Multimodal identity tracking in a smart room

The automatic detection, tracking, and identification of multiple people in intelligent environments are important building blocks on which smart interaction systems can be designed. Those could be, e.g., gesture recognizers, head pose estimators or far-field speech recognizers and dialog systems. In this paper, we present a system which is capable of tracking multiple people in a smart room environment while inferring their identities in a completely automatic and unobtrusive way. It relies on a set of fixed and active cameras to track the users and get close-ups of their faces for identification, and on several microphone arrays to determine active speakers and steer the attention of the system. Information coming asynchronously from several sources, such as position updates from audio or visual trackers and identification events from identification modules, is fused at higher level to gradually refine the room’s situation model. The system has been trained on a small set of users and showed good performance at acquiring and keeping their identities in a smart room environment. An erratum to this article can be found at     

Person re-identification in TV series using robust face recognition and user feedback

In this paper, we present a system for person re-identification in TV series. In the context of video retrieval, person re-identification refers to the task where a user clicks on a person in a video frame and the system then finds other occurrences of the same person in the same or different videos. The main characteristic of this scenario is that no previously collected training data is available, so no person-specific models can be trained in advance. Additionally, the query data is limited to the image that the user clicks on. These conditions pose a great challenge to the re-identification system, which has to find the same person in other shots despite large variations in the person’s appearance. In the study, facial appearance is used as the re-identification cue, since, in contrast to surveillance-oriented re-identification studies, the person can have different clothing in different shots. In order to increase the amount of available face data, the proposed system employs a face tracker that can track faces up to full profile views. This makes it possible to use a profile face image as query image and also to retrieve images with non-frontal poses. It also provides temporal association of the face images in the video, so that instead of using single images for query or target, whole tracks can be used. A fast and robust face recognition algorithm is used to find matching faces. If the match result is highly confident, our system adds the matching face track to the query set. Finally, if the user is not satisfied with the number of returned results, the system can present a small number of candidate face images and lets the user confirm the ones that belong to the queried person. These features help to increase the variation in the query set, making it possible to retrieve results with different poses, illumination conditions, etc. The system is extensively evaluated on two episodes of the TV series Coupling, showing very promising results.     

Digital bundling

Increasingly, we see that firms offer many items on information-intensive channels and the Internet. Especially with low-marginal-cost digital goods, bundling may be a beneficial strategy. Different bundles may help customers become more discriminating and maximize profits. However, the marketplace evidence provides mixed observation of bundling rigorously pursued. In this study, we provide a general framework to analyze when and how bundling may be beneficial. We compare and contrast the firm and customer characteristics on bundling strategy. We analyze when individual sales can be beneficial. We find that when costs do not increase relative to the bundle valuation, firms find it beneficial to limit the number of bundles offered in the market. A low (zero) marginal cost firm benefits from offering just one big bundle. Such a firm obtains a higher profit compared to a firm that offers many bundles. With high marginal costs, the number of bundles increases with increasing customer valuation and/or heterogeneity. When a firm offers all the bundles, prices and profit increase as customer heterogeneity and valuation increase. When the number of offered items is high, individual sale (unbundling) may be preferred over bundling and bundling becomes an inferior strategy. Interestingly, a firm may benefit from unbundling the items when customers have higher valuations.     

Automatic determination of traffic accidents based on KMC-based attribute weighting

In this study, the traffic accidents recognizing risk factors related to the environmental (climatological) conditions that are associated with motor vehicles accidents on the Konya-Afyonkarahisar highway with the aid of Geographical Information Systems (GIS) have been determined using the combination of K-means clustering (KMC)-based attribute weighting (KMCAW) and classifier algorithms including artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS). The dynamic segmentation process in ArcGIS9.0 from the traffic accident reports recorded by District Traffic Agency has identified the locations of the motor vehicle accidents. The attributes obtained from this system are day, temperature, humidity, weather conditions, and month of occurred traffic accidents. The traffic accident dataset comprises five attributes (day, temperature, humidity, weather conditions, and month of occurred traffic accidents) and 358 observations including 179 without accident and 179 with accident. The proposed comprises two stages. In the first stage, the all attributes of dataset have been weighted using KMCAW method. The aims of this weighting method are both to increase the classification performance of used classifier algorithm and to transform from linearly non-separable traffic accidents dataset to a linearly separable dataset. In the second stage, after weighting process, ANN and ANFIS classifier algorithms have been separately used to determine the case of traffic accidents as with accident or without accident. In order to evaluate the performance of proposed method, the classification accuracy, sensitivity, specificity and area under the ROC (Receiver Operating Characteristic) curves (AUC) values have been used. While ANN and ANFIS classifiers obtained the overall prediction accuracies of 53.93 and 38.76%, respectively, the combination of KMCAW and ANN and the combination of KMCAW and ANFIS achieved the overall prediction accuracies of 74.15 and 55.06% on the prediction of traffic accidents. The experimental results have demonstrated that the proposed attribute weighting method called KMCAW is a robust and effective data pre-processing method in the prediction of traffic accidents on Konya-Afyonkarahisar highway in Turkey.     

Performance of DSRC systems using conventional channel estimation at high velocities

In this paper, we present a study of the dedicated short range communications (DSRC) receiver performance under varying signal to noise ratio, velocity, symbol durations and packet lengths. Conventional channel estimation, which is used in IEEE 802.11a, assumes static channel characteristics for the entire packet duration. That is found to be infeasible for high velocity DSRC applications. Simulation results show that the packet-error-rate increases with the increase in relative velocity. Viterbi decoding substantially improves the performance, but the sensitivity to Doppler shift still exists. Analysis and simulation results show that extending the symbol duration or increasing packet length results in an increase of the packet-error-rate. These results may serve as benchmarks for future DSRC channel estimation methods.     

Multi-Agent System-Based Fuzzy Controller Design with Genetic Tuning for a Mobile Manipulator Robot in the Hand Over Task

This paper presents an application of the multi-agent system approach to a service mobile manipulator robot that interacts with a human during an object delivery and hand-over task in two dimensions. The base, elbow and shoulder of the robot are identified as three different agents, and are controlled using fuzzy control. The control variables of the controllers are linear velocity of the base, angular velocity of the elbow, and angular velocity of the shoulder. Main inputs to the system are the horizontal and vertical distances between the human and robot hands. These are input to all three agents. In developing the fuzzy control rules, effective delivery and avoidance of contact with humans, not to cause physical damage, are considered. The membership functions of the fuzzy controllers are tuned by using genetic algorithms. In tuning, the performance is calculated considering the distance deviation from the direct path, time spent to reach the human hand and energy consumed by the actuators. The proposed multi-agent system structure based on fuzzy control for the object delivery task succeeded in both effective and safe delivery.     

Pharmacokinetic application of fuzzy structure identification and reasoning

The aim of this paper is to introduce the algorithm proposed in [Fuzzy Sets and System, 2002] and some further modifications. Its applications presented in [ibid; A comparison of five approaches for lithium dose and serum concentration prediction, IFSA-NAFIPS 2001, pp. 104-110] is reviewed as a full collection of its use in pharmacokinetic analysis. First a recently developed fuzzy system modeling algorithm and approximate reasoning tool are introduced along with the modifications. Later the performance of the proposed algorithm is tested in two different data sets and compared with some well-known algorithms from the literature. In the comparison, individualized pharmacokinetic data (i.e., alprazolam data) and population pharmacokinetic data (i.e., lithium data) are used. The comparisons demonstrate that the proposed algorithm can be successfully applied in pharmacokinetic modeling.     

Low‐Temperature Deposition of Hexagonal Boron Nitride via Sequential Injection of Triethylboron and N2/H2 Plasma

Hexagonal boron nitride (hBN) thin films were deposited on silicon and quartz substrates using sequential exposures of triethylboron and N₂/H₂ plasma in a hollow‐cathode plasma‐assisted atomic layer deposition reactor at low temperatures (≤450°C). A non‐saturating film deposition rate was observed for substrate temperatures above 250°C. BN films were characterized for their chemical composition, crystallinity, surface morphology, and optical properties. X‐ray photoelectron spectroscopy (XPS) depicted the peaks of boron, nitrogen, carbon, and oxygen at the film surface. B 1s and N 1s high‐resolution XPS spectra confirmed the presence of BN with peaks located at 190.8 and 398.3 eV, respectively. As deposited films were polycrystalline, single‐phase hBN irrespective of the deposition temperature. Absorption spectra exhibited an optical band edge at ~5.25 eV and an optical transmittance greater than 90% in the visible region of the spectrum. Refractive index of the hBN film deposited at 450°C was 1.60 at 550 nm, which increased to 1.64 after postdeposition annealing at 800°C for 30 min. These results represent the first demonstration of hBN deposition using low‐temperature hollow‐cathode plasma‐assisted sequential deposition technique.     

Language Change Quantification Using Time-separated Parallel Translations

We introduce a systematic approach to language change quantificationby studying unconsciously used language features in time-separatedparallel translations. For this purpose, we use objective stylemarkers such as vocabulary richness and lengths of words, wordstems and suffixes, and employ statistical methods to measuretheir changes over time. In this study, we focus on the changein Turkish in the second half of the twentieth century. To obtainword stems, we first introduce various stemming techniques andshow that they are highly effective. Our statistical analysesshow that over time, for both text and lexicon, the length ofTurkish words has become significantly longer, and word stemshave become significantly shorter. We also show that suffixlengths have become significantly longer for types and the vocabularyrichness based on word stems has shrunk significantly. Theseobservations indicate that in contemporary Turkish one woulduse more suffixes to compensate for the fewer stems to preservethe expressive power of the language at the same level. Ourapproach can be adapted for quantifying the change in otherlanguages.