Digital Scheme Research Articles

A new self-embedding digital watermarking encryption scheme

A new self-embedding digital watermarking encryption scheme

Sludges against Ponzi schemes

Pyramid schemes are adapting to the digitalisation era and mimicking various organisations. The organisers of these schemes quite often appeal to such human qualities as greed and the desire to get rich quickly. Thus, it is becoming increasingly relevant to develop new methods for countering pyramid schemes taking into account modern realities and behavioural characteristics of an individual decisionmaker. The focus of the research is on selecting sludge tools to fight against digital pyramid schemes. Methodologically, the study relies on the concepts of behavioural economics, in particular, of the nudge theory (nudges vs sludges). The methods include brainstorming, focus groups. The evidence comes from primary qualitative research, in-depth interviews with both non-professional investors and experts in the field of financial market held in 2022. The analysis allowed defining the concept of sludge and present the types of sludge tools. The paper provides an original list of sludge tools as well as a set of practical tools to counter pyramid schemes, which, in particular, consists of legal restrictions, monitoring of Ponzi schemes’ activities, financial enlightenment, a legal information system, self-coordination of investors, and an individual approach. The paper concludes that sludge tools differ in terms of the length of impact and the speed of obtaining a result. Short-term sludges generate a fast effect and prevent from investing in pyramid schemes without preliminary preparation of participants; they, for instance, include placing a poster with a warning against Ponzi schemes, ensuring a careful reading of the contract, mandatory familiarisation with the List of entities with detected signs of illegal activities in the financial market created by the Bank of Russia, taking a risk test for assessing the probability of a decreased price or lost asset, ensuring advice of a financial counsellor as an obligatory stage of investment. The findings contribute to the understanding of the need for proactive (preventive) measures’ development aimed at countering fraudulent schemes.

Continuous Refinement-based Digital Pathology Image Assistance Scheme in Medical Decision-Making Systems.

Digital pathology images' extensive cellular information provide a trustworthy foundation for tumor diagnosis. With the aid of computer-aided diagnostics, pathologists can locate crucial information more quickly. The cascade structure refines the segmentation results by utilizing its multi-task and multi-stage characteristics. However, cascade-based models require downsampling and cropping of patches during the inference process due to the ultra-high resolution and complex structure of pathology images. This not only increases the cost and computation time but also results in the loss of cellular details and corrupts the global contextual information. This study proposes a Digital Pathology Image Assistance Program (CRSDPI) for medical decision-making systems that is based on continuous improvement. After locating the region of interest using the maximum inter-class variance method, the pictures are preprocessed to account for the impacts of staining inconsistencies and sensitivity variations on the model's performance. Ultimately, we create a two-phase continuously refined segmentation network (TCRNet) by combining an enhanced continuous refinement model with a coarse segmentation network built on a pyramid scene parsing network. The coarse segmentation network introduces an auxiliary loss term to speed up convergence, and the refined model introduces an implicit function to reduce computational cost and reconstruct more details. The TCRNet model refines the target by successively aligning the features without the need to take cascading decoder operations after encoder. Experiments conducted on digital pathology images of breast cancer and osteosarcoma demonstrate the superior prediction accuracy and computational speed of our strategy.

Thailand in 2023

On the face of it, Thai politics in 2023 was groundbreaking. The event that encapsulated the year was the stunning electoral victory of a radical party, Move Forward. That party was eventually blocked from forming a government by an alliance that included Pheu Thai and the royalist-conservative elite. With Pheu Thai at the head of government, populist policies, most notably the digital wallet scheme, became the centerpiece for economic development. The marriage of convenience between the supposedly reformist Pheu Thai and the conservative forces, as well as the return of economic populism, gave the impression that not much had changed in Thailand. But compared to past years, 2023 was momentous.

Baseline Calibration Scheme Embedded in Single-Slope ADC for Gas Sensor Applications

This paper introduces a single-slope analog-to-digital converter (SS ADC) with an embedded digital baseline calibration scheme designed to improve the accuracy and reliability of gas sensor measurements. The proposed SS ADC effectively leverages an up/down counter mechanism to ensure stable signal extraction from gas sensors, despite variations in the baseline distribution. The proposed SS ADC initiates with a down counting operation to capture the initial output value of the gas sensor, which, after A/D conversion, is stored as a reference point for future readings. Subsequent gas sensor output values are derived by performing an up counting operation from this baseline reference. This approach allows for real-time correction of the baseline during the SS A/D conversion process, obviating the need for complex post-processing and baseline correction algorithms. The proposed SS ADC with the baseline calibration scheme was designed using a 0.18 μm standard CMOS process to confirm its feasibility. It demonstrated a signal-to-noise and distortion ratio (SNDR) of 57.56 dB and a spurious-free dynamic range (SFDR) of 59.02 dB, resulting in an effective number of bits (ENOB) of 9.27 bits in the post-simulation level. The proposed SS ADC has a total power consumption of 1.649 mW. This work offers an efficient solution to the baseline distribution problem in gas sensors, facilitating more reliable and accurate gas detection systems.

Shapley’s Value as a Resource Optimization Strategy for Digital Radio Transmission over IBOC FM

The hybrid in-band on-channel (IBOC) transmission system, which serves as a digital audio radio scheme, facilitates the simultaneous transmission of analog FM and digital audio. In IBOC systems, broadcasters transmit signals within allocated channel bands, posing a challenge in bandwidth allocation for digital audio transmission. To address this, cooperative game theory, supported by the bankruptcy game and Shapley’s value, is proposed as a strategy to optimize bandwidth allocation at each node or station. This approach considers service demand, the number of stations, and radio channel conditions in the FM band. The paper presents a scenario under saturated traffic conditions to evaluate the degree of optimization achievable using the Shapley value under clearly defined traffic and channel conditions. The results indicate that cooperative game theory, with the support of the Shapley value, offers an excellent alternative for optimizing bandwidth in digital broadcasting over IBOC in FM, with potential applicability in other digital radio broadcasting systems.

Evaluation of cryoprotein investigation using a digital external quality assurance scheme.

Background: Robust preanalytical and analytical processes are critical for the detection of cryoproteins. There is significant variation in practice in the detection, analysis and reporting. Results: A survey in 2018 of 137 laboratories participating in the UK National External Quality Assessment Service (UK NEQAS) (6) quality control program showed significant variation in the laboratory processes which highlighted the need for standardisation of the detection, analysis and reporting of cryoglobulins.Conclusion: The first available EQA scheme aiming to harmonise practice for cryoprotein testing has been developed by UK NEQAS and laboratories should participate in an appropriate EQA scheme to fulfil requirements for ISO accreditation.

Modified DNA-based Cryptography System in the Cloud: Deep Maxout-based Fined Tuned Key Generation

Cloud security is a set of practices and tools created to address both internal and external security threats to businesses. Organizations must have cloud security as they implement their digital transformation schemes and include cloud-based tools and services in their infrastructure. Cryptography is a mechanism for preventing illegal access to data. In this paper, modified DNA-based cryptography (MDNAC), which is defined as data hiding with respect to DNA sequence is used. The steps involved in the proposed MDNAC is: encryption and decryption with optimal key generation. A way of converting plain text into cipher text is known as encryption. Two components make up the encryption process: a key and an encryption algorithm. For the encryption algorithm, we employed a modified DNA algorithm. In the decryption phase, the reverse operation is performed to get the plain text from the cipher text. Moreover, a deep learning model is used for generating the keys; the model used is deep maxout. To ensure the appropriate key generation process, the weights of the deep maxout are optimally tuned by the new feedback assisted Archimedes optimization (FAAO) algorithm. Based on the generated keys, the encryption process takes place. Finally, the performance of MDNAC is evaluated using conventional methods with respect to different measures. Additionally, the MDNAC obtained a correlation value of 0.20297 for the mean case scenario, despite the fact that the corresponding values are 0.02%, 0.17%, 0.2%, 0.7%, 0.12%,0.41%, 0.86%, and 0.46% as compared to the other models such as FAT, AOA, BMO, COOT, BOA, SSO, WOA, and LES respectively.

Efficient Cumulant-Based Automatic Modulation Classification Using Machine Learning.

This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier's performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks' end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications.

A reconfigurable RF front-end for 5G direct sampling receivers with an optimized calibration scheme

This paper presents a reconfigurable radio frequency front-end (RFFE) tailored for direct RF sampling receivers operating within Frequency Range 1 (FR-1) of the 5G spectrum. It consists of a balun-LNA, a noise-cancelling, current-reuse, Q-enhanced filter, and a programmable gain amplifier (PGA). Fabricated in 22-nm FD-SOI technology, the RFFE covers the entire frequency range from 1.7 to 6.4 GHz with a tunable bandwidth ranging from 50 MHz to 1.2 GHz. The experimental results confirm that the RFFE achieves 3.9 dB NF, 55 dB ultimate OOB rejection, −4 dBm IB-IIP3, and 23 dBm OOB-IIP3. Furthermore, a digital calibration scheme is proposed to compensate for process, voltage, and temperature (PVT) variations, achieving lower than 2 % error in 6.61 µs on average. Subsequently, a proximal policy optimization (PPO) agent is employed to choose the optimal policy for the successive adjustments of quality factors and resonance frequencies of the filter’s resonators. As a result, the proposed reinforcement learning algorithm reduces the calibration’s convergence time by 59 %.

A Hybrid Digital Twin Scheme for the Condition Monitoring of Industrial Collaborative Robots

Industrial collaborative robots play an essential role in smart manufacturing because they improve productivity while also ensuring workplace safety. However, the development of prognostic and health management systems to ensure the reliability of these robots has been a major challenge due to the lack of fault data. This paper proposed a digital twin scheme based on the fusion of the robot kinematic and dynamic models’ information down to the powertrains (i.e., the joints motor, and gear) along with the control algorithms and uncertainty accommodation based upon deep learning. The presented digital twin concept has the potential to propel simulation-based fault prediction. We also highlight and discuss challenges and opportunities around the development of the hybrid digital twin for condition monitoring of industrial collaborative robots.

Nonce generation techniques in Schnorr multi-signatures: Exploring EdDSA-inspired approaches

<abstract> <p>This paper proposes a deterministic nonce generation technique to address the catastrophic issues associated with nonce reuse in message signing and to enhance the efficiency of Schnorr multi-signature schemes. Additionally, this research aims to reduce computational complexity and bandwidth requirements in digital and multi-signature schemes while maintaining robust security against common attacks. The proposed method was inspired by the EdDSA approach. The methodology includes a comprehensive mathematical analysis of digital signature algorithms and a rigorous examination of their vulnerabilities to well-known cryptographic attacks. This analysis evaluates the effectiveness and robustness of the proposed nonce generation technique within the frameworks of the Schnorr digital signature and the two-round MuSig schemes. Techniques and tools employed in this research involve deterministically generating nonces by hashing the private key and subsequently hashing the result with the message. Furthermore, it is proposed to exclude the public nonce R from the challenge calculations and to allow signers to directly prove possession of their secret keys through the aggregated public key, thereby eliminating the need for non-interactive zero-knowledge (NIZK) proofs. The findings demonstrate significant reductions in computational complexity and operational requirements, thereby improving bandwidth efficiency and making this method well-suited for resource-constrained devices. The approach also exhibits strong resistance to various attacks, including nonce reuse, key cancellation, rogue keys, and virtual machine rewinding.</p> </abstract>

GIS technologies for assessing hydroecological state of rivers in the Aral sea basin

This article analyzes the results of a study on the difficulties and possibilities of using information technology to support the adoption of novel approaches in hydroecology and hydropower. Additionally, it examines the advancement of geographic information systems (GIS) for hydroecological and hydro-technical monitoring and their utilization as educational tools for students. Researching the utilization of information systems to develop environmentally friendly approaches for managing hydraulic infrastructure. The paper centred on the present subject of developing GIS systems for hydro-ecological monitoring, which are valuable tools for improving the information systems of the Aral Sea basin. Furthermore, it assists in optimizing models to create ecologically sustainable approaches for utilizing hydrotechnical constructions. Utilizing the amalgamation of findings from many scientific studies inside a single GIS database may be both feasible and advantageous, serving as a versatile instrument for modeling. An illustration of this methodology may be seen as a unified system where the amalgamation yields outcomes: the water-salt balancing system across all rivers in Uzbekistan. The digital mapping system and schemes are used to build ecologically sustainable methods for operating hydraulic and hydroelectric plants in Uzbekistan. The Aral Sea basin monitoring system (ASBMS) is a computerized system that uses ArcView to monitor the hydroecological quality of surface waters in the Aral Sea basin. Keywords: ecological situation, hydroecology, hydroecological maps, GIS technology, Aral Sea basin.

Optimization Exploration of Digital Identity Authentication Algorithm Based on Blockchain

Abstract In order to solve the problems of the cumbersome user authentication process, insecure identity information, and vulnerable authentication system in traditional identity authentication, a distributed identity authentication method based on blockchain technology is proposed, which utilizes decentralization and is not easy to tamper with characteristics of blockchain and asymmetric encryption to improve the security and stability of user identity authentication system. On this basis, a digital identity authentication optimization scheme is designed, including the use of a hash chain to improve the data structure and the consistency of node data by optimizing the consensus mechanism to enhance authentication security and efficiency. Finally, the automation of authentication is also achieved by introducing smart contracts. The experimental results show that the computational spend of blockchain technology is 55.47% and 97.36% less than the two traditional algorithms, and the overall security score is 22, while the conventional scheme is only 12.5. The blockchain-based authentication mechanism improves the reliability and efficiency of the authentication system.

Digital Control Design Scheme for an LCL-Type Inverter Based on QPR Controller With GVWF Under Weak Grid Conditions

Digital Control Design Scheme for an LCL-Type Inverter Based on QPR Controller With GVWF Under Weak Grid Conditions

Розробка цифрового інтерфейсу для керування імпульсним блоком живлення

Most switching power supplies are not designed to change their output voltage. However, situations arise where a different voltage from the power source is needed. The purpose of this article is to develop a practical interface scheme for digitally controlling the output voltage of a switching power supply with an analog feedback loop. The article presents the results of the research of isolated flyback switch-mode power supply (SMPS) circuits, determines the method of controlling the output voltage of the SMPS, and proposes a digital interface for controlling the output voltage of the power supply. The emphasis was on using readily available, inexpensive components with minimal modification to the existing design. The study revealed limitations in existing switching power supply designs regarding effective output voltage control. Modifying the internal reference source is often not an available option. Therefore, influencing the feedback circuit emerges as a simple and viable control method. All power supply feedback loops include a voltage divider measuring the output voltage of switching power supply. This research demonstrates that controlling output voltage can be achieved by injecting or draining current within or from output voltage divider of power supply. Draining current increases the output voltage, while injecting decreases it. The additional resistor only requires a single connection point within the existing circuit. The proposed method provides a linear dependence of the output voltage increase on the value of the injected/drained current. It is shown that the digital interface scheme depends on the number of discrete levels of the required output voltage. For a small number of levels, field-effect transistors controlled by a positional or binary code can be used to control the power supply, and with a significant number of levels, it is advisable to use ready-made digital-to-analog converters.

The role of the Internet in the evolution of fraud: a historical aspect

This article presents a thorough examination of how the Internet has altered the landscape of fraud, with particular focus on increasingly sophisticated digital schemes. It examines how the internet serves both criminals and cybersecurity efforts alike. Beginning with an exploration of fraud's historical roots from its early manifestations in pre-digital to digital times and continuing into modernity, this article details its development alongside technological progress. Utilizing various research methodologies (historical and statistical analysis), such as tracking its progression across time as well as any countermeasures implemented during that period.Significant findings of this research highlight the rise of internet-enabled fraud forms such as phishing, identity theft and cyber extortion; emphasizing their global and anonymous nature. Furthermore, research indicates the need for multidisciplinary approaches that combine technological, legislative and educational strategies in combatting internet fraud. This highlights machine learning technologies like artificial intelligence in detecting fraud along with greater public awareness campaigns to build cyber literacy literacy levels among society at large.

Digital healthcare framework for patients with disabilities based on deep federated learning schemes

Utilizing digital healthcare services for patients who use wheelchairs is a vital and effective means to enhance their healthcare. Digital healthcare integrates various healthcare facilities, including local laboratories and centralized hospitals, to provide healthcare services for individuals in wheelchairs. In digital healthcare, the Internet of Medical Things (IoMT) allows local wheelchairs to connect with remote digital healthcare services and generate sensors from wheelchairs to monitor and process healthcare. Recently, it has been observed that wheelchair patients, when older than thirty, suffer from high blood pressure, heart disease, body glucose, and others due to less activity because of their disabilities. However, existing wheelchair IoMT applications are straightforward and do not consider the healthcare of wheelchair patients with their diseases during their disabilities. This paper presents a novel digital healthcare framework for patients with disabilities based on deep-federated learning schemes. In the proposed framework, we offer the federated learning deep convolutional neural network schemes (FL-DCNNS) that consist of different sub-schemes. The offloading scheme collects the sensors from integrated wheelchair bio-sensors as smartwatches such as blood pressure, heartbeat, body glucose, and oxygen. The smartwatches worked with wearable devices for disabled patients in our framework. We present the federated learning-enabled laboratories for data training and share the updated weights with the data security to the centralized node for decision and prediction. We present the decision forest for centralized healthcare nodes to decide on aggregation with the different constraints: cost, energy, time, and accuracy. We implemented a deep CNN scheme in each laboratory to train and validate the model locally on the node with the consideration of resources. Simulation results show that FL-DCNNS obtained the optimal results on the sensor data and minimized the energy by 25%, time 19%, cost 28%, and improved the accuracy of disease prediction by 99% as compared to existing digital healthcare schemes for wheelchair patients.

A novel blockchain-based digital forensics framework for preserving evidence and enabling investigation in industrial Internet of Things

To address challenges in digital evidence collection and responsibility determination for industrial safety accidents involving industrial Internet of Things (IIoT) device nodes, this paper proposes a blockchain-based digital forensic scheme within the IIoT communication architecture. The scheme utilizes a decentralized blockchain storage mechanism to enable remote storage of digital forensic data. Additionally, it leverages smart contract mechanisms to facilitate efficient retrieval and tracing of related evidence chains. To enhance data security of IIoT device nodes, a token mechanism is implemented for access control. Moreover, to meet real-time evidence acquisition requirements in IIoT, an efficient batch consensus mechanism is proposed. Experimental simulations demonstrate the superiority of the novel consensus algorithm compared to the traditional Delegated Proof-of-Stake (DPOS) consensus in the proposed scheme for the IIoT environment. It meets speed requirements for evidence collection, ensuring tamper-proof, non-repudiable, and permanent storage of digital forensic data. Consequently, the application of blockchain technology for judicial access and evidence storage has made significant contributions to digital forensics within the IIoT context.

Motivational factors to paying zakat through institutions: a multigroup analysis of urban and suburban muzakki based on digital payment scheme

Purpose This study aims to empirically explore several factors that encourage muzakki (zakat payers) to pay their zakat through institutions by elaborating on their extrinsic and intrinsic motivations as the composite factors regarding the attitude and intention improvement of muzakki. This study specifically studies zakat payment via digital means and categorizes the muzakki groups into two (urban and suburban) to be considered in the results. Design/methodology/approach Overall, this study gathers the data from 298 muzakki using a partial least squares technique the multigroup analysis to compare the analysis. Findings This study found that different sociodemographic aspects will result in varied performances of motivation in using technology between the two groups. Furthermore, positive preference aspects, such as muzakki’s attitude, can be a catalyst in improving their motivation to pay zakat through institutions. Practical implications The findings of this study can be used as a foundation to improve the technology-based services that will be more accessible and reachable. Provision of technical follow-ups regarding the utilization of technology, including community-based digital platform socializations, availability of online customer service that will respond to muzakki’s needs and synergy between stakeholders, are the primary obligations that a zakat institution must fulfill. Originality/value As far as the researchers are concerned, the studies focusing on the motivational factors and attitude of muzakki as an intervention in paying zakat via institutions are limited in numbers, especially studies on digital payment. In this study, however, classifying the groups into two will help gain a deeper understanding of this topic.