Shared Scheme Research Articles

Intelligent Control System for Brain-Controlled Mobile Robot Using Self-Learning Neuro-Fuzzy Approach.

Brain-computer interface (BCI) provides direct communication and control between the human brain and physical devices. It is achieved by converting EEG signals into control commands. Such interfaces have significantly improved the lives of disabled individuals suffering from neurological disorders-such as stroke, amyotrophic lateral sclerosis (ALS), and spinal cord injury-by extending their movement range and thereby promoting self-independence. Brain-controlled mobile robots, however, often face challenges in safety and control performance due to the inherent limitations of BCIs. This paper proposes a shared control scheme for brain-controlled mobile robots by utilizing fuzzy logic to enhance safety, control performance, and robustness. The proposed scheme is developed by combining a self-learning neuro-fuzzy (SLNF) controller with an obstacle avoidance controller (OAC). The SLNF controller robustly tracks the user's intentions, and the OAC ensures the safety of the mobile robot following the BCI commands. Furthermore, SLNF is a model-free controller that can learn as well as update its parameters online, diminishing the effect of disturbances. The experimental results prove the efficacy and robustness of the proposed SLNF controller including a higher task completion rate of 94.29% (compared to 79.29%, and 92.86% for Direct BCI and Fuzzy-PID, respectively), a shorter average task completion time of 85.31 s (compared to 92.01 s and 86.16 s for Direct BCI and Fuzzy-PID, respectively), and reduced settling time and overshoot.

Technical note: A GPU-based shared Monte Carlo method for fast photon transport in multi-energy x-ray exposures.

The Monte Carlo (MC) method is an accurate technique for particle transport calculation due to the precise modeling of physical interactions. Nevertheless, the MC method still suffers from the problem of expensive computational cost, even with graphics processing unit (GPU) acceleration. Our previous works have investigated the acceleration strategies of photon transport simulation for single-energy CT. But for multi-energy CT, conventional individual simulation leads to unnecessary redundant calculation, consuming more time. This work proposes a novel GPU-based shared MC scheme (gSMC) to reduce unnecessary repeated simulations of similar photons between different spectra, thereby enhancing the efficiency of scatter estimation in multi-energy x-ray exposures. The shared MC method selects shared photons between different spectra using two strategies. Specifically, we introduce spectral region classification strategy to select photons with the same initial energy from different spectra, thus generating energy-shared photon groups. Subsequently, the multi-directional sampling strategy is utilized to select energy-and-direction-shared photons, which have the same initial direction, from energy-shared photon groups. Energy-and-direction-shared photons perform shared simulations, while others are simulated individually. Finally, all results are integrated to obtain scatter distribution estimations for different spectral cases. The efficiency and accuracy of the proposed gSMC are evaluated on the digital phantom and clinical case. The experimental results demonstrate that gSMC can speed up the simulation in the digital case by ∼37.8% and the one in the clinical case by ∼20.6%, while keeping the differences in total scatter results within 0.09%, compared to the conventional MC package, which performs an individual simulation. The proposed GPU-based shared MC simulation method can achieve fast photon transport calculation for multi-energy x-ray exposures.

Q-A2NN: Quantized All-Adder Neural Networks for Onboard Remote Sensing Scene Classification

Performing remote sensing scene classification (RSSC) directly on satellites can alleviate data downlink burdens and reduce latency. Compared to convolutional neural networks (CNNs), the all-adder neural network (A2NN) is a novel basic neural network that is more suitable for onboard RSSC, enabling lower computational overhead by eliminating multiplication operations in convolutional layers. However, the extensive floating-point data and operations in A2NNs still lead to significant storage overhead and power consumption during hardware deployment. In this article, a shared scaling factor-based de-biasing quantization (SSDQ) method tailored for the quantization of A2NNs is proposed to address this issue, including a powers-of-two (POT)-based shared scaling factor quantization scheme and a multi-dimensional de-biasing (MDD) quantization strategy. Specifically, the POT-based shared scaling factor quantization scheme converts the adder filters in A2NNs to quantized adder filters with hardware-friendly integer input activations, weights, and operations. Thus, quantized A2NNs (Q-A2NNs) composed of quantized adder filters have lower computational and memory overheads than A2NNs, increasing their utility in hardware deployment. Although low-bit-width Q-A2NNs exhibit significantly reduced RSSC accuracy compared to A2NNs, this issue can be alleviated by employing the proposed MDD quantization strategy, which combines a weight-debiasing (WD) strategy, which reduces performance degradation due to deviations in the quantized weights, with a feature-debiasing (FD) strategy, which enhances the classification performance of Q-A2NNs through minimizing deviations among the output features of each layer. Extensive experiments and analyses demonstrate that the proposed SSDQ method can efficiently quantize A2NNs to obtain Q-A2NNs with low computational and memory overheads while maintaining comparable performance to A2NNs, thus having high potential for onboard RSSC.

Sharing the Shared Rides: Multi-Party Carpooling Supported Strategy-Proof Double Auctions

Multi-party carpooling emerges as a burgeoning shared transportation scheme whereby the trip shared by each driver is shared among multi-party riders whose itineraries coincide. Confronting the information asymmetry and the voluntary self-interested nature of bilateral participants in matching and pricing operations, this study designs Multi-party cArpooling SupporTed stratEgy-pRoof (MASTER) double auction mechanisms considering personalized carpooling constraints. First, in a scheduled carpooling scenario, two [Formula: see text] mechanisms that masterfully blend the ideas of the famed trade reduction method and multi-stage approach are proposed which implement distinct group bid determination approaches for responding to different market conditions. Second, in an on-demand carpooling scenario, two parameterized [Formula: see text] mechanisms that integrate frustration-based promotion to proactively prioritize matching and deferentially compensate riders based on their waits are contrived which also endow the platform with operational flexibility to agilely pursue alterable operational objectives by adjusting promotion strength. We prove theoretically that the proposed mechanisms satisfy strategy proofness, budget balance, individual rationality, and asymptotic efficiency under mild conditions. Experimental results reveal that multi-party carpooling constitutes a multi-win solution under higher rider-driver ratios whilst it could be detrimental to drivers otherwise, which can be ameliorated by favoring the driver side in determining promotion strength. Simulation studies manifest that our proposed auction mechanisms could bring benefits concerning allocation efficiency and service responsiveness compared with their academic and practical counterparts. We also shed light on choosing among alternative mechanisms according to market conditions and operational orientations.

Analyzing shared mobility markets in Europe: A comparative analysis of shared mobility schemes across 311 European cities

The progression of shared mobility across Europe is remarkable. While station-based car and bike sharing have a more extended history, particularly in major European (capital) cities, recent advancements in modal types and operational models have significantly transformed the shared mobility landscape. Rapid expansion by private organisations has broadened access to shared mobility services across Europe. However, not all European cities are considered potentially viable markets due to local factors such as stringent regulatory frameworks and unfavourable economic conditions. The composition of the local offerings influences how citizens use these services, impacting travel behaviour and the local transport networks differently. Therefore, understanding the availability of various shared mobility schemes across Europe is essential for comprehending the market structure, its development, the providers' decision drivers, and the potential consequences for local transportation systems.First, this paper presents data on various segments and features of the shared mobility market across European cities with more than 100.000 inhabitants. Second, two cluster approaches, i.e. k-means and latent class clustering analysis (LCCA), are conducted to structure this European market. Third, the contextual characteristics, such as socio-demographics, the built environment and the geography, are compared among the clusters using Dunn testing.The results depict that the market is very fragmented, ranging from cities with a minimal offer (i.e. one type of modality available) to cities with a very competitive market consisting of numerous modalities and operators.As expected, the most comprehensive offer of shared mobility is found in cities with the highest economic potential, measured by GDP per capita and population size. However, these cities tend to impose stricter regulations and invest in public schemes, especially for bike and car sharing, affecting the share of private operators. This may explain why private scooter sharing companies are willing to operate in smaller cities that initially seem to lack the economic conditions to accommodate a profitable sharing scheme. In cities where scooters are absent, mainly in Dutch cities, free-floating moped- and bike-sharing schemes have acted as a surrogate. Still, the comprehensiveness of the offer in these cities is considerable, suggesting that even with strict regulatory frameworks, other factors like infrastructure can create an attractive environment for operators. Overall, shared mobility is well-developed in European cities, meaning that many people are already aware of or have access to some form of shared mobility. This provides opportunities for other less-developed modalities, such as cargo bicycles, to further expand and offer specific use cases for car replacement. Therefore, future research could follow up on market developments to understand how various segments evolve and to examine the role of different policy frameworks more thoroughly.

SDN enabled role based shared secret scheme for virtual machine security in cloud environment

Cloud computing has given a new direction towards the usage of resources based on the demand without depending on the location. Even though there are many advantages with cloud computing there are challenges and security being one among them. Especially at Infrastructure as a Service (IaaS) level, where the actual resources are shared, security has given more importance. Robust access control mechanisms are to be applied to safeguard the cloud environment. In this paper, Software Defined Network (SDN) enabled role-based access control along with trust-based model is proposed. This model considers the roles of the users and provides the finely grained access to the virtual machines in the cloud. Secret shares are shared fairly among the users based on two parameters namely trust and roles assigned to the participants. Secret has to be reconstructed to access the Virtual Machine (VM) and to reconstruct, the secret shares are taken from different trusted users at different levels. Cloud service provider will be unaware of the secret shares which are distributed among the participant users. SDN Controller is responsible for taking care of share generation, distribution of shares among the user participants and reconstruction of secret. In order to avoid malicious user participants, trust evaluator periodically checks the trust value. The security analysis prove that scheme is more secure and efficient in comparison with other approaches.

B+-Tree-Quantization-Based Shared Secret Key Extraction Scheme from Atmospheric Optical Wireless Channel

B+-Tree-Quantization-Based Shared Secret Key Extraction Scheme from Atmospheric Optical Wireless Channel

Shared Coupling-Bridge Scheme for Weakly Supervised Local Feature Learning

Shared Coupling-Bridge Scheme for Weakly Supervised Local Feature Learning

Decentralized coded caching for shared caches using erasure coding

In a client–server architecture data traffic varies through time. It exhibits congestion of network resources at peak hours while the network remains rather under-utilized during the off-peak hours. One of the potential techniques to smooth out this data traffic over time is to load part of the data into cache memories distributed across the network. In the classical cache framework, each user is associated to a cache and the caching process is centrally coordinated. However, for large networks, decentralized caching is more practical since decentralized caching does not require the knowledge of all the active users in the network during the prefetching phase which is a necessity for centralized caching. Furthermore, with an aim to reduce cache under-utilization in a network, caches are allowed to serve multiple clients. Such a scheme in which multiple users are allowed to share a cache is referred to as the shared caching scheme. Shared caching scheme has been explored in both centralized and decentralized setting with centralized scheme giving a superior performance. To improve the performance of the decentralized caching scheme we implement precoding at the server’s end. Precoding is done by encoding the data stored in the server using a Maximum Distance Separable (MDS) erasure code. The coded caching framework in this paper is denoted as MDS precoded decentralized shared caching. We propose a delivery scheme and derive a delivery rate for the proposed scheme. Using techniques from index coding, a lower bound is obtained for all MDS precoded decentralized shared caching linear delivery schemes delivering certain contents. The proposed lower bound is shown to meet the proposed delivery rate. The achievable delivery rate proposed in this paper is also shown to match with the rate obtained for the optimal centralized shared caching scheme for small and large cache memory sizes.

A cost-effective and high-efficient EV shared fast charging scheme with hierarchical coordinated operation strategy for addressing difficult-to-charge issue in old residential communities

Charging infrastructures are being deployed widely in urban and rural areas to solve the so-called “last one-kilometer” charging problem. However, for old residential communities, the existing difficult-to-charge issue is not easily solved solely by the widespread deployment of charging infrastructure owing to limited spaces, complex reconstruction, and huge costs. In this context, this paper introduces a cost-effective and high-efficient shared fast charging scheme for old residential communities. The comprised registration-based queuing charging mechanism is proposed to schedule electric vehicle (EV) charging. An integrated and multi-stage power electronic device called smart interlinking unit (SIU) is introduced for grid reconstruction. The SIU-based charging station architecture features flexible control capability, enabling DC power supply for terminal EV chargers. For this architecture, a novel hierarchical coordinated operation strategy is designed, comprising the upper-level real-time vehicle-to-grid (V2G) dispatching and lower-level coordination strategies. Case studies demonstrate that the proposed scheme could accommodate 40 % EV penetration, while exhibiting a significant decline in system investment and occupied area compared to the slow charging scheme. Additionally, a 1.3 % improvement in average transfer efficiency is verified under this architecture. The proposed strategy is proven to realize a prominent power peak-shaving and valley-filling, effective transformer load balance, and DC voltage restoration.

Motivational Patterns and Personal Characteristics of Potential Carsharing Users: A Qualitative Analysis

In the last decade, in Europe and the US, carsharing has become a mainstream transportation mode offering a sustainable solution to serious urban problems such as pollution, economic crisis, congestion, and parking. In Greece, carsharing is currently entering its commercial phase. Planners and providers strive to gain an insight into the factors influencing the use of carsharing to effectively implement carsharing systems (CSS). In this context, understanding the motives and usage conditions are considered necessary. Based on a qualitative analysis (semi-constructed interviews, n = 52), this paper identifies motivational patterns as well as personal characteristics of potential users that can be further explored through quantitative research methods. During the data analysis process, participants’ responses were classified into categories that revealed not only the factors that motivated them but also unveiled the challenges they face when utilizing carsharing schemes. These factors were the following: familiarity, comfort, mindset, everyday life, usability, and economy. Next, these factors were analyzed further based on the personal characteristics of the respondents preparing the ground for quantitative research in future research initiatives. Notably, the present findings could be beneficial to operators, policymakers, and stakeholders endeavoring to appraise shared mobility schemes in Greece and Mediterranean countries in general.

Exploring the carbon inequality embodied in China’s interregional trade based on a human well-being equity perspective

China’s interregional trade leads to interregional carbon inequality and impacts the flow and distribution of interregional human well-being. Few studies have been conducted that construct human well-being from a multidimensional perspective and explore the inequality of carbon emissions in interregional trade based on a human well-being equity perspective. This study applies a multiregional input‒output (MRIO) model to analyze the flow of carbon emissions and human well-being and capital between regions, explores the carbon inequality embodied in interregional trade based on the regional environmental inequality (REI) index, and finally proposes a shared responsibility scheme for carbon emissions and policy implications based on a human well-being equity perspective. The results demonstrate that the transfer of carbon emissions and human well-being through regional trade is realized through consumption. Different transfer paths of carbon emissions and human well-being lead to significant carbon inequality in interregional trade. Regions such as the West and North, which suffer more significant inequality in trade, receive carbon emissions mainly from carbon-intensive industries; regions such as the East coast and South coast, which benefit more from trade, transfer large amounts of carbon emissions by importing products from industrial sectors with a high carbon emission intensity and relatively low value and by acquiring more human well-being and capital. This study provides evidence regarding the current status of carbon inequality based on human well-being equity in China; fills the gap in existing research; and provides a reference for addressing the inequality of carbon emissions and human well-being and each type of capital between regions and formulating a fair and effective carbon reduction policy. This study provides an overall solution for sustainable development in China.

Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Design

To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further computation. By utilizing the same signals for both radar sensing and data transmission, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">integrated sensing and communication</i> (ISAC) technique enables simultaneous data collection and delivery in the physical layer. By exploiting the analog-wave addition property in a multi-access channel, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">over-the-air computation</i> (AirComp) has been proposed as a communication approach that also enables function computation. The promising performances of ISAC and AirComp motivate the current work on developing a framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">integrated sensing, communication, and computation over-the-air</i> (ISCCO). Two schemes are designed to support <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple-input-multiple-output</i> (MIMO) ISCCO simultaneously, namely the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">separated and shared</i> schemes. The separated scheme splits antenna array for radar sensing and AirComp, while all the antennas transmit a joint waveform for both radar sensing and AirComp in the shared scheme. The performance of radar sensing is evaluated by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mean squared error</i> (MSE) of the estimated target response matrix, while the MSE of the estimated function is adopted as the metric to evaluate the performance of the coupled communication and computation in AirComp. The design challenge of MIMO ISCCO lies in the joint optimization of beamformers at both the IoT devices and the server, which results in a non-convex problem. To solve this problem, an algorithmic solution based on the technique of semidefinite relaxation is proposed. The results reveal that the beamformer at each sensor needs to account for supporting dual-functional signals in the shared scheme, while dedicated beamformers for sensing and AirComp are needed to mitigate the mutual interference between the two functionalities in the separated scheme. The application of ISCCO on target location estimation is further demonstrated via simulation.

A multi-subgroup decision-making method for design selection based on subjective reports and objective physiological index data

Selecting an optimal product design scheme requires considering multiple criteria and engaging the wisdom of crowds. However, existing studies regarding design selection rarely considered the participation of group wisdom, and few studies considered subjective reports and physiological index data comprehensively for evaluation. This study developed a multi-subgroup decision-making method based on the participation of designers, experts, and customers. Multi-modal aggregation strategies are applied to aggregate subjective evaluations of both individuals and groups represented in probabilistic linguistic term sets (PLTSs), objective eye movement, and Electroencephalogram feature data. Specifically, a probabilistic linguistic information aggregation operator based on the Choquet integral is proposed to aggregate the values of subjective criteria with correlations, and the double normalization-based multi-aggregation method is then applied to aggregate objective criteria values, which can avoid information loss in the normalization and aggregation. Finally, the Choquet integral-based Borda operator is developed to integrate the evaluation results of multiple subgroups. An example of evaluating intelligent shared vehicle design schemes is presented to validate the proposed method.

Multi-Antenna Coded Caching for Shared Caches With Arbitrary User-to-Cache Association

We consider the multi-antenna shared cache setup where the server is equipped with multiple transmit antennas and is connected to a set of users that are assisted with a smaller number of helper nodes. The helper nodes serve as caches that are shared among the users. Each user gets connected to exactly one helper cache. For this setting, under uncoded cache placement, an optimal multi-antenna coded caching scheme supporting all types of user-to-cache association is known in the literature which is applicable only when the number of antennas is at most the number of users connected to the least occupied cache. The other known multi-antenna shared caching schemes considered only uniform association of users to helper caches. In this work, we propose a multi-antenna coded caching scheme for the shared cache setting with more diverse user-to-cache associations where the number of antennas is not less than the number of users connected to the least occupied cache. Under certain scenarios, our scheme achieves the same performance exhibited by some optimal schemes.

Combining CNN and transformers for full-reference and no-reference image quality assessment

Most deep learning approaches for image quality assessment use regression from deep features extracted by CNN (Convolutional Neural Networks). However, non-local information is usually neglected in existing methods. Motivated by the recent success of transformers in modeling contextual information, we propose a hybrid framework that utilizes a vision transformer backbone to extract features and a CNN decoder for quality estimation. We propose a shared feature extraction scheme for both FR and NR settings. A two-branch structured attentive quality predictor is devised for quality prediction. Evaluation experiments on various IQA datasets, including LIVE, CSIQ and TID2013, LIVE-Challenge, KADID-10 K, and KONIQ-10 K, show that our proposed models achieve outstanding performance for both FR and NR settings.

Stochastic-Skill-Level-Based Shared Control for Human Training in Urban Air Mobility Scenario

This paper proposes a novel stochastic-skill-level-based shared control framework to assist human novices to emulate human experts in complex dynamic control tasks. The proposed framework aims to infer stochastic-skill-levels (SSLs) of the human novices and provide personalized assistance based on the inferred SSLs. SSL can be assessed as a stochastic variable which denotes the probability that the novice will behave similarly to experts. We propose a data-driven method which can characterize novice demonstrations as a novice model and expert demonstrations as an expert model, respectively. Then, our SSL inference approach utilizes the novice and expert models to assess the SSL of the novices in complex dynamic control tasks. The shared control scheme is designed to dynamically adjust the level of assistance based on the inferred SSL to prevent frustration or tedium during human training due to poorly imposed assistance. The proposed framework is demonstrated by a human subject experiment in a human training scenario for a remotely piloted urban air mobility (UAM) vehicle. The results show that the proposed framework can assess the SSL and tailor the assistance for an individual in real-time. The proposed framework is compared to practice-only training (no assistance) and a baseline shared control approach to test the human learning rates in the designed training scenario with human subjects. A subjective survey is also examined to monitor the user experience of the proposed framework.

Distributed Hierarchical Shared Control for Flexible Multirobot Maneuver Through Dense Undetectable Obstacles.

When teleoperating a multirobot system (MRS) in outdoor environments, human operators can often detect obstacles that are not detected by robots and spot emergencies faster than robots do. However, the lack of efficient methods for operators to manipulate an MRS has limited the number of robots in a human-robot team. To handle this problem, a distributed hierarchical shared control scheme is proposed, aiming to provide a safe and flexible control interface for a few human operators to interact with a large MRS. The proposed hierarchical control scheme employs a two-layered structure. In the upper layer, intention field networks are designed to generate virtual human control signals. Two functionalities for human teleoperation, called: 1) group management and 2) motion intervention, are realized using intention fields, allowing the operators to split the robot formation into different groups and steer individual robots away from immediate danger. In parallel, a blending-based shared control algorithm is designed in the lower layer to resolve the conflict between human intervention inputs and autonomous formation control signals. The input-to-output stability (IOS) of the proposed distributed hierarchical shared control scheme is proved by exploiting the properties of weighting functions. Results from a usability testing experiment and a physical experiment are also presented to validate the effectiveness and practicability of the proposed method.

A Novel Method for Authentication Using Chaotic Behaviour of Chua’s Oscillator in (n,k) Secret Shared Data Scheme for Secure Communication

In the current scenario, the utilization, and advancement of security algorithms in communication models have been increased. The use of chaotic circuit-based secure algorithms can give a new direction to develop a highly secure communication model. Security in any communication model can be provided by various available methods. The main feature of any security method is to introduce randomness at every step of the algorithm. Generally, the security algorithms deal with random numbers that are generated using a random number generator with predefined logic in the conventional secure communication model. In this article, the chaotic behavioral circuit is used to handle the randomness. The three main classifications of security control are management security, operational security, and physical security controls. The chaotic behavior of high-dimensional Chua’s circuit has been used to implement these three security controls. The challenging task is to design and deploy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(n,k$</tex-math> </inline-formula> ) secret sharing scheme using Chua’s oscillator. The novel authenticator code function based on Chua’s oscillator is proposed in this paper. This authenticator code function is having almost the same characteristics as of hash code, but with a higher degree of security.

A deep ensemble learning method for single finger-vein identification.

Finger-vein biometrics has been extensively investigated for personal verification. Single sample per person (SSPP) finger-vein recognition is one of the open issues in finger-vein recognition. Despite recent advances in deep neural networks for finger-vein recognition, current approaches depend on a large number of training data. However, they lack the robustness of extracting robust and discriminative finger-vein features from a single training image sample. A deep ensemble learning method is proposed to solve the SSPP finger-vein recognition in this article. In the proposed method, multiple feature maps were generated from an input finger-vein image, based on various independent deep learning-based classifiers. A shared learning scheme is investigated among classifiers to improve their feature representation captivity. The learning speed of weak classifiers is also adjusted to achieve the simultaneously best performance. A deep learning model is proposed by an ensemble of all these adjusted classifiers. The proposed method is tested with two public finger vein databases. The result shows that the proposed approach has a distinct advantage over all the other tested popular solutions for the SSPP problem.