MEBOOK: A Novel Device Using Video Self-Modeling to Enhance Literacy Among Children with ASD

The aim is to build a video application, MEBOOK, to overlay features of the protagonist on the face of the reader and replace the scene background with animated video pertinent to the story

The Influences of Relationship Marketing in the Housing Brokerage Market

Many companies and salesmen try to build and maintain long-term relationship with their customer. The influences of relationship marketing activities are important issue of the housing brokerage Market. The aims of study are to learn the impacts of relationship marketing on customer satisfaction and customer loyalty in the housing brokerage Market. The researchers survey the customers and use the regression analysis method to test the relationships between relationship marketing, customer satisfaction and customer loyalty in Taiwan. The research results show that the relationship marketing positively impacts on customer satisfaction and customer loyalty, then customer satisfaction positively impacts on customer loyalty. Customer satisfaction plays a mediating role between relationship marketing and customer loyalty. Besides, the study confirm that brokers’ expertise moderates the relationship of relationship marketing process. The findings suggest that the brokers’ relationship marketing and expertise empirically impact on the customers

A Robust RGBD Slam System for 3D Environment with Planar Surfaces

With the increasing popularity of RGB-depth (RGB-D) sensors such as the Microsoft Kinect, there have been much research on capturing and reconstructing 3D environments using a movable RGB-D sensor. The key process behind these kinds of simultaneous location and mapping (SLAM) systems is the iterative closest point or ICP algorithm, which is an iterative algorithm that can estimate the rigid movement of the camera based on the captured 3D point clouds. While ICP is a well-studied algorithm, it is problematic when it is used in scanning large planar regions such as wall surfaces in a room. The lack of depth variations on planar surfaces makes the global alignment an ill-conditioned problem. In this paper, we present a novel approach for registering 3D point clouds by combining both color and depth information. Instead of directly searching for point correspondences among 3D data, the proposed method first extracts features from the RGB images, and then back-projects the features to the 3D space to identify more reliable correspondences. These color correspondences form the initial input to the ICP procedure which then proceeds to refine the alignment. Experimental results show that our proposed approach can achieve better accuracy than existing SLAMs in reconstructing indoor environments with large planar surfaces

Automatic Content Generation for Video Self Modeling

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him or herself. Its effectiveness in rehabilitation and education has been repeatedly demonstrated but technical challenges remain in creating video contents that depict previously unseen behaviors. In this paper, we propose a novel system that re-renders new talking-head sequences suitable to be used for VSM treatment of patients with voice disorder. After the raw footage is captured, a new speech track is either synthesized using text-to-speech or selected based on voice similarity from a database of clean speeches. Voice conversion is then applied to match the new speech to the original voice. Time markers extracted from the original and new speech track are used to re-sample the video track for lip synchronization. We use an adaptive re-sampling strategy to minimize motion jitter, and apply bilinear and optical-flow based interpolation to ensure the image quality. Both objective measurements and subjective evaluations demonstrate the effectiveness of the proposed techniques

Automatic Video Self Modeling for Voice Disorder

Video self modeling (VSM) is a behavioral intervention technique in which a learner models a target behavior by watching a video of him- or herself. In the field of speech language pathology, the approach of VSM has been successfully used for treatment of language in children with Autism and in individuals with fluency disorder of stuttering. Technical challenges remain in creating VSM contents that depict previously unseen behaviors. In this paper, we propose a novel system that synthesizes new video sequences for VSM treatment of patients with voice disorders. Starting with a video recording of a voice-disorder patient, the proposed system replaces the coarse speech with a clean, healthier speech that bears resemblance to the patient’s original voice. The replacement speech is synthesized using either a text-to-speech engine or selecting from a database of clean speeches based on a voice similarity metric. To realign the replacement speech with the original video, a novel audiovisual algorithm that combines audio segmentation with lip-state detection is proposed to identify corresponding time markers in the audio and video tracks. Lip synchronization is then accomplished by using an adaptive video re-sampling scheme that minimizes the amount of motion jitter and preserves the spatial sharpness. Results of both objective measurements and subjective evaluations on a dataset with 31 subjects demonstrate the effectiveness of the proposed techniques

A Fast and Robust Extrinsic Calibration for RGB-D Camera Networks

From object tracking to 3D reconstruction, RGB-Depth (RGB-D) camera networks play an increasingly important role in many vision and graphics applications. Practical applications often use sparsely-placed cameras to maximize visibility, while using as few cameras as possible to minimize cost. In general, it is challenging to calibrate sparse camera networks due to the lack of shared scene features across different camera views. In this paper, we propose a novel algorithm that can accurately and rapidly calibrate the geometric relationships across an arbitrary number of RGB-D cameras on a network. Our work has a number of novel features. First, to cope with the wide separation between different cameras, we establish view correspondences by using a spherical calibration object. We show that this approach outperforms other techniques based on planar calibration objects. Second, instead of modeling camera extrinsic calibration using rigid transformation, which is optimal only for pinhole cameras, we systematically test different view transformation functions including rigid transformation, polynomial transformation and manifold regression to determine the most robust mapping that generalizes well to unseen data. Third, we reformulate the celebrated bundle adjustment procedure to minimize the global 3D reprojection error so as to fine-tune the initial estimates. Finally, our scalable client-server architecture is computationally efficient: the calibration of a five-camera system, including data capture, can be done in minutes using only commodity PCs. Our proposed framework is compared with other state-of-the-arts systems using both quantitative measurements and visual alignment results of the merged point clouds

CPEB4-Dependent Neonate-Born Granule Cells Are Required for Olfactory Discrimination

The rodent olfactory bulb (OB) contains two distinct populations of postnatally born interneurons, mainly granule cells (GCs), to support local circuits throughout life. During the early postnatal period (i.e., 2 weeks after birth), GCs are mostly produced locally from progenitor cells in the OB with a proportion of them deriving from proliferating cells in the rostral migratory stream (RMS). Afterward, the replenishment of GCs involves differentiated neuroblasts from the subventricular zone (SVZ) in a process known as adult neurogenesis. Although numerous studies have addressed the role of SVZ-born GCs in olfactory behaviors, the function of GCs produced early postnatally in the OB remains elusive. Our previous study demonstrated that the translational regulator, cytoplasmic polyadenylation element-binding protein 4 (CPEB4), is a survival factor exclusively for neonate-born but not SVZ/adult-derived GCs, so CPEB4-knockout (KO) mice provide unique leverage to study early postnatal-born GC-regulated olfactory functions. CPEB4-KO mice with hypoplastic OBs showed normal olfactory sensitivity and short-term memory, but impaired ability to spontaneously discriminate two odors. Such olfactory dysfunction was recapitulated in specific ablation of Cpeb4 gene in inhibitory interneurons but not in excitatory projection neurons or SVZ-derived interneurons. The continuous supply of GCs from adult neurogenesis eventually restored the OB size but not the discrimination function in 6-month-old KO mice. Hence, in the early postnatal OB, whose function cannot be replaced by adult-born GCs, construct critical circuits for odor discrimination

Rate adaptation for 802.11 multiuser mimo networks

In multiuser MIMO (MU-MIMO) networks, the optimal bit rate of a user is highly dynamic and changes from one packet to the next. This breaks traditional bit rate adaptation algorithms, which rely on recent history to predict the best bit rate for the next packet. To address this problem, we introduce TurboRate, a rate adaptation scheme for MU-MIMO LANs. TurboRate shows that clients in a MU-MIMO LAN can adapt their bit rate on a per-packet basis if each client learns two variables: its SNR when it transmits alone to the access point, and the direction along which its signal is received at the AP. TurboRate also shows that each client can compute these two variables passively without exchanging control frames with the access point. A TurboRate client then annotates its packets with these variables to enable other clients to pick the optimal bit rate and transmit concurrently to the AP. A prototype implementation in USRP-N200 shows that traditional rate adaptation does not deliver the gains of MU-MIMO WLANs, and can interact negatively with MU-MIMO, leading to low throughput. In contrast, enabling MU-MIMO with TurboRate provides a mean throughput gain of 1.7x and 2.3x, for 2-antenna and 3-antenna APs respectively.National Science Council (China) (contract No. NSC 100-2221-E-001-005-MY2)National Science Foundation (U.S.) (NSF Grant CNS-1117194

Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels

Motion mode (M-mode) recording is an essential part of echocardiography to measure cardiac dimension and function. However, the current diagnosis cannot build an automatic scheme, as there are three fundamental obstructs: Firstly, there is no open dataset available to build the automation for ensuring constant results and bridging M-mode echocardiography with real-time instance segmentation (RIS); Secondly, the examination is involving the time-consuming manual labelling upon M-mode echocardiograms; Thirdly, as objects in echocardiograms occupy a significant portion of pixels, the limited receptive field in existing backbones (e.g., ResNet) composed from multiple convolution layers are inefficient to cover the period of a valve movement. Existing non-local attentions (NL) compromise being unable real-time with a high computation overhead or losing information from a simplified version of the non-local block. Therefore, we proposed RAMEM, a real-time automatic M-mode echocardiography measurement scheme, contributes three aspects to answer the problems: 1) provide MEIS, a dataset of M-mode echocardiograms for instance segmentation, to enable consistent results and support the development of an automatic scheme; 2) propose panel attention, local-to-global efficient attention by pixel-unshuffling, embedding with updated UPANets V2 in a RIS scheme toward big object detection with global receptive field; 3) develop and implement AMEM, an efficient algorithm of automatic M-mode echocardiography measurement enabling fast and accurate automatic labelling among diagnosis. The experimental results show that RAMEM surpasses existing RIS backbones (with non-local attention) in PASCAL 2012 SBD and human performances in real-time MEIS tested. The code of MEIS and dataset are available at https://github.com/hanktseng131415go/RAME