Fusing Fixed and Hint Landmarks on Crowd Paths for Automatically Constructing Wi-Fi Fingerprint Database

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.

Influence of Landmarks on Spatial Memory in Short-nosed Fruit Bat, Cynopterus sphinx

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.

Street-Level Landmarks Acquisition Based on SVM Classifiers

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.

Street-Level IP Geolocation Algorithm Based on Landmarks Clustering

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.

Estimation of Accumulated Degree Day Value of Six Landmarks within the Pupal Stage of Lucilia sericata

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.

Resettlement Project Turns Cityscape Patches to Landmarks

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.

Endoscopic identification of endoluminal esophageal landmarks for radial and longitudinal orientation and lesion location

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.

Visual attention and clustering-based automatic selection of landmarks using single camera

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.

Landmarks-Driven Triplet Representation for Facial Expression Similarity

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.

Image-Based Mobile Robot Guidance System by Using Artificial Ceiling Landmarks

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.

Data-driven approach to learning salience models of indoor landmarks by using genetic programming

In landmark-based way-finding,determining the most salient landmark from several candidates at decision points is challenging.To overcome this problem,current approaches usually rely on a linear model to measure the salience of landmarks.However,linear models are not always able to establish an accurate quantitative relationship between the attributes of a landmark and its perceived salience.Furthermore,the numbers of evaluated scenes and of volunteers participating in the testing of these models are often limited.With the aim of overcoming these gaps,we propose learning a non-linear salience model by means of genetic programming.We compared our proposed approach with conventional algorithms by using photographs of two hundred test scenes collected from two shopping malls.Two hundred volunteers who were not in these environments were asked to answer questionnaires about the collected photographs.The results from this experiment showed that in 76%of the cases,the most salient landmark(according to the volunteers’perception)was correctly predicted by our proposed approach.This accuracy rate is considerably higher than the ones achieved by conventional linear models.

Shanghai Landmarks

The city’s architecture,like Shanghai itself,combines the classic and the modern Best of the old and the new The Bund, Yuyuan Garden and Xintiandi are called Shanghai’s three

Design of an enhanced visual odometry by building and matching compressive panoramic landmarks online

Efficient and precise localization is a prerequisite for the intelligent navigation of mobile robots. Traditional visual localization systems, such as visual odometry （VO） and simultaneous localization and mapping （SLAM）, suffer from two shortcomings： a drift problem caused by accumulated localization error, and erroneous motion estimation due to illumination variation and moving objects. In this paper, we propose an enhanced VO by introducing a panoramic camera into the traditional stereo-only VO system. Benefiting from the 360° field of view, the panoramic camera is responsible for three tasks： （1） detect- ing road junctions and building a landmark library online; （2） correcting the robot＇s position when the landmarks are revisited with any orientation; （3） working as a panoramic compass when the stereo VO cannot provide reliable positioning results. To use the large-sized panoramic images efficiently, the concept of compressed sensing is introduced into the solution and an adap- tive compressive feature is presented. Combined with our previous two-stage local binocular bundle adjustment （TLBBA） stereo VO, the new system can obtain reliable positioning results in quasi-real time. Experimental results of challenging long-range tests show that our enhanced VO is much more accurate and robust than the traditional VO, thanks to the compressive panoramic landmarks built online.

Biologically inspired collective construction with visual landmarks

We describe our research in using environmental visual landmarks as the basis for completing simple robot construction tasks.Inspired by honeybee visual navigation behavior,a visual template mechanism is proposed in which a natural landmark serves as a visual reference or template for distance determination as well as for navigation during collective construction.To validate our proposed mechanism,a wall construction problem is investigated and a minimalist solution is given.Experimental results show that,using the mechanism of a visual template,a collective robotic system can successfully build the desired structure in a decentralized fashion using only local sensing and no direct communication.In addition,a particular variable,which defines tolerance for alignment of the structure,is found to impact the system performance.By decreasing the value of the variable,system performance is improved at the expense of a longer construction time.The visual template mechanism is appealing in that it can use a reference point or salient object in a natural environment that is new or unexplored and it could be adapted to facilitate more complicated building tasks.

The Role of Greenery, Layering, and Landmarks in the Regulations on Urban Skylines

Many scholars have conducted visual analyses of urban skylines, but little attention has been paid to the quantitative measures regarding specific design elements within the skyline. This article aims to help urban designers and regulators improve the skyline and investigate which factors can make urban skylines more pleasant for people. Computer generated images of skylines are tested for three factors including greenery, layering, and landmarks. For the data collection, a questionnaire was used, as one of the simple and effective methods to gather results. The authors use statistics as a method of measuring the degree of people's preferences. The study finds that the proportions of landmarks in the overall skyline, the height of layers, and the percentage of greenery deserve special attention. The authors also discuss the current problems of skyline design in typical Chinese cities according to the above findings.

Customized Convolutional Neural Network for Accurate Detection of Deep Fake Images in Video Collections

The motivation for this study is that the quality of deep fakes is constantly improving,which leads to the need to develop new methods for their detection.The proposed Customized Convolutional Neural Network method involves extracting structured data from video frames using facial landmark detection,which is then used as input to the CNN.The customized Convolutional Neural Network method is the date augmented-based CNN model to generate‘fake data’or‘fake images’.This study was carried out using Python and its libraries.We used 242 films from the dataset gathered by the Deep Fake Detection Challenge,of which 199 were made up and the remaining 53 were real.Ten seconds were allotted for each video.There were 318 videos used in all,199 of which were fake and 119 of which were real.Our proposedmethod achieved a testing accuracy of 91.47%,loss of 0.342,and AUC score of 0.92,outperforming two alternative approaches,CNN and MLP-CNN.Furthermore,our method succeeded in greater accuracy than contemporary models such as XceptionNet,Meso-4,EfficientNet-BO,MesoInception-4,VGG-16,and DST-Net.The novelty of this investigation is the development of a new Convolutional Neural Network(CNN)learning model that can accurately detect deep fake face photos.

A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the patient.It affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of mind.Normally,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of bipolar.In this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based model.Facial Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry emotions.Experiments have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental state.The experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,respectively.Overall,the outcomes demonstrated the stated method’s superiority over the current best practices.

基于分布式聚类的Peer-to-Peer路由算法

提出了分布式聚类算法,该算法自动对地理邻近的对等结点聚类,消除了集中式聚类算法的单点瓶颈问题,及分布式Binning算法的landmark放置问题;提出了一种基于该聚类的邻居路由算法,该算法消除了na?ve算法路由效率低、IP-snoop算法实现困难及处理开销大、directed算法需要处理超级节点失效时容错问题。实验结果表明,基于分布式聚类的Peer-to-Peer(P2P)路由算法比现有的结构化路由算法减少了近一倍的路由跳转和路由时延。

High speed robust image registration and localization using optimized algorithm and its performances evaluation

Local invariant algorithm applied in downward-looking image registration,usually computes the camera＇s pose relative to visual landmarks.Generally,there are three requirements in the process of image registration when using these approaches.First,the algorithm is apt to be influenced by illumination.Second,algorithm should have less computational complexity.Third,the depth information of images needs to be estimated without other sensors.This paper investigates a famous local invariant feature named speeded up robust feature（SURF）,and proposes a highspeed and robust image registration and localization algorithm based on it.With supports from feature tracking and pose estimation methods,the proposed algorithm can compute camera poses under different conditions of scale,viewpoint and rotation so as to precisely localize object＇s position.At last,the study makes registration experiment by scale invariant feature transform（SIFT）,SURF and the proposed algorithm,and designs a method to evaluate their performances.Furthermore,this study makes object retrieval test on remote sensing video.For there is big deformation on remote sensing frames,the registration algorithm absorbs the Kanade-Lucas-Tomasi（KLT） 3-D coplanar calibration feature tracker methods,which can localize interesting targets precisely and efficiently.The experimental results prove that the proposed method has a higher localization speed and lower localization error rate than traditional visual simultaneous localization and mapping（vSLAM） in a period of time.

发现和学习不可复位动态系统的预测状态表示的一种新算法

提出了一种发现和学习不可复位动态系统的预测状态表示的新算法.在证明系统的任意landmark均可作为系统的初始状态的基础上,利用发现的landmark确定系统在任意时间步所处的经历,然后采用蒙特卡罗方法估计任意经历下任意检验发生的概率,解决了在不可复位动态系统中,经历下检验发生的概率难以获取问题,进而发现和学习不可复位动态系统的预测状态表示.实验结果表明,本文算法获得的系统的预测状态表示在预测精度上明显优于suffix-history算法,验证了所提算法的有效性.