Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Safe Area Detection Method for Autonomous Vehicles in Hub Airport

The application of autonomous driving technology will improve the level of airport autonomy and intelligent operation. In order to improve safety, autonomous vehicles need to accurately detect what portion of area inside the lane is free from obstacles. This paper proposes a safe area detection method, which uses a safe threshold to combine the lane detection model and the SSD object detection model to mark the safe area for airport autonomous vehicles. Finally, in the built airport simulation platform, we mainly compare the proposed method with the FCN area detection method. The simulation results show the proposed method improves accuracy rate by 7.91%, and speed by 17.90%, which can meet the requirements of real-time detection in hub airport.
ALOHA: A Unified Platform-Aware Evaluation Method for CNNs Execution on Heterogeneous Systems at the Edge

CNN design and deployment on embedded edge-processing systems is an error-prone and effort-hungry process, that poses the need for accurate and effective automated assisting tools. In such tools, pre-evaluating the platform-aware CNN metrics such as latency, energy cost, and throughput is a key requirement for successfully reaching the implementation goals imposed by use-case constraints. Especially when more complex parallel and heterogeneous computing platforms are considered, currently utilized estimation methods are inaccurate or require a lot of characterization experiments and efforts. In this paper, we propose an alternative method, designed to be flexible, easy to use, and accurate at the same time. Considering a modular platform and execution model that adequately describes the details of the platform and the scheduling of different CNN operators on different platform processing elements, our method captures precisely operations and data transfers and their deployment on computing and communication resources, significantly improving the evaluation accuracy. We have tested our method on more than 2000 CNN layers, targeting an FPGA-based accelerator and a GPU platform as reference example architectures. Results have shown that our evaluation method increases the estimation precision by up to <inline-formula> <tex-math notation="LaTeX">$5\times $ </tex-math></inline-formula> for execution time, and by <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> for energy, compared to other widely used analytical methods. Moreover, we assessed the impact of the improved platform-awareness on a set of neural architecture search experiments, targeting both hardware platforms, and enforcing 2 sets of latency constraints, performing 5 trials on each search space, for a total number of 20 experiments. The predictability is improved by <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula>, reaching, with respect to alternatives, selection results clearly more similar to those obtained with on-hardware measurements.
Symmetry-constrained Non-negative Matrix Factorization Approach for Highly-Accurate Community Detection

A community structure is a fundamental property of complex networks and its detection plays an important role in exploring and understanding such networks. Due to its great interpretability, a symmetric and non-negative matrix factorization (SNMF) model is frequently adopted to perform community detection tasks. However, it adopts a single latent factor (LF) matrix to construct the approximation of a given undirected matrix to ensure its absolute symmetry at the expense of shrinking its solution space. This paper proposes a symmetry-constrained NMF (SCNMF) method, with two-fold ideas: a) modeling the approximate symmetry of an undirected network by introducing an equality-constraint on LF matrices into an NMF framework; and b) using graph-regularization to extract the features regarding the intrinsic geometric structure of a network. Extensively empirical studies on six real-world social networks from industrial applications demonstrate that the proposed SCNMF-based detector achieves higher accuracy for community detection than state-of-the-art models.
A Survey on Perspective-n-Point Problem

Determining the position and orientation of the camera is a crucial problem with the rapidly developing technology in visual Simultaneous Localization and Mapping (SLAM), augmented reality and 3D reconstruction. Solving the PnP (perspective-n-point) problem is an effective method to calculate the pose of the camera and is also the most widely used method in many applications. In this paper, the methods for PnP problem, including special PnP problem and general PnP problem are summarized firstly. Then, due to importance of performing PnP methods in practical applications, ability to handle outliers for PnP methods is studied. Finally, the main problems of the current researches on PnP problem are presented.
A Soft Robotic Glove for Hand Rehabilitation using Pneumatic Actuators with Jamming Structure

In order to solve the problem that the current soft robotic glove for hand rehabilitation can only assist the movement of the fingers in a single direction and the output force is insufficient under lower air pressure, a soft pneumatic actuator coupled with a granular jamming system is designed. First of all, based on the analysis of the hand movement requirements, the design requirements of the soft robotic glove are proposed, and the structure design is carried out. Next, the deformation analysis of the pneumatic structure is performed based on the Yeoh model, and the actuator is simulated by ABAQUS. Finally, the manufacturing method of the actuator and the soft robotic glove is given, experiments of actuator performance and grasping experiments of the soft robotic glove are carried out. The results show that the maximum bending angle of the driver can reach 138°, the side swing angle can reach 60°, and the maximum output force can reach 5.7N under the pressure of 200kPa, which meets the requirements of hand rehabilitation and can assist in grasping objects.
Conceptual Design and Feasibility Test of Foldable Robotic Arms for Collaborative Work: FRAC

A new type of wearable robot is introduced to provide supplementary arms for assisting the worker's tasks. The supplementary arm is metamorphic called Foldable Robotic Arms for Collaboration (FRAC) to extend the operational degrees-of-freedom. FRAC provides additional two arms for a worker to augment the tasks with the environment where the additional arms are necessary. Initially, the FRAC is folded for saving the workspace to allow more space for human arms to interact with the environment. It is unfolded when the worker needs extra arms to hold the object. The strategy for assisting the worker is described, and kinematics and simulation analysis of the folding and unfolding are performed. FRAC is developed for its feasible functionality by performing the conceptual drilling and screwing operation for construction work with the FRAC.
Blind Separation of Heart Sound Convolutive Mixtures Utilizing Independent Vector Analysis

Separation of heart sound signals from the recorded mixtures has become a hot research topic in the clinical diagnosis of heart diseases. To better understand the current health status of patients, it is of great significance to develop intelligent auscultation methods to improve the effectiveness of hearing and assist clinicians in practice. In this paper, an optimization convolutive blind source separation algorithm utilizing independent vector analysis is proposed for separation of the heart sound mixtures. In the algorithm, a denoising approach is firstly used to reduce the impact of the additive white Gaussian noise. Then, using the short time Fourier transform, the mixing signals in time domain are transmitted into the frequency domain. Afterwards, the unmixng matrix is updated using Newton’s method, and the heart sound sources are reconstructed based on the estimated unmixing matrix. Experimental results show that the algorithm obtains better separation performance than the baseline methods. Especially, it has better superiority in the strong reverberant environment.
STP Approach to Formulate k-Externally Stable Sets of Graphs

In this paper the structure of graphs in terms of k-externally stable set (k-ESS) is investigated based on a new matrix product, called semi-tensor product of matrices. By defining an eigenvector and an eigenvalue of node subset of a graph, three necessary and sufficient conditions of k-ESS are proposed in a matrix form. Using these conditions, the concepts of k-ESS matrix are introduced. The k-ESS matrix provides the complete information of the corresponding structures of a graph. Further, an algorithm is designed to find all the structures of a graph by conducting a series of matrix operation. The proposed method and results may offer a new way to investigate the problems related to graph structures in the field of network systems.
A Ground-Based Interferometric Synthetic Aperture Radar Design and Experimental Study for Surface Deformation Monitoring

A new ground-based interferometric synthetic aperture radar has been developed by iRadar Sdn Bhd, in collaboration with the Centre for Remote Sensing and Surveillance Technologies, Multimedia University, Malaysia. This ground-based radar operates at Ku-band, single VV-polarization, with range resolution up to 0.175 m and azimuth resolution of 5.81 mrad at 1 km. Indoor test and outdoor field measurements have been conducted to verify the performance and specifications of the developed ground-based synthetic aperture radar (GBSAR). This system can be deployed for surface deformation monitoring especially at the high-risk slope for landslide monitoring and detection. This article presents the basic theory of the GBSAR system, the hardware description, as well as some indoor and outdoor field measurement results of the developed system.
Combining Surgical Navigation and 3D Printing for Less Invasive Pelvic Tumor Resections

Surgical interventions for musculoskeletal tumor resection are particularly challenging in the pelvic region due to their anatomical complexity and proximity to vital structures. Several techniques, such as surgical navigation or patient-specific instruments (PSIs), have been introduced to ensure accurate resection margins. However, their inclusion usually modifies the surgical approach making it more invasive. In this study, we propose to combine both techniques to reduce this invasiveness while improving the precision of the intervention. PSIs are used for image-to-patient registration and the installation of the navigation’s reference frame. We tested and validated the proposed setup in a realistic surgical scenario with six cadavers (12 hemipelvis). The data collected during the experiment allowed us to study different resection scenarios, identifying the patient-specific instrument configurations that optimize navigation accuracy. The mean values obtained for maximum osteotomy deviation or MOD (maximum distance between the planned and actual osteotomy for each simulated scenario) were as follows: for ilium resections, 5.9 mm in the iliac crest and 1.65 mm in the supra-acetabular region, and for acetabulum resections, 3.44 mm, 1.88 mm, and 1.97 mm in the supra-acetabular, ischial and pubic regions, respectively. Additionally, those cases with image-to-patient registration error below 2 mm ensured MODs of 2 mm or lower. Our results show how combining several PSIs leads to low navigation errors and high precision while providing a less invasive surgical approach.
Improved Indoor Fingerprinting Localization Method Using Clustering Algorithm and Dynamic Compensation

Many indoor fingerprinting localization methods are based on signal-domain distances with large localization error and low stability. An improved fingerprinting localization method using a clustering algorithm and dynamic compensation was proposed. In the offline stage, the fingerprint database was built and clustered based on offline hybrid distance and an affinity propagation clustering algorithm. Furthermore, clusters were adjusted using transition regions and a given radius, as well as updating the corresponding position and fingerprint of the cluster centroid. In the online stage, the lost received signal strength (RSS) in the reference fingerprint would be dynamically compensated by using a minimum RSS value, rather than a fixed one. Online signal-domain distance was calculated for cluster identification based on RSS readings and compensated reference fingerprint. Then, K reference points with minimum online signal-domain distances were selected, and affinity propagation clustering was reused by position-domain distances to choose the position-concentrated sub-cluster for location estimation. Experimental results show that the proposed method outperforms state-of-the-art fingerprinting methods, with the mean error of 2.328 m, the root mean square error of 1.865 m and the maximum error of 10.722 m in a testbed of 3200 square meters. The improvement rates, in terms of accuracy and stability, are more than 21% and 13%, respectively.
Shading Style Assessment for Vessel Wall and Lumen Visualization

Current blood vessel rendering usually depicts solely the surface of vascular structures and does not visualize any interior structures. While this approach is suitable for most applications, certain cardiovascular diseases, such as aortic dissection would beneﬁt from a more comprehensive visualization. In this work, we investigate different shading styles for the visualization of the aortic inner and outer wall, including the dissection ﬂap. Finding suitable shading algorithms, techniques, and appropriate parameters is time-consuming when practitioners ﬁne-tune them manually. Therefore, we build a shading pipeline using well-known shading algorithms such as Blinn-Phong, Oren-Nayar, Cook-Torrance, Toon, and extended Lit-Sphere shading with techniques such as the Fresnel effect and screen space ambient occlusion. We interviewed six experts from various domains to ﬁnd the best combination of shadings for preset combinations that maximize user experience and the applicability in clinical settings.
Programming and Synthesis for Software-defined FPGA Acceleration: Status and Future Prospects

FPGA-based accelerators are increasingly popular across a broad range of applications, because they offer massive parallelism, high energy efficiency, and great flexibility for customizations. However, difficulties in programming and integrating FPGAs have hindered their widespread adoption. Since the mid 2000s, there has been extensive research and development toward making FPGAs accessible to software-inclined developers, besides hardware specialists. Many programming models and automated synthesis tools, such as high-level synthesis, have been proposed to tackle this grand challenge. In this survey, we describe the progression and future prospects of the ongoing journey in significantly improving the software programmability of FPGAs. We first provide a taxonomy of the essential techniques for building a high-performance FPGA accelerator, which requires customizations of the compute engines, memory hierarchy, and data representations. We then summarize a rich spectrum of work on programming abstractions and optimizing compilers that provide different trade-offs between performance and productivity. Finally, we highlight several additional challenges and opportunities that deserve extra attention by the community to bring FPGA-based computing to the masses.
Modeling and Processing of Smart Point Clouds of Cultural Relics with Complex Geometries

The digital documentation of cultural relics plays an important role in archiving, protection, and management. In the field of cultural heritage, three-dimensional (3D) point cloud data is effective at expressing complex geometric structures and geometric details on the surface of cultural relics, but lacks semantic information. To elaborate the geometric information of cultural relics and add meaningful semantic information, we propose a modeling and processing method of smart point clouds of cultural relics with complex geometries. An information modeling framework for complex geometric cultural relics was designed based on the concept of smart point clouds, in which 3D point cloud data are organized through the time dimension and different spatial scales indicating different geometric details. The proposed model allows smart point clouds or a subset to be linked with semantic information or related documents. As such, this novel information modeling framework can be used to describe rich semantic information and high-level details of geometry. The proposed information model not only expresses the complex geometric structure of the cultural relics and the geometric details on the surface, but also has rich semantic information, and can even be associated with documents. A case study of the Dazu Thousand-Hand Bodhisattva Statue, which is characterized by a variety of complex geometries, reveals that our proposed framework is capable of modeling and processing the statue with excellent applicability and expansibility. This work provides insights into the sustainable development of cultural heritage protection globally.
Nut Unfastening by Robotic Surface Exploration

In this paper, we present a novel concept and primary investigations regarding automated unfastening of hexagonal nuts by means of surface exploration with a compliant robot. In contrast to the conventional industrial approaches that rely on custom-designed motorised tools and mechanical tool changers, we propose to use robot fingers to position, grasp and unfasten unknown random-sized hexagonal nuts, which are arbitrarily positioned in the robot’s task space. Inspired by how visually impaired people handle unknown objects, in this work, we use information observed from surface exploration to devise the unfastening strategy. It combines torque monitoring with active compliance for the robot fingers to smoothly explore the object’s surface. We implement a shape estimation technique combining scaled iterative closest point and hypotrochoid approximation to estimate the location as well as contour profile of the hexagonal nut so as to accurately position the gripper fingers. We demonstrate this work in the context of dismantling an electrically driven vehicle battery pack. The experiments are conducted using a seven degrees of freedom (DoF)–compliant robot fitted with a two-finger gripper to unfasten four different sized randomly positioned hexagonal nuts. The obtained results suggest an overall exploration and unfastening success rate of 95% over an average of ten trials for each nut.
Defining a Model for Integrating Indoor and Outdoor Network Data to Support Seamless Navigation Applications

Spaces are continuous realms where human beings freely navigate, such as from indoor to outdoor and optionally to another indoor space. However, currently available data models to represent space for navigation do not entirely reflect this continuity of freedom and movement. Data conversion or complications in implementation hinder current approaches to link indoor space with outdoor space due to the variety of present data models. Furthermore, this representation of indoor–outdoor connection becomes oversimplified during the integration process. Consequently, location-based applications based on these datasets are limited in conveying mobility within these spaces and aiding navigation activity. This paper defines a framework for integrating indoor and outdoor navigable space to enable seamless navigation. This model enables the connection between indoor and outdoor navigation networks. We describe the connections between these networks through spatial relationships, which can be generalized to represent various cases of indoor–outdoor transitional spaces. Using sample datasets, we demonstrate the framework’s potential to provide a seamless connection between indoor and outdoor space in a route analysis experiment.
The Integration of Linguistic and Geospatial Features Using Global Context Embedding for Automated Text Geocoding

Geocoding is an essential procedure in geographical information retrieval to associate place names with coordinates. Due to the inherent ambiguity of place names in natural language and the scarcity of place names in textual data, it is widely recognized that geocoding is challenging. Recent advances in deep learning have promoted the use of the neural network to improve the performance of geocoding. However, most of the existing approaches consider only the local context, e.g., neighboring words in a sentence, as opposed to the global context, e.g., the topic of the document. Lack of global information may have a severe impact on the robustness of the model. To fill the research gap, this paper proposes a novel global context embedding approach to generate linguistic and geospatial features through topic embedding and location embedding, respectively. A deep neural network called LGGeoCoder, which integrates local and global features, is developed to solve the geocoding as a classification problem. The experiments on a Wikipedia place name dataset demonstrate that LGGeoCoder achieves competitive performance compared with state-of-the-art models. Furthermore, the effect of introducing global linguistic and geospatial features in geocoding to alleviate the ambiguity and scarcity problem is discussed.
Comparison of GPU-based Methods for Handling Point Cloud Occlusion

Three-dimensional point clouds have conventionally been used along with several sources of information. This fusion can be performed by projecting the point cloud into the image plane and retrieving additional data for each point. Nevertheless, the raw projection omits the occlusion caused by foreground surfaces, thus assigning wrong information to 3D points. For large point clouds, testing the occlusion of each point from every viewpoint is a time-consuming task. Hence, we propose several algorithms implemented in GPU and based on the use of z-buffers. Given the size of nowadays point clouds, we also adapt our methodologies to commodity hardware by splitting the point cloud into several chunks. Finally, we compare their performance through the response time.
Assessing Place Type Similarities Based on Functional Signatures Extracted from Social Media Data

Place types are often used to query places or retrieve data in gazetteers. Existing gazetteers do not use the same place type classification schemes, and the various typing schemes can cause difficulties in data alignment and matching. Different place types may share some level of similarities. However, previous studies have paid little attention to the place type similarities. This study proposes an analytical approach to measuring similarities between place types in multiple typing schemes based on functional signatures extracted from web-harvested place descriptions. In this study, a functional signature consists of three component signature factors: place affordance, events, and key-descriptors. The proposed approach has been tested in a case study using Twitter data. The case study finds high similarity scores between some pairs of types and summarizes the situations when high similarities could occur. The research makes two innovative contributions: First, it proposes a new analytical approach to measuring place type similarities. Second, it demonstrates the potential and benefits of using location-based social media data to better understand places.