In order to study the relationship between landmarks and spatial memory in short-nosed fruit bat, Cynopterus sphinx （Megachiroptera, Pteropodidae）, we simulated a foraging environment in the laboratory. Different la...In order to study the relationship between landmarks and spatial memory in short-nosed fruit bat, Cynopterus sphinx （Megachiroptera, Pteropodidae）, we simulated a foraging environment in the laboratory. Different landmarks were placed to gauge the spatial memory of C. sphinx. We changed the number of landmarks every day with 0 landmarks again on the fifth day （from 0, 2, 4, 8 to 0）. Individuals from the control group were exposed to the identical artificial foraging environment, but without landmarks. The results indicated that there was significant correlation between the time of the first foraging and the experimental days in both groups （Pearson Correlation： experimental group： r=-0.593, P〈0.01; control group： r=-0.581, P〈0.01）. There was no significant correlation between the success rates of foraging and the experimental days in experimental groups （Pearson Correlation： r=0.177, P〉0.05）, but there was significant correlation between the success rates of foraging and the experimental days in the control groups （Pearson Correlation： r=0.445, P〈0.05）. There was no significant difference for the first foraging time between experimental and control groups （GLM： F0.05,1=4.703, P〉0.05）; also, there was no significant difference in success rates of foraging between these two groups （GLM： F0.05,1=0.849,P〉0.05）. The results of our experiment suggest that spatial memory in C. sphinx was formed gradually and that the placed landmarks appeared to have no discernable effects on the memory of the foraging space.展开更多
The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable pr...The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.展开更多
In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their promotion.In this ...In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their promotion.In this paper,a novel approach based on crowd paths to solve this problem is presented,which collects and constructs automatically fingerprints database for anonymous buildings through common crowd customers.However,the accuracy degradation problem may be introduced as crowd customers are not professional trained and equipped.Therefore,we define two concepts:fixed landmark and hint landmark,to rectify the fingerprint database in the practical system,in which common corridor crossing points serve as fixed landmark and cross point among different crowd paths serve as hint landmark.Machinelearning techniques are utilized for short range approximation around fixed landmarks and fuzzy logic decision technology is applied for searching hint landmarks in crowd traces space.Besides,the particle filter algorithm is also introduced to smooth the sample points in crowd paths.We implemented the approach on off-the-shelf smartphones and evaluate the performance.Experimental results indicate that the approach can availably construct WiFi fingerprint database without reduce the localization accuracy.展开更多
High-density street-level reliable landmarks are one of the important foundations for street-level geolocation.However,the existing methods cannot obtain enough street-level landmarks in a short period of time.In this...High-density street-level reliable landmarks are one of the important foundations for street-level geolocation.However,the existing methods cannot obtain enough street-level landmarks in a short period of time.In this paper,a street-level landmarks acquisition method based on SVM(Support Vector Machine)classifiers is proposed.Firstly,the port detection results of IPs with known services are vectorized,and the vectorization results are used as an input of the SVM training.Then,the kernel function and penalty factor are adjusted for SVM classifiers training,and the optimal SVM classifiers are obtained.After that,the classifier sequence is constructed,and the IPs with unknown service are classified using the sequence.Finally,according to the domain name corresponding to the IP,the relationship between the classified server IP and organization name is established.The experimental results in Guangzhou and Wuhan city in China show that the proposed method can be as a supplement to existing typical methods since the number of obtained street-level landmarks is increased substantially,and the median geolocation error using evaluated landmarks is reduced by about 2 km.展开更多
AIM To characterize esophageal endoluminal landmarks to permit radial and longitudinal esophageal orientation and accurate lesion location.METHODS Distance from the incisors and radial orientation were estimated for t...AIM To characterize esophageal endoluminal landmarks to permit radial and longitudinal esophageal orientation and accurate lesion location.METHODS Distance from the incisors and radial orientation were estimated for the main left bronchus and the left atrium landmarks in 207 consecutive patients using white light examination.A sub-study was also performed using white light followed by endoscopic ultrasound(EUS)in 25 consecutive patients to confirm the findings.The scope orientation throughout the exam was maintained at the natural axis,where the left esophageal quadrant corresponds to the area between 6 and 9 o'clock.When an anatomical landmark was identified,it was recorded with a photograph and its quadrant orientation and distance from the incisors were determined.The reference points to obtain the distances and radial orientation were as follows:the midpoint of the left main bronchus and the most intense pulsatile zone of the left atrium.With the video processor system set to moderate insufflation,measurements were obtained at the end of the patients'air expiration.RESULTS The left main bronchus and left atrium esophageal landmarks were identified using white light in 99%and 100%of subjects at a mean distance of 25.8 cm(SD2.3),and 31.4 cm(SD 2.4)from the incisors,respectively.The left main bronchus landmark was found to be a tubular,concave,non-pulsatile,esophageal external compression,occupying approximately 1/4 of the circumference.The left atrium landmark was identified as a round,convex,pulsatile,esophageal external compression,occupying approximately 1/4 of the circumference.Both landmarks were identified using white light on the anterior esophageal quadrant.In the substudy,the left main bronchus was identified in 24(92%)patients at 25.4 cm(SD2.1)and 26.7 cm(SD 1.9)from the incisors,by white light and EUS,respectively.The left atrium was recognized in all patients at 30.5 cm(SD 1.9),and 31.6 cm(SD2.3)from the incisors,by both white light and EUS,respectively.EUS confirmed that the landmarks corresponded to these two structures,respectively,and that they were located on the anterior esophageal wall.The Bland-Altman plot demonstrated high agreement between the white light and EUS measurements.CONCLUSION This study provides an endoscopic characterization of esophageal landmarks corresponding to the left main bronchus and left atrium,to permit radial and longitudinal orientation and accurate lesion location.展开更多
Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays.However,this principle is often invalid in real Internet environment,which ...Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays.However,this principle is often invalid in real Internet environment,which leads to unreliable geolocation results.To improve the accuracy and reliability of locating IP in real Internet,a street-level IP geolocation algorithm based on landmarks clustering is proposed.Firstly,we use the probes to measure the known landmarks to obtain their delay vectors,and cluster landmarks using them.Secondly,the landmarks are clustered again by their latitude and longitude,and the intersection of these two clustering results is taken to form training sets.Thirdly,we train multiple neural networks to get the mapping relationship between delay and location in each training set.Finally,we determine one of the neural networks for the target by the delay similarity and relative hop counts,and then geolocate the target by this network.As it brings together the delay and geographical coordinates clustering,the proposed algorithm largely improves the inconsistency between them and enhances the mapping relationship between them.We evaluate the algorithm by a series of experiments in Hong Kong,Shanghai,Zhengzhou and New York.The experimental results show that the proposed algorithm achieves street-level IP geolocation,and comparing with existing typical streetlevel geolocation algorithms,the proposed algorithm improves the geolocation reliability significantly.展开更多
An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoo...An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoor environment. First, a modified visual attention method was proposed to automatically select a candidate region as a more useful landmark. In visual attention, candidate landmark regions were selected with different characteristics of ambient color and intensity in the image. Then, the more useful landmarks were selected by combining the candidate regions using clustering. As generally implemented, automatic landmark selection by vision-based simultaneous localization and mapping(SLAM) results in many useless landmarks, because the features of images are distinguished from the surrounding environment but detected repeatedly. These useless landmarks create a serious problem for the SLAM system because they complicate data association. To address this, a method was proposed in which the robot initially collected landmarks through automatic detection while traversing the entire area where the robot performed SLAM, and then, the robot selected only those landmarks that exhibited high rarity through clustering, which enhanced the system performance. Experimental results show that this method of automatic landmark selection results in selection of a high-rarity landmark. The average error of the performance of SLAM decreases 52% compared with conventional methods and the accuracy of data associations increases.展开更多
The facial landmarks can provide valuable information for expression-related tasks.However,most approaches only use landmarks for segmentation preprocessing or directly input them into the neural network for fully con...The facial landmarks can provide valuable information for expression-related tasks.However,most approaches only use landmarks for segmentation preprocessing or directly input them into the neural network for fully connection.Such simple combination not only fails to pass the spatial information to network,but also increases calculation amounts.The method proposed in this paper aims to integrate facial landmarks-driven representation into the triplet network.The spatial information provided by landmarks is introduced into the feature extraction process,so that the model can better capture the location relationship.In addition,coordinate information is also integrated into the triple loss calculation to further enhance similarity prediction.Specifically,for each image,the coordinates of 68 landmarks are detected,and then a region attention map based on these landmarks is generated.For the feature map output by the shallow convolutional layer,it will be multiplied with the attention map to correct the feature activation,so as to strengthen the key region and weaken the unimportant region.Finally,the optimized embedding output can be further used for downstream tasks.Three embeddings of three images output by the network can be regarded as a triplet representation for similarity computation.Through the CK+dataset,the effectiveness of such an optimized feature extraction is verified.After that,it is applied to facial expression similarity tasks.The results on the facial expression comparison(FEC)dataset show that the accuracy rate will be significantly improved after the landmark information is introduced.展开更多
The present paper investigates the pupal development times ofLucilia sericata which were studied in the laboratory at six different constant temperatures （20, 22, 24, 26, 28 ℃ each ± ℃）. Lower thresholds （tL...The present paper investigates the pupal development times ofLucilia sericata which were studied in the laboratory at six different constant temperatures （20, 22, 24, 26, 28 ℃ each ± ℃）. Lower thresholds （tL） for development were estimated from the linear regression of the developmental rates on each temperature. These data have made it possible to calculate the ADD （Accumulated Degree-Days） necessary for L. sericata to complete the larval stage and to achieve adult emergence. The minimal duration of development from oviposition to adult emergence was found to be inversely related to temperature. Additionally, six landmarks in pupal development are showed and for each of the landmarks the ADD value was calculated for every rearing temperature involved. These data assist in calculating the duration of the pupal stage based on morphological characteristics and would be of great value for future forensic entomological casework.展开更多
This paper presents an image-based mobile robot guidance system in an indoor space with installed artificial ceiling landmarks. The overall system, including an omni-directional mobile robot motion control, landmark i...This paper presents an image-based mobile robot guidance system in an indoor space with installed artificial ceiling landmarks. The overall system, including an omni-directional mobile robot motion control, landmark image processing and image recognition, is implemented on a single FPGA chip with one CMOS image sensor. The proposed feature representation of the artificial ceiling landmarks is invariant with respect to rotation and translation. One unique feature of the proposed ceiling landmark recognition system is that the feature points of landmarks are determined by topological information from both the foreground and background. To enhance recognition accuracy, landmark classification is performed after the mobile robot is moved to a position such that the ceiling landmark is located in the upright- top corner position of the robot’s camera image. The accuracy of the proposed artificial ceiling landmark recognition system using the nearest neighbor classification is 100% in our experiments.展开更多
Lao Zhang was a retired construction worker in Hohhot, capital of north China＇s Inner Mongolian Autonomous Region. The old man had worked to build apartment buildings all his life but never expected, even in a dream,...Lao Zhang was a retired construction worker in Hohhot, capital of north China＇s Inner Mongolian Autonomous Region. The old man had worked to build apartment buildings all his life but never expected, even in a dream, to be able to set his home in one of them.展开更多
A robot landmark navigation system based on monocular camera was researched theoretically and experimentally.First the landmark setting and its data structure in programming was given;then the coordinates of them gett...A robot landmark navigation system based on monocular camera was researched theoretically and experimentally.First the landmark setting and its data structure in programming was given;then the coordinates of them getting by robot and global localization of the robot was described;finally experiments based on Pioneer III mobile robot show that this system can work well at different topographic situation without lose of signposts.展开更多
基金supported by the National Natural Science Foundation of China(NSFC,No30800102)Natural Science Foundation of Hainan Province(309026)
文摘In order to study the relationship between landmarks and spatial memory in short-nosed fruit bat, Cynopterus sphinx （Megachiroptera, Pteropodidae）, we simulated a foraging environment in the laboratory. Different landmarks were placed to gauge the spatial memory of C. sphinx. We changed the number of landmarks every day with 0 landmarks again on the fifth day （from 0, 2, 4, 8 to 0）. Individuals from the control group were exposed to the identical artificial foraging environment, but without landmarks. The results indicated that there was significant correlation between the time of the first foraging and the experimental days in both groups （Pearson Correlation： experimental group： r=-0.593, P〈0.01; control group： r=-0.581, P〈0.01）. There was no significant correlation between the success rates of foraging and the experimental days in experimental groups （Pearson Correlation： r=0.177, P〉0.05）, but there was significant correlation between the success rates of foraging and the experimental days in the control groups （Pearson Correlation： r=0.445, P〈0.05）. There was no significant difference for the first foraging time between experimental and control groups （GLM： F0.05,1=4.703, P〉0.05）; also, there was no significant difference in success rates of foraging between these two groups （GLM： F0.05,1=0.849,P〉0.05）. The results of our experiment suggest that spatial memory in C. sphinx was formed gradually and that the placed landmarks appeared to have no discernable effects on the memory of the foraging space.
基金supported by the National Science and Technology Council,Taiwan,with grant numbers NSTC 114-2622-8-992-007-TD1 and 112-2811-E-992-003-MY3.
文摘The global population is rapidly expanding,driving an increasing demand for intelligent healthcare systems.Artificial intelligence(AI)applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend.Among these applications,mouth motion tracking and mouth-state detection represent an important direction,providing valuable support for diagnosing neuromuscular disorders such as dysphagia,Bell’s palsy,and Parkinson’s disease.In this study,we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices.The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit(BiGRU)and Comprehensive Learning Particle Swarm Optimization(CLPSO).We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics,including accuracy,precision,recall,F1-score,cosine similarity,ROC–AUC,and the precision–recall curve.The proposed method achieved an impressive accuracy of 96.57%with an excellent precision of 98.25%on our self-collected dataset,outperforming traditional models and related works in the same field.These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems,contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.
基金partially sponsored by National Key Project of China (No.2012ZX03001013-003)
文摘In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their promotion.In this paper,a novel approach based on crowd paths to solve this problem is presented,which collects and constructs automatically fingerprints database for anonymous buildings through common crowd customers.However,the accuracy degradation problem may be introduced as crowd customers are not professional trained and equipped.Therefore,we define two concepts:fixed landmark and hint landmark,to rectify the fingerprint database in the practical system,in which common corridor crossing points serve as fixed landmark and cross point among different crowd paths serve as hint landmark.Machinelearning techniques are utilized for short range approximation around fixed landmarks and fuzzy logic decision technology is applied for searching hint landmarks in crowd traces space.Besides,the particle filter algorithm is also introduced to smooth the sample points in crowd paths.We implemented the approach on off-the-shelf smartphones and evaluate the performance.Experimental results indicate that the approach can availably construct WiFi fingerprint database without reduce the localization accuracy.
基金The work presented in this paper is supported by the National Key R&D Program of China[Nos.2016YFB0801303,2016QY01W0105]the National Natural Science Foundation of China[Nos.U1636219,U1804263,61602508,61772549,U1736214,61572052]Plan for Scientific Innovation Talent of Henan Province[No.2018JR0018].
文摘High-density street-level reliable landmarks are one of the important foundations for street-level geolocation.However,the existing methods cannot obtain enough street-level landmarks in a short period of time.In this paper,a street-level landmarks acquisition method based on SVM(Support Vector Machine)classifiers is proposed.Firstly,the port detection results of IPs with known services are vectorized,and the vectorization results are used as an input of the SVM training.Then,the kernel function and penalty factor are adjusted for SVM classifiers training,and the optimal SVM classifiers are obtained.After that,the classifier sequence is constructed,and the IPs with unknown service are classified using the sequence.Finally,according to the domain name corresponding to the IP,the relationship between the classified server IP and organization name is established.The experimental results in Guangzhou and Wuhan city in China show that the proposed method can be as a supplement to existing typical methods since the number of obtained street-level landmarks is increased substantially,and the median geolocation error using evaluated landmarks is reduced by about 2 km.
基金(in part)a grant in aid from the Emura Foundation for the Promotion of Cancer Research,No.01221
文摘AIM To characterize esophageal endoluminal landmarks to permit radial and longitudinal esophageal orientation and accurate lesion location.METHODS Distance from the incisors and radial orientation were estimated for the main left bronchus and the left atrium landmarks in 207 consecutive patients using white light examination.A sub-study was also performed using white light followed by endoscopic ultrasound(EUS)in 25 consecutive patients to confirm the findings.The scope orientation throughout the exam was maintained at the natural axis,where the left esophageal quadrant corresponds to the area between 6 and 9 o'clock.When an anatomical landmark was identified,it was recorded with a photograph and its quadrant orientation and distance from the incisors were determined.The reference points to obtain the distances and radial orientation were as follows:the midpoint of the left main bronchus and the most intense pulsatile zone of the left atrium.With the video processor system set to moderate insufflation,measurements were obtained at the end of the patients'air expiration.RESULTS The left main bronchus and left atrium esophageal landmarks were identified using white light in 99%and 100%of subjects at a mean distance of 25.8 cm(SD2.3),and 31.4 cm(SD 2.4)from the incisors,respectively.The left main bronchus landmark was found to be a tubular,concave,non-pulsatile,esophageal external compression,occupying approximately 1/4 of the circumference.The left atrium landmark was identified as a round,convex,pulsatile,esophageal external compression,occupying approximately 1/4 of the circumference.Both landmarks were identified using white light on the anterior esophageal quadrant.In the substudy,the left main bronchus was identified in 24(92%)patients at 25.4 cm(SD2.1)and 26.7 cm(SD 1.9)from the incisors,by white light and EUS,respectively.The left atrium was recognized in all patients at 30.5 cm(SD 1.9),and 31.6 cm(SD2.3)from the incisors,by both white light and EUS,respectively.EUS confirmed that the landmarks corresponded to these two structures,respectively,and that they were located on the anterior esophageal wall.The Bland-Altman plot demonstrated high agreement between the white light and EUS measurements.CONCLUSION This study provides an endoscopic characterization of esophageal landmarks corresponding to the left main bronchus and left atrium,to permit radial and longitudinal orientation and accurate lesion location.
基金the National Key R&D Program of China 2016YFB0801303(F.L.received the grant,the sponsors’website is https://service.most.gov.cn/)by the National Key R&D Program of China 2016QY01W0105(X.L.received the grant,the sponsors’website is https://service.most.gov.cn/)+5 种基金by the National Natural Science Foundation of China U1636219(X.L.received the grant,the sponsors’website is http://www.nsfc.gov.cn/)by the National Natural Science Foundation of China 61602508(J.L.received the grant,the sponsors’website is http://www.nsfc.gov.cn/)by the National Natural Science Foundation of China 61772549(F.L.received the grant,the sponsors’website is http://www.nsfc.gov.cn/)by the National Natural Science Foundation of China U1736214(F.L.received the grant,the sponsors’website is http://www.nsfc.gov.cn/)by the National Natural Science Foundation of China U1804263(X.L.received the grant,the sponsors’website is http://www.nsfc.gov.cn/)by the Science and Technology Innovation Talent Project of Henan Province 184200510018(X.L.received the grant,the sponsors’website is http://www.hnkjt.gov.cn/).
文摘Existing IP geolocation algorithms based on delay similarity often rely on the principle that geographically adjacent IPs have similar delays.However,this principle is often invalid in real Internet environment,which leads to unreliable geolocation results.To improve the accuracy and reliability of locating IP in real Internet,a street-level IP geolocation algorithm based on landmarks clustering is proposed.Firstly,we use the probes to measure the known landmarks to obtain their delay vectors,and cluster landmarks using them.Secondly,the landmarks are clustered again by their latitude and longitude,and the intersection of these two clustering results is taken to form training sets.Thirdly,we train multiple neural networks to get the mapping relationship between delay and location in each training set.Finally,we determine one of the neural networks for the target by the delay similarity and relative hop counts,and then geolocate the target by this network.As it brings together the delay and geographical coordinates clustering,the proposed algorithm largely improves the inconsistency between them and enhances the mapping relationship between them.We evaluate the algorithm by a series of experiments in Hong Kong,Shanghai,Zhengzhou and New York.The experimental results show that the proposed algorithm achieves street-level IP geolocation,and comparing with existing typical streetlevel geolocation algorithms,the proposed algorithm improves the geolocation reliability significantly.
文摘An improved method with better selection capability using a single camera was presented in comparison with previous method. To improve performance, two methods were applied to landmark selection in an unfamiliar indoor environment. First, a modified visual attention method was proposed to automatically select a candidate region as a more useful landmark. In visual attention, candidate landmark regions were selected with different characteristics of ambient color and intensity in the image. Then, the more useful landmarks were selected by combining the candidate regions using clustering. As generally implemented, automatic landmark selection by vision-based simultaneous localization and mapping(SLAM) results in many useless landmarks, because the features of images are distinguished from the surrounding environment but detected repeatedly. These useless landmarks create a serious problem for the SLAM system because they complicate data association. To address this, a method was proposed in which the robot initially collected landmarks through automatic detection while traversing the entire area where the robot performed SLAM, and then, the robot selected only those landmarks that exhibited high rarity through clustering, which enhanced the system performance. Experimental results show that this method of automatic landmark selection results in selection of a high-rarity landmark. The average error of the performance of SLAM decreases 52% compared with conventional methods and the accuracy of data associations increases.
文摘The facial landmarks can provide valuable information for expression-related tasks.However,most approaches only use landmarks for segmentation preprocessing or directly input them into the neural network for fully connection.Such simple combination not only fails to pass the spatial information to network,but also increases calculation amounts.The method proposed in this paper aims to integrate facial landmarks-driven representation into the triplet network.The spatial information provided by landmarks is introduced into the feature extraction process,so that the model can better capture the location relationship.In addition,coordinate information is also integrated into the triple loss calculation to further enhance similarity prediction.Specifically,for each image,the coordinates of 68 landmarks are detected,and then a region attention map based on these landmarks is generated.For the feature map output by the shallow convolutional layer,it will be multiplied with the attention map to correct the feature activation,so as to strengthen the key region and weaken the unimportant region.Finally,the optimized embedding output can be further used for downstream tasks.Three embeddings of three images output by the network can be regarded as a triplet representation for similarity computation.Through the CK+dataset,the effectiveness of such an optimized feature extraction is verified.After that,it is applied to facial expression similarity tasks.The results on the facial expression comparison(FEC)dataset show that the accuracy rate will be significantly improved after the landmark information is introduced.
文摘The present paper investigates the pupal development times ofLucilia sericata which were studied in the laboratory at six different constant temperatures （20, 22, 24, 26, 28 ℃ each ± ℃）. Lower thresholds （tL） for development were estimated from the linear regression of the developmental rates on each temperature. These data have made it possible to calculate the ADD （Accumulated Degree-Days） necessary for L. sericata to complete the larval stage and to achieve adult emergence. The minimal duration of development from oviposition to adult emergence was found to be inversely related to temperature. Additionally, six landmarks in pupal development are showed and for each of the landmarks the ADD value was calculated for every rearing temperature involved. These data assist in calculating the duration of the pupal stage based on morphological characteristics and would be of great value for future forensic entomological casework.
文摘This paper presents an image-based mobile robot guidance system in an indoor space with installed artificial ceiling landmarks. The overall system, including an omni-directional mobile robot motion control, landmark image processing and image recognition, is implemented on a single FPGA chip with one CMOS image sensor. The proposed feature representation of the artificial ceiling landmarks is invariant with respect to rotation and translation. One unique feature of the proposed ceiling landmark recognition system is that the feature points of landmarks are determined by topological information from both the foreground and background. To enhance recognition accuracy, landmark classification is performed after the mobile robot is moved to a position such that the ceiling landmark is located in the upright- top corner position of the robot’s camera image. The accuracy of the proposed artificial ceiling landmark recognition system using the nearest neighbor classification is 100% in our experiments.
文摘Lao Zhang was a retired construction worker in Hohhot, capital of north China＇s Inner Mongolian Autonomous Region. The old man had worked to build apartment buildings all his life but never expected, even in a dream, to be able to set his home in one of them.
基金supported by the project of 'the research of Ad Hoc net routing algorithm base-on Antcolony algorithm in several robots' system(CSTC,2008BB2081)
文摘A robot landmark navigation system based on monocular camera was researched theoretically and experimentally.First the landmark setting and its data structure in programming was given;then the coordinates of them getting by robot and global localization of the robot was described;finally experiments based on Pioneer III mobile robot show that this system can work well at different topographic situation without lose of signposts.
