noise (DBSCAN), hierarchical DBSCAN, and ordering points to identify the clustering structure (OPTICS), evidenced by higher silhouette and Davies–Bouldin index scores. The results highlight SS-DBSCAN’s ability to capture intrinsic clustering structures accurately, providing deeper insights across various research domains. SS-DBSCAN’s scalability and adaptability to diverse data densities make it a valuable tool for analyzing large, complex datasets.

Compression purpose to reduce the redundancy data as small as possible and speed up the data transmission process. To solve the size problem in saving data and transmission process, we use Run Length Encoding and Fibonacci Code algorithm to do compression process. Run Length Encoding and Fibonacci Code algorithm is a type of lossless data compression used in this research, which performance will be measured by comparison parameters of the Compression Ratio (CR), Redundancy (RD), Space Saving (SS) and Compression Time. The compression process is only done on image files with Bitmap format (*.bmp) and encode using Run Length Encoding or Fibonacci Code, then perform the compression process. The final result of the compression is file with extension *.rle or *.fib which contains compressed information that can be decompressed back. The output of the decompression result is an original image file that is stored with *.bmp extension. Fibonacci algorithm will give a better compressed size on image color, while in a grayscale image Run Length Encoding will give a better compressed size. Based on the results of research at two different types of images, each algorithm has its own advantages. Fibonacci Code algorithm is better for color image compression while Run-Length algorithm Encoding is better for grayscale image compression.

The potato tuber moth (PTM), Phthorimaea operculella (Zeller), is an important pest of Solanaceae crops and especially devastating to potatoes. There is no significant difference in morphological characteristics of PTM from the first to third instar larvae; therefore, it is difficult to directly determine the number of instars of this pest based on morphology. In the present study, head capsule width and length and mandible width of 340 PTM individuals were measured. Density‐based spatial clustering of applications with noise (DBSCAN) clustering was used for instar grouping. The results of DBSCAN clustering were compared with those obtained using Gaussian mixture models and k‐means clustering; the results of the three clustering methods were verified using Brooks–Dyar rule, Crosby rule and linear regression model. The clusters obtained using the three methods were the same and comprised four PTM instars with three morphological characteristics. Moreover, the results of the three methods fit the Brooks–Dyar rule, Crosby rule, frequency analysis and logarithmic regression model well. Head capsule width was the best morphological characteristic for determining the number of instars of PTM, and this characteristic may be used for determining PTM instars in the field. These results show that the DBSCAN clustering method is a promising tool for the identification of insect instars.

China's social media platform, Sina Weibo, like Twitter, hosts a considerable amount of big data: messages, comments, pictures. Collecting and analyzing information from this treasury of human behavior data is a challenge, although the message exchange on the network is readable by everyone through the web or app interface. The official Application Programming Interface (API) is the gateway to access and download public content from Sina Weibo and is used to collect messages for all mainland China. The nearby_timeline() request is used to harvest only messages with associated location information. This technical note serves as a reference for researchers who do not speak Mandarin but want to collect data from this rich source of information. Ways of data visualization are presented as a point cloud, density per areal unit, or clustered using Density‐Based Spatial Clustering of Applications with Noise (DBSCAN). The relation of messages to census information is also given.

The accurate estimation of the probability distribution of traction current harmonics in EMUs is crucial for preventing electromagnetic interference (EMI), managing high‐speed railway signaling systems, and analyzing traction system power quality. Although kernel density estimation (KDE) has been a longstanding flexible, nonparametric estimation method, it faces significant challenges, including high computational demands and difficulties in selecting appropriate bandwidths. This article introduces the Gaussian mixture model (GMM) as a novel approach for the probabilistic estimation of traction current harmonics, thereby providing a scientific foundation for harmonic analysis. To improve the precision and efficiency of harmonic interference assessments, we initialize the GMM parameters and the expectation‐maximization (EM) algorithm with the density‐based spatial clustering of applications with noise (DBSCAN). The effectiveness of our method is confirmed through the Kolmogorov–Smirnov (K–S) goodness‐of‐fit test, root mean square error (RMSE), and R 2 statistics, demonstrating that our approach provides greater stability and faster computation than existing methods.

Accurate and efficient registration of unmanned aerial vehicle light detection and ranging (UAV‐lidar) and terrestrial lidar (T‐lidar) data is crucial for forest structure parameter extraction. This study proposes a novel method based on a starburst pattern for the automatic registration of UAV‐lidar and T‐lidar data in forest scenes. It employs density‐based spatial clustering of applications with noise (DBSCAN) for individual tree identification, constructs starburst patterns separately from both lidar sources, and utilises polar coordinate rotation and matching to achieve coarse registration. Fine registration is achieved using the iterative closest point (ICP) algorithm. Experimental results demonstrate that the starburst‐pattern‐based method achieves the desired registration accuracy (average coarse registration error of 0.157 m). Further optimisation with ICP yields slight improvements with an average fine registration error of 0.149 m. Remarkably, the proposed method is insensitive to the individual tree detection number when exceeding 10, and the tree position error has minimal impact on registration accuracy. Furthermore, our proposed method outperforms two existing methods in T‐lidar and UAV‐lidar registration over forest environments.

Privacy protection in electronic healthcare applications is an important consideration due to the sensitive nature of personal health data. Internet of Health Things (IoHT) networks have privacy requirements within a healthcare setting. However, these networks have unique challenges and security requirements (integrity, authentication, privacy and availability) must also be balanced with the need to maintain efficiency in order to conserve battery power, which can be a significant limitation in IoHT devices and networks. Data are usually transferred without undergoing filtering or optimization, and this traffic can overload sensors and cause rapid battery consumption when interacting with IoHT networks. This consequently poses restrictions on the practical implementation of these devices. As a solution to address the issues, this paper proposes a privacy-preserving two-tier data inference framework, this can conserve battery consumption by reducing the data size required to transmit through inferring the sensed data and can also protect the sensitive data from leakage to adversaries. Results from experimental evaluations on privacy show the validity of the proposed scheme as well as significant data savings without compromising the accuracy of the data transmission, which contributes to energy efficiency of IoHT sensor devices.

One of the main challenges of modern computer systems is to overcome the ever more prominent limitations of disk I/O and memory bandwidth, which today are thousands-fold slower than computational speeds. In this paper, we investigate reducing memory bandwidth and overall I/O and memory access times by using multithreaded compression and decompression of large datasets. Since the goal is to achieve a significant overall speedup of I/O, both level of compression achieved and efficiency of the compression and decompression algorithms, are of importance. Several compression methods for efficient disk access for large seismic datasets are implemented and empirically tested on on several modern CPUs and GPUs, including the Intel i7 and NVIDIA c2050 GPU. To reduce I/O time, both lossless and lossy symmetrical compression algorithms as well as hardware alternatives, are tested. Results show that I/O speedup may double by using an SSD vs. HDD disk on larger seismic datasets. Lossy methods investigated include variations of DCT-based methods in several dimensions, and combining these with lossless compression methods such as RLE (Run-Length Encoding) and Huffman encoding. Our best compression rate (0.16%) and speedups (6 for HDD and 3.2 for SSD) are achieved by using DCT in 3D and combining this with a modified RLE for lossy methods. It has an average error of 0.46% which is very acceptable for seismic applications. A simple predictive model for the execution time is also developed and shows an error of maximum 5% vs. our obtained results. It should thus be a good tool for predicting when to take advantage of multithreaded compression. This model and other techniques developed in this paper should also be applicable to several other data intensive applications.

We present a fast compression technique for natural language texts. The novelties are that (1) decompression of arbitrary portions of the text can be done very efficiently, (2) exact search for words and phrases can be done on the compressed text directly, using any known sequential pattern-matching algorithm, and (3) word-based approximate and extended search can also be done efficiently without any decoding. The compression scheme uses a semistatic word-based model and a Huffman code where the coding alphabet is byte-oriented rather than bit-oriented. We compress typical English texts to about 30% of their original size, against 40% and 35% forCompressandGzip, respectively. Compression time is close to that ofCompressand approximately half of the time ofGzip, and decompression time is lower than that ofGzipand one third of that ofCompress. We present three algorithms to search the compressed text. They allow a large number of variations over the basic word and phrase search capability, such as sets of characters, arbitrary regular expressions, and approximate matching. Separators and stopwords can be discarded at search time without significantly increasing the cost. When searching for simple words, the experiments show that running our algorithms on a compressed text is twice as fast as running the best existing software on the uncompressed version of the same text. When searching complex or approximate patterns, our algorithms are up to 8 times faster than the search on uncompressed text. We also discuss the impact of our technique in inverted files pointing to logical blocks and argue for the possibility of keeping the text compressed all the time, decompressing only for displaying purposes.

Huffman coding is an important part of image compression technology, the image compression platform is based on GUI, and Huffman is also widely used. This paper introduces the basic principle of the Huffman algorithm, compares it with arithmetic coding and run length encoding, and expounds on the application of these three algorithms in JPEG compression. The AC algorithm combined block-based, fine texture models and adaptive arithmetic coding in the given an example. The RLE algorithm used automatic threshold, direction judgment, and selective value counts to improve its compression efficiency. JPEG algorithm adopted an adaptive quantization table to reduce the distortion rate of image compression. This paper proves the possibility of a better compression rate and distortion rate of the hybrid compression algorithm by demonstrating the improved example of the basic image compression algorithm. In the future, the improved basic algorithms can be combined based on the original JPEG algorithm, and different algorithms can be integrated on the GUI to face different use environments.

RLH: Bitmap compression technique based on run-length and Huffman encoding

This paper aims to study and compare the compression efficiencies of wavelet-bitmap and wavelet-Huffman. Both methods use wavelet to deduct a portion of the information in the image that can endure losing it without a significant disturbance in the image itself. Both methods deal with thresholding of wavelet coefficients to produce as much as possible zero coefficients for the purpose of higher compression, then encode the non zero coefficients by using Huffman encoding and bitmap drawing of the coefficients distribution resulted from thresholding inside the analyzed image. The processing time is calculated using MATLAB. Results were taken to compare the efficiencies of the compression between the two methods. In this work we present a method based on wavelet transforms using Huffman encoding and bitmap encoding. Different combinations of parameters and transforms have been compared. The result shows that the time of compression in wavelet-bitmap method is less than the time of compression which is taken by wavelet-Huffman method. Also the compression ratio in the wavelet-Huffman is greater than the other method. The quality in both methods is relatively equal.

Direct command generation for CNC machinery based on data compression techniques

The need to improve smart health systems to monitor the health situation of patients has grown as a result of the spread of epidemic diseases, the ageing of the population, the increase in the number of patients, and the lack of facilities to treat them. This led to an increased demand for remote healthcare systems using biosensors. These biosensors produce a large volume of sensed data that will be received by the edge of the Internet of Medical Things (IoMT) to be forwarded to the data centers of the cloud for further treatment. An edge-fog computing-enabled lossless electroencephalogram (EEG) data compression with epileptic seizure detection in IoMT networks is proposed in this article. The proposed approach achieves three functionalities. First, it reduces the amount of sent data from the edge to the fog gateway using lossless EEG data compression based on a hybrid approach of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering and Huffman encoding (KCHE) at the edge gateway. Second, it decides the epileptic seizure situation of the patient at the fog gateway based on the epileptic seizure detector-based Naive Bayes (ESDNB) algorithm. Third, it reduces the size of IoMT EEG data delivered to the cloud using the same lossless compression algorithm in the first step. Various measures implemented to show the effectiveness of the suggested approach and the comparison results confirm that the KCHE reduces the amount of EEG data transmitted to the fog and cloud platform and produces a suitable detection of an epileptic seizure. The average of compression power of the proposed KCHE is four times the average of compression power of other methods for all EEG records ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Z, F, N, O$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> ). Furthermore, the proposed ESDNB outperforms the other methods in terms of accuracy, where it provides accuracy from 99.53 % up to 99.99 % using the data set of Bonn University.