Central Processing Unit Research Articles

Oral biomechanical feature recognition method and system for vocal pronunciation resonance

This paper discusses a vocal pronunciation resonance recognition method based on oral biomechanical features, aiming to collect the movement data inside the oral cavity through high-precision sensors, and combine support vector machines and convolutional neural networks to achieve precise recognition and optimization of pronunciation resonance features. First, by using high-precision sensors such as tongue position sensors and soft palate movement sensors, key biomechanical data such as tongue position, oral morphology and soft palate movement of singers during pronunciation are obtained in real-time. Then, feature extraction and analysis are performed to extract biomechanical features such as movement amplitude, frequency, and speed of oral organs, and a resonance feature recognition model is constructed using support vector machines and convolutional neural networks to recognize and optimize the resonance area during pronunciation. Finally, this paper designs and develops an automated recognition system that can provide real-time feedback on the singer’s pronunciation data, and provide personalized training suggestions based on the recognition results to optimize the pronunciation resonance effect. The experimental results show that the resonance recognition method based on biomechanical data can significantly improve the accuracy and personalization of pronunciation, and help improve the efficiency and scientificity of vocal training. The system has good stability, with a response time of less than 500 ms and a CPU (Central Processing Unit) usage rate of no more than 70%. This method provides effective technical support for the digitalization and personalized optimization of vocal pronunciation, and has high practical value and promotion potential.

Optimized Design and Applications of Arithmetic Logic Units: Addressing Power Efficiency and Performance in Diverse Computing Applications

In modern computer systems, the Arithmetic Logic Unit (ALU) is the core component of the central processing Unit (CPU) and performs basic tasks such as arithmetic operations, logic operations, and data transmission. With the development of information technology, the demand for computing is growing, the requirements for computing accuracy, speed and energy consumption are becoming more stringent. The design and optimization of ALU is directly related to the overall performance and energy efficiency of the system, so it becomes the focus of research and the key to technological breakthroughs. This paper focuses on summarizing and exploring the application scenarios, development, and optimization prospects of ALUs. This study reviews various application contexts, including embedded systems, quantum computing, and high-performance processors, highlighting how tailored ALU designs can meet the demands of each. In the field of optimization strategies, Gate Diffusion Input (GDI) technology, reversible logic, and Single Electron Transistor (SET) technology were discussed as a way to reducing power consumption and improving processing speeds.

Thinking beyond substances: Why behavioral "addiction" research must move past substance use disorder paradigms.

In this viewpoint article, the authors contend that behavioral addiction (BA) research must move past substance use disorder paradigms. Under the most liberal definitions of BA, activities such as eating, exercise, work, smartphone use, and a litany of others could all become addictions. Abundant clinical evidence shows that people may frequently engage in behaviors in ways that become impairing. Yet, frequency of engagement in a behavior is insufficient evidence that addiction is occurring and may be of little evidentiary value at all. There are also severe problems with assuming equivalence between all behavioral processes and substance use. The conceptual problems manifest in methodological problems, meaning that many of the methodological approaches used in substance use research are likely not valid for BA research. Given abundant evidence that the behavior patterns commonly referred to as BAs are associated with distress and impairment, BAs are likely to continue to garner interest in both clinical care and clinical science. To better understand the phenomenology of BAs, research should first start with systematic and multimethod investigations among patients reporting such problems. That is, rather than simply co-opting methods and measures from substance use disorder (SUD) research, BA research should carefully consider the signs and symptoms reported by people experiencing real impairments from excessive and dysregulated behavioral engagements. Additionally, BA researchers should seek to engage with larger theoretical perspectives on psychopathology regarding the core processes that seem to be driving such impairments. For BA research to achieve scientific legitimacy, explain clinical phenomena, and, ultimately, reduce human suffering, the study of such disorders must move beyond SUD paradigms and addiction framing alone and instead strive for riskier tests of validity. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Comparative between the binary thresholding technique and the Otsu method for the people detection

In image detection processes where there is a variation in brightness between pixels, techniques are required to obtain optimal and adaptable threshold values for these variations. Therefore, a comparison between the binary thresholding technique and the adaptive method of Otsu is made, in videos with dynamic and static background, weighing the response time of the algorithm, memory used, requirement of the central processing unit and hits in the detections, in the languages of Python and M (Matlab). The techniques in Python present better results in terms of response time and memory space; while, when using Matlab, the lowest percentage of machine requirement is presented. Also, the Otsu method improves the percentage of hits in 12.89 % and 11.3 % for videos with dynamic and static background, with respect to the binary thresholding technique.

Efficiently accelerated bioimage analysis with NanoPyx, a Liquid Engine-powered Python framework

AbstractThe expanding scale and complexity of microscopy image datasets require accelerated analytical workflows. NanoPyx meets this need through an adaptive framework enhanced for high-speed analysis. At the core of NanoPyx, the Liquid Engine dynamically generates optimized central processing unit and graphics processing unit code variations, learning and predicting the fastest based on input data and hardware. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to reactive microscopy and computing fields requiring efficiency.

Apache Spark based distributed clustering for big data analytic with application to 3D road network

The vast amount of data stored nowadays has turned big data analytics into a very promising research field. Clustering is an essential step in data analysis, widely used for classification, collecting statistics, and acquiring insights in specific domains of knowledge. However, the most of existing algorithms based on Lloyd-Forgy’s method, have an enormously huge average-case complexity while clustering data sets with a large number of features, which may be superpolynomial time (NP-hard) and are severely constrained in terms of speed, productivity, and adaptability. Aiming to improve Lloyd-Forgy’s clustering performance, K-means++ algorithms, a variety of algorithm-level optimizations which is not been well-studied, is discussed along with very promising gaussian mixture model (GMM) and soft clustering based Fuzzy C-means (FCM). Further, for fast and distributed data processing and to leverage the benefits of big data platforms, such as Apache Spark, Spark-based clustering methods are applied on three-dimensional (3D) road network data set which is collected from UCI repository. However, Spark-based clustering research is still in infancy. The distributed computation tests are conducted by allocating two core processors and one databricks unit (DBU) with 15 GB memory and measuring execution times, as well as root mean square error (RMSE), mean absolute error (MAE), clustering accuracy, and silhouette values. The results are promising and provide new research directions in the field of spark-based clustering on big data.

CPUs and GPUs in Smart Home Devices: Performance, Applications, and Future Directions

This paper explores the performance and applications of central processing units (CPUs) and graphics processing units (GPUs) in smart home devices. It delves into the background and trends of smart home evolution, highlighting the applied technologies and the roles of CPU and GPU. The literature review presents advancements in CPU and GPU technologies for smart home devices, including their developments in home data processing, image recognition, and device interconnection. The methodology section discusses the architecture design of CPU and GPU in smart home devices, emphasizing multi-core design, parallel processing, and cache optimization. The applications of CPUs and GPUs are examined in smart home appliances and home entertainment systems, including their roles in device control, energy efficiency management, intelligent operation interfaces, 4K video processing, game graphics rendering, and intelligent recommendation systems. The experiment evaluates the performance of CPUs and GPUs in smart home devices through a simulated home environment and specific performance indicators. Finally, the conclusion emphasizes the importance of high-performance computing chips in smart home devices. It suggests future research directions, including optimizing performance, integrating AI technology, and developing new computing chips.

Detection of DDOS Attack in Cloud Computing Environment using Artificial Neural Network

One of the most severe threats against cloud systems is Distributed Denial of Service (DDoS) attacks. DDoS attacks create a type of resource crippling by flooding the system with abnormal traffic, hence overwhelming all the resources like memory, CPU, and network bandwidth, bringing the services out of reach to legitimate users. It becomes quite difficult to detect such attacks when the requests originate from hundreds of geographically dispersed sources. As such, we propose an ANN-based DDoS attack detection approach that leans on machine learning to enhance the accuracy and efficiency in detecting attacks. The ANN model is designed to pick out underlying patterns in the network traffic and distinguish between legitimate and malicious activities that might originate from either side, thus reducing false positives and false negatives. A particular advantage of cloud computing is its scalability, through which changing demands can be catered to, but it needs robust security against DDoS attacks in order to maintain the service. This ANN-based approach especially focuses on ethical AI principles and sustainability, while being non-discriminatory in access to services. For it reduces errors to ensure fairness in detection, fine-tuning for energy efficiency also turns out to lead to a smaller ecological footprint.

A closed-box kernel function for numerical simulation of transient heat conduction

A new kernel function, termed the closed-box kernel function, has been developed to address numerical simulation of transient heat conduction in the same medium. Firstly, this method is versatile and not limited to specific industrial scenarios or designated materials. Secondly, the method solves the spatial temperature at each time point only once, eliminating the need for multiple iterations. Thirdly, the method allows for controlled and adjustable simulation speed of heat conduction while maintaining high accuracy. After 24,700 iterations in the transient process, the relative error in temperature values is merely 0.000072. Fourthly, this numerical technique can break data dependency between spatial nodes, making it suitable for parallel computing with Graphics Processing Units (GPUs). The average speedup achieved is 94.0712 times faster compared to Central Processing Unit (CPU) computations. Finally, we provide mathematical examples to verify the correctness and accuracy of this numerical approach and present a computational framework for parallel computing in the Visual Studio (VS) programming environment to demonstrate its practicality in engineering applications.

Nonlinear system dynamics simulation of thermal processes in the VVER-1200 reactor core

Abstract This study presents developing and validating a nonlinear dynamic model for simulating thermal processes in the VVER-1200 reactor core, a third-generation pressurized water reactor with enhanced safety features and improved thermal efficiency. The model employs a point kinetic mathematical approach, incorporating key neutronic parameters extracted from previous analyses of the VVER-1200 under normal operating conditions. These parameters, including neutron flux distribution, power peaking factors, and reactivity coefficients, were integrated into the reactor kinetic model to capture complex feedback mechanisms. In this study, the variation of reactor power with control rod insertion is examined under normal operating conditions. The analysis focuses on how different levels of control rod insertion influence reactor performance, providing insights into optimizing operational efficiency and safety. Monte Carlo code MCNP6 was used to estimate neutronic and kinetic parameters for the proposed model. The model’s validation involved comparing simulation results against detailed Monte Carlo N-Particle calculations, specifically focusing on power changes due to reactivity variations induced by control rod movements. Results showed strong agreement between the nonlinear dynamic model and MCNP calculations. The validated model provides valuable insights into the VVER-1200 core’s thermal behavior under steady-state and transient conditions. It can investigate critical hot spots, identify thermal limits, and assess system perturbation responses. The model’s computational efficiency makes it a practical tool for further optimization and safety analyses of the VVER-1200 design.

The case for neurons: a no-go theorem for consciousness on a chip.

We apply the methodology of no-go theorems as developed in physics to the question of artificial consciousness. The result is a no-go theorem which shows that under a general assumption, called dynamical relevance, Artificial Intelligence (AI) systems that run on contemporary computer chips cannot be conscious. Consciousness is dynamically relevant, simply put, if, according to a theory of consciousness, it is relevant for the temporal evolution of a system's states. The no-go theorem rests on facts about semiconductor development: that AI systems run on central processing units, graphics processing units, tensor processing units, or other processors which have been designed and verified to adhere to computational dynamics that systematically preclude or suppress deviations. Whether our result resolves the question of AI consciousness on contemporary processors depends on the truth of the theorem's main assumption, dynamical relevance, which this paper does not establish.

ADAPTIVE FILTERING AND MACHINE LEARNING METHODS IN NOISE SUPPRESSION SYSTEMS, IMPLEMENTED ON THE SoC

Context. Modern video conferencing systems work in different noise environments, so preservation of speech clarity and provision of quick adaptation to changes in this environment are relevant tasks. During the development of embedded systems, finding a balance between resource consumption, performance, and signal quality obtained after noise suppression is necessary. Systems on a chip allow us to use the power of both processor cores available on the hardware platform and FPGAs to perform complex calculations, which contributes to increasing the speed or reducing the load on the central SoC cores. Objective. To conduct a comparative analysis of the noise suppression quality in audio signals by an adaptive filtering algorithm and a filtering algorithm using machine learning based on the RNNoise neural network in noise suppression devices on the technological platform SoC. Method. Evaluation using objective metrics and spectrogram analysis using the Librosa library in Python. Neural network training and model design are performed on the basis of Python and Torch tools. The Vitis IDE package was used for the neural network implementation on the platform SoC. Results. The analysis of two noise suppression methods using the adaptive Wiener filter and the RNNoise neural network was performed. In the considered scenarios, it was determined that the neural network shows better noise suppression results according to the analysis of spectrograms and objective metrics. Conclusions. A comparative analysis of the effectiveness of noise suppression algorithms based on adaptive filters and a neural network was performed for scenarios with different noise environments. The results of objective SIGMOS metrics were obtained to evaluate the quality of the received audio signal. In addition, the possibility of running the RNNoise neural network on the technological platform SoC ZYNQ 7000 was verified.

VECTOR-LOGIC FAULT SIMULATION

Context. The technological trends of Design&Test computing for the IT industry and academic science are determined by the following directions: in-memory computing, immersive computing, AI computing, focused on energy saving and reduction of computing time when providing services. A mechanism for simulating faults as addresses on smart data structures is proposed, which eliminates the algorithm for simulating input test sets to obtain a test map for logic functionality. The proposed mechanism is focused on the service of SoC IP-cores under the control of the IEEE 1500 standard, which can be perceived positively by engineers in the EDA market. Objective. The purpose of the research is time- and energy-saving mechanisms for simulating malfunctions, such as addresses, by using read-write transactions of in-memory computing to build a test map of any functionality on smart data structures. Method. Smart data structures are represented by a logical vector and its derivatives in the form of truth tables and matrices. The test map is a matrix whose coordinates are determined by the combinations of all logical faults that are tested on the binary sets of the comprehensive test. The construction of the test map is focused on the architecture of in-memory computing based on read-write transactions, which makes the simulation mechanism economical in terms of simulation time and energy consumption due to the absence of a central processor. A logical vector as a single component of input data does not require synthesis into a technologically permitted structure of elements. Synthesis of smart data structures based on four matrix operations creates a fault test map like addresses for any logic. Results. Deductive matrix vectors are effectively used to model faults as addresses in digital structures of any configuration, including convergent branches and feedback loops. The resulting test map is used to find the minimum fault-checking test of the input variables. The proposed fault simulation mechanism technologically easily fits into the architecture of in-memory computing and uses only read-write transactions. The vector logic engine can also be used to test graph structures that are described by a truth table or a logical vector. The truth table addresses used for fault simulation are effectively used for processorless processing of large data in the in-memory computing architecture. Conclusions. Scientific novelty – a vector-logic in-memory computing mechanism for building a test map is proposed, characterized by the construction of intelligent data structures that reset the fault modeling algorithm. The proposed mechanism has no analogues in the design & test industry in terms of simplicity and predictability of data structure sizes and the absence of a test set modeling algorithm. The practical significance is determined by the application of the mechanism for testing logical functionalities of any complexity to solve verification tasks. Prospects of the research – increasing the object of diagnosis to the scheme, i.e. building a test map of the scheme logical structure.

Manufacturing innovation for Industry 4.0: an innovation capability perspective

PurposeThe number of small and medium-sized manufacturing companies that have successfully embraced the digital transformation envisioned by the Fourth Industrial Revolution (Industry 4.0) remains low. This paper argues that one reason is the significant innovation required in manufacturing systems to undergo such a transformation. This innovation demands capabilities vastly different from those traditionally employed for continuous improvements in manufacturing systems. The conventional development of manufacturing systems emphasizes resilience, robustness, and efficiency, typically thriving in stable and predictable conditions. However, developing a manufacturing system under highly complex and unpredictable circumstances requires new capabilities. We term this “manufacturing innovation”. At this stage, learning from successful cases is a valuable step towards unifying scattered evidence and developing coherent knowledge of how SMEs successfully do manufacturing innovation in the context of Industry 4.0.Design/methodology/approachWe conducted a multiple case study involving seven small and medium-sized Danish manufacturing companies to investigate successful manufacturing innovation in the context of Industry 4.0. Cross-case analysis identified four critical propositions regarding the capabilities contributing positively to manufacturing innovation.FindingsThe research findings highlight various capabilities for successful manufacturing innovation in the context of Industry 4.0. They suggest that such significant digital transformation of manufacturing systems begins with radical innovations in enabling processes rather than core processes. A flexible approach facilitates it, often operationalized through iterative methods. Moreover, the accumulation of knowledge from previous manufacturing innovation initiatives forms a foundational basis for strategically approaching Industry 4.0, suggesting that experience in manufacturing development generally enhances the capacity to adopt Industry 4.0 technologies effectively.Research limitations/implicationsThe results underscore the need for viewing digital transformation towards Industry 4.0 as a manufacturing innovation process, which relies on significantly different organizational capabilities than those supporting continuous manufacturing development. This insight has two implications for research in this domain; (1) Innovation process models must be developed to support radical systemic innovation, gradual learning and agile processes in manufacturing, and (2) Industry 4. 0 technologies enable new potential, but the actualization of this potential is dependent on organizational competences.Practical implicationsThe findings also offer several practical implications. Identifying patterns of best practices provides much-needed inspiration and insight into how manufacturing innovation for Industry 4.0 may be approached. While we agree with studies showing that competencies are one of the biggest challenges for companies to get started, our results also suggest that by using a flexible approach, companies can build competencies gradually and as needed, which can yield the right results over time. Furthermore, the findings suggest that a specific starting point for manufacturing companies may be enabling processes rather than core processes. This new understanding of the types of solutions companies manage to progress with may suggest that the technologies here are more mature or that there is greater motivation to get started. This implication is supported by the result that a long-term strategy is needed, but that it must be operationalized into smaller solutions to avoid biting off more than they can chew initially. While other researchers have also pointed this out, we provide a deeper understanding of why it is necessary and how it can be operationalized.Originality/valueThe article is one of the first to make a qualitative study on multiple cases to understand how manufacturing companies successfully introduced manufacturing innovation for Industry 4.0.

A Scalable, Multi-Core, Multi-Function, Integrated CMOS/Memristor Sensor Interface for Neural Sensing Applications

This paper presents the architecture, design, and testing results of a scalable, multi-core, multi-function sensor interface, integrating CMOS technology and memristor elements for efficient neuromorphic and bio-inspired analysis. The architecture leverages the high-density and non-volatile properties of memristors to support different analysis functions. Each processing core is equipped with hybrid CMOS/memristor arrays, enabling real-time parallel acquisition and analysis, and each can be configured independently. The system facilitates communication between cores and is fully scalable. The first implementation supports 16 input channels, storing 256 neural signal samples, and the second implementation supports 576 input channels, storing 9k neural signal samples.

What Is the Process? A Metamodel of the Requirements Elicitation Process Derived from a Systematic Literature Review

Requirements elicitation is a fundamental process in software engineering, essential for aligning software products with user needs and project objectives. As software projects become more complex, effective elicitation methods are vital for capturing accurate and comprehensive requirements. Despite the variety of available elicitation methods, practitioners face persistent challenges such as capturing tacit knowledge, managing diverse stakeholder needs, and addressing ambiguities in requirements. Moreover, although elicitation is recognized as a core process for gathering and analyzing system objectives, there is a lack of a unified and systematic framework to guide practitioners—especially newcomers—through the activity. To address these challenges, we provide a comprehensive analysis of existing elicitation methods, aiming to contribute to better alignment between software products and project objectives, ultimately improving software engineering practices. We do so by performing a systematic literature review identifying crosscutting steps, common techniques, tools, and approaches that define the core activities of the elicitation process. We synthesize our findings into a metamodel that structures software elicitation processes. This review uncovers various elicitation methods—such as collaborative workshops, interviews, and prototyping—each demonstrating unique strengths in different project contexts. It also highlights significant limitations, including stakeholder misalignment and incomplete requirements capture, which continue to reduce the effectiveness of elicitation processes. Finally, our study seeks to contribute to understanding requirements elicitation methods by providing a comprehensive view of their current strengths and limitations through a metamodel enabling the structuring and optimization of elicitation processes.

Liquid-liquid phase separation in hepatocellular carcinoma.

Liquid-liquid phase separation (LLPS) drives the formation of membraneless intracellular compartments within both cytoplasm and nucleus. These compartments can form distinct physicochemical environments, and in particular display different concentrations of proteins, RNA, and macromolecules compared to the surrounding cytosol. Recent studies have highlighted the significant role of aberrant LLPS in cancer development and progression, impacting many core processes such as oncogenic signalling pathways, transcriptional dysregulation, and genome instability. In hepatocellular carcinoma (HCC), aberrant formation of biomolecular condensates has been observed in a number of preclinical models, highlighting their significance as an emerging factor in understanding cancer biology and its molecular underpinnings. In this review, we summarize emerging evidence and recent advances in understanding the role of LLPS in HCC, with a particular focus on the regulation and dysregulation of cytoplasmic and nuclear condensates in cancer cells. We finally discuss how an emerging understanding of phase separation processes in HCC opens up new potential treatment avenues.

The Potential of AI Technology in Marketing

With the rapid development and increasing popularity of artificial intelligence technology, its influence has penetrated into every corner of the social economy. The marketing field has also ushered in unprecedented changes. The introduction of AI technology has not only reshaped the strategies and execution methods of traditional marketing, but also opened up new paths for the future development of marketing. This paper conducts a case study on Amazon to explore the wide application direction of AI technology in the field of marketing, especially focusing on how AI learning systems play a key role in various core processes of marketing, and the emerging development trends emerging in this process. Through research, it is found that AI technology still has great development potential in marketing in many aspects such as market and customers. At the same time, the challenges that come with it cannot be ignored. It requires joint efforts from both inside and outside the industry to ensure that AI technology can develop healthily and sustainably in the field of marketing. From a practical perspective, this study helps companies better understand and apply AI technology to improve the efficiency and effectiveness of marketing, so as to formulate more scientific and effective marketing strategies. From a theoretical perspective, this study helps enrich marketing theory and promote the development of marketing disciplines. The introduction of AI technology provides new tools and methods for marketing, but also brings new challenges and problems. In-depth research on these issues will help improve the marketing theory system and provide theoretical support for future marketing practices.

Real-Time Information Acquisition and Processing Method for Penetration Information Based on Multi-Information Fusion

To improve the real-time performance and the target adaptability of penetration fuze detonation control systems, and to enhance the system fusion processing capability for multi-sensor information, this paper uses a modular design concept to construct a miniaturized (ø38[Formula: see text]mm[Formula: see text]×[Formula: see text]4[Formula: see text]mm) fuze detonation control system that is capable of real-time processing of data from multiple information sources. The core component of this system is the GD32E230 microcontroller, which features a high dominant frequency and low power consumption. This device is integrated with a ferroelectric memory and signal processing circuits that match the sensors. To address the issue of unclear traditional acceleration signal penetration and the difficulties associated with the identification of these signals, the approach in this paper improves feature recognition accuracy through rapid acquisition and fusion of multiple types of sensor output signal, and self-adaptive identification of multilayered targets and single-layer thick targets is achieved. During the programming of the embedded system, the hardware register is operated directly, the instruction execution sequence is optimized, and the program execution efficiency is improved by using the function characteristic that some microcontroller unit peripherals do not occupy the central processing unit when working, thus allowing the intended purpose of improving the system’s real-time performance to be achieved. A semi-physical simulation method is then used to verify the performance of the penetration fuze detonation control system. The results obtained show that the system has 100%-layer counting accuracy for multilayered targets and a relative error of less than 1% for the calculated residual velocities of single-layer thick targets, thus validating the effectiveness of the system.

Sybil Attack-Resistant Blockchain-Based Proof-of-Location Mechanism with Privacy Protection in VANET.

In this paper, we propose a Proof-of-Location (PoL)-based location verification scheme for mitigating Sybil attacks in vehicular ad hoc networks (VANETs). For this purpose, we employ smart contracts for storing the location information of the vehicles. This smart contract is maintained by Road Side Units (RSUs) and acts as a ground truth for verifying the position information of the neighboring vehicles. To avoid the storage of fake location information inside the smart contract, vehicles need to solve unique computational puzzles generated by the neighboring RSUs in a limited time frame whenever they need to report their location information. Assuming a vehicle has a single Central Processing Unit (CPU) and parallel processing is not allowed, it can solve a single computational puzzle in a given time period. With this approach, the vehicles with multiple fake identities are prevented from solving multiple puzzles at a time. In this way, we can mitigate a Sybil attack and avoid the storage of fake location information in a smart contract table. Furthermore, the RSUs maintain a dedicated blockchain for storing the location information of neighboring vehicles. They take part in mining for the purpose of storing the smart contract table in the blockchain. This scheme guarantees the privacy of the vehicles, which is achieved with the help of a PoL privacy preservation mechanism. The verifier can verify the locations of the vehicles without revealing their privacy. Experimental results show that the proposed mechanism is effective in mitigating Sybil attacks in VANET. According to the experiment results, our proposed scheme provides a lower fake location registration probability, i.e., lower than 10%, compared to other existing approaches.