Showing papers in "IEEE Transactions on Consumer Electronics in 2015"
TL;DR: This paper presents a magnetic resonance wireless power transfer (WPT) system that uses three coils, a planar receiver and operates at 6.78 MHz, effective power transfer is ensured by establishing an impedance matching condition for this WPT system.
Abstract: This paper presents a magnetic resonance wireless power transfer (WPT) system that uses three coils, a planar receiver and operates at 6.78 MHz,. Effective power transfer is ensured by establishing an impedance matching condition for this WPT system. A metamaterial (MTM) array having dimensions of 20 cm Ⅹ 30 cm is also positioned near the load coil to concentrate the magnetic field and enhance the transfer efficiency. The result is a maximal improvement of 27% in the transfer efficiency at a transfer distance of 50 cm. The impact of a ground plane on the transfer efficiency is also examined. By utilizing the MTM array, making slits on the ground plane and increasing the gap between the ground plane and the load coil, it is possible to mitigate this impact. The highest transfer efficiency improvement is about 55% at a distance of 20 cm with the ground plane. A practical laptop model is fabricated to verify the impact of the load coil angle and position on the transfer efficiency. The result shows that the maximum transfer efficiency with the laptop model is 47.58% with the load coil angle of 90 degree.
97 citations
TL;DR: An Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks is proposed and significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed forHome automation networks.
Abstract: With the fast development of wireless communications, ZigBee and semiconductor devices, home automation networks have recently become very popular. Since typical consumer products deployed in home automation networks are often powered by tiny and limited batteries, one of the most challenging research issues is concerning energy reduction and the balancing of energy consumption across the network in order to prolong the home network lifetime for consumer devices. The introduction of clustering and sink mobility techniques into home automation networks have been shown to be an efficient way to improve the network performance and have received significant research attention. Taking inspiration from nature, this paper proposes an Ant Colony Optimization (ACO) based clustering algorithm specifically with mobile sink support for home automation networks. In this work, the network is divided into several clusters and cluster heads are selected within each cluster. Then, a mobile sink communicates with each cluster head to collect data directly through short range communications. The ACO algorithm has been utilized in this work in order to find the optimal mobility trajectory for the mobile sink. Extensive simulation results from this research show that the proposed algorithm significantly improves home network performance when using mobile sinks in terms of energy consumption and network lifetime as compared to other routing algorithms currently deployed for home automation networks.
80 citations
TL;DR: A cepstrum-smoothing-based load disaggregation method to effectively deal with the simultaneous on/off events of multiple appliances is presented and a data acquisition system is developed to obtain only the characteristic signals of appliances and to filter the noise inputted from the power supply.
Abstract: Various load disaggregation methods have been developed for monitoring home appliance loads to save energy in a smart home. Most of load disaggregation methods are designed to focus on the on/off events of single appliance. In reality, however, multiple appliances can be turned on/off simultaneously. Load disaggregation can be complicated by the simultaneous on/off events of multiple appliances. This paper presents a cepstrum-smoothing-based load disaggregation method to effectively deal with the simultaneous on/off events of multiple appliances. Further, a data acquisition system is developed to obtain only the characteristic signals of appliances and to filter the noise inputted from the power supply. Test results are provided to demonstrate the effectiveness of the proposed cepstrumsmoothing- based load disaggregation method1.
56 citations
TL;DR: A range of factors that affect the quality of iris images are reviewed and Iris size, image quality and acquisition wavelength are found to be key factors for useful authentication performance.
Abstract: As a near ideal biometric, iris authentication is widely used and mobile acquisition techniques are known. But iris acquisition on handheld imaging devices, such as smartphones, poses multiple, unique challenges. In this paper, a range of factors that affect the quality of iris images are reviewed. Iris size, image quality and acquisition wavelength are found to be key factors. Experimental results are presented confirming the lower limits of iris size for useful authentication performance. The authentication workflow for handheld devices is described. A case study on a current smartphone model is presented, including calculation of the pixel resolution that can be achieved with a visible-only optical system. Based on these analyses, system requirements for unconstrained acquisition in smartphones are discussed. Several design strategies are presented and key research challenges are outlined together with potential solutions.
54 citations
TL;DR: An interactive framework of visualizing and authoring IoT in indoor environments such as home or small office and shows that the visual authoring is easy to use, understandable, and also preferred to typical text-based script programming.
Abstract: Internet of things recently emerges as a common platform and service for consumer electronics. This paper presents an interactive framework of visualizing and authoring IoT in indoor environments such as home or small office. Building blocks of the framework are virtual sensors and actuators that abstract physical things and their virtual behaviors on top of their physical networks. Their behaviors are abstracted and programmed through visual authoring tools on the web, which allows a casual consumer to easily monitor and define their behaviors even without knowing the underlying physical connections. The user study performed to assess the usability of the visual authoring showed that the visual authoring is easy to use, understandable, and also preferred to typical text-based script programming.
46 citations
TL;DR: An oneM2M standards-compliant device software platform for consumer electronics based on the Internet of Things, called &Cube, that leverages a standardized resource model and REST APIs to work with oneM 2M service platforms, leading to interoperability across various IoT consumer electronics built on the &Cube.
Abstract: The highly-fragmented and non-standardized landscape of the Internet of Things industry results in forcing both IoT developers and end-users to have to choose their proprietary consumer electronics by a company, eventually becoming a barrier to build an unfragmented IoT ecosystem. This paper proposes an oneM2M standards-compliant device software platform for consumer electronics based on the Internet of Things, called &Cube. It leverages a standardized resource model and REST APIs to work with oneM2M service platforms, leading to interoperability across various IoT consumer electronics built on the &Cube. The growing adoption of the &Cube in consumer electronics will help lower the barriers for the manufacturers and developers to create innovative products and entirely new services.
44 citations
TL;DR: A motion adaptive temporal filtering based on a Kalman structured updating is presented, which works directly on the color filter array (CFA) raw video for achieving low memory consumption1.
Abstract: In this paper, a novel approach for noise reduction and enhancement of extremely low-light video is proposed. For noise removal, a motion adaptive temporal filtering based on a Kalman structured updating is presented. Dynamic range of denoised video is increased by adjustment of RGB histograms using Gamma correction with adaptive clipping thresholds. Finally, residual noise is removed using a nonlocal means (NLM) denoising filter. The proposed method works directly on the color filter array (CFA) raw video for achieving low memory consumption1.
41 citations
TL;DR: A novel mobile agent-based cross-layer anomaly detection scheme, which takes into account stochastic variability in cross- layer data obtained from received data packets, and defines fuzzy logic-based soft boundaries to characterize behavior of sensor nodes is introduced.
Abstract: Despite the rapid advancements in consumer electronics, the data transmitted by sensing devices in a smart home environment are still vulnerable to anomalies due to node faults, transmission errors, or attacks. This affects the reliability of the received sensed data and may lead to the incorrect decision making at both local (i.e., smart home) and global (i.e., smart city) levels. This study introduces a novel mobile agent-based cross-layer anomaly detection scheme, which takes into account stochastic variability in cross-layer data obtained from received data packets, and defines fuzzy logic-based soft boundaries to characterize behavior of sensor nodes. This cross-layer design approach empowers the proposed scheme to detect both node and link anomalies, and also effectively transmits mobile agents by considering the communication link-state before transmission of the mobile agent. The proposed scheme is implemented on a real testbed and a modular application software is developed to manage the anomaly detection system in the smart home. The experimental results show that the proposed scheme detects cross-layer anomalies with high accuracy and considerably reduces the energy consumption caused by the mobile agent transmission in the poor communication link-state situations.
40 citations
TL;DR: A novel OD method based on a new structure, called Deformable Grid (DG), which is initially a regular gridshaped, but it can be deformed gradually depending on the motion of the object in the scene.
Abstract: To assist visually impaired people, a variety of obstacle detection (OD) methods with the monocular vision have been developed. Most conventional OD methods detect the obstacle by using the variation of the motion vector or the object size between two consecutive image frames. However, the OD performance using the short-term variation is significantly affected by the tracking error, which leads to inaccurate OD results. To cope with this problem, this paper presents a novel OD method based on a new structure, called Deformable Grid (DG). The DG is initially a regular gridshaped, but it can be deformed gradually depending on the motion of the object in the scene. The proposed method detects the object at risk of collision based on the degree of the deformation of the DG. Experimental results demonstrate that the proposed OD method outperforms the conventional ones in terms of processing time as well as accuracy.
39 citations
TL;DR: The proposed biometric system is shown to possess a range of desirable operation characteristics for real-world use, which include reliable in identification, convenient to use, high in security, fast in response, low in cost, and simple for installation.
Abstract: In consideration of an increasing demand to adopt the biometric technology to minimize potential security risks, the paper presents a consumer-level biometric system for automatic physical access control. Through development of a near infrared imaging device for biometric acquisition of dorsal hand vein patterns and computation efficient image processing methods, the proposed system is shown to possess a range of desirable operation characteristics for real-world use, which include reliable in identification, convenient to use, high in security, fast in response, low in cost, and simple for installation.
32 citations
TL;DR: This proposal outperforms traditional VLC schemes, especially in Non-Line-of-Sight reception where around 3 dB of gain, with respect to traditional schemes, can be obtained for unoptimized parameters, and larger than 3 dB could easily be achieved.
Abstract: In this paper, a novel cooperative transmission and reception scheme in Visible Light Communications (VLC) is proposed and evaluated. This new scheme provides improvements and reliability in large indoor scenarios, such as corridors, laboratories, shops or conference rooms, where the coverage needs to be obtained by using different access points when VLC is used. The main idea behind the proposal is a simple cooperative transmission scheme where the receiver terminal will obtain the signal from different access points at the same time. This proposal outperforms traditional VLC schemes, especially in Non-Line-of-Sight reception where around 3 dB of gain, with respect to traditional schemes, can be obtained for unoptimized parameters, and larger than 3 dB could easily be achieved. The cooperation is studied in terms of the percentage of light coming from the main access point and a parameter called sidelobes’ amplitude level. The performance is evaluated according to the location within the atto-cell.
TL;DR: This paper focuses on the design and implementation of the MAC layer of wireless embedded systems that are powered by energy harvesting, providing novel protocol features and practical experiences to designers of consumer electronics who opt for tailoring their own protocol solutions instead of using the standards.
Abstract: While energy consumption is widely considered the primary challenge of wireless networked devices, energy harvesting emerges as a promising way of powering the Internet of Things (IoT). In the Medium Access Control (MAC) layer of the communication stack, energy harvesting introduces spatial and temporal uncertainty in the availability of energy. In this context, this paper focuses on the design and implementation of the MAC layer of wireless embedded systems that are powered by energy harvesting; providing novel protocol features and practical experiences to designers of consumer electronics who opt for tailoring their own protocol solutions instead of using the standards.
TL;DR: The proposed session key agreement protocol does not suffer from the forgery attack and the password guessing attack as compared to other protocols in the literature, and has an improved tradeoff for computational cost, communication cost and security.
Abstract: A Universal Serial Bus (USB) Mass Storage Device (MSD), often termed a USB flash drive, is ubiquitously used to store important information in unencrypted binary format. This low cost consumer device is incredibly popular due to its size, large storage capacity and relatively high transfer speed. However, if the device is lost or stolen an unauthorized person can easily retrieve all the information. Therefore, it is advantageous in many applications to provide security protection so that only authorized users can access the stored information. In order to provide security protection for a USB MSD, this paper proposes a session key agreement protocol after secure user authentication. The main aim of this protocol is to establish session key negotiation through which all the information retrieved, stored and transferred to the USB MSD is encrypted. This paper not only contributes an efficient protocol, but also does not suffer from the forgery attack and the password guessing attack as compared to other protocols in the literature. This paper analyses the security of the proposed protocol through a formal analysis which proves that the information is stored confidentially and is protected offering strong resilience to relevant security attacks. The computational cost and communication cost of the proposed scheme is analyzed and compared to related work to show that the proposed scheme has an improved tradeoff for computational cost, communication cost and security.
TL;DR: Security and performance analysis results confirm that the proposed protocol could solve security problems of the previously introduced NFC security protocol with a marginal computational cost increase and proposes a new secure pseudonym-based NFC protocol that eliminates vulnerabilities of the previous security protocol.
Abstract: Near Field Communication (NFC) has been used for short range communications in a number of applications for consumer electronics devices. Specifically, NFC has been used in electronic payment systems. To ensure secure communications, security protocols for various NFC applications have been proposed. Recently, a conditional privacy preserving security protocol was introduced. However this paper demonstrates that the protocol is vulnerable to two impersonations attacks and then proposes a new secure pseudonym-based NFC protocol that eliminates vulnerabilities of the previous security protocol. Security and performance analysis results confirm that the proposed protocol could solve security problems of the previously introduced NFC security protocol with a marginal computational cost increase1.
TL;DR: The proposed approach generates over- and under-exposed images by making use of a novel adaptive histogram separation scheme and utilizes a fuzzy logic based approach at the fusion stage which takes visibility of the inputs pixels into account.
Abstract: In this work, a high dynamic range (HDR) image generation method using a single input image is presented. The proposed approach generates over- and under-exposed images by making use of a novel adaptive histogram separation scheme. Thus, it becomes possible to eliminate ghosting effects which generally occur when several input image containing camera/object motion are utilized in HDR imaging. Additionally, it is proposed to utilize a fuzzy logic based approach at the fusion stage which takes visibility of the inputs pixels into account. Since the proposed approach is computationally light-weight, it is possible to implement it on mobile devices such as smart phones and compact cameras. Experimental results show that the proposed approach is able to provide ghost-free and improved HDR performance compared to the existing methods1.
TL;DR: Analysis of the adaption of iris biometrics for unconstrained, hand-held devices such as smartphones is investigated and preliminary results indicate that there are challenges to achieve a reliable recognition performance from the images captured using this device.
Abstract: As a robust method of person authentication, iris biometrics is making its way in to consumer devices such as smartphones. Current iris image acquisition devices typically work under controlled environment and constrained acquisition conditions. In this paper, the adaption of iris biometrics for unconstrained, hand-held devices such as smartphones is investigated. A prototype device is presented with full system description. This device is equipped with a single image sensor with both visible and NIR sensing capabilities. The device is analysed in terms of its optical properties and iris imaging capabilities. Preliminary results indicate that there are challenges to achieve a reliable recognition performance from the images captured using this device. Current system acquires images with marginal optical quality and spatial resolution in an unconstrained acquisition scenario for iris recognition. Nevertheless, the analyses presented in this paper indicate a similar camera module with improved optics and sensor could combine iris biometrics with conventional front camera functions such as video call and the capture of selfie images.
TL;DR: A home appliance control framework based on a smart TV set-top box that supports a WebSocket protocol-based data communication and comprises a Web server where each set- top box's network location is registered.
Abstract: Most modern smart TV set-top boxes support internet connection and allow the user to install and run more advanced application programs or plugins/add-ons based on a specific platform. It means that a smart TV set-top box can be a good candidate to act as a hub that combines a wide variety of solutions for smart home services. This paper proposes a home appliance control framework based on a smart TV set-top box. As a core component, a home gateway software module is installed on the smart TV set-top box. And the architecture of the home gateway module is designed so that a home appliance control driver is installed only when necessary. To enable the user to control their home appliances in any place using a Web-based application or a Web browser, the proposed framework supports a WebSocket protocol-based data communication and comprises a Web server where each set-top box’s network location is registered. The proposed framework is verified with a demonstration on a small-scale home model. Both IP-based and serial communication-based home appliances are included in the demonstration system. The demonstration examples show that consumers can control various types of home appliances in any place through a smart TV set-top box solution.
TL;DR: A hand-shaped guide window is proposed for fast processing of image acquisition, valley point detection and verification to deal with problems in mobile palmprint recognition, and an improved version of the Competitive Code is proposed in order to cope with the variation in mobile images.
Abstract: Limited processing power, hand pose variations, complicated backgrounds and changing illumination are some of the inherent problems in mobile palmprint recognition. In this paper, a hand-shaped guide window is proposed for fast processing of image acquisition, valley point detection and verification to deal with such problems. Also, an improved version of the Competitive Code is proposed in order to cope with the variation in mobile images, and two practical verification scenarios are suggested to further improve the verification performance. A palmprint database was established using mobile phones for experiments, and an equal error rate (EER) of 2.88% was achieved by using the conventional 1-to-1 matching strategy. By applying the suggested practical scenarios, the verification accuracy was improved to achieve an EER of 0.97%.
TL;DR: Simulations show that compared to the classic Iterative Clipping and Filtering (ICF) technique, ICTF can dramatically decrease the number of required iterations to reach the desired PAPR with low complexity.
Abstract: In this paper, an efficient Iterative Companding Transform and Filtering (ICTF) technique is proposed for reducing the Peak-to-Average Power Ratio (PAPR) of Orthogonal Frequency Division Multiplexing (OFDM) signal. By means of a specially designed iterative procedure, ICTF is able to obtain both an improved Bit Error Rate (BER) and minimized Out-of-Band Interference (OBI) while reducing the PAPR significantly. A comprehensive theoretical analysis is presented, and some important results such as the achievable PAPR gain, impact of companding distortion, and selection criteria for companding parameters and maximum iteration number are derived. In particular, it is shown that the ICTF without de-companding at the receiver can offer a good BER performance. Simulations show that compared to the classic Iterative Clipping and Filtering (ICF) technique, ICTF can dramatically decrease the number of required iterations to reach the desired PAPR with low complexity. In addition, the companded OFDM symbols by the proposed ICTF technique have less in-band distortion, and lower out-of-band spectral regrowth than traditional companding schemes.
TL;DR: A novel algorithm for the detection and tracking of low relative speed moving vehicles as it combines with motion vector based moving object detection to form a complete solution for an Advanced Driver Assistance System given its reduction in cost and complexity.
Abstract: This paper presents a novel algorithm for the detection and tracking of low relative speed moving vehicles. The proposed algorithm is particularly suitable for massproduced in-vehicle devices as it combines with motion vector based moving object detection to form a complete solution for an Advanced Driver Assistance System given its reduction in cost and complexity. The algorithm utilizes motion vectors that are readily available from video encoder output. The region of interest for detection is reduced by ignoring the area above the vanishing line of the captured image, evaluation of the amplitude of motion vectors and identification of the road region. During the evaluation process, a binary image is generated by comparing the gray-level of the captured image to the gray-level of the detected road region. Subsequently, the horizontal and vertical contours of specific areas inside the region of interest are evaluated. Test results show the effectiveness of the algorithm with more than 90% detection rate and the suitability for real-time use with cycle time of less than 66ms.
TL;DR: G gesture recognition algorithms that use an inertial sensor worn on the forearm are examined, showing that the Markov Chain based algorithms outperformed the Hidden Markov Model.
Abstract: Wearable wireless devices and ubiquitous computing are expected to grow significantly in the coming years. Standard inputs such as a mouse and keyboard are not well suited for such mobile systems and gestures are seen as an effective alternative to these classic input styles. This paper examines gesture recognition algorithms that use an inertial sensor worn on the forearm. The recognition algorithms use the sensor's quaternion orientation in either a Hidden Markov Model or Markov Chain based approach. A set of six gestures were selected to fit within the context of an active video game. Despite the fact that the Hidden Markov Model is one of the most commonly used methods for gesture recognition, the experiments showed that the Markov Chain based algorithms outperformed the Hidden Markov Model. The Markov Chain algorithm obtained an average accuracy of 95%, while also having a much faster computation time, making it better suited for real time applications.
TL;DR: The proposed filter bank essentially is designed to provide reliable recognition under cleaning robot ego noise through the result of event spectrum analysis and the experimental results indicate that the features extracted by the proposed filterbank are more robust than the conventional ones.
Abstract: Due to its mobile capability when performing house-cleaning function in absence of home owners, a cleaning robot has sufficient capacity to be fully utilized as an automatic surveillance system for indoor security. While many research efforts have been made recently to provide a robot understand the auditory environment, there are still many obstacles to overcome. One of the most serious challenges encountered in providing accurate auditory scene analysis is the presence of robot ego noise. Robot ego noise is primarily generated by the embedded motors on a robot during its operation. This paper proposes a new filterbank design based on discriminative distances within event-to-noise and event-to-event, respectively. The proposed filterbank essentially is designed to provide reliable recognition under cleaning robot ego noise through the result of event spectrum analysis. The experimental results indicate that the features extracted by the proposed filterbank are more robust than the conventional ones.
TL;DR: This paper investigates the performance of different machine learning strategies to arbitrary downsize video pre-encoded with the high efficiency video coding standard (HEVC), which exploits correlation between input and output coding information to predict the splitting behavior of HEVC coding units.
Abstract: Nowadays, broadcasters deliver ultra-high resolution video to their consumers. This live video is sent to a set-top box for display on a television. However, if one or more users in the home want to view the same video on their personal mobile devices with a lower display resolution and limited processing power, decoding the original ultra-high resolution video would result in stuttering and quickly drain the battery life on these devices. To enable a satisfactory consumer experience, the resolution of the video stream should be adapted to the target mobile device at the set-top box. The aim of this paper is to investigate the performance of different machine learning strategies to arbitrary downsize video pre-encoded with the high efficiency video coding standard (HEVC). These machine learning techniques exploit correlation between input and output coding information to predict the splitting behavior of HEVC coding units. Several machine learning algorithms are optimized. Additionally, both online and offline training strategies are tested. Of the tested algorithms, online-trained random forests achieve the best compression-efficiency with a bit rate increase of 5.4% and an average complexity reduction of 70%1.
TL;DR: A machine-learning-integrated load control scheme for improved performance and reliability by dynamic capacity adjustment and interactive load heuristic that tries to reduce the power deviation while keeping the temperature violation ratio and switching counts within an acceptable range.
Abstract: Load scheduling over cyclic electrical devices can reduce the peak power demand. In this paper, we propose a machine-learning-integrated load control (MILC) scheme for improved performance and reliability. By dynamic capacity adjustment and interactive load heuristic, MILC tries to reduce the power deviation while keeping the temperature violation ratio and switching counts within an acceptable range. A prototype of the proposed scheme has been implemented and, through experiments using load traces from a real home, we evaluate the performance of MILC. The results show that MILC reduces the peak demand from 4993 W to 4236 W and successfully decreases the power deviation by 12.1% on average.
TL;DR: A dynamic garbage collection scheme based on past update times is proposed for NAND flash-based consumer electronics that can achieve a balance between the garbage collection overhead and the degree of wear leveling.
Abstract: NAND flash memory has become the dominant storage device for consumer electronics such as smart phones, portable personal computers, and digital cameras. Because NAND flash memory often introduces an out-of-place update scheme to solve its erase-before-write hardware constraint, these consumer electronics devices should perform garbage collection to reclaim garbage and obtain free space. However, existing garbage collection schemes usually suffer from the high garbage collection overhead and the high degree of wear leveling. In this paper, a dynamic garbage collection scheme based on past update times is proposed for NAND flash-based consumer electronics. The proposed scheme can achieve a balance between the garbage collection overhead and the degree of wear leveling. Experimental results show that the proposed scheme is better than existing garbage collection schemes in terms of the number of copy operations, the number of erase operations, the energy consumption, and the degree of wear leveling.
TL;DR: Experimental results show that the proposed mechanism performs better than existing hot data identification mechanisms in terms of the false identification ratio between hot data and memory-space overheads.
Abstract: Hot data identification plays a very important role in NAND flash memory because it can improve the efficiency of garbage collection and decrease the degree of wear leveling. Existing hot data identification mechanisms have drawbacks in terms of their memory-space overhead and the true identification of hot data. To address these problems, this paper proposes a novel hot data identification mechanism. This mechanism mainly consists of kernel density estimation and a hot degree function. The kernel density estimation is used to build the kernel density function of the read and write operations by monitoring them in the NAND flash memory. That is, the kernel density function can be used for preliminary estimation of the probability distribution of the hot and cold data in NAND flash memory. After preliminary estimation of the kernel density function, the hot degree function is introduced to accurately identify the hot data in the NAND flash memory. Experimental results show that the proposed mechanism performs better than existing hot data identification mechanisms in terms of the false identification ratio between hot data and memory-space overheads.
TL;DR: An intelligent screen system for the user to interactively enjoy different sceneries of remote places that is aware of the current environment, and the weather condition in the remote scenery video is associated with that of the local place in different ways to bring the user a natural and pleasant viewing experience.
Abstract: With the growth of the popularity of large screens in the consumer market and the enrichment of the online and offline video resources, enjoying a dynamic remote scenery at home has become an interesting application. This paper presents an intelligent screen system for the user to interactively enjoy different sceneries of remote places. Different from existing works, the system is aware of the current environment, and the weather condition in the remote scenery video is associated with that of the local place in different ways to bring the user a natural and pleasant viewing experience. Meanwhile, the system is able to transform the layout of the scenery video according to the user's position and viewing angle, making the user feel like viewing the outside scenery through a real window when walking and looking around in front of the screen. A prototype consisting of a large screen and several low-cost sensors is implemented and a series of tests are conducted. The results show that the proposed system can effectively improve the users' experience on natural scenery viewing and make the process more pleasant. The system fits for screens with various sizes and can be easily installed, making it a scalable and ease-of-use consumer device in smart home1.
TL;DR: Simulations demonstrate that MLMS outperforms traditional LMS, Normalized LMS (NLMS), and Generalized Normalized Gradient Descent (GNGD) algorithms in terms of the Normalized Mean Square Error (NMSE) and out-of-band Power Spectral Density (PSD) under the noisy feedback condition for the wideband PAs.
Abstract: As one of the most promising linearization techniques, adaptive Digital Predistortion (PD) has been widely utilized in modern wireless communication systems for improving the efficiency of Power Amplifier (PA). In view of the non-stationary signal environment for the wideband PAs, an efficient indirect learning adaptive PD is proposed in the paper based on the Memory Polynomial Model (MPM). The coefficients of the proposed PD can be effectively identified by the Modified Least Mean Square (MLMS) learning algorithm. In addition, more stable convergence and lower steady-state error can be achieved simultaneously for the PAs with deep memory effects by adopting the variable step-size parameter. Theoretical analysis results regarding the learning stability, convergence behavior, and selection criteria of initial settings are derived. Simulations demonstrate that MLMS outperforms traditional LMS, Normalized LMS (NLMS), and Generalized Normalized Gradient Descent (GNGD) algorithms in terms of the Normalized Mean Square Error (NMSE) and out-of-band Power Spectral Density (PSD) under the noisy feedback condition for the wideband PAs1.
TL;DR: This paper introduces the world's first programmable video-processing platform for the enhancement of the video quality of the 8K (7680 × 4320) Ultra High Definition (UHD) TV that operates at a maximum rate of 60 frames per second.
Abstract: This paper introduces the world’s first programmable video-processing platform for the enhancement of the video quality of the 8K (7680 x 4320) Ultra High Definition (UHD) TV that operates at a maximum rate of 60 frames per second. To support the massive computational load and memory bandwidth of 8K video, several key features have been implemented in the proposed platform such as symmetric multi-cluster architecture for data partitioning, a ring-data path between the clusters to support data pipelining, on-the-fly processing architecture for the reduction of the DDR bandwidth, and flexible hardware accelerators that facilitate the computation of common kernels by video-qualityenhancement algorithms. In addition, within a context of continuously evolving and changing UHD-TV video algorithms, system flexibility is crucial to support new algorithms and enhance competitiveness. The programmability of the main core of the proposed platform the reconfigurable processor (RP) makes it possible to upgrade the algorithms even after the hardware design is fixed. The proposed platform was embedded into the System on Chip (SoC), and a new 8K UHD-TV model that features this programmable solution is expected to appear on the market in the near future.
TL;DR: In this paper, a personalized TV program guide based on neural network is described and it is shown how class imbalance information can be exploited in learning the user preferences to improve the system performance and increases the user satisfaction.
Abstract: When TV recommender systems perform well, number of interactions in which their users expressed positive feedback on the recommended content is expected to be greater than the number of negative ones. This is known as class imbalance and, paradoxically, it degrades the system performance by making the identification of the programs the user will dislike increasingly difficult. As the misclassification of the unwanted content is easily perceived by TV viewers, it should be avoided by all means. In this paper, a personalized TV program guide based on neural network is described. It is shown how class imbalance information can be exploited in learning the user preferences. This not only improves the system performance, but increases the user satisfaction as well.1