Hardware/software partitioning algorithm based on the combination of genetic algorithm and tabu search

To solve the hardware/software (HW/SW) partitioning problem of a single Central Processing Unit (CPU) system, a hybrid algorithm of Genetic Algorithm (GA) and Tabu Search(TS) is studied. Firstly, the concept hardware orientation is proposed and then used in creating the initial colony of GA and the mutation, which reduces the randomicity of initial colony and the blindness of search. Secondly, GA is run, the crossover and mutation probability become smaller in the process of GA, thus they not only ensure a big search space in the early stages, but also save the good solution for later browsing. Finally, the result of GA is used as initial solution of TS, and tabu length adaptive method is put forward in the process of TS, which can improve the convergence speed. From experimental statistics, the efficiency of proposed algorithm outperforms comparison algorithm by up to 25% in a large-scale problem, what is more, it can obtain a better solution. In conclusion, under specific conditions, the proposed algorithm has higher efficiency and can get better solutions

Management of Temporally and Spatially Correlated Failures in Federated Message Oriented Middleware for Resilient and QoS-Aware Messaging Services.

PhDMessage Oriented Middleware (MOM) is widely recognized as a promising solution for the communications between heterogeneous distributed systems. Because the resilience and quality-of-service of the messaging substrate plays a critical role in the overall system performance, the evolution of these distributed systems has introduced new requirements for MOM, such as inter domain federation, resilience and QoS support. This thesis focuses on a management frame work that enhances the Resilience and QoS-awareness of MOM, called RQMOM, for federated enterprise systems. A common hierarchical MOM architecture for the federated messaging service is assumed. Each bottom level local domain comprises a cluster of neighbouring brokers that carry a local messaging service, and inter domain messaging are routed through the gateway brokers of the different local domains over the top level federated overlay. Some challenges and solutions for the intra and inter domain messaging are researched. In local domain messaging the common cause of performance degradation is often the fluctuation of workloads which might result in surge of total workload on a broker and overload its processing capacity, since a local domain is often within a well connected network. Against performance degradation, a combination of novel proactive risk-aware workload allocation, which exploits the co-variation between workloads, in addition to existing reactive load balancing is designed and evaluated. In federated inter domain messaging an overlay network of federated gateway brokers distributed in separated geographical locations, on top of the heterogeneous physical network is considered. Geographical correlated failures are threats to cause major interruptions and damages to such systems. To mitigate this rarely addressed challenge, a novel geographical location aware route selection algorithm to support uninterrupted messaging is introduced. It is used with existing overlay routing mechanisms, to maintain routes and hence provide more resilient messaging against geographical correlated failures

Semantic Map Building Based on Object Detection for Indoor Navigation

Building a map of the environment is a prerequisite for mobile robot navigation. In this paper, we present a semantic map building method for indoor navigation of a robot using only the image sequence acquired by a mon‐ ocular camera installed on the robot. First, a topological map of the environment is created, where each key frame forms a node of the map represented as visual words (VWs). The edges between two adjacent nodes are built from relative poses obtained by performing a novel pose estimation approach, called one-point RANSAC camera pose estimation (ORPE). Then, taking advantage of an improved deformable part model (iDPM) for object detection, the topological map is extended by assigning semantic attributes to the nodes. Extensive experimental evaluations demonstrate the effectiveness of the proposed monocular SLAM method

Click on Mask: A Labor-efficient Annotation Framework with Level Set for Infrared Small Target Detection

Infrared Small Target Detection is a challenging task to separate small targets from infrared clutter background. Recently, deep learning paradigms have achieved promising results. However, these data-driven methods need plenty of manual annotation. Due to the small size of infrared targets, manual annotation consumes more resources and restricts the development of this field. This letter proposed a labor-efficient and cursory annotation framework with level set, which obtains a high-quality pseudo mask with only one cursory click. A variational level set formulation with an expectation difference energy functional is designed, in which the zero level contour is intrinsically maintained during the level set evolution. It solves the issue that zero level contour disappearing due to small target size and excessive regularization. Experiments on the NUAA-SIRST and IRSTD-1k datasets reveal that our approach achieves superior performance. Code is available at https://github.com/Li-Haoqing/COM.Comment: 4 pages, 5 figures, references adde

Contour Detection-based Discovery of Mid-level Discriminative Patches for Scene Classification

Feature extraction and representation is a key step in scene classification. In this paper, a contour detection-based mid-level features learning method is proposed for scene classification. First, a sketch tokens-based contour detection scheme is proposed to initialize seed blocks for learning mid-level patches and the patches with more contour pixels are selected as seed blocks. The procedure is demonstrated to be helpful for scene classification. Next, the seed blocks are employed to train an exemplar SVM to discover other similar occurrences and an entropy-rank criterion is utilized to mine the discriminative patches. Finally, scene categories are identified by matching the discriminative patches and testing images. Extensive experiments on the MIT Indoor-67 dataset, the 15-scene dataset and the UIUC-sports dataset show that the proposed approach yields better performance than other state-of-the-art counterparts

The motion characteristics of a cylinder vehicle in the oblique water-exit process

The hydrodynamic model of a vehicle exiting the water surface obliquely has been analyzed. The analyzed object is a cylinder vehicle and its motion characteristics. Two methods have been used to simulate the water-exit process under the same conditions: Numerical Simulation Method (NSM) and Theoretical Model Solution Method (TMSM). The comparison results of the two methods can validate the hydrodynamic model founded in this paper. Different initial angles and different initial velocities have been simulated by this hydrodynamic model and the numerical simulation has been analyzed. The analysis reveals the rule of change of altitude and position of the vehicle in the water-exit process, and its motion after it exits the water surface. This paper explains why it is more difficult for the vehicle to exit the water obliquely than vertically. The results show that the hydrodynamic model of the water exiting vehicle can be used to research the exiting water motion characteristics. The models simulate the physics of motion realistically and this hydrodynamic model can be used as a foundation for the future research of the stability and control of a vehicle exiting the water

Multi-channel and multi-scale mid-level image representation for scene classification

Convolutional neural network (CNN)-based approaches have received state-of-the-art results in scene classification. Features from the output of fully connected (FC) layers express one-dimensional semantic information but lose the detailed information of objects and the spatial information of scene categories. On the contrary, deep convolutional features have been proved to be more suitable for describing an object itself and the spatial relations among objects in an image. In addition, the feature map from each layer is max-pooled within local neighborhoods, which weakens the invariance of global consistency and is unfavorable to scenes with highly complicated variation. To cope with the above issues, an orderless multi-channel mid-level image representation on pre-trained CNN features is proposed to improve the classification performance. The mid-level image representation of two channels from the FC layer and the deep convolutional layer are integrated at multi-scale levels. A sum pooling approach is also employed to aggregate multi-scale mid-level image representation to highlight the importance of the descriptors beneficial for scene classification. Extensive experiments on SUN397 and MIT 67 indoor datasets demonstrate that the proposed method achieves promising classification performance

THREE DIMENSIONAL KINEMATIC ANALYSIS IN WOMEN’S SHOT PUTT: INFLUENCE OF HEAD MOVEMENT ON TECHNIQUE

The influence of head motion of shot put during competition was studied in this study. Six female elite shot putter was recruited as subjects in this study. Shots performed by six female elite shot putter were filmed. Three-dimensional analysis was employed to determine the angle of head, trunk, shoulder rotation and height of shot at different throwing phase. Results found that the raising of head position in every phase influenced the movements limb and trunk. Therefore, it is necessary for coach and athletes to pay more attention on the head movement in shot put

ILNet: Low-level Matters for Salient Infrared Small Target Detection

Infrared small target detection is a technique for finding small targets from infrared clutter background. Due to the dearth of high-level semantic information, small infrared target features are weakened in the deep layers of the CNN, which underachieves the CNN's representation ability. To address the above problem, in this paper, we propose an infrared low-level network (ILNet) that considers infrared small targets as salient areas with little semantic information. Unlike other SOTA methods, ILNet pays greater attention to low-level information instead of treating them equally. A new lightweight feature fusion module, named Interactive Polarized Orthogonal Fusion module (IPOF), is proposed, which integrates more important low-level features from the shallow layers into the deep layers. A Dynamic One-Dimensional Aggregation layers (DODA) are inserted into the IPOF, to dynamically adjust the aggregation of low dimensional information according to the number of input channels. In addition, the idea of ensemble learning is used to design a Representative Block (RB) to dynamically allocate weights for shallow and deep layers. Experimental results on the challenging NUAA-SIRST (78.22% nIoU and 1.33e-6 Fa) and IRSTD-1K (68.91% nIoU and 3.23e-6 Fa) dataset demonstrate that the proposed ILNet can get better performances than other SOTA methods. Moreover, ILNet can obtain a greater improvement with the increasement of data volume. Training code are available at https://github.com/Li-Haoqing/ILNet