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"title": "Laughter Type Recognition from Whole Body Motion",
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"abstract": "Despite the importance of laughter in social interactions it remains little studied in affective computing. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received almost no attention. The aim of this study is twofold: first an investigation into observers' perception of laughter states (hilarious, social, awkward, fake, and non-laughter) based on body movements alone, through their categorization of avatars animated with natural and acted motion capture data. Significant differences in torso and limb movements were found between animations perceived as containing laughter and those perceived as nonlaughter. Hilarious laughter also differed from social laughter in the amount of bending of the spine, the amount of shoulder rotation and the amount of hand movement. The body movement features indicative of laughter differed between sitting and standing avatar postures. Based on the positive findings in this perceptual study, the second aim is to investigate the possibility of automatically predicting the distributions of observer's ratings for the laughter states. The findings show that the automated laughter recognition rates approach human rating levels, with the Random Forest method yielding the best performance.",
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"content": "Despite the importance of laughter in social interactions it remains little studied in affective computing. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received almost no attention. The aim of this study is twofold: first an investigation into observers' perception of laughter states (hilarious, social, awkward, fake, and non-laughter) based on body movements alone, through their categorization of avatars animated with natural and acted motion capture data. Significant differences in torso and limb movements were found between animations perceived as containing laughter and those perceived as nonlaughter. Hilarious laughter also differed from social laughter in the amount of bending of the spine, the amount of shoulder rotation and the amount of hand movement. The body movement features indicative of laughter differed between sitting and standing avatar postures. Based on the positive findings in this perceptual study, the second aim is to investigate the possibility of automatically predicting the distributions of observer's ratings for the laughter states. The findings show that the automated laughter recognition rates approach human rating levels, with the Random Forest method yielding the best performance.",
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"abstract": "The problem of illumination normalization in the field of computer vision is a subject that has been studied steadily so far. However, the treatment of strong illumination has not been done to date. In this paper, we present a method of illumination normalization using a deep learning method, the conditional generative adversarial network (CGAN), to reconstruct accurate 3D skin textures from a single image toward efficient haptic palpation. After normalizing the illumination through the conditional generative adversarial network in which the input image is conditioned to obtain the desired output image, bilateral filtering that removes noise while preserving the boundaries enhances fine wrinkles. As the refinement process of enhancing skin textures, intrinsic image decomposition is performed to obtain a shading layer image with histogram equalization that is merged into the normalized image obtained by the illumination normalization process to enhance skin tactile properties (wrinkle and roughness). Through these processes, we can obtain a depth image with normalized illumination and enhanced skin wrinkle texture. Using this, the depth of the skin surface texture is restored in three dimensions precisely. The superiority of the illumination normalization method proposed in this paper over three other illumination normalization methods (CIDRE, LDCT, and TT) is verified through comparison. We also confirm the illumination normalization performance of our method through restoration of the three-dimensional skin surface.",
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"abstract": "With maturing technologies and availability of the sensor-enriched device, the driving force behind handheld augmented reality (HAR) technology will be leaning towards the experience technology can bring. Though attentions are gathered on usability and conventions for this technology, the user experience cannot be ignored. It will be more commonly available in the hands of the public and become a technology that is not solely used by experts. It will be important to approach the technology with friendlier and more user-centered focus to bring it out of research labs and into people's life. In this paper, we will introduce design exploration constructs inspired from a method that is commonly used in the field of the industrial design to guide designers of HAR application to explore different aspects of user experience. The purpose of proposing AEIOU is to provide a platform to create encompassing user experience that is beyond the usability considerations or the existing design conventions. The advantage of having such a platform is to provide a starting ground for discussion and betterment of HAR application. The use of these constructs is discussed in relation to the design process of AR browser.",
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"normalizedAbstract": "With maturing technologies and availability of the sensor-enriched device, the driving force behind handheld augmented reality (HAR) technology will be leaning towards the experience technology can bring. Though attentions are gathered on usability and conventions for this technology, the user experience cannot be ignored. It will be more commonly available in the hands of the public and become a technology that is not solely used by experts. It will be important to approach the technology with friendlier and more user-centered focus to bring it out of research labs and into people's life. In this paper, we will introduce design exploration constructs inspired from a method that is commonly used in the field of the industrial design to guide designers of HAR application to explore different aspects of user experience. The purpose of proposing AEIOU is to provide a platform to create encompassing user experience that is beyond the usability considerations or the existing design conventions. The advantage of having such a platform is to provide a starting ground for discussion and betterment of HAR application. The use of these constructs is discussed in relation to the design process of AR browser.",
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"abstract": "Diminished Reality (DR) has been considered as a sub-technology of Mixed and Augmented Reality (AR/MR). While AR/MR means technologies that add and/or overlay visual information onto images of real scene for providing users to enhance their visual experiences with the added/overlaid information, DR aims the similar enhanced visual experiences by deleting visual information from the images of real scene. Adding and deleting visual information might be considered as same technical issues, but they are actually totally different. In DR, visual information that is hidden by the deleted object should be recovered for filling into the deleted area. This recovery of the hidden area is not required for general adding/overlaying based AR/MR, but should be one of the typical issues for achieving DR. Camera pose estimation and tracking is a typical issue in AR/MR, but the condition of the scene and required performance for DR are not always the same as AR/MR. For example, the object to be diminished/removed should be detected and tracked while the camera is freely moving for DR. In this tutorial, challenging technical issues for DR are addressed, such as recovery of hidden area, detecting and tracking the object to be removed/diminished, tracking camera poses, illumination matching and re-lighting, etc. In addition to those technical issues for DR, a survey of applications of DR, expected futures with DR, and human factors of DR are also presented.",
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"abstract": "High-radix, low-diameter, hierarchical networks based on the Dragonfly topology are common picks for building next generation HPC systems. However, effective tools are lacking for analyzing the network performance and exploring the design choices for such emerging networks at scale. In this paper, we present visual analytics methods that couple data aggregation techniques with interactive visualizations for analyzing large-scale Dragonfly networks. We create an interactive visual analytics system based on these techniques. To facilitate effective analysis and exploration of network behaviors, our system provides intuitive, scalable visualizations that can be customized to show various traffic characteristics and correlate between different performance metrics. Using high-fidelity network simulation and HPC applications communication traces, we demonstrate the usefulness of our system with several case studies on exploring network behaviors at scale with different workloads, routing strategies, and job placement policies. Our simulations and visualizations provide valuable insights for mitigating network congestion and inter-job interference.",
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"abstract": "With the increasing diffusion of Internet probing technologies, a large amount of regularly collected traceroutes are available for Internet Service Providers (ISPs) at low cost. We introduce a practically applicable methodology and algorithm that, given solely an arbitrary set of traceroutes, spot routing paths that change similarly over time, aggregate them into inferred events, and report each event along with the impacted observation points and a small set of IP addresses that can help identify its cause. The formal model at the basis of our methodology revolves around the notion of empathy, a relation that binds similarly behaving traceroutes. The correctness and completeness of our approach are based on structural properties that are easily expressed in terms of empathic measurements. We perform experiments with data from public measurement infrastructures like RIPE Atlas, showing the effectiveness of our algorithm in distilling events from a large amount of traceroute data. We also validate the accuracy of the inferred events against ground-truth knowledge of routing changes originating from induced and spontaneous routing events. Given these promising results, we believe our methodology can be an effective aid for ISPs to detect and track routing changes affecting many users (with potentially adverse effects on their connection quality).",
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"abstract": "Critical networked services are usually regulated by Service Level Agreements (SLAs). In order to ensure SLAs are being met, it is necessary to monitor Service Level Objectives (SLOs). Active measurement mechanisms are usually chosen to perform this monitoring task, which requires measurement probes to be activated in network devices. However, these probes are expensive in terms of computational resources consumption, thus, active measurement mechanisms usually can cover only a fraction of what could be measured, which can lead to SLA violations being missed. Besides that, highly dynamic networking patterns require the ongoing selection of the candidate network destinations for probing and their respective prioritization, a practice that is not well suited for human administrators because configuring the probes is labor-intensive and error-prone. A possibility to improve the detection of SLA violations is the employment of Peer-to-Peer (P2P) technology in order to steer tasks related to a distributed decision making process for probe activation. In this context, a P2P management overlay can be used to coordinate the probe activation and to share measurement results among the network devices. For a node to rely on measurement data from a peer to determine which probes to configure, it needs to know which of the peers are best correlated with itself, i.e., which nodes have the most significance in terms of being indicative of service level violations that might be observed by the node itself. We propose an autonomic P2P solution to coordinate the placement of active measurement probes in large-scale networks. The edge nodes of the network cooperate via a P2P management overlay to determine what destinations should be monitored. Each edge node determines autonomously what destinations to probe considering local measurements and measurement data from other edge nodes. The measurements considered are traffic information from passive measurement results and past service level measurement results from active measurement results. The proposed solution is evaluated using simulation and the results show its feasibility and interesting features.",
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"content": "Critical networked services are usually regulated by Service Level Agreements (SLAs). In order to ensure SLAs are being met, it is necessary to monitor Service Level Objectives (SLOs). Active measurement mechanisms are usually chosen to perform this monitoring task, which requires measurement probes to be activated in network devices. However, these probes are expensive in terms of computational resources consumption, thus, active measurement mechanisms usually can cover only a fraction of what could be measured, which can lead to SLA violations being missed. Besides that, highly dynamic networking patterns require the ongoing selection of the candidate network destinations for probing and their respective prioritization, a practice that is not well suited for human administrators because configuring the probes is labor-intensive and error-prone. A possibility to improve the detection of SLA violations is the employment of Peer-to-Peer (P2P) technology in order to steer tasks related to a distributed decision making process for probe activation. In this context, a P2P management overlay can be used to coordinate the probe activation and to share measurement results among the network devices. For a node to rely on measurement data from a peer to determine which probes to configure, it needs to know which of the peers are best correlated with itself, i.e., which nodes have the most significance in terms of being indicative of service level violations that might be observed by the node itself. We propose an autonomic P2P solution to coordinate the placement of active measurement probes in large-scale networks. The edge nodes of the network cooperate via a P2P management overlay to determine what destinations should be monitored. Each edge node determines autonomously what destinations to probe considering local measurements and measurement data from other edge nodes. The measurements considered are traffic information from passive measurement results and past service level measurement results from active measurement results. The proposed solution is evaluated using simulation and the results show its feasibility and interesting features.",
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"doi": "10.1109/TrustCom.2012.140",
"title": "Efficient Internet Mapping in the Presence of Network Load Balancers",
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"abstract": "Traceroute is a popular tool that has many applications in network troubleshooting and mapping. We propose in this paper a six-step methodology for creating a more efficient profile-based probing strategy. The basic idea behind our methodology is examining how often routing changes occur in the routing path at the different times of the day. This helps in giving a high probing weight for parts of the routing path that change frequently while lower probing weight is assigned for parts that change rarely in the different time periods of the day. Our experimental results show that our approach exploits regularities in the routing changes and achieves great savings in the number of probes. In fact, our approach achieves a 66% reduction in the number of probes compared to classical Traceroute that is launched periodically. This is an important enhancement for systems that scan the Internet repeatedly. Furthermore, we show another enhancement that cuts scanning time by 90%. This is achieved by scanning multiple hop levels at the same time after marking the probes in order to match them with the received responses.",
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"content": "Traceroute is a popular tool that has many applications in network troubleshooting and mapping. We propose in this paper a six-step methodology for creating a more efficient profile-based probing strategy. The basic idea behind our methodology is examining how often routing changes occur in the routing path at the different times of the day. This helps in giving a high probing weight for parts of the routing path that change frequently while lower probing weight is assigned for parts that change rarely in the different time periods of the day. Our experimental results show that our approach exploits regularities in the routing changes and achieves great savings in the number of probes. In fact, our approach achieves a 66% reduction in the number of probes compared to classical Traceroute that is launched periodically. This is an important enhancement for systems that scan the Internet repeatedly. Furthermore, we show another enhancement that cuts scanning time by 90%. This is achieved by scanning multiple hop levels at the same time after marking the probes in order to match them with the received responses.",
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"abstract": "Traceroute is the most widely used Internet path analysis tool today to study the topology of the Internet and to diagnose routing failures as well as poor performance events. A major limitation of traceroute when the destination is not controllable by the user is its inability to measure reverse paths, i.e., the path from any given destination back to the source. This is a major drawback for ISPs, who need to understand the performance of the Internet paths connecting popular services (e.g., YouTube and Facebook) to their customers. Even if public servers and distributed measurement platforms can provide partial reverse path visibility through ad-hoc measurements, there is still a need for a structured approach capable of analyzing the performance of Internet paths connecting any pair of nodes (servers, routers, hosts, etc.). While the problem of reverse traceroute has been addressed in the past, proposed techniques rely on IP address spoofing - which might lead to security concerns, and assume the availability of certain route-tracking options -, which might not be available. In this paper, we introduce and evaluate DisNETPerf, a new tool which provides exactly the same type of information as traceroute, but for paths connecting arbitrarily selected nodes. DisNETPerf works by firstly locating probes (i.e., measurement points) that are the closest to a given target node, using them to perform traceroute measurements from the target point-of-view to a given destination for path performance monitoring and troubleshooting purposes. We propose two techniques for probe location, and demonstrate that the reverse path (from server to users) can be measured with very high accuracy in certain scenarios. We also analyze relevant characteristics of Internet paths and distributed measurement platforms, which reinforce the applicability and relevance of DisNETPerf in current Internet.",
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"title": "Statistical Optimal Hash-Based Longest Prefix Match",
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"abstract": "Longest Prefix Match (LPM) is a basic and important function for current network devices. Hash-based approaches appear to be excellent candidate solutions for LPM with the capability of fast lookup speed and low latency. The number of hash table probes, i.e. the search path of a hash-based LPM algorithm, directly determines the lookup performance. In this paper, we propose Ω-LPM to improve the lookup performance by optimizing the search path of the hash-based LPM. Ω-LPM first reconstructs the forwarding table to support random search, then it applies a dynamic programming algorithm to find the shortest search path based on the statistics of the matching probabilities. Ω-LPM concretely reduces the number of hash table probes via searching most of the packets in optimal search paths. Even in the worst case, the upper bound of the average search path of Ω-LPM is 1 + log2(N), here N is the length of the longest prefix in the routing table. The case studies of the name lookup in Named Data Networking and the IP lookup in current Internet demonstrate that Ω-LPM can shorten 61.04% and 86.88% search paths compared with the basic hash-based methods of name lookup and IP lookup, respectively, furthermore Ω-LPM reduces 32.3% probes of the name lookup and 73.55% probes of the IP lookup compared with the optimal linear search. ThLongest Prefix Match (LPM) is a basic and important function for current network devices. Hash-based approaches appear to be excellent candidate solutions for LPM with the capability of fast lookup speed and low latency. The number of hash table probes, i.e. the search path of a hash-based LPM algorithm, directly determines the lookup performance. In this paper, we propose Ω-LPM to improve the lookup performance by optimizing the search path of the hash-based LPM. Ω-LPM first reconstructs the forwarding table to support random search, then it applies a dynamic programming algorithm to find the shortest search path based on the statistics of the matching probabilities. Ω-LPM concretely reduces the number of hash table probes via searching most of the packets in optimal search paths. Even in the worst case, the upper bound of the average search path of Ω-LPM is 1 + log2(N), here N is the length of the longest prefix in the routing table. The case studies of the name lookup in Named Data Networking and the IP lookup in current Internet demonstrate that Ω-LPM can shorten 61.04% and 86.88% search paths compared with the basic hash-based methods of name lookup and IP lookup, respectively, furthermore Ω-LPM reduces 32.3% probes of the name lookup and 73.55% probes of the IP lookup compared with the optimal linear search. The experimental results conducted on extensional name tables and IP tables also show that Ω-LPM has both low memory overhead and excellent scalability.e experimental results conducted on extensional name tables and IP tables also show that Ω-LPM has both low memory overhead and excellent scalability.",
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"content": "Longest Prefix Match (LPM) is a basic and important function for current network devices. Hash-based approaches appear to be excellent candidate solutions for LPM with the capability of fast lookup speed and low latency. The number of hash table probes, i.e. the search path of a hash-based LPM algorithm, directly determines the lookup performance. In this paper, we propose Ω-LPM to improve the lookup performance by optimizing the search path of the hash-based LPM. Ω-LPM first reconstructs the forwarding table to support random search, then it applies a dynamic programming algorithm to find the shortest search path based on the statistics of the matching probabilities. Ω-LPM concretely reduces the number of hash table probes via searching most of the packets in optimal search paths. Even in the worst case, the upper bound of the average search path of Ω-LPM is 1 + log2(N), here N is the length of the longest prefix in the routing table. The case studies of the name lookup in Named Data Networking and the IP lookup in current Internet demonstrate that Ω-LPM can shorten 61.04% and 86.88% search paths compared with the basic hash-based methods of name lookup and IP lookup, respectively, furthermore Ω-LPM reduces 32.3% probes of the name lookup and 73.55% probes of the IP lookup compared with the optimal linear search. ThLongest Prefix Match (LPM) is a basic and important function for current network devices. Hash-based approaches appear to be excellent candidate solutions for LPM with the capability of fast lookup speed and low latency. The number of hash table probes, i.e. the search path of a hash-based LPM algorithm, directly determines the lookup performance. In this paper, we propose Ω-LPM to improve the lookup performance by optimizing the search path of the hash-based LPM. Ω-LPM first reconstructs the forwarding table to support random search, then it applies a dynamic programming algorithm to find the shortest search path based on the statistics of the matching probabilities. Ω-LPM concretely reduces the number of hash table probes via searching most of the packets in optimal search paths. Even in the worst case, the upper bound of the average search path of Ω-LPM is 1 + log2(N), here N is the length of the longest prefix in the routing table. The case studies of the name lookup in Named Data Networking and the IP lookup in current Internet demonstrate that Ω-LPM can shorten 61.04% and 86.88% search paths compared with the basic hash-based methods of name lookup and IP lookup, respectively, furthermore Ω-LPM reduces 32.3% probes of the name lookup and 73.55% probes of the IP lookup compared with the optimal linear search. The experimental results conducted on extensional name tables and IP tables also show that Ω-LPM has both low memory overhead and excellent scalability.e experimental results conducted on extensional name tables and IP tables also show that Ω-LPM has both low memory overhead and excellent scalability.",
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"normalizedAbstract": "Longest Prefix Match (LPM) is a basic and important function for current network devices. Hash-based approaches appear to be excellent candidate solutions for LPM with the capability of fast lookup speed and low latency. The number of hash table probes, i.e. the search path of a hash-based LPM algorithm, directly determines the lookup performance. In this paper, we propose Ω-LPM to improve the lookup performance by optimizing the search path of the hash-based LPM. Ω-LPM first reconstructs the forwarding table to support random search, then it applies a dynamic programming algorithm to find the shortest search path based on the statistics of the matching probabilities. Ω-LPM concretely reduces the number of hash table probes via searching most of the packets in optimal search paths. Even in the worst case, the upper bound of the average search path of Ω-LPM is 1 + log2(N), here N is the length of the longest prefix in the routing table. The case studies of the name lookup in Named Data Networking and the IP lookup in current Internet demonstrate that Ω-LPM can shorten 61.04% and 86.88% search paths compared with the basic hash-based methods of name lookup and IP lookup, respectively, furthermore Ω-LPM reduces 32.3% probes of the name lookup and 73.55% probes of the IP lookup compared with the optimal linear search. ThLongest Prefix Match (LPM) is a basic and important function for current network devices. Hash-based approaches appear to be excellent candidate solutions for LPM with the capability of fast lookup speed and low latency. The number of hash table probes, i.e. the search path of a hash-based LPM algorithm, directly determines the lookup performance. In this paper, we propose Ω-LPM to improve the lookup performance by optimizing the search path of the hash-based LPM. Ω-LPM first reconstructs the forwarding table to support random search, then it applies a dynamic programming algorithm to find the shortest search path based on the statistics of the matching probabilities. Ω-LPM concretely reduces the number of hash table probes via searching most of the packets in optimal search paths. Even in the worst case, the upper bound of the average search path of Ω-LPM is 1 + log2(N), here N is the length of the longest prefix in the routing table. The case studies of the name lookup in Named Data Networking and the IP lookup in current Internet demonstrate that Ω-LPM can shorten 61.04% and 86.88% search paths compared with the basic hash-based methods of name lookup and IP lookup, respectively, furthermore Ω-LPM reduces 32.3% probes of the name lookup and 73.55% probes of the IP lookup compared with the optimal linear search. The experimental results conducted on extensional name tables and IP tables also show that Ω-LPM has both low memory overhead and excellent scalability.e experimental results conducted on extensional name tables and IP tables also show that Ω-LPM has both low memory overhead and excellent scalability.",
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"Dynamic Programming",
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"Internet",
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"Dynamic Programming Algorithm",
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"Named Data Networking",
"IP Lookup",
"Internet",
"IP Networks",
"Engines",
"Probes",
"Heuristic Algorithms",
"Routing",
"Computers",
"Information Science",
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"Router",
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"Forwarding"
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"fullName": "Huichen Dai",
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{
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"fullName": "Hao Wu",
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"abstract": "Unwanted or unusual delay changes on an Internet path can happen, though possibly rarely. In order to understand and manage the network to potentially avoid such abrupt changes, it is crucial and challenging to locate which part of the path has contributed critically to such delay disruptions so that some corresponding actions may be taken. To tackle this challenge, the present paper proposes a simple and novel approach. The proposed approach relies only on end-to-end measurements of the path. Specifically, the end-to-end measurements include end-to-end delay measurement and traceroute measurement.With the former, abrupt delay increases are flagged and a delay disruption matrix is constructed. With the latter, a routing matrix for the end-to-end path is formed. The key idea of the proposed approach is to link the routing matrix to the delay disruption matrix and make use of compressed sensing theory to infer critical hops in the routing matrix which contribute to the abrupt delay increases. To demonstrate its effectiveness, the proposed approach is applied to a real Internet path. The results are encouraging, showing that the proposed approach is able to locate the hops that have the most significant impact on or contribute the most to abrupt increases on the end-to-end delay of the measured Internet path.",
"abstracts": [
{
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"content": "Unwanted or unusual delay changes on an Internet path can happen, though possibly rarely. In order to understand and manage the network to potentially avoid such abrupt changes, it is crucial and challenging to locate which part of the path has contributed critically to such delay disruptions so that some corresponding actions may be taken. To tackle this challenge, the present paper proposes a simple and novel approach. The proposed approach relies only on end-to-end measurements of the path. Specifically, the end-to-end measurements include end-to-end delay measurement and traceroute measurement.With the former, abrupt delay increases are flagged and a delay disruption matrix is constructed. With the latter, a routing matrix for the end-to-end path is formed. The key idea of the proposed approach is to link the routing matrix to the delay disruption matrix and make use of compressed sensing theory to infer critical hops in the routing matrix which contribute to the abrupt delay increases. To demonstrate its effectiveness, the proposed approach is applied to a real Internet path. The results are encouraging, showing that the proposed approach is able to locate the hops that have the most significant impact on or contribute the most to abrupt increases on the end-to-end delay of the measured Internet path.",
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"title": "Fire Detection Based on Image Processing in Coal Mine",
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"abstract": "Fire detection is very important in safety area, and at all times it is a difficult problem in coal mine. In this paper, a real-time fire-detector algorithm is proposed. In early stage there is no evident flame, and the temperature is low, the visible light is scarce, so the signal is difficult to achieve by ordinary color CCD. Here infrared CCD is employed as a probe, and obtained infrared image is input the computer, then fire recognition is determined based on image processing. Firstly, the grey scale binary image is partitioned by entropy threshold method and then smoothed by Median filtering. Then significant characteristics of flame are extracted and after normalization the foregoing data are input neutral network for recognition. All of the above clues are combined to reach a final decision. Experiment results show that the proposed method is successful in detecting fire or flames. In addition, it reduces the false alarms remarkably. It is also shown that the method works in a variety of conditions. The system has high validity and accuracy.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Fire detection is very important in safety area, and at all times it is a difficult problem in coal mine. In this paper, a real-time fire-detector algorithm is proposed. In early stage there is no evident flame, and the temperature is low, the visible light is scarce, so the signal is difficult to achieve by ordinary color CCD. Here infrared CCD is employed as a probe, and obtained infrared image is input the computer, then fire recognition is determined based on image processing. Firstly, the grey scale binary image is partitioned by entropy threshold method and then smoothed by Median filtering. Then significant characteristics of flame are extracted and after normalization the foregoing data are input neutral network for recognition. All of the above clues are combined to reach a final decision. Experiment results show that the proposed method is successful in detecting fire or flames. In addition, it reduces the false alarms remarkably. It is also shown that the method works in a variety of conditions. The system has high validity and accuracy.",
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"fno": "06063289",
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"abstract": "A 2/3 inch interline CCD image sensor for NTSC system with 250000 pixels has been developed. This device adopts buried channel CCD and vertical overflow drain structure for high charge transfer efficiency/low noise and good antiblooming/smear reduction capability respectively. It was fabricated by a double-p-well double-poly double-metal technology, and 1.5 mu m design rule process. Gettering was also applied for dark current reduction. On chip color filter, Y/Cy/G complementary color, checkerboard pattern, was developed for color image sensing.<>",
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"abstract": "In order to make up for the defects of traditional temperature measurement methods, CCD cameras are gradually applied to the measurement of temperature fields of high-temperature objects. In this paper, by establishing the furnace flame radiation energy imaging and temperature imaging model, an algorithm for reconstructing the temperature field is proposed, and the feasibility of reconstructing the three-dimensional temperature field of a large furnace chamber is analyzed and demonstrated. On this basis, a breakthrough in real-time visualization and detection technology of the three-dimensional temperature field in the combustion chamber of an aero-engine is realized.",
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"abstract": "A system for the computer-aided guided interactive diagnosis of rare diseases is presented. It is based on dynamic taxonomies, a knowledge management model that allows the guided interactive exploration of complex information bases. The problem of the diagnosis of pathologies is recasted as a problem of exploring and thinning out candidate pathologies on the basis of clinical signs and other observable features. Our early experience with this tool is discussed both in terms of advantages with respect to existing approaches, and in terms of extensions and improvements.",
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"abstract": "Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training. While these methods achieve reasonable improvements in performance, they typically perform category-agnostic domain alignment, thereby resulting in negative transfer of features. To overcome this issue, in this work, we attempt to incorporate category information into the domain adaptation process by proposing Memory Guided Attention for Category-Aware Domain Adaptation (MeGA-CDA). The proposed method consists of employing category-wise discriminators to ensure category-aware feature alignment for learning domain-invariant discriminative features. However, since the category information is not available for the target samples, we propose to generate memory-guided category-specific attention maps which are then used to route the features appropriately to the corresponding category discriminator. The proposed method is evaluated on several benchmark datasets and is shown to outperform existing approaches.",
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"abstract": "Automation can provide significant performance improvements in digital manufacturing systems that customize shapes of implants and prosthetic devices to the anatomy of a patient. The challenge, however, lies in the ability of an automatic solution to adapt to anatomical variations of a given object category. This paper presents a hierarchical framework that generalizes the digital design of anatomical surface models in terms of a small number of prototypes. The latter are derived from the local shape information of constituent parts via shape matching and clustering and then associated with one operation that dictates how a shape undergoes modification. We demonstrate the proposed technique through application to typical hearing aid design operations with promising results.",
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"abstract": "We present a novel and reliable approach for complex object acquisition and surface registration using hybrid geometric shape features in a hierarchical 3D shape approximation and segmentation approach. First, instead of relying on one type of scanned data, we propose to use hybrid data provided that it can support both global and local geometric shape features. The scanned low-resolution global data supplies the global shape prior for registering the high-resolution local surface patches. Local surfaces can thus be optimally registered requiring less overlap and reducing uncertainty. Second, we cannot directly register huge volumes of data simultaneously due to the memory bottlenecks. We segment the global low-resolution model into several meaningful sub-shapes extending a hierarchical algorithm. The local surfaces can be registered on the sub-shapes respectively and all sub-shapes can be merged and rendered after registration. To verify the reliability of the approach, various 3D models have been acquired. The experiments show compelling results by reconstructing very detailed models of complex objects. The approach can be applied to practical 3D modeling applications.",
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