Data Storage Research Articles

Securing Anomaly Detection for Process-Based Time Series

Widespread innovation from artificial intelligence and machine learning (AI/ML) tools presents a lucrative opportunity for the nuclear industry to improve state-of-the-art analyses (e.g. condition monitoring, remote operation, etc.) due to increased data visibility. In recent years, the risk posed by collaborative data exchange has received increased attention due, in part, to a potential adversary’s ability to reverse-engineer intercepted data using domain knowledge and AI/ML tools. While the efficacy of typical encryption has been proven during passive communication and data storage, collaborative exchange typically requires decryption for extended analyses by a third-party, which poses an intrinsic risk due to these trustworthiness concerns. The directed infusion of data (DIOD) 1 1 A.Al Rashdan and H.Abdel-Khalik, Deceptive Infusion of Data, Non-Provisional Patent, Application No. 63/227,389, September 2022. paradigm presented in this paper discusses a novel data masking technique that relies on preserving the usable information of proprietary data while concealing its identity via reduced-order modeling. In contrast to existing state-of-the-art data masking methods, DIOD does not impose limiting assumptions, computational overheads, or induced uncertainties, thereby allowing for secure and flexible data-level security that does not alter the inferential content of the data. This paper focuses on the application of DIOD to a process-based simulation wherein a leaking reservoir with a controlled inlet pump is simulated under various experimental conditions with the goal of producing masked data that preserve the information given by anomalies. These experiments included the injection of statistically significant anomalies, subtle anomalies that occurred over an extended period, and the addition of several independent anomalous states. Each experiment showed that a classifier will identify the same anomalies whether it analyzes the original or masked data. An additional experiment also tested the case of corrupted labeling information wherein labels were arbitrarily randomized, and the loss in labeling accuracy was about the same for both datasets. Each of these experiments show that data obfuscated by DIOD may be utilized in the place of real data for a variety of condition monitoring scenarios with no loss in performance.

FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm

This work presents a novel approach that harnesses the capabilities of field programmable gate arrays (FPGA) to enable instantaneous monitoring of electroencephalography (EEG) signals for the detection and prediction of seizure patterns. The integration of FPGA technology with the perceptron learning algorithm holds promises for enhancing real-time healthcare monitoring systems and contributing to improved patient care and safety. The proposed work addresses the challenges presented at predicting seizures from EEG data. The first challenge is to handle massive EEG data that poses memory issues. To tackle this, the research employs cellular automata (Rule 90 and Rule 150) to reduce data size by 85.71%, making it more manageable. The second is to implement a linearly classified perceptron algorithm using FPGA to predict and detect seizures from real-time EEG data. The third is clock synchronization between stored data and real EEG data for comparison purposes. The proposed system is continuously compared to real-time EEG signals using a single perceptron neural network, and alert signals are generated based on preactivation values, with a continuous alert issued when the value reaches 3/4th sample match. These early alerts empower individuals to take preventive actions. The model is implemented using an FPGA Zynq-7000 series, which consumes 270mW of power. The design utilizes 118 Lookup Tables (LUTs).

Effect of chromium at B site substitution on CaTiO3 perovskite ceramics for data storage application

Effect of chromium at B site substitution on CaTiO3 perovskite ceramics for data storage application

Review of neuromorphic computing based on NAND flash memory.

The proliferation of data has facilitated global accessibility, which demands escalating amounts of power for data storage and processing purposes. In recent years, there has been a rise in research in the field of neuromorphic electronics, which draws inspiration from biological neurons and synapses. These electronics possess the ability to perform in-memory computing, which helps alleviate the limitations imposed by the 'von Neumann bottleneck' that exists between the memory and processor in the traditional von Neumann architecture. By leveraging their multi-bit non-volatility, characteristics that mimic biology, and Kirchhoff's law, neuromorphic electronics offer a promising solution to reduce the power consumption in processing vector-matrix multiplication tasks. Among all the existing nonvolatile memory technologies, NAND flash memory is one of the most competitive integrated solutions for the storage of large volumes of data. This work provides a comprehensive overview of the recent developments in neuromorphic computing based on NAND flash memory. Neuromorphic architectures using NAND flash memory for off-chip learning are presented with various quantization levels of input and weight. Next, neuromorphic architectures for on-chip learning are presented using standard backpropagation and feedback alignment algorithms. The array architecture, operation scheme, and electrical characteristics of NAND flash memory are discussed with a focus on the use of NAND flash memory in various neural network structures. Furthermore, the discrepancy of array architecture between on-chip learning and off-chip learning is addressed. This review article provides a foundation for understanding the neuromorphic computing based on the NAND flash memory and methods to utilize it based on application requirements.

Implementation and optimization of SpMV algorithm based on SW26010P many-core processor and stored in BCSR format

The irregular distribution of non-zero elements of large-scale sparse matrix leads to low data access efficiency caused by the unique architecture of the Sunway many-core processor, which brings great challenges to the efficient implementation of sparse matrix–vector multiplication (SpMV) computing by SW26010P many-core processor. To address this problem, a study of SpMV optimization strategies is carried out based on the SW26010P many-core processor. Firstly, we design a memorized data storage transformation strategy to transform the matrix in CSR storage format into BCSR (Block Compressed Sparse Row) storage. Secondly, the dynamic task scheduling method is introduced to the algorithm to realize the load balance between slave cores. Thirdly, the LDM memory is refined and designed, and the slave core dual cache strategy is optimized to further improve the performance. Finally, we selected a large number of representative sparse matrices from the Matrix Market for testing. The results show that the scheme has obviously speedup the processing procedure of sparse matrices with various sizes and sizes, and the master–slave speedup ratio can reach up to 38 times. The optimization method used in this paper has implications for other complex applications of the SW26010P many-core processor.

Multilevel Anti-counterfeiting Barcode with Enhanced Information Encryption Based on Stimulus-Responsive Digital Polymers.

In response to the escalating challenges of counterfeiting due to technological and socioeconomic advancements, a novel trilevel anti-counterfeiting Quick Response (QR) code system has been developed. This system integrates digital polymers with QR code and stimulus-responsive chromophores, i.e., rhodamine B (RB), rhodamine 6G (R6G), and spiropyran (SP), to provide a sophisticated security solution. This advanced barcode remains concealed until specific stimuli reveal it and can be scanned by a smartphone, enabling first and second level anti-counterfeiting. For the third level of security, the encrypted information within the digital polymers can only be deciphered using tandem mass spectrometry. This innovative approach not only enhances security features but also offers reversible visibility and a complex verification process. This trilevel system surpasses traditional single-level anti-counterfeiting methods and holds significant potential for future applications in protecting brand authenticity and managing data storage, contributing new concepts and techniques to the field of high-security anti-counterfeiting materials.

Early detection of cardiorespiratory complications and training monitoring using wearable ECG sensors and CNN

This research study demonstrates an efficient scheme for early detection of cardiorespiratory complications in pandemics by Utilizing Wearable Electrocardiogram (ECG) sensors for pattern generation and Convolution Neural Networks (CNN) for decision analytics. In health-related outbreaks, timely and early diagnosis of such complications is conclusive in reducing mortality rates and alleviating the burden on healthcare facilities. Existing methods rely on clinical assessments, medical history reviews, and hospital-based monitoring, which are valuable but have limitations in terms of accessibility, scalability, and timeliness, particularly during pandemics. The proposed scheme commences by deploying wearable ECG sensors on the patient’s body. These sensors collect data by continuously monitoring the cardiac activity and respiratory patterns of the patient. The collected raw data is then transmitted securely in a wireless manner to a centralized server and stored in a database. Subsequently, the stored data is assessed using a preprocessing process which extracts relevant and important features like heart rate variability and respiratory rate. The preprocessed data is then used as input into the CNN model for the classification of normal and abnormal cardiorespiratory patterns. To achieve high accuracy in abnormality detection the CNN model is trained on labeled data with optimized parameters. The performance of the proposed scheme is evaluated and gauged using different scenarios, which shows a robust performance in detecting abnormal cardiorespiratory patterns with a sensitivity of 95% and specificity of 92%. Prominent observations, which highlight the potential for early interventions include subtle changes in heart rate variability and preceding respiratory distress. These findings show the significance of wearable ECG technology in improving pandemic management strategies and informing public health policies, which enhances preparedness and resilience in the face of emerging health threats.

HYPSTAR: a hyperspectral pointable system for terrestrial and aquatic radiometry

Optical Earth observation satellites provide vast amounts of data on a daily basis. The top-of-atmosphere radiance measured by these satellites is usually converted to bottom-of-atmosphere radiance or reflectance which is then used for deriving numerous higher level products used for monitoring environmental conditions, climate change, stock of natural resources, etc. The increase of available remote sensing data impacts decision-making on both regional and global scales, and demands appropriate quality control and validation procedures. A HYperspectral Pointable System for Terrestrial and Aquatic Radiometry (HYPSTAR®) has been designed to provide automated, in-situ multiangular reflectance measurements of land and water targets. HYPSTAR-SR covers 380–1020 nm spectral range at 3 nm spectral resolution and is used at water sites. For land sites the HYPSTAR-XR variant is used with the spectral range extended to 1680 nm at 10 nm spectral resolution. The spectroradiometer has multiplexed radiance and irradiance entrances, an internal mechanical shutter, and an integrated imaging camera for capturing snapshots of the targets. The spectroradiometer is mounted on a two-axis pointing system with 360° range of free movement in both axes. The system also incorporates a stable light emitting diode as a light source, used for monitoring the stability of the radiometric calibration during the long-term unattended field deployment. Autonomous operation is managed by a host system which handles data acquisition, storage, and transmission to a central WATERHYPERNET or LANDHYPERNET server according to a pre-programmed schedule. The system is remotely accessible over the internet for configuration changes and software updates. The HYPSTAR systems have been deployed at 10 water and 11 land sites for different periods ranging from a few days to a few years. The data are automatically processed at the central servers by the HYPERNETS processor and the derived radiance, irradiance, and reflectance products with associated measurement uncertainties are distributed at the WATERHYPERNET and LANDHYPERNET data portals.

Research on the application and prospect of RFID technology

As Radio Frequency Identification (RFID) has the advantages of fast scanning speed, small size, large data storage, high security, and other technologies, it is widely used in various fields to upgrade working efficiency. Meanwhile, as one of the research topics of the Internet of Things (IoT), Radio Frequency Identification also provides the possibility for the development and application scope of IoT. Based on the application principle of Radio Frequency Identification technology, this paper discusses the application of Radio Frequency Identification in the two fields of intelligent libraries and intelligent logistics through literature review and theoretical analysis. Through the analysis of the current situation of RFID application in the library management and logistics industry, this paper discusses the advantages and shortcomings of radio frequency identification technology, and according to the existing problems the future direction of development of the corresponding improvement proposals to promote the further improvement of the application of radio frequency identification technology.

Self-Assembled Chiral Polymers Exhibiting Amplified Circularly Polarized Electroluminescence.

Circularly polarized electroluminescence (CPEL) is highly promising in realm of 3D display and optical data storage. However, designing a groundbreaking chiral material with high comprehensive CPEL performance remains a formidable challenge. In this work, a pair of chiral polymers with self-assembled behavior is designed by integrating a chiral BN-moiety into polyfluorene backbone, named R-PBN and S-PBN, respectively. The chiral polymers show narrowband emission centered at 490 nm with full-width half maximum (FWHM) of 29 nm and high photoluminescence quantum yield (PLQY) of 79%. After thermal annealing treatment, the chiral polymers undergo self-assembly, exhibiting amplified circularly polarized luminescence (CPL) with asymmetry factor (|glum|) of up to 0.11. Moreover, the solution-processed nondoped CP-OLEDs based on the chiral polymers as emitting layers exhibit maximum external quantum efficiency (EQEmax) of 9.8%, intense CPEL activities with |gEL| of up to 0.07, and small FWHM of 36 nm, simultaneously. This represents the first case of self-assembled chiral polymers that combines high EQE, large gEL value and narrowband emission.

A blockchain architecture with smart contracts for an additive symbiotic network - a case study

AbstractAdopting innovative technologies such as blockchain and additive manufacturing can help organisations promote the development of additive symbiotic networks, thus pursuing higher sustainable goals and implementing circular economy strategies. These symbiotic networks correspond to industrial symbiosis networks in which wastes and by-products from other industries are incorporated into additive manufacturing processes. The adoption of blockchain technology in such a context is still in a nascent stage. Using the case study method, this research demonstrates the adoption of blockchain technology in an additive symbiotic network of a real-life context. The requirements to use a blockchain network are identified, and an architecture based on smart contracts is proposed as an enabler of the additive symbiotic network under study. The proposed solution uses the Hyperledger Fabric Attribute-Based Access Control as the distributed ledger technology. Even though this solution is still in the proof-of-concept stage, the results show that adopting it would allow the elimination of intermediary entities, keep available tracking records of the resources exchanged, and improve trust among the symbiotic stakeholders (that do not have any trust or cooperation mechanisms established before the symbiotic relationship). This study highlights that the complexity associated with introducing a novel technology and the technology’s immaturity compared to other data storage technologies are some of the main challenges related to using blockchain technology in additive symbiotic networks.

Self‐Assembled Chiral Polymers Exhibiting Amplified Circularly Polarized Electroluminescence

Circularly polarized electroluminescence (CPEL) is highly promising in realm of 3D display and optical data storage. However, designing a groundbreaking chiral material with high comprehensive CPEL performance remains a formidable challenge. In this work, a pair of chiral polymers with self‐assembled behavior is designed by integrating a chiral BN‐moiety into polyfluorene backbone, named R‐PBN and S‐PBN, respectively. The chiral polymers show narrowband emission centered at 490 nm with full‐width half maximum (FWHM) of 29 nm and high photoluminescence quantum yield (PLQY) of 79%. After thermal annealing treatment, the chiral polymers undergo self‐assembly, exhibiting amplified circularly polarized luminescence (CPL) with asymmetry factor (|glum|) of up to 0.11. Moreover, the solution‐processed nondoped CP‐OLEDs based on the chiral polymers as emitting layers exhibit maximum external quantum efficiency (EQEmax) of 9.8%, intense CPEL activities with |gEL| of up to 0.07, and small FWHM of 36 nm, simultaneously. This represents the first case of self‐assembled chiral polymers that combines high EQE, large gEL value and narrowband emission.

Enhancement of cyber security in IoT based on ant colony optimized artificial neural adaptive Tensor flow

ABSTRACT The Internet of Things (IoT) is a network that connects various hardware, software, data storage, and applications. These interconnected devices provide services to businesses and can potentially serve as entry points for cyber-attacks. The privacy of IoT devices is increasingly vulnerable, particularly to threats like viruses and illegal software distribution lead to the theft of critical information. Ant Colony-Optimized Artificial Neural-Adaptive Tensorflow (ACO-ANT) technique is proposed to detect malicious software illicitly disseminated through the IoT. To emphasize the significance of each token in source duplicate data, the noise data undergoes processing using tokenization and weighted attribute techniques. Deep learning (DL) methods are then employed to identify source code duplication. Also the Multi-Objective Recurrent Neural Network (M-RNN) is used to identify suspicious activities within an IoT environment. The performance of proposed technique is examined using Loss, accuracy, F measure, precision to identify its efficiency. The experimental outcomes demonstrate that the proposed method ACO-ANT on Malimg dataset provides 12.35%, 14.75%, 11.84% higher precision and 10.95%, 15.78%, 13.89% higher f-measure compared to the existing methods. Further, leveraging block chain for malware detection is a promising direction for future research the fact that could enhance the security of IoT and identify malware threats.

Research on airport baggage anomaly retention detection technology based on machine vision, edge computing, and blockchain

AbstractAirport checked luggage entails specific requirements for speed, stability, and reliability. The issue of abnormal retention of checked luggage presents a significant challenge to aviation safety and transportation efficiency. Traditional luggage monitoring systems exhibit limitations in terms of accuracy and timeliness. To address this challenge, this paper proposes a real‐time detection and alerting of luggage anomaly retention based on the YOLOv5 object detection model, leveraging visual algorithms. By eliminating cloud servers and deploying multiple edge servers to establish a private chain, images of anomalously retained luggage are encrypted and stored on the chain. Data users can verify the authenticity of accessed images through anti‐tampering algorithms, ensuring the security of data transmission and storage. The deployment of edge computing servers can significantly reduce algorithm latency and enhance real‐time performance. This solution employs computer vision technology and an edge computing framework to address the speed and stability of checked luggage transportation. Furthermore, blockchain technology greatly enhances system security during operation. A model trained on a sample set of 4600 images achieved a luggage recognition rate of 96.9% and an anomaly detection rate of 95.8% in simulated test videos.

Robust Ferrimagnetism and Ferroelectricity in 2D ɛ-Fe2O3 Semiconductor with Ultrahigh Ordering Temperature.

2D single-phase multiferroic materials with the coexistence of electric and spin polarization offer a tantalizing potential for high-density multilevel data storage. One of the current limitations for application is the scarcity of the materials, especially those combine ferromagnetism and ferroelectricity at high temperatures. Here, robust ferrimagnetism and ferroelectricity in 2D ɛ-Fe2O3 samples with both single-crystalline and polycrystalline form are demonstrated. Interestingly, the polycrystalline nanosheets also exhibit easily switchable ferroelectric polarizations comparable to that of single crystals. The existence of grain boundary does not hinder the switching and retention of ferroelectric polarization. Furthermore, the ɛ-Fe2O3 nanosheets show ferrimagnetic and ferroelectric Curie temperatures up to 800K, which reaches record highs in 2D single-phase multiferroic materials. This work provides important progress in the exploration of 2D high-temperature single-phase multiferroics for potentially compact high-temperature information nanodevices.

Resilience eDBT: Development and Usability Evaluation of a Dialectical Behavior Therapy Skills Training App for Eating Disorders.

Dialectical behavior therapy (DBT) is an evidence-based treatment for disorders characterized by recurrent binge eating. Yet, access to specialized treatment like DBT remains limited. To increase the accessibility of DBT, we developed a DBT skills training app (Resilience: eDBT) for the management of eating disorder (ED) symptoms. This paper delineates the developmental process of Resilience and tests its usability. Descriptive information on the development and features of Resilience is provided, including its framework, content structure and delivery formats, functionality, data storage procedure, and privacy protocols. Usability was assessed via a mixed methods approach in 10 symptomatic individuals. Qualitative data were organized based on an existing framework, which included six themes: usability, visual design, user engagement, content, therapeutic persuasiveness, and therapeutic alliance. Resilience demonstrated good usability via a Systems Usability Scale score of 85.5, which exceeded the recommended cutoff of 68. Positive aspects of the app, according to interview data, were the ease of use and the visual design, while the addition of peer support was suggested as an opportunity for improvement. A novel DBT-based app may serve as an acceptable, low-intensity option or adjunct to traditional treatment for targeting ED symptoms that emerge in daily life. However, notable limitations include the small sample size and the single time point at which the usability assessment was conducted.

ANDROID HEALTHCARE APPLICATION

The healthcare systems is pivotal in improving the accessibility and management of healthcare services. This paper focuses on the creation of a comprehensive appointment management system to streamline various healthcare interactions. Modern healthcare challenges such as scheduling appointments, ordering medications, and accessing health information necessitate an integrated approach. The proposed system includes modules for user registration, lab test management, medication management, doctor search and appointment booking, health education articles, order tracking, and user logout functionalities. Utilizing user-friendly interfaces and robust backend infrastructure, the system ensures secure user registration, easy browsing of lab tests and medications, efficient search for specialist doctors, convenient appointment booking, access to health articles, and order tracking. Key features include personalized user profiles, real-time appointment scheduling, and secure data storage using a SQLite database. Keywords: Healthcare management, Mobile Applications, Software Engineering

Functionality Expansion of Guided Mode Radiation via On-Chip Metasurfaces.

On-chip metasurfaces play a crucial role in bridging the guided mode and free-space light, enabling full control over the wavefront of scattered free-space light in an optimally compact manner. Recently, researchers have introduced various methods and on-chip metasurfaces to engineer the radiation of guided modes, but the total functions that a single metasurface can achieve are still relatively limited. In this work, we propose a novel on-chip metasurface design that can multiplex up to four distinct functions. We can efficiently control the polarization state, phase, angular momentum, and beam profile of the radiated waves by tailoring the geometry of V-shaped nanoantennas integrated on a slab waveguide. We demonstrate several innovative on-chip metasurfaces for switchable focusing/defocusing and for multifunctional generators of orbital angular momentum beams. Our on-chip metasurface design is expected to advance modern integrated photonics, offering applications in optical data storage, optical interconnection, augmented reality, and virtual reality.

Integrating NoSQL, Hilbert Curve, and R*-Tree to Efficiently Manage Mobile LiDAR Point Cloud Data

The widespread use of Light Detection and Ranging (LiDAR) technology has led to a surge in three-dimensional point cloud data; although, it also poses challenges in terms of data storage and indexing. Efficient storage and management of LiDAR data are prerequisites for data processing and analysis for various LiDAR-based scientific applications. Traditional relational database management systems and centralized file storage struggle to meet the storage, scaling, and specific query requirements of massive point cloud data. However, NoSQL databases, known for their scalability, speed, and cost-effectiveness, provide a viable solution. In this study, a 3D point cloud indexing strategy for mobile LiDAR point cloud data that integrates Hilbert curves, R*-trees, and B+-trees was proposed to support MongoDB-based point cloud storage and querying from the following aspects: (1) partitioning the point cloud using an adaptive space partitioning strategy to improve the I/O efficiency and ensure data locality; (2) encoding partitions using Hilbert curves to construct global indices; (3) constructing local indexes (R*-trees) for each point cloud partition so that MongoDB can natively support indexing of point cloud data; and (4) a MongoDB-oriented storage structure design based on a hierarchical indexing structure. We evaluated the efficacy of chunked point cloud data storage with MongoDB for spatial querying and found that the proposed storage strategy provides higher data encoding, index construction and retrieval speeds, and more scalable storage structures to support efficient point cloud spatial query processing compared to many mainstream point cloud indexing strategies and database systems.

Robust Electrothermal Switching of Optical Phase‐Change Materials through Computer‐Aided Adaptive Pulse Optimization

Electrically tunable optical devices present diverse functionalities for manipulating electromagnetic waves by leveraging elements capable of reversibly switching between different optical states. This adaptability in adjusting their responses to electromagnetic waves after fabrication is crucial for developing more efficient and compact optical systems for a broad range of applications, including sensing, imaging, telecommunications, and data storage. Chalcogenide‐based phase‐change materials (PCMs) have shown great promise due to their stable, nonvolatile phase transition between amorphous and crystalline states. Nonetheless, optimizing the switching parameters of PCM devices and maintaining their stable operation over thousands of cycles with minimal variation can be challenging. Herein, the critical role of PCM pattern as well as electrical pulse form in achieving reliable and stable switching is reported on, extending the operational lifetime of the device beyond 13000 switching events. To achieve this, a computer‐aided algorithm that monitors optical changes in the device and adjusts the applied voltage in accordance with the phase transformation process is developed, thereby significantly enhancing the lifetime of these reconfigurable devices. The findings reveal that patterned PCM structures show significantly higher endurance compared to blanket PCM thin films.