It is well known that trust relationships between entities are uncertain in pervasive computing. However, previous researchers did not attach enough importance to uncertainty in their trust models. Therefore, their models always seem arbitrary when they set some specific values to various trust levels. To remedy this problem, we declare that uncertainty should be taken into account while modeling trust. This paper presents such an approach, in which we model trust based on cloud theory, which is a promising theory to describe uncertain concepts. In other words, we regard trust between entities as a cloud, say trust cloud. Based on such a definition of trust, we further propose algorithms to compute propagated trust clouds and aggregated trust clouds, which are needed in case of trust reasoning for entities in pervasive computing environments. Besides, we also propose an approach to use our cloud-based trust model in pervasive applications, which is helpful to put our model into practice in trust-based security mechanisms.
In recent years, WiFi fingerprint-based localization has received much attention due to its deployment practicability. Although existing works show WiFi fingerprinting can achieve good localization accuracy, the experiments were conducted under their own testbeds within a small area and a short period. In this work, we investigate the impact of different indoor environmental factors, such as temporal and spatial similarity, on the performance of WiFi fingerprinting. We find that, WiFi fingerprinting is highly environment-sensitive. In an open space, it is quite challenging to find spatially varying but temporally stable signatures for adjacent reference locations. To address this issue, we propose a heatmap-based WiFi fingerprinting (called HMF) by utilizing layout construction as an additional input to improve WiFi fingerprint localization in open space environment. Our experimental results show, HMF can improve existing WiFi fingerprinting schemes like Radar and Horus by 28% and 80% in moderately open space, e.g., a wide corridor.
With the integration of helmets and functional accessories, wearers' fatigue would be accelerated. Therefore, the suitability and comfort of helmets become the most critical factors for the final promotion and application. In this work, the effects of average pressure distribution(APD) for five different types of helmets on five areas (front, rear, left, right, and top) of the head surface under static and multi-coupled degrees of freedom rotation conditions (30mm vertical vibration, 15 ° pitching movement, 15 ° flip movement, 15 ° azimuth movement) were analyzed. The results show that #B helmet has a uniform distribution of APD on the head, making it the most comfortable, while the #E helmet has the most uneven APD on the head, with greater pressure on the top of the head compared to the other four helmets, indicating that the comfort of the E helmet is poor, It may be that the pad system of #E helmet does not restrain and support the circumference of the head, the entire mass of the helmet acts on the top area of the head. Combined with many wearers ’ feedback suggestions, the dispersity of helmet pressure (DHP) under static conditions and helmet-following(HF) under dynamic conditions are considered as two extremely critical indicators for assessing helmet comfort. The results indicated that the DHP was positively correlated with HF performance, The smaller the DHP of the helmet in static state, the better the HF stability of the helmet in dynamic state. Therefore, this present work proposes indicators that affect helmet wearing comfort from the perspective of ergonomics, which can objectively and quantitatively evaluate helmet wearing comfort in the market.
The proposal of a high-intensity muon source driven by the CiADS linac, which has the potential to be one of the state-of-the-art facilities, is presented in this paper. We briefly introduce the development progress of the superconducting linac of CiADS. Then the consideration of challenges related to the high-power muon production target is given and the liquid lithium jet muon production target concept is proposed, for the first time. The exploration of the optimal target geometry for surface muon production efficiency and the investigation into the performance of liquid lithium jet target in muon rate are given. Based on the comparison between the liquid lithium jet target and the rotation graphite target, from perspectives of surface muon production efficiency, heat processing ability and target geometry compactness, the advantages of the new target concept are demonstrated and described comprehensively. The technical challenges and the feasibility of the free-surface liquid lithium target are discussed.
The century-old development of the automotive industry has spawned one of the greatest Cyber-Physical Systems (CPSs) in the future-unmanned vehicles. The vehicle can obtain environmental information through different sensors, map it to the virtual coordinate system of the vehicle body to make decisions, and finally generate control instructions. However, a series of factors, such as complex road scenes, defective and irregular target sparse sampling, and large coding space, pose challenges to accurate, efficient, and stable perception results. To overcome the most challenging problem of dynamic target tracking, this paper designs a two-stage detection model based on non-uniform polar voxelization sampling of irregular 3D point cloud, which is used with local registration-based search to achieve efficient multi-target tracking. Non-uniform voxelization not only balances the spatial sampling and encoding efficiency of the point cloud for the backbone, but also adapts to the feature aggregation of the detection head, thereby achieving double acceleration. Finally, we tested our model on KITTI Tracking data. The comparison results show that the calculation speed of the final model is greatly improved and the tracking accuracy is competitive in all categories.
A high-intensity muon source driven by a continuous-wave superconducting linac holds the potential to significantly advance the intensity frontier of muon sources. Alongside advancements in accelerator technologies, breakthroughs in muon production target and collection schemes are essential. After a brief introduction to the development of the accelerator-driven system superconducting linac, a novel muon production target is proposed, utilizing a free-surface liquid lithium jet capable of handling the heat power generated by a proton beam with an energy of 600 MeV and a current of 5 mA. It is predicted by our simulation studies that the lithium target is more efficient in surface muon production compared to the rotating graphite target. The parameter space of the front end consisting of a lithium target and a large-aperture capture solenoid is explored, from the perspective of production efficiency, capture efficiency, and characteristics of the surface muon beam. Published by the American Physical Society 2024
Infrastructure-assisted autonomous driving is an emerging paradigm that expects to significantly improve the driving safety of autonomous vehicles. The key enabling technology for this vision is to fuse LiDAR results from the roadside infrastructure and the vehicle to improve the vehicle's perception in real time. In this work, we propose VIPS, a novel lightweight system that can achieve decimeter-level and real-time (up to 100 ms) perception fusion between driving vehicles and roadside infrastructure. The key idea of VIPS is to exploit highly efficient matching of graph structures that encode objects' lean representations as well as their relationships, such as locations, semantics, sizes, and spatial distribution. Moreover, by leveraging the tracked motion trajectories, VIPS can maintain the spatial and temporal consistency of the scene, which effectively mitigates the impact of asynchronous data frames and unpredictable communication/compute delays. We implement VIPS end-to-end based on a campus smart lamppost testbed. To evaluate the performance of VIPS under diverse situations, we also collect two new multi-view point cloud datasets using the smart lamppost testbed and an autonomous driving simulator, respectively. Experiment results show that VIPS can extend the vehicle's perception range by 140% within 58 ms on average, and delivers a 4X improvement in perception fusion accuracy and 47X data transmission saving over existing approaches. A video demo of VIPS based on the lamppost dataset is available at https://youtu.be/zW4oi_EWOu0.
Cover Caption: The cover image is based on the Full Article Are Arch-conforming Insoles a Good Fit for Diabetic foot? Insole Customized Design by Using Finite Element Analysis by Jianwei Niu et al., https://doi.org/10.1002/hfm.20841.
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