Autonomous Vehicle Control Using Lidar and Camera with CAN Network

An autonomous vehicle operates in a dynamically changing environment,where multiple sensors must work in a cooperative mode. In these scenarios reliability of the communication protocol carries a lot of importance in real time data transfer. In this paper, CAN communication is used to demonstrate sensor integration using a LIDAR and a camera. Also demonstrated is a novel method for object detection, obstacle avoidance and navigation of an autonomous RC vehicle.

ACR-MLM:a privacy-preserving framework for anonymous and confidential rewarding in blockchain-based multi-level marketing

Network marketing is a trading technique that provides companies with the opportunity to increase sales.With the increasing number of Internet-based purchases,several threats are increasingly observed in this field,such as user privacy violations,company owner(CO)fraud,the changing of sold products’information,and the scalability of selling networks.This study presents the concept of a blockchain-based market called ACR-MLM that functions based on the multi-level marketing(MLM)model,through which registered users receive anonymous and confidential rewards for their own and their subgroups’sales.Applying a public blockchain as the ACR-MLM framework’s infrastructure solves existing problems in MLM-based markets,such as CO fraud(against the government or its users),user privacy violations(obtaining their real names or subgroup users),and scalability(when vast numbers of users have been registered).To provide confidentiality and scalability to the ACR-MLM framework,hierarchical identity-based encryption(HIBE)was applied with a functional encryption(FE)scheme.Finally,the security of ACR-MLM is analyzed using the random oracle(RO)model and then evaluated.

Blockchain and its derived technologies shape the future generation of digital businesses: a focus on decentralized finance and the Metaverse

Without a doubt,blockchain,as one of the most valuable technological advancements,has been introduced over the past decade and has played a significant role in the industrial revolution.The emergence of other technologies derived from blockchain,such as decentralized finance(DeFi)and the Metaverse,has fundamentally transformed people’s daily lives and profoundly impacted future versions of digital businesses.This study explored the evolution of digital businesses in the near future,with a specific focus on the two primary technologies mentioned above.First,we reviewed DeFi-based technologies,including GameFi,SciFi,SocialFi,and others which serve as foundational building blocks for future jobs and businesses.Second,we examined Metaverse-based jobs,such as Metaverse-based academies and markets which are expected to be launched as commonly used businesses.Ultimately,this study provides several guidelines,such as how to use DeFi 2.0 and apply centralized decentralized finance(CeDeFi)-based platforms.Additionally,it offers future directions for launching these businesses,including controlling the progress of artificial intelligence(AI)in practical applications,utilizing cloud-assisted models for the Metaverse,and providing conditional privacy for future Metaverse-based businesses.

Tracking performance of large margin classifier in automatic modulation classification with a software radio environment

Automatic modulation classification is the process of identification of the modulation type of a signal in a general environment. This paper proposes a new method to evaluate the tracking performance of large margin classifier against signal-tonoise ratio （SNR）, and classifies all forms of primary user＇s signals in a cognitive radio environment. For achieving this objective, two structures of a large margin are developed in additive white Gaussian noise （AWGN） channels with priori unknown SNR. A combination of higher order statistics and instantaneous characteristics is selected as effective features. Simulation results show that the classification rates of the proposed structures are well robust against environmental SNR changes.

Augmented input estimation in multiple maneuvering target tracking

This paper presents augmented input estimation(AIE)for multiple maneuvering target tracking.Multi-target tracking(MTT)is based on two main parts,data association and estimation.In data association(DA),the best observations are assigned to the considered tracks.In real conditions,the number of observations is more than targets and also locations of observations are often so scattered that the association between targets and observations cannot be done simply.In this case,for general MTT problems with unknown numbers of targets,we present a Markov chain Monte-Carlo DA(MCMCDA)algorithm that approximates the optimal Bayesian filter with low complexity in computations.After DA,estimation and tracking should be done.Since in general cases,many targets can have maneuvering motions,then AIE is proposed to cover both the non-maneuvering and maneuvering parts of motion and the maneuver detection procedure is eliminated.This model with an input estimation(IE)approach is a special augmentation in the state space model which considers both the state vector and the unknown input vector as a new augmented state vector.Some comparisons based on the Monte-Carlo simulations are also made to evaluate the performances of the proposed method and other older methods in MTT.

Blockchain-based reporting protocols as a collective monitoring mechanism in DAOs

As a recent decentralized business model,Distributed Autonomous Organization(DAO)challenges the management paradigms of conventional organizations and represents the future of democracy-based systems.However,DAOs are facing challenges that can be divided into two categories,including policy-based and technical challenges.Detecting misbehaving nodes is a crucial challenge that is considered in both categories.Designing mechanisms to report malicious behaviors while maintaining anonymity is essential for the survival and efficiency of DAOs.In this paper,we clarify the problem,and introduce Blockchain-Based Anonymous Reporting(BBAR)as a collective monitoring mechanism in DAOs with its requirements and implementation methods.

A new image watermarking framework based on levels-directions decomposition in contourlet representation

With the development of digital information technologies,robust watermarking framework is taken into real consideration as a challenging issue in the area of image processing,due to the large applicabilities and its utilities in a number of academic and real environments.There are a wide range of solutions to provide image watermarking frameworks,while each one of them is attempted to address an efficient and applicable idea.In reality,the traditional techniques do not have sufficient merit to realize an accurate application.Due to the fact that the main idea behind the approach is organized based on contourlet representation,the only state-of-the-art materials that are investigated along with an integration of the aforementioned contourlet representation in line with watermarking framework are concentrated to be able to propose the novel and skilled technique.In a word,the main process of the proposed robust watermarking framework is organized to deal with both new embedding and de-embedding processes in the area of contourlet transform to generate watermarked image and the corresponding extracted logo image with high accuracy.In fact,the motivation of the approach is that the suggested complexity can be of novelty,which consists of the contourlet representation,the embedding and the corresponding de-embedding modules and the performance monitoring including an analysis of the watermarked image as well as the extracted logo image.There is also a scrambling module that is working in association with levels-directions decomposition in contourlet embedding mechanism,while a decision maker system is designed to deal with the appropriate number of sub-bands to be embedded in the presence of a series of simulated attacks.The required performance is tangibly considered through an integration of the peak signal-to-noise ratio and the structural similarity indices that are related to watermarked image.And the bit error rate and the normal correlation are considered that are related to the extracted logo analysis,as well.Subsequently,the outcomes are fully analyzed to be competitive with respect to the potential techniques in the image colour models including hue or tint in terms of their shade,saturation or amount of gray and their brightness via value or luminance and also hue,saturation and intensity representations,as long as the performance of the whole of channels are concentrated to be presented.The performance monitoring outcomes indicate that the proposed framework is of significance to be verified.

A creative concept for designing and simulating quaternary logic gates in quantum-dot cellular automata

New technologies such as quantum-dot cellular automata(QCA) have been showing some remarkable characteristics that standard complementary-metal-oxide semiconductor(CMOS) in deep sub-micron cannot afford. Modeling systems and designing multiple-valued logic gates with QCA have advantages that facilitate the design of complicated logic circuits. In this paper, we propose a novel creative concept for quaternary QCA(QQCA). The concept has been set in QCASim, the new simulator developed by our team exclusively for QCAs’ quaternary mode. Proposed basic quaternary logic gates such as MIN, MAX, and different types of inverters(SQI, PQI, NQI, and IQI) have been designed and verified by QCASim. This study will exemplify how fast and accurately QCASim works by its handy set of CAD tools. A 1×4 decoder is presented using our proposed main gates.Preference points such as the minimum delay, area, and complexity have been achieved in this work. QQCA main logic gates are compared with quaternary gates based on carbon nanotube field-effect transistor(CNFET). The results show that the proposed design is more efficient in terms of latency and energy consumption.

Introducing scalable 1-bit full adders for designing quantum-dot cellular automata arithmetic circuits

Designing logic circuits using complementary metal-oxide-semiconductor(CMOS)technology at the nano scale has been faced with various challenges recently.Undesirable leakage currents,the short-effect channel,and high energy dissipation are some of the concerns.Quantum-dot cellular automata(QCA)represent an appropriate alternative for possible CMOS replacement in the future because it consumes an insignificant amount of energy compared to the standard CMOS.The key point of designing arithmetic circuits is based on the structure of a 1-bit full adder.A low-complexity full adder block is beneficial for developing various intricate structures.This paper represents scalable 1-bit QCA full adder structures based on cell interaction.Our proposed full adders encompass preference aspects of QCA design,such as a low number of cells used,low latency,and small area occupation.Also,the proposed structures have been expanded to larger circuits,including a 4-bit ripple carry adder(RCA),a 4-bit ripple borrow subtractor(RBS),an add/sub circuit,and a 2-bit array multiplier.All designs were simulated and verified using QCA Designer-E version 2.2.This tool can estimate the energy dissipation as well as evaluate the performance of the circuits.Simulation results showed that the proposed designs are efficient in complexity,area,latency,cost,and energy dissipation.

Level-direction decomposition analysis with a focus on image watermarking framework

This research addresses the new level-direction decomposition in the area of image watermarking as the further development of investigations. The main process of realizing a watermarking framework is to generate a watermarked image with a focus on contourlet embedding representation. The approach performance is evaluated through several indices including the peak signal-to-noise ratio and structural similarity, whereby a set of attacks are carried out using a module of simulated attacks. The obtained information is analyzed through a set of images, using different color models, to enable the calculation of normal correlation. The module of the inverse of contourlet embedding representation is correspondingly employed to obtain the present watermarked image, as long as a number of original images are applied to a scrambling module, to represent the information in disorder. This allows us to evaluate the performance of the proposed approach by analyzing a complicated system, where a decision making system is designed to find the best level and the corresponding direction regarding contourlet embedding representation. The results are illustrated in appropriate level-direction decomposition. The key contribution lies in using a new integration of a set of subsystems, employed based upon the novel mechanism in contourlet embedding representation, in association with the decision making system. The presented approach is efficient compared with state-of-the-art approaches, under a number of serious attacks. A number of benchmarks are obtained and considered along with the proposed framework outcomes. The results support our ideas.