Subset selection under budget constraints is critical in applications like multi-robot patrolling, crime deterrence, and targeted marketing, where multiple agents must jointly select targets and plan feasible routes. We formalize this challenge as Multi-Subset Selection with Budget-Constrained Routing (MSS-BCR), involving complex, non-additive cost structures that defy traditional methods. We propose GRIP, a graph-based framework integrating spatial reward fields and policy learning to enable coordinated, budget-aware target selection and routing. GRIP uses attention-based embeddings and constraint-triggered pruning with utility recovery to produce high-quality, feasible solutions. Experiments based on multiple synthetic and real-world datasets show GRIP outperforms baselines in reward efficiency and scalability across varied scenarios.

Vision-and-Language Navigation (VLN) plays a critical role in tasks of embodied AI, particularly in unseen environments following natural language instructions. Recent advancements leverage large language models (LLMs) to improve the accuracy and generalizability of VLN systems by encoding image sequences as dense token representations. However, this tokenization approach incurs substantial computational overhead due to two key inefficiencies: 1) ego-centric camera views often include navigation-irrelevant re- gions (e.g., sky or distant backgrounds), and 2) high-frame-rate image sequences introduce temporal redundancy. To address these challenges, we propose Spatial-Temporal Efficient Visual Token Pruning (STEP-Nav), a unified frame- work that simultaneously prunes redundant visual tokens and fine-tunes VLN models to preserve navigation performance. In particular, STEP-Nav incorporates a distance- and content-aware token evaluation mechanism to remove irrelevant tokens at the spatial level, along with temporal level similarity-based filtering to reduce redundancy across sequential frames. To ensure pruning does not harm task performance, we introduce a distortion-aware fine-tuning strategy that aligns pruned-token representations with their full-token counterparts while maintaining navigation accuracy. Experiments on the R2R and RxR benchmarks using Navid-CE and NavGPT-2 as base models demonstrate that STEP-Nav preserves over 95% of the performance while reducing 66.7% of tokens, outperforming existing token pruning baselines.

Vehicle trajectory prediction is important for automated vehicles to understand driving scenarios. This paper proposes an encoder-decoder network-based parameterized transfer learning (EDN-PTL) model to predict vehicle trajectory. To improve trajectory prediction accuracy, the motion interaction between the target vehicle and the surrounding vehicles is considered, and a multidimensional spatiotemporal input expansion (MSIA) strategy is proposed to extend the feature dimensions. Additionally, global and local scale features, as well as long and short horizon features, are extracted and used for interactive vehicle trajectory prediction by a CNN and LSTM-based encoder-decoder network (CNN-LSTM-EDN). Moreover, the features extracted by CNN-LSTM-EDN are integrated using a stacked convolutional social pooling network (SCSPN). To enhance the environmental adaptability of the trajectory prediction model, a PTL strategy is proposed to enable transfer learning capabilities of EDN-PTL. Based on the PTL strategy, trajectory prediction accuracy is maintained even when applied to untrained environments. The proposed EDN-PTL model is validated on three types of publicly available naturalistic datasets and compared with several baselines and state-of-the-art (SOTA) methods. The validation results demonstrate that the proposed EDN-PTL achieves better prediction accuracy, robustness, and environmental adaptability compared to the baselines and SOTA methods.

Energy optimization constitutes a paramount design consideration in the realm of embedded systems development since these devices are inherently constrained by finite battery resources. Designing and developing an effective energy-aware scheduling approach is a desirable work to provide excellent processing capability while keeping the energy consumption under control. Although previous approaches can obtain reasonable scheduling solutions for tasks with energy consumption constraints, they are computationally expensive and have deficiencies in effectiveness or efficiency due to unfair or inefficient energy pre-assignment strategies. In this article, we study the energy-aware workflow scheduling problem and present a three-stage list-based approach to minimize the schedule length of workflows in heterogeneous distributed embedded systems. First, the workflow applications and energy consumption of processors are modelled, and the energy-aware workflow scheduling problem is formulated as a non-linear mixed integer programming one with various dependency and energy constraints. Then, with an effective task prioritization strategy and a reasonable energy pre-assignment strategy, a three-stage list-based scheduling approach is proposed to schedule the tasks and minimize the schedule length of workflows. Experiments on randomly-generated and real-life workflows demonstrate that our proposed approach constantly outperforms the existing approaches and our algorithm can, respectively, reduce the normalized schedule length and the deviation ratio by 16.7% and 7.6% in average.

Search time is an important metric for regional coverage searches conducted by unmanned clusters. This paper proposes an improved proximal policy optimization (IPPO) algorithm to decrease search time while ensuring the coverage rate for a heterogeneous cluster consisting of unmanned ground vehicles and unmanned aerial vehicles (UGV-UAV). The models of the UGV-UAV cluster, search scenario, and search constraints are first developed. Then, the IPPO algorithm is designed to simultaneously learn the cross-domain actions of UGVs and UAVs. The main advantage of the IPPO is that it can achieve the cross-domain cooperative learning (CDCL) mechanism, thus ensuring the collaboration consistency of the UGV-UAV cluster and enhancing search efficiency. To analyze the IPPO-based regional coverage search performance, three state-of-the-art (SOTA) methods are selected for comparison. The validation results demonstrate that the proposed method outperforms these SOTA methods in terms of both search time and coverage rate.

Collaborative perception effectively mitigates issues such as limited field of view and occlusion by enabling multiple agents to share perceptual information. Despite its advantages, challenges persist in complex environments due to factors such as limited communication bandwidth and noisy poses, which may potentially degrade system performance. Meanwhile, a substantial amount of simulation data is widely adopted in collaborative perception to achieve high precision and real-time detection. However, the domain gap between simulated and real-world environments may result in weakened collaborative performance and hindered generalization ability. In this work, we focus on the multi-agent collaborative perception problem and propose a quantized communication and transferable fusion framework, namedQCTF, to efficiently minimize the bandwidth overhead and enhance real-world perception by leveraging unlabeled data for improved adaptability. First, we present a quantized communication method that employs multi-scale residual indices and an optimized codebook to extract robust representations while minimizing bandwidth usage. Then, we design a channel-aware selection strategy that adjusts the bandwidth volume and compensates for the quantized representation by combining the prioritized critical features with the channel dimension. Finally, we adopt a transferable fusion module to effectively bridge the simulation-to-reality domain gaps and improve perceptual capability through multi-scale adaptation discriminators. Experiments on both simulated and real-world datasets are conducted to evaluate the effectiveness of the proposed framework, and the results demonstrate that our approach consistently outperforms the existing methods in limited communication bandwidth and domain adaptation scenarios.

Due to their high flexibility and strong maneuverability, unmanned aerial vehicles (UAVs) have attracted lots of attention and are widely employed in many fields. Especially in target encirclement applications, UAVs have shown great advantages in adaptability and reliability, and can efficiently fly to and evenly surround the targets in complex and dynamic environments. In this paper, we concentrate on the cooperative target encirclement problem of heterogeneous UAVs and try to propose a multi-agent reinforcement learning approach to solve the problem. First, with the models of heterogeneous UAVs and obstacles, we analyze the collision avoidance, motion continuity, and energy consumption constraints of UAVs, and formulate the cooperative target encirclement problem as a multi-constraint combinatorial optimization one. Then, inspired by the humans’ learning experience that curiosity provides a powerful motivator for humans to explore, discover, and acquire new knowledge, we propose an extrinsic-and-intrinsic reward-based multi-agent reinforcement learning approach to cooperatively control the behaviors of UAVs and achieve the target encirclement missions. Simulation experiments with randomly generated environments are conducted to evaluate the performance of our approach, and the results show that our approach has a significant advantage in terms of average reward, encirclement success rate, encirclement time, and encirclement energy consumption.

Unmanned Aerial Vehicles (UAVs) are emerging as a pivotal component in the field of intelligent transportation systems. Leveraging virtual-physical interactions, digital twin technology significantly enhances the adaptability of UAVs in complex traffic environments. However, current approaches still pose three major challenges: contextual adaptability, timely responsiveness, and effective multi-UAV coordination. In this paper, we introduce EnFlexiTwin, a digital twin enhancement assistance platform seamlessly integrated with AdaSor, a lightweight adaptive data selector. EnFlexiTwin automates the construction of incremental learning datasets, enabling real-time enhancements that allow digital twins to adapt to new time series data while preserving historical knowledge. We test EnFlexiTwin on a real-world dataset from low-altitude small-parcel delivery. The results show improved performance and adaptability of digital twins. Furthermore, time-varying simulations on real-world dataset and experiments on a practical air-ground cooperative UAV swarm application highlight that EnFlexiTwin achieves superior enhancements under varying real-time requirements and swarm scale compared to baseline approaches.

Human-machine cooperation can simultaneously leverage the strengths of both human drivers and machines, making it a promising solution for improving driving safety, comfort, and experience. This paper designs a heterogeneous synchronous reinforcement learning (HSRL)-based human-machine shared steering decision-making (HMSSDM) strategy for intelligent vehicles. First, the vehicle dynamics, which incorporate steering characteristics, are built to quantify human driver’s steering behavior. Additionally, the scenario-oriented driving constraints (SODCs) are established to demonstrate driving constraints from traffic participants, roadside obstacles, and traffic signs. Second, to enhance the rationality and reliability of steering behaviors, the human driver’s steering behavior is evaluated using a fuzzy logic strategy, and HSRL is proposed to simultaneously determine steering actions and allocate driving authority between the human driver and machine. The main advantage of HSRL is its ability to perform both continuous domain learning (CDL) and discrete domain learning (DDL) simultaneously. Finally, the proposed method is validated using a human and hardware-in-the-loop (HHiL) experimental platform. The comparison results demonstrate that the proposed method outperforms the comparison methods in terms of driving safety, comfort and experience.

Lane recognition is an important component of autonomous driving system and advanced driving assistance system (ADAS) for intelligent vehicles. In complex driving conditions, accurate and fast lane recognition is a challenging issue. In this paper, a vision-based geometric model (VBGM) is proposed for accurate and fast lane recognition in complex conditions. The framework of the VBGM includes an image preprocessing stage and a lane recognition stage. In the image preprocessing stage, the region of interest (ROI) is extracted from the original image, and the original image is transformed into an undistorted greyscale image. In the lane recognition stage, the lane contour is first extracted using the Roberts operator. Then, to accurately and quickly recognize the lane marking, a lane recognition coordinate system (LRCS) and a rotational LRCS (R-LRCS) are constructed. The distracting contours in abnormal regions are padded based on the LRCS using a contextual frames correlation (CFC) strategy, and the midpoints of the lane contour are identified based on the R-LRCS. Finally, an adaptive-order polynomial fitting model is built to fit the lane marking according to the midpoints in the LRCS. To evaluate the effectiveness of the proposed method, two state-of-the-art methods are selected for comparison. The comparative results indicate that the proposed method possesses a higher recognition rate and speed for lane recognition in complex conditions.

Multiagent routing problems, arising from practical applications, such as logistics, transportation, and emergency response, face challenges due to the exponential growth of the search space with increasing problem scales. This article proposes RouteMaker to address the often-overlooked multiagent routing problems involving dedicated multiple depots. RouteMaker leverages role-interaction-based graph neural network (RIGNN) to realize effective locations assignments and integrates an advanced planner to plan travel path for each agent. RouteMaker is trained on small-scale problems and can produce comparable or superior approximate optimal solutions compared with the best heuristic baselines. Notably, the learned RouteMaker generalizes seamlessly to large-scale problems and real-world problems without the need for fine-tuning, delivering significantly higher quality solutions in relatively less time. For scenarios involving 40 agents and 1000 locations, RouteMaker achieves over $600\times $ speed improvement and more than 88% cost reduction, compared with the representative classical heuristic solver (ORTools).

Heterogeneous multiprocessor architecture is frequently employed as an economical and efficient means of providing excellent parallel processing capabilities while keeping production cost and power consumption under control. Although this architecture achieves significant performance enhancement and cost reduction, it results in a serious task allocation and scheduling problem, especially for periodic tasks with data dependencies, all of which should be reasonably scheduled and executed in a timely manner such that their deadlines and dependence requirements could be satisfied even if the worst happens. In this article, we concentrate on the non-preemptive scheduling problem of periodic tasks with data dependencies upon heterogeneous multiprocessor platforms. First, with models of data-dependent tasks and heterogeneous processors, we analyze the time, space, precedence, and data dependence constraints of tasks and design an exact formulation based on the mixed integer linear programming to completely explore the solution space and produce the optimal solutions. Then, by constructing a directed acyclic graph to depict the dependence relationship of jobs generated by tasks, we propose an efficient off-line list-based scheduling algorithm to provide a reasonable time and processor allocation for each job, with a view to minimizing the completion time of jobs. Experiments with randomly generated tasks are performed to evaluate the effectiveness and efficiency of the proposed algorithm, and the experimental results show that our algorithm can averagely enhance the scheduling success ratio by 28.5%, and, respectively, reduce the task completion time and the deviation ratio by 23.3% and 17.2%, on average.

Camera-LiDAR fusion models significantly enhance perception performance in autonomous driving. The fusion mechanism leverages the strengths of each modality while minimizing their weaknesses. Moreover, in practice, camera-LiDAR fusion models utilize pre-trained backbones for efficient training. However, we argue that directly loading single-modal pre-trained camera and LiDAR backbones into camera-LiDAR fusion models introduces similar feature redundancy across modalities due to the nature of the fusion mechanism. Unfortunately, existing pruning methods are developed explicitly for single-modal models, and thus, they struggle to effectively identify these specific redundant parameters in camera-LiDAR fusion models. In this paper, to address the issue above on camera-LiDAR fusion models, we propose a novelty pruning framework Alternative Modality Masking Pruning (AlterMOMA), which employs alternative masking on each modality and identifies the redundant parameters. Specifically, when one modality parameters are masked (deactivated), the absence of features from the masked backbone compels the model to reactivate previous redundant features of the other modality backbone. Therefore, these redundant features and relevant redundant parameters can be identified via the reactivation process. The redundant parameters can be pruned by our proposed importance score evaluation function, Alternative Evaluation (AlterEva), which is based on the observation of the loss changes when certain modality parameters are activated and deactivated. Extensive experiments on the nuScene and KITTI datasets encompassing diverse tasks, baseline models, and pruning algorithms showcase that AlterMOMA outperforms existing pruning methods, attaining state-of-the-art performance.

Collaborative 3D object detection by sharing features among agents significantly enhances performance compared to single-agent detection. However, directly sharing full-sized features introduces a large communication bandwidth load. To address this challenge, existing collaborative methods adopt a request-response framework, where the ego agent sends a request, and collaborative agents respond with only the necessary parts of the features after analyzing the request. However, the frequent communication in this request-response cycle impacts real-time system performance in real-world environments by increasing overall processing time and raising the risk of message loss and communication delays. To address this challenge and enable real-time system implementation, we propose a request-free collaborative 3D object detection framework that eliminates the request-response cycle through a novel request-free response generator, named Position and Occlusion Response Generator (PORG). PORG consists of two specialized components, Position-aware Mask Generator (PaMG) and Occlusion-aware Feature Mask Generator (OaMG), which use attention mechanisms to generate the necessary response features without the request from the ego agent. To evaluate the efficiency of our proposed PORG, we conducted evaluations on both public datasets and real-world settings. We provide system implementation for both the request-response and request-free frameworks on Jetson Orin Series embedded devices, and extensive evaluation shows that PORG outperforms the baselines, achieving higher Average Precision (AP) with lower communication bandwidth in public datasets and superior real-time performance on embedded devices.

Multiedge cooperative computing that combines constrained resources of multiple edges into a powerful resource pool has the potential to deliver great benefits, such as a tremendous computing power, improved response time, and more diversified services. However, the mass heterogeneous resources composition and lack of scheduling strategies make the modeling and cooperating of multiedge computing system particularly complicated. This article first proposes a system-level state evaluation model to shield the complex hardware configurations and redefine the different service capabilities at heterogeneous edges. Second, an integer linear programming model is designed to cater for optimally dispatching the distributed arriving requests. Finally, a learning-based lightweight real-time scheduler, CoRaiS is proposed. CoRaiS embeds the real-time states of the multiedge system and requests information, and combines the embeddings with a policy network to schedule the requests, so that the response time of all requests can be minimized. Evaluation results verify that the CoRaiS can make a high-quality scheduling decision in real-time, and can be generalized to other multiedge computing system, regardless of the system scales. Characteristic validation also demonstrates that the CoRaiS successfully learns to balance loads, perceive real-time state and recognize heterogeneity while scheduling.

Converting natural language text into executable SQL queries significantly impacts the healthcare domain, specifically when applied to electronic medical records. Given that electronic medical records store extensive patient information in a relational multitable database, developing a Text-to-SQL parser would enable the correlation of intricate medical terminology through semantic parsing. A major challenge is designing a versatile Text2SQL parser applicable to new databases. A critical step towards this goal involves schema linking - accurately identifying references to previously unseen columns or tables during SQL creation. In response to these key challenges, we propose a novel framework—Linking Information Enhanced Text2SQL Parsing on Complex Electronic Medical Records (LI-EMRSQL). This model leverages the Poincaré distance metric detection procedure, utilizing induced relations to enhance the performance of pre-existing graph-based parsers and improve schema linkage. To enhance the generalizability of LI-EMRSQL, the detection process is completely unsupervised and does not necessitate additional parameters. On two conventional Text2SQL datasets and two EMRs Text2SQL datasets, the system delivers SOTA performance. Furthermore, notable enhancements in the model's comprehension and alignment of schemas are observed.

The regulation time is an important index for formation switching control of connected and automated vehicle (CA V) platoon. This paper proposes a time-aware formation control (T AFC) strategy to improve the formation switching performance of CA V platoon. To construct an effective information sharing mechanism among the vehicles in the platoon, a unidirectional weighted graph is designed to construct the relation of the CA V platoon and calculate the impact factor between two different vehicles. Based on the unidirectional weighted graph, the time-aware requirement is converted to the regulation order problem, and the regulation order which corresponding to the minimum time is designed. According to the T AFC, the qualitative regulation strategy of the CA V platoon and the quantitative tune-up strategy of the vehicles are determined. In order to analyze the performance of the TAFC strategy, two state-of-art methods are selected as the benchmarked methods. The validation results demonstrate the proposed method possesses better performance for formation switching control compared with the benchmarked methods.

Summary Cloud computing has been widely adopted in practical applications due to its strong calculating ability and high parallel feature. Although cloud computing can achieve significant cost reduction and flexibility enhancement, it results in a serious task scheduling problem. As one of the key techniques for automate management of cloud resources, task scheduling plays an important role in improving system utilization and supporting load balancing. In this article, we focus on the scheduling problem of independent tasks in cloud environment with heterogeneous and distributed resources. First, with models of resources and tasks, we present an exact formulation based on linear programming to fully search solution space and produce optimal allocation schemes for tasks. Then, inspired from the differential evolution method, we propose a population‐based approach to allocate tasks to their suitable resources such that the total time cost would be minimized. Experiments with multi‐task sets are conducted to show the convergence and efficiency of the proposed approach.

Abstract Distributed heterogeneous systems have been widely adopted in industrial applications by providing high scalability and performance while keeping complexity and energy consumption under control. However, along with the increase in the number of computing nodes, the energy consumption of distributed heterogeneous systems dramatically grows and is extremely hard to predict. Energy‐conscious task scheduling, which tries to assign appropriate priorities and processors to tasks such that the system energy requirement would be met, has received extensive attention in recent years. However, many approaches reduce energy consumption by extending the completion time. In this article, we focus on the scheduling problem of energy‐conscious tasks in distributed heterogeneous computing systems and provide an efficient approach to mitigate energy consumption while minimizing the overall makespan of parallel applications. First, based on the heterogeneous earliest finish time, a fitness function is proposed to balance the makespan and energy consumption. Then, by improving the crossover and mutation operations of the traditional genetic algorithm, we proposed an efficient scheduling approach named energy‐conscious genetic algorithm to optimize the priorities and processor allocation of tasks, with objectives of minimizing the system energy and makespan. Experiment results on real‐world applications and simulations with randomly generated task graphs demonstrate that the proposed approach outperforms in energy‐saving and makespan reducing.

The energy utilization efficiency of autonomous electric vehicles is seriously affected by the longitudinal motion control performance. However, the longitudinal motion control is constrained by the driving scene. This article proposes an energy-saving optimization and control (ESOC) method to improve the energy utilization efficiency of autonomous electric vehicles. In ESOC, the constraints from the driving scene are thoroughly considered, and the autonomous driving scene constraints are mapped to the vehicle dynamics and control domain. On this basis, the efficiency self-searching method and the multiconstraint energy-saving control strategy are designed. The main ideology of the proposed ESOC is that the energy utilization efficiency of an autonomous electric vehicle can be improved by optimizing and controlling the operation point distribution of the powertrain efficiency. The experimental results demonstrate that the operation point distribution of the autonomous electric vehicle's powertrain efficiency can be well optimized by the proposed ESOC, and the energy consumption results indicate that the proposed ESOC outperforms the state-of-the-art methods.

Unmanned aerial vehicles (UAVs) have been widely applied in civilian and military applications due to their high autonomy and strong adaptability. Although UAVs can achieve effective cost reduction and flexibility enhancement in the development of large-scale systems, they result in a serious path planning and task allocation problem. Coverage path planning, which tries to seek flight paths to cover all of regions of interest, is one of the key technologies in achieving autonomous driving of UAVs and difficult to obtain optimal solutions because of its NP-Hard computational complexity. In this paper, we study the coverage path planning problem of autonomous heterogeneous UAVs on a bounded number of regions. First, with models of separated regions and heterogeneous UAVs, we propose an exact formulation based on mixed integer linear programming to fully search the solution space and produce optimal flight paths for autonomous UAVs. Then, inspired from density-based clustering methods, we design an original clustering-based algorithm to classify regions into clusters and obtain approximate optimal point-to-point paths for UAVs such that coverage tasks would be carried out correctly and efficiently. Experiments with randomly generated regions are conducted to demonstrate the efficiency and effectiveness of the proposed approach.

Due to the high maneuverability and strong adaptability, autonomous unmanned aerial vehicles (UAVs) are of high interest to many civilian and military organizations around the world. Automatic path planning which autonomously finds a good enough path that covers the whole area of interest, is an essential aspect of UAV autonomy. In this study, we focus on the automatic path planning of heterogeneous UAVs with different flight and scan capabilities, and try to present an efficient algorithm to produce appropriate paths for UAVs. First, models of heterogeneous UAVs are built, and the automatic path planning is abstracted as a multi-constraint optimization problem and solved by a linear programming formulation. Then, inspired by the density-based clustering analysis and symbiotic interaction behaviours of organisms, an adaptive clustering-based algorithm with a symbiotic organisms search-based optimization strategy is proposed to efficiently settle the path planning problem and generate feasible paths for heterogeneous UAVs with a view to minimizing the time consumption of the search tasks. Experiments on randomly generated regions are conducted to evaluate the performance of the proposed approach in terms of task completion time, execution time and deviation ratio.

With the growing complexities of modern SoC designs and increasingly shortened time-to-market requirements, new design paradigms such as outsourced design services have emerged. Design abstraction level has also been raised from RTL to ESL. Modern SoC designs in ESL often integrate a variety of third-party behavioral intellectual properties, as well as intensively utilizing EDA tools to improve design productivity. However, this new design trend makes modern SoCs more vulnerable to hardware Trojan attacks. Although hardware Trojan detection has been studied for more than a decade in RTL and lower levels, it has only recently gained attention in ESL designs. In this paper, we present a novel approach for generating test cases by selective concolic testing to detect hardware Trojans in ESL. We have evaluated our approach on an open source benchmark that includes various types of hardware Trojans. The experimental results demonstrate that our approach is able to detect hardware Trojans effectively and efficiently.

Heterogeneous multiprocessor platforms have been widely adopted as an efficient approach to providing high instruction throughput while keeping power and complexity under control. Although this approach can achieve improved performance for large-scale real-time systems, it results in a complex task scheduling problem. All tasks should be scheduled according to a proper strategy such that their deadlines will be met even in the worst case situations. In this work, we study the non-preemptive scheduling problem of periodic tasks with data dependency upon heterogeneous multiprocessor platforms. We first analyze the space, time and precedence constraints of tasks, and propose an exact formulation to determine the schedulability of tasks. Then, inspired from the Heterogeneous Earliest Finish Time (HEFT) algorithm, we present a list-based scheduling heuristic to schedule the jobs generated by the periodic tasks and minimize the jobs' finish time. The proposed approach is efficient and can help in guiding the design of heterogeneous multiprocessor systems.

Integrated modular design has been widely adopted as an approach to facilitating the development process of large-scale real-time systems. Although this approach can achieve enhanced design reuse and reduced time consumption, it results in a complex task allocation and scheduling problem. All tasks should be integrated into a shared platform according to a proper schedule, such that their deadlines will be met even under the worst case situations. In this paper, we study the allocation and scheduling problem of strictly periodic tasks in multi-core real-time systems. We first propose a necessary and sufficient condition to determine whether a new task is schedulable on a processor without changing the start times of the existing tasks. Then, based on the condition derived previously, we present a task assignment algorithm, which not only provides valid start times and processor allocations for all tasks, but also obtains the minimum number of processors required by the system. Finally, simulation experiments with randomly generated task sets are conducted to show the high efficiency and reliability of the proposed approach.