Data Loss Research Articles

Design and performance analysis of manchester coder-based body channel communication using FPGA

In general, the Body Channel Communication (BCC) is used to transmit human physiological signals over vast distances. Also, the Error-correcting codes can detect and rectify faults in long-range data transmission. Data is encoded in a predetermined manner for secure transmission. In this paper, a Manchester encoding-based high throughput architecture is proposed for a body channel communication system with a low-power operation mode. Further, the Manchester encoder is designed for high throughput performance with a low complexity architecture, and an analog front-end processing circuit which includes two stage pre-amplifiers. Also, the transmission rate on the BCC transceiver side is boosted by the maximum Consecutive Identical Digit (CID), which is limited by Manchester codes. The Manchester code format is generated by encoding the sensed data. Additionally, a full gain buffer is used to reduce data loss and hardware overhead. Results demonstrate that the suggested Manchester encoder is implemented in 90 nano meter (nm) technology, and the 10 Megabits per second (Mbps) data rate is obtained with a configurable operating frequency ranging from 1 to 100 Megahertz (MHz).

ANALYSIS OF ROUTING PROTOCOLS CHARACTERISTICS IN AD-HOC NETWORK

Background. Wireless ad-hoc networks are becoming increasingly prevalence in remote areas, in extreme environments, even in military operations, and in scenarios where setting up infrastructure networks is not possible. Research of ad-hoc routing protocols problems allows improving the efficiency of their operation in conditions of high variability in packet loss or instability of network operation when the speed of users changes. Objective. The purpose of the paper is analysis of packet loss dependency from a network operation time, study of a user speed influence on a network efficiency, and research of network operation efficiency with different routing protocols. Methods. The method of routing protocols efficiency evaluation is the simulation of their operation in an ad-hoc network on a test data set and research of a network indicators dependency in time under different loads and changing mobility of users. Results. The conducted research demonstrated that user’s mobility at different speeds significantly affects the network operation as a whole. The instability of users' positions leads to a significant increase in route search time and packet transmission time. Among researched GPSR, DSDV, and AODV protocols, the latter proved to be the best because it has the lowest percentage of data loss and the lowest average time of message send and receive operations. Conclusions. The work is dedicated to the actual problem of developing and setting parameters of ad-hoc network. Received research results indicate the need to choose the optimal routing protocol depending on specific application conditions, such as user movement speed and network stability. The proposed solutions can be the first stage of complex processing of packets in the mobile network and justify the choice of AODV protocol as a basis for further improvement.

Application of improved and efficient image repair algorithm in rock damage experimental research

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm’s operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis.

Post-Quantum Secure Identity-Based Signature Scheme with Lattice Assumption for Internet of Things Networks.

The Internet of Things (IoT) plays an essential role in people's daily lives, such as healthcare, home, traffic, industry, and so on. With the increase in IoT devices, there emerge many security issues of data loss, privacy leakage, and information temper in IoT network applications. Even with the development of quantum computing, most current information systems are weak to quantum attacks with traditional cryptographic algorithms. This paper first establishes a general security model for these IoT network applications, which comprises the blockchain and a post-quantum secure identity-based signature (PQ-IDS) scheme. This model divides these IoT networks into three layers: perceptual, network, and application, which can protect data security and user privacy in the whole data-sharing process. The proposed PQ-IDS scheme is based on lattice cryptography. Bimodal Gaussian distribution and the discrete Gaussian sample algorithm are applied to construct the fundamental difficulty problem of lattice assumption. This assumption can help resist the quantum attack for information exchange among IoT devices. Meanwhile, the signature mechanism with IoT devices' identity can guarantee non-repudiation of information signatures. Then, the security proof shows that the proposed PQ-IDS can obtain the security properties of unforgeability, non-repudiation, and non-transferability. The efficiency comparisons and performance evaluations show that the proposed PQ-IDS has good efficiency and practice in IoT network applications.

Viewing preferences of ASD children on paintings

The eye movement patterns of children with autism spectrum disorder (ASD) based on high-precision, high-sampling-rate professional eye trackers have been widely studied. Still, the equipment used in these studies is expensive and requires skilled operators, and the stimuli are focused on pictures or videos created out of ASD group. We utilized a previously developed eye-tracking device using a tablet, and the double-column paintings with one column from children with ASD, and the other from typically developing (TD) children as stimuli, to investigate the preference of ASD children for the paintings created within their group. This study collected eye movement data from 82 children with ASD and 102 TD children, and an adaptive eye movement classification algorithm was applied to the data aligned by the sampling rate, followed by feature extraction and statistical analysis from the aspects of time, frequency, range, and clustering. Statistical tests indicate that apart from displaying more pronounced non-compliance during the experiment, resulting in a higher data loss rate, children with ASD did not show significant preferences in viewing the two types of paintings compared to TD children. Therefore, we tend to believe that there is no significant difference in preference for ASD and TD paintings showcasing as diptych from the two groups of children using our eye tracking device, and their feature values indicate that they do not have a viewing preference for the paintings.

Efficient and Reliable Emergency Routing for Wireless Body Area Networks

AbstractThe development of Wireless Body Area Networks (WBAN) is currently in progress with the aim of facilitating communication among small body sensors. This data can be employed to offer prompt or timely aid to individuals who are not just in a critical state but also those who are unable to promptly commute to medical facilities due to physical limitations, congested roadways, or their place of residence. Another crucial requirement for WBAN is their capability to deliver quality of service for diverse forms of network traffic. Privacy and system security are crucial to protecting patient records throughout use and storage by healthcare providers. This study introduces a novel emergency data handling method for emergency healthcare communication networks. Furthermore, the proposed system provides Reliable Emergency Routing to ensure secure transfer of emergency and non‐emergency WBAN data rewards are used to establish routes to securely transmit non‐emergency and emergency data in highly exposed situations while minimizing data loss and delay. Simulations showed that the recommended approach outperformed current protocols in energy consumption, latency, packet delivery ratio throughput, and communication overhead.

An adaptive MLP-based joint optimization of resource allocation and relay selection in device-to-device communication using hybrid meta-heuristic algorithm

Enhancement in both energy efficiency and spectral efficiency in cellular networks is made possible by means of an advanced technique called device-to device (D2D) communication. The enhancement of these efficiencies is done by utilizing the cellular user (CU) resources once again to make communication with nearby cellular devices in an effective manner by spectral means. As the D2D communication technology is capable of providing a direct communication link with nearby devices effectively with enhanced spectral efficacy, this approach is considered as an ideal solution for the futuristic cellular communication network. A flexible and reliable relay-assisted communication by means of D2D technology is required that acts as an intermediate relay when the attenuation between the channels across the D2D devices becomes high. The throughput of the system is increased by utilizing D2D communication technology as it uses direct data transmission within cellular devices. When the cellular user is far apart from one another, the data loss in the D2D communication system is minimized with the utilization of the relay. When the channels are good, then the relay nodes (RNs) serve the cellular user. However, it is noted that the D2D systems are affected by issues such as higher consumption of energy and spectral sharing. Also, the sum rate gets degraded as a result of mutual interference between resource-sharing cellular devices in the relay-assisted D2D communication system. The transmission of the data in a traditional relay-assisted D2D communication system is carried out between the D2D receiver (DR) and D2D transmitter (DT) only with the utilization of its own energy by the RN. The issues in relay selection and resource allocation in the conventional joint resource allocation schemes are tackled by executing a scheme for performing the task of optimal relay selection and joint resource allocation. The enhancement of the overall sum rate in the D2D communication is the main motive behind the implemented scheme. This goal is attained along with the minimization of the link rates in the cellular and D2D networks. The ideal selection of the relay and the execution of the joint resource allocation are done with the utilization of a new optimization scheme called the hybrid flow direction with the chameleon swarm algorithm (HFDCSA), in which the flow direction algorithm (FDA) is fused along with chameleon swarm algorithm. This optimal selection of the relays is assisted by considering constraints like the network’s sum rate and energy efficiency in the network to achieve high performance. The data obtained from distinct sources are given to the adaptive multi-layer perceptron (AMLP) in which optimal resource allocation and the relay selection are performed with the help of the suggested HFDCSA. The parameters in the MLP are tuned by the same HFDCSA. Finally, the performance validation is conducted in the stage to verify the working of the suggested approach.

High Power Pulsed LED Driver for Vibration Measurements.

Vibration measurements pose specific experimental challenges to be faced. In particular, optical methods can be used to obtain full-field vibration information. In this scenario, stereo-camera systems can be developed to obtain 3D displacement measurements. As vibration frequency increases, the common approach is to reduce camera exposure time to avoid blurred images, which can lead to under-exposed images and data loss, as well as issues with the synchronization of the stereo pair. Both of these problems can be solved by using high-intensity light pulses, which can produce high-quality images and guarantee camera synchronization since data is saved by both cameras only during the short-time light pulse. To this extent, high-power Light-Emitting Diodes (LEDs) can be used, but even if the LED itself can have a fast response time, specific electronic drivers are needed to ensure the desired timing of the light pulse. In this paper, a circuit is specifically designed to achieve high-intensity short-time light pulses in the range of 1 µs. A prototype of the designed board was assembled and tested to check its capability to respect the specification. Three different measurement methods are proposed and validated to achieve short-time light pulse measurements: shunt voltage measurement, direct photodiode measurement with a low-cost sensor, and indirect pulse measurement through a low-frame-rate digital camera.

DMF2Net: Dynamic multi-level feature fusion network for heterogeneous remote sensing image change detection

With the rapid development of remote sensing data fusion technology, heterogeneous remote sensing image (HRSI) change detection (CD) has become a frontier field. The powerful nonlinear information processing capability of deep neural network provides the possibility for image domain conversion of HRSIs. However, the majority of existing methods rely on stacking network layers to extract deep high-level semantic features to accomplish the mutual conversion of image domains. The effective extraction and utilization of shallow features have not been fully considered. At the same time, the dependence between deep and shallow features has not been deeply explored. Therefore, these algorithms severely restrict the performance of CD. A new HRSI-CD network (DMF2Net) based on multi-level feature fusion is proposed in this paper to address these issues. DMF2Net uses central difference convolution to extract fine-grained features from shallow layers of images. It is capable of capturing the intrinsic detail features of image by aggregating intensity and gradient information. The dynamic multi-level feature fusion method is used to learn the fusion weights from the features, which are then used to guide the organic fusion of shallow and deep semantic features. This can preserve more position and detail features of the image, which can prevent the loss of small data during image conversion and enhance the model’s ability to detect subtle changes. On this basis, a new DMF2Net-based method for detecting change in bi-temporal HRSIs is proposed. We conducted extensive experiments on four publicly available HRSI-CD datasets. The experimental results showed that the proposed CD framework achieves significant improvement over the most advanced methods. The project file of the proposed framework will be obtained from https://github.com/cwlnnu/DMF2Net.

Transformer-customer relationship identification based on deep Gaussian mixture model in low-voltage distribution system

Accurate transformer-customer relationship is critical for better operation and management of low-voltage distribution system. It is of high cost to establish and check the profiles of transformer-customer relationship by manual maintenance, which is often mistakenly archived too. This paper proposes a novel automatic identification method based on a deep Gaussian mixture model with voltage data. The improved model is built with feature extraction network, clustering network and splitting and merging network, which can adjust the number of transformers adaptively and dynamically. It can also identify outliers of the data and effectively solve the uncertainty and imbalance of samples. Finally, the proposed method is tested in a practical power supply district which has 2752 customers with 9 transformers. Five different test cases are set to verify its effectiveness. The method can dynamically adjust to the actual number of transformer under different initial clustering numbers. The experimental results demonstrate that our model achieves an accuracy of up to 98.8% in profiling unlabeled users, while achieving an error correction accuracy of 99% in the case of imbalanced users in the transformer area. To further validate the superiority of our model, we conducted robustness experiments under different user scales and data loss scenarios. The results consistently outperform current state-of-the-art algorithms.

Koopman dynamic-oriented deep learning for invariant subspace identification and full-state prediction of complex systems

One strategy for predicting the state of nonlinear dynamical systems (typically of high dimensionality) is global linearization, such as utilizing the Koopman analysis model to transform the system state into an invariant subspace that evolves linearly. A critical challenge in the Koopman model is designing or deriving observation functions, typically nonlinear, to linearize the dynamical systems. To address the challenge, this study proposes a model called SKCAE (Skip-connected Koopman Convolutional AutoEncoder) that combines guidance of physics and data to learn observation functions accurately and efficiently. The novelties of SKCAE are twofold: (1) a coordinate-transforming main network for the construction of nonlinear observation functions, enabling the explicit identification of the low-dimensional dynamics that dominate the system's dynamical evolution, and (2) a Koopman dynamics-oriented subnetwork for quantitatively interpreting the system's intrinsic mechanisms from the frequency perspective and efficiently predicting the system state. Four numerical cases with discrete/continuous spectra on Eulerian/Lagrangrian descriptions are studied, i.e., fixed-point attractor, Duffing oscillator, fluid flow past a cylinder, and channel turbulence. Results demonstrate that SKCAE achieves significant improvement (up to a hundred times) in accuracy compared to conventional models and possesses remarkable capability in handling scenarios with data noises and/or loss, owing to its intrinsic advantage in retaining a broad range of frequency components of a dynamical system.

Data oversampling and feature selection for class imbalanced datasets

Introduction: Significant advancements and modifications have been implemented in data classification (DC) in the past few decades. Due to their infinite quantity and imbalance, data becomes challenging for classification. The biggest concern in DM (Data Mining) is Class Imbalance (CI). To avoid these issues in recent work proposed map reduce based data parallelization of class imbalanced datasets. Methods: A novel Over Sampling (OS) technique called Minority Oversampling in Kernel Canonical Correlation Adaptive Subspaces (MOKCCAS) has been suggested with the objective to minimize data loss throughout (FSP) Feature Space Projections. This technique takes advantage of the constant Feature Extraction (FE) capability of a version of the ASSOM (Adaptive Subspace Self-Organizing Maps) that is derived from Kernel Canonical Correlation Analysis (KCCA). And in classification, Feature Selection (FS) plays an important role because the acquired dataset might contain large volume of samples, utilizing all features of samples from the dataset for classification will decrease the classifier performance. And then data parallelization will be done by using map reduce framework to solve this computation requirement problem.Result: Then proposes a feature selection model using Mutated whale optimization (MWO) methods and produces features and reduces the time consumption. Finally proposed class balancing model will be tested using uniform distribution based enhanced adaptive neuro fuzzy inference system (UDANFIS). Test outcomes validate the efficiency of the suggested technique by precision, recall, accuracy and Error Rate (ER).Conclusion: The study subsequently suggests a novel OS approach called MOKCCAS to lessen the loss of data throughout feature space projection.

Application of The Waterfall Method to the Website-Based JM Leather & Shoes Point of Sales Information System

Trading business is a type of business that has the basic concept of buying supplies of goods and reselling them with the aim of making a profit. In the current era, people continue to flock to set up a place of business, this is due to changes in market conditions as well as increasingly sophisticated economy and technology. JM Leather & Shoes continues to supervise and record the processing of goods data, supplier data, expenditure data, sales transactions, purchase transactions, and the preparation of reports which are always evaluated every month by superiors. During this time, both cashiers and administration record in the ledger. This of course causes various problems. Where the process of making reports takes a relatively long time, because the administration must first adjust the real stock at the place of business with the stock in the records until finally the difference between the two is found. One effort that can be made is to create an application that can help facilitate business process activities and minimize the occurrence of errors that could have a negative impact on the company's future. With this system, the data processing process from searching, adding, changing, to creating reports as evaluation material becomes easier and faster. The risk of data loss or damage can also be reduced to a minimum because it has been stored in the database, making it more effective and efficient. Future researchers are expected to add features for forecasting the items that are most and least desired by customers.

Regression trees for interval‐censored failure time data based on censoring unbiased transformations and pseudo‐observations

AbstractInterval‐censored data arise when a failure process is under intermittent observation and failure status is only known at assessment times. We consider the development of predictive algorithms when training samples involve interval censoring. Using censoring unbiased transformations and pseudo‐observations, we define observed data loss functions, which are unbiased estimates of the corresponding complete data loss functions. We show that regression trees based on these loss functions can recover the tree structure and yield good predictive accuracy. An application is given to a study involving individuals with psoriatic arthritis where the aim is to identify genetic markers useful for the prediction of axial disease within 10 years of a baseline assessment.

A Report on Hazards of Computer Viruses

Computer viruses are one of the major risks that an individual and a community face to the safe living of a household, enterprise, and government. These malicious software programs are developed to work like viruses: they reproduce themselves and spread, thus damaging the system, data loss and the loss of money. This research paper focuses on the various types of dangers that computer viruses pose, such as data corruption, theft of personal information, system disruption, and reputational damage. The hazards of computer viruses are the main thing that people and organizations need to be aware of in order to protect themselves and their systems from these hazards. Furthermore, computer viruses can also steal sensitive information, such as passwords, credit card numbers, and personal documents. This theft of personal information can result in financial loss and identity theft. Additionally, viruses can cause system damage, leading to system crashes or malfunctions. This can disrupt normal operations and lead to costly repairs or replacements.

Advances in Cybersecurity and Reliability

In recent years, the significant increase in financial and data losses impacting individuals and businesses has highlighted the pressing need to tackle cybersecurity challenges in today’s digital environment [...]

Off-Grid Electric Vehicle Charging Station with Integrated Local Server OCPP Protocol as a Management System

Abstract Electric vehicles are widely regarded as pivotal in driving the sustainability of transportation networks forward, thanks to their capacity to diminish carbon emissions, enhance air quality, and bolster the robustness of electricity grids. The accessibility of charging infrastructure and the subjective norms that endorse electric mobility actively shape the electric vehicles acceptance. In this study, Our main goal is to provide off-grid electric vehicle charging infrastructures and the data communication protocols that connect to servers. We analyze the specifications of the OCPP (Open Charge Point Protocol) with an emphasis on its applicabillity for electric charging stations for vehicles. Our research concludes that off-grid electric vehicle charging systems can be effectively applied to small electric vehicles such as electric motorcycles, scooters, and bicycles. The OCPP data communication protocol can also support interactions between small electric vehicle charging stations and central server management systems (CSMS). Furthermore, we tested the electric vehicle charging process for a duration of two hours, and the charging station consistently produced stable voltage, current, and power output, matching the inverter outputs and fulfilling the specifications required by electric vehicle charging adapters. Analysis of throughput data indicates a positive correlation between the number of operational ports at a charging station and the volume of data processed by the server. However, beyond a certain threshold a decline in data transactions was observed, attributable to data loss.

Secure Routing and Reliable Packets Transmission In MANET Using Fast Recursive Transfer Algorithm

Mobile Ad-hoc Network (MANET) autonomous operation can be multi-hop it is the infrastructure-less wireless network. Security is one of the biggest challenges in Mobile Adhoc Network. The MANETs security there are considerations must be so that the routing protocol in order to protect the secure data transmission. In the routing and security that is an important aspect for in a MANETs, existing method routing protocol, however, is not enough to security requirements. The proposed method using Fast Recursive Transmission Algorithm (FRTA) used designed to maximize the data security, routing optimization, minimizing the impact of malicious attack using Collision Detection Avoid Algorithm (CDAA) activity over the MANET and select the best path. Table-driven routing protocols, also known as proactive routing protocols, mandate that every node on the network keep up-to-date routing data. To maintain consistent routing information for network nodes, these protocols propagate frequent updates to the routing table throughout the network, necessitating changes to the network topology. There's a lot of overhead from these upgrades. The suggested FRTA algorithm lowers node data loss rates and increases network energy efficiency. Table-driven routing protocols, also known as proactive routing protocols, mandate that every node on the network keep up-to-date routing data. To maintain consistent routing information for network nodes, these protocols propagate frequent updates to the routing table throughout the network, necessitating changes to the network topology. There's a lot of overhead from these upgrades. The suggested FRTA algorithm lowers node data loss rates and increases network energy efficiency. The proposed method shows high performance than other existing evaluations of the most advanced security and routing energy, end-to-end delay, packet transfer rate, packet loss

Which pooling method is better: Max, Avg, or Concat (Max, Avg)

Pooling is a non-linear operation that aggregates the results of a given region to a single value. This method effectively removes extraneous details in feature maps while keeping the overall information. As a result, the size of feature maps is reduced, which decreases computing costs and prevents overfitting by eliminating irrelevant data. In CNN models, the max pooling and average pooling methods are commonly utilized. The max pooling selects the highest value within the pooling area and aids in preserving essential features of the image. However, it ignores the other values inside the pooling region, resulting in a significant loss of information. The average pooling computes the average values within the pooling area, which reduces data loss. However, by failing to emphasize critical pixels in the image, it may result in the loss of significant features. To examine the performance of pooling methods, this study comprised the experimental analysis of multiple models, i.e. shallow and deep, datasets, i.e. Cifar10, Cifar100, and SVHN, and pool sizes, e.g. $2x2$, $3x3$, $10x10$. Furthermore, the study investigated the effectiveness of combining two approaches, namely Concat (Max, Avg), to minimize information loss. The findings of this work provide an important guideline for selecting pooling methods in the design of CNNs. The experimental results demonstrate that pooling methods have a considerable impact on model performance. Moreover, there are variances based on the model and pool size.

Improving unbalanced image classification through fine-tuning method of reinforcement learning

Image classification, especially unbalanced image classification, holds considerable promise for practical applications. Existing research focuses mainly on enhancing the effectiveness of classifiers through approaches such as data resampling and loss function adjustments. To date, most of the approaches address the class unbalanced issue by transforming unbalanced data distributions into balanced [id=SecondEdit]ones. Consequently, a critical challenge is to directly develop high-performance classifiers that are adaptive to diverse unbalanced data distributions. In this paper, for unbalanced image classification tasks, we propose a Reinforcement Learning Fine-tuning approach to Unbalanced image Classification (RLF-UC). Specifically, we train classification pretraining models on [d=FirstEdit]fivethree unbalanced datasetsid=SecondEdit], and train [d=FirstEdit]the correspondinga reward function model designed to optimize fine-tuning policy. Then, we train a reinforcement learning fine-tuning classification model and optimize its policy to maximize the cumulative expected reward. Finally, we guide the model to prioritize minority category knowledge and incorporate distribution distance constraints, which are derived from the disparities between the [d=SecondEdit]pre-trainedpretrained model and the fine-tuning model to dynamically adjust the fine-tuning classifier model. Experimental results on [d=FirstEdit]fivethree reprocessed unbalanced image datasets demonstrate that our RLF-UC method provides comparable or better classification and generalization capabilities than other baselines.