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  • 1.
    Ahlström, Eric
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Holmqvist, Lucas
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Goswami, Prashant
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Comparing Traditional Key Frame and Hybrid Animation2017Ingår i: SCA '17 Proceedings of the ACM SIGGRAPH / Eurographics Symposium on Computer Animation, ACM Digital Library, 2017, artikel-id nr. a20Konferensbidrag (Refereegranskat)
    Abstract [en]

    In this research the authors explore a hybrid approach which usesthe basic concept of key frame animation together with proceduralanimation to reduce the number of key frames needed for an animationclip. The two approaches are compared by conducting anexperiment where the participating subjects were asked to ratethem based on their visual appeal.

  • 2.
    Bertilsson, Emil
    et al.
    Blekinge Tekniska Högskola.
    Goswami, Prashant
    Blekinge Tekniska Högskola.
    Dynamic Creation of Multi-resolution Triangulated Irregular Network2016Ingår i: Proceedings of SIGRAD 2016 / [ed] M. Hayashi, 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Triangulated irregular network (TIN) can produce terrain meshes with a reduced triangle count compared toregular grid. At the same time, TIN meshes are more challenging to optimize in real-time in comparison to otherapproaches. This paper explores efficient generation of view-dependent, adaptive TIN meshes for terrain duringruntime with no or minimal preprocessing. This is achieved by reducing the problem of mesh simplification to thatof inexpensive 2D Delaunay triangulation and lifting it back to 3D. The approach and its efficiency is validatedwith suitable datasets.

  • 3. Che, X.
    et al.
    Niu, Y.
    Shui, B.
    Fu, J.
    Fei, G.
    Goswami, Prashant
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Zhang, Y.
    A novel simulation framework based on information asymmetry to evaluate evacuation plan2015Ingår i: The Visual Computer, ISSN 0178-2789, E-ISSN 1432-2315, Vol. 31, nr 6-8, s. 853-861Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In this paper, we present a novel framework to simulate the crowd behavior under emergency situations in a confined space with multiple exits. In our work, we take the information asymmetry into consideration, which is used to model the different behaviors presented by pedestrians because of their different knowledge about the environment. We categorize the factors influencing the preferred velocity into two groups, the intrinsic and extrinsic factors, which are unified into a single space called influence space. At the same time, a finite state machine is employed to control the individual behavior. Different strategies are used to compute the preferred velocity in different states, so that our framework can produce the phenomena of decision change. Our experimental results prove that our framework can be employed to analyze the factors influencing the escape time, such as the number and location of exits, the density distribution of the crowd and so on. Thus it can be used to design and evaluate the evacuation plans. © 2015 Springer-Verlag Berlin Heidelberg

  • 4.
    Frid Kastrati, Mattias
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Goswami, Prashant
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Selective rasterized ray-traced reflections on the GPU2016Ingår i: Eurographics Proceedings STAG 2016 / [ed] Andrea Giachetti and Silvia Biasotti and Marco Tarini, Eurographics - European Association for Computer Graphics, 2016Konferensbidrag (Refereegranskat)
    Abstract [en]

    Ray-tracing achieves impressive effects such as realistic reflections on complex surfaces but is also more computationally expensive than classic rasterization. Rasterized ray-tracing methods can accelerate ray-tracing by taking advantage of the massive parallelization available in the rasterization pipeline on the GPU. In this paper, we propose a selective rasterized raytracing method that optimizes the rasterized ray-tracing by selectively allocating computational resources to reflective regions in the image. Our experiments suggest that the method can speed-up the computation by up to 4 times and also reduce the memory footprint by almost 66% without affecting the image quality. We demonstrate the effectiveness of our method using complex scenes and animations.

  • 5.
    Goswami, Prashant
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier. BTH.
    Real-time landscape-size convective clouds simulation2015Ingår i: Proceedings of the 19th ACM Symposium on Interactive 3D Graphics, ACM, 2015, s. 135-Konferensbidrag (Refereegranskat)
  • 6.
    Goswami, Prashant
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Eliasson, André
    Franzén, Pontus
    Implicit Incompressible SPH on the GPU2015Ingår i: Proceedings of Workshop on Virtual Reality Interaction and Physical Simulation (VRIPHYS), Eurographics - European Association for Computer Graphics, 2015Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents CUDA-based parallelization of implicit incompressible SPH (IISPH) on the GPU. Along with the detailed exposition of our implementation, we analyze various components involved for their costs. We show that our CUDA version achieves near linear scaling with the number of particles and is faster than the multi-core parallelized IISPH on the CPU. We also present a basic comparison of IISPH with the standard SPH on GPU.

  • 7.
    Goswami, Prashant
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap. BTH.
    Markowicz, Christian
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Hassan, Ali
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Real-time particle-based snow simulation on the GPU2019Ingår i: Eurographics Symposium on Parallel Graphics and Visualization / [ed] Hank Childs and Stefan Frey, Porto: Eurographics - European Association for Computer Graphics, 2019Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents a novel real-time particle-based method for simulating snow on the GPU. Our method captures compressionand bonding between snow particles, and incorporates the thermodynamics to model the realistic behavior of snow. Thepresented technique is computationally inexpensive, and is capable of supporting rendering in addition to physics simulation athigh frame rates. The method is completely parallel and is implemented using CUDA. High efficiency and its simplicity makesour method an ideal candidate for integration in existing game SDK frameworks.

  • 8.
    Goswami, Prashant
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Neyret, Fabrice
    Univ. Grenoble, FRA.
    Real-time landscape-size convective clouds simulation and rendering2017Ingår i: Proceedings of Workshop on Virtual Reality Interaction and Physical Simulation, Eurographics - European Association for Computer Graphics, 2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    This paper presents an efficient, physics-based procedural model for the real-time animation and visualization of cumulusclouds at landscape size. We couple a coarse Lagrangian model ofair parcelswith a procedural amplification using volumetricnoise. Our Lagrangian model draws an aerologyi.e.,the atmospheric physics of hydrostatic atmosphere with thermodynamicstransforms, augmented by a model of mixing between parcels and environment. In addition to the particle-particle interactions,we introduce particle-implicit environment interactions. In contrast to the usual fluid simulation, we thus do not need to samplethe transparent environment, a key property for real-time efficiency and scalability to large domains. Inheriting from the high-level physics of aerology, we also validate our simulation by comparing it to predictive diagrams, and we show how the user caneasily control key aspects of the result such as the cloud base and top altitude. Our model is thus fast, physical and controllable.

  • 9.
    Peng, Cong
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Goswami, Prashant
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datavetenskap.
    Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology2019Ingår i: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, nr 8, artikel-id 1747Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

  • 10.
    Peng, Cong
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Goswami, Prashant
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Bai, Guohua
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    An Ontological Approach to Integrate Health Resources from Different Categories of Services2018Ingår i: HEALTHINFO 2018, The Third International Conference on Informatics and Assistive Technologies for Health-Care, Medical Support and Wellbeing, International Academy, Research and Industry Association (IARIA), 2018, s. 48-54Konferensbidrag (Refereegranskat)
    Abstract [en]

    Effective and convenient self-management of health requires collaborative utilization of health data from different services provided by healthcare providers, consumer-facing products and even open data on the Web. Although health data interoperability standards include Fast Healthcare Interoperability Resources (FHIR) have been developed and promoted, it is impossible for all the different categories of services to adopt in the near future. The objective of this study aims to apply Semantic Web technologies to integrate the health data from heterogeneously built services. We present an Web Ontology Language (OWL)-based ontology that models together health data from FHIR standard implemented services, normal Web services and Linked Data. It works on the resource integration layer of the presented layered integration architecture. An example use case that demonstrates how this method integrates the health data into a linked semantic health resource graph with the proposed ontology is presented.

  • 11.
    Peng, Cong
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Goswami, Prashant
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Bai, Guohua
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Fuzzy Matching of OpenAPI Described REST Services2018Ingår i: Procedia Computer Science, Elsevier, 2018, Vol. 126, s. 1313-1322Konferensbidrag (Refereegranskat)
    Abstract [en]

    The vast amount of Web services brings the problem of discovering desired services for composition and orchestration. The syntactic service matching methods based on the classical set theory have a difficulty to capture the imprecise information. Therefore, an approximate service matching approach based on fuzzy control is explored in this paper. A service description matching model to the OpenAPI specification, which is the most widely used standard for describing the defacto REST Web services, is proposed to realize the fuzzy service matching with the fuzzy inference method developed by Mamdani and Assilian. An evaluation shows that the fuzzy service matching approach performed slightly better than the weighted approach in the setting context.

  • 12.
    Peng, Cong
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Goswami, Prashant
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Bai, Guohua
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Linking Health Web Services as Resource Graph by Semantic REST Resource Tagging2018Ingår i: Procedia Computer Science / [ed] Shakshuki E.,Yasar A., Elsevier, 2018, Vol. 141, s. 319-326Konferensbidrag (Refereegranskat)
    Abstract [en]

    Various health Web services host a huge amount of health data about patients. The heterogeneity of the services hinders the collaborative utilization of these health data, which can provide a valuable support for the self-management of chronic diseases. The combination of REST Web services and Semantic Web technologies has proven to be a viable approach to address the problem. This paper proposes a method to add semantic annotations to the REST Web services. The service descriptions and the resource representations with semantic annotations can be transformed into a resource graph. It integrates health data from different services, and can link to the health-domain ontologies and Linked Open Health Data to support health management and imaginative applications. The feasibility of out method is demonstrated by realizing with OpenAPI service description and JSON-LD representation in an example use case.

  • 13.
    Sun, Bin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Cheng, Wei
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Bai, Guohua
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Goswami, Prashant
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Correcting and complementing freeway traffic accident data using mahalanobis distance based outlier detection2017Ingår i: Technical Gazette, ISSN 1330-3651, E-ISSN 1848-6339, Vol. 24, nr 5, s. 1597-1607Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A huge amount of traffic data is archived which can be used in data mining especially supervised learning. However, it is not being fully used due to lack of accurate accident information (labels). In this study, we improve a Mahalanobis distance based algorithm to be able to handle differential data to estimate flow fluctuations and detect accidents and use it to support correcting and complementing accident information. The outlier detection algorithm provides accurate suggestions for accident occurring time, duration and direction. We also develop a system with interactive user interface to realize this procedure. There are three contributions for data handling. Firstly, we propose to use multi-metric traffic data instead of single metric for traffic outlier detection. Secondly, we present a practical method to organise traffic data and to evaluate the organisation for Mahalanobis distance. Thirdly, we describe a general method to modify Mahalanobis distance algorithms to be updatable. © 2017, Strojarski Facultet. All rights reserved.

  • 14.
    Sun, Bin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Cheng, Wei
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Goswami, Prashant
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Bai, Guohua
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Short-Term Traffic Forecasting Using Self-Adjusting k-Nearest Neighbours2018Ingår i: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, nr 1, s. 41-48Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting.However, kNN parameters self-adjustment has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training. We used realworld data with more than one-year traffic records to conduct experiments. The results show that DP-kNN can perform better than manually adjusted kNN and other benchmarking methods with regards to accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.

  • 15.
    Sun, Bin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Liyao, Ma
    University of Jinan, CHI.
    Wei, Cheng
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Wei, Wen
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för teknik och estetik.
    Prashant, Goswami
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Guohua, Bai
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    An Improved k-Nearest Neighbours Method for Traffic Time Series Imputation2017Konferensbidrag (Refereegranskat)
    Abstract [en]

    Intelligent transportation systems (ITS) are becoming more and more effective, benefiting from big data. Despite this, missing data is a problem that prevents many prediction algorithms in ITS from working effectively. Much work has been done to impute those missing data. Among different imputation methods, k-nearest neighbours (kNN) has shown excellent accuracy and efficiency. However, the general kNN is designed for matrix instead of time series so it lacks the usage of time series characteristics such as windows and weights that are gap-sensitive. This work introduces gap-sensitive windowed kNN (GSW-kNN) imputation for time series. The results show that GSW-kNN is 34% more accurate than benchmarking methods, and it is still robust even if the missing ratio increases to 90%.

  • 16.
    Sun, Bin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Wei, Cheng
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Liyao, Ma
    University of Jinan, CHN.
    Prashant, Goswami
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Anomaly-Aware Traffic Prediction Based on Automated Conditional Information Fusion2018Ingår i: Proceedings of 21st International Conference on Information Fusion, IEEE conference proceedings, 2018Konferensbidrag (Refereegranskat)
    Abstract [en]

    Reliable and accurate short-term traffic prediction plays a key role in modern intelligent transportation systems (ITS) for achieving efficient traffic management and accident detection. Previous work has investigated this topic but lacks study on automated anomaly detection and conditional information fusion for ensemble methods. This works aims to improve prediction accuracy by fusing information considering different traffic conditions in ensemble methods. In addition to conditional information fusion, a day-week decomposition (DWD) method is introduced for preprocessing before anomaly detection. A k-nearest neighbours (kNN) based ensemble method is used as an example. Real-world data are used to test the proposed method with stratified ten-fold cross validation. The results show that the proposed method with incident labels improves predictions up to 15.3% and the DWD enhanced anomaly-detection improves predictions up to 8.96%. Conditional information fusion improves ensemble prediction methods, especially for incident traffic. The proposed method works well with enhanced detections and the procedure is fully automated. The accurate predictions lead to more robust traffic control and routing systems.

  • 17.
    Sun, Bin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Wei, Cheng
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Prashant, Goswami
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Guohua, Bai
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    An Overview of Parameter and Data Strategies for K-Nearest Neighbours Based Short-Term Traffic Prediction2017Ingår i: ACM International Conference Proceeding Series Volume Part F133326, Association for Computing Machinery (ACM), 2017, s. 68-74Konferensbidrag (Refereegranskat)
    Abstract [en]

    Modern intelligent transportation systems (ITS) requires reliable and accurate short-term traffic prediction. One widely used method to predict traffic is k-nearest neighbours (kNN). Though many studies have tried to improve kNN with parameter strategies and data strategies, there is no comprehensive analysis of those strategies. This paper aims to analyse kNN strategies and guide future work to select the right strategy to improve prediction accuracy. Firstly, we examine the relations among three kNN parameters, which are: number of nearest neighbours (k), search step length (d) and window size (v). We also analysed predict step ahead (m) which is not a parameter but a user requirement and configuration. The analyses indicate that the relations among parameters are compound especially when traffic flow states are considered. The results show that strategy of using v leads to outstanding accuracy improvement. Later, we compare different data strategies such as flow-aware and time-aware ones together with ensemble strategies. The experiments show that the flowaware strategy performs better than the time-aware one. Thus, we suggest considering all parameter strategies simultaneously as ensemble strategies especially by including v in flow-aware strategies.

  • 18.
    Sun, Bin
    et al.
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Wei, Cheng
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Prashant, Goswami
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Guohua, Bai
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för kreativa teknologier.
    Flow-Aware WPT k-Nearest Neighbours Regression for Short-Term Traffic Prediction2017Ingår i: Proceedings - IEEE Symposium on Computers and Communications, Institute of Electrical and Electronics Engineers (IEEE), 2017, Vol. 07, s. 48-53, artikel-id 8024503Konferensbidrag (Refereegranskat)
    Abstract [en]

    Robust and accurate traffic prediction is critical in modern intelligent transportation systems (ITS). One widely used method for short-term traffic prediction is k-nearest neighbours (kNN). However, choosing the right parameter values for kNN is problematic. Although many studies have investigated this problem, they did not consider all parameters of kNN at the same time. This paper aims to improve kNN prediction accuracy by tuning all parameters simultaneously concerning dynamic traffic characteristics. We propose weighted parameter tuples (WPT) to calculate weighted average dynamically according to flow rate. Comprehensive experiments are conducted on one-year real-world data. The results show that flow-aware WPT kNN performs better than manually tuned kNN as well as benchmark methods such as extreme gradient boosting (XGB) and seasonal autoregressive integrated moving average (SARIMA). Thus, it is recommended to use dynamic parameters regarding traffic flow and to consider all parameters at the same time.

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