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  Subjects -> ELECTRONICS (Total: 207 journals)
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IEEE Transactions on Consumer Electronics
Journal Prestige (SJR): 0.53
Citation Impact (citeScore): 3
Number of Followers: 46  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0098-3063
Published by IEEE Homepage  [228 journals]
  • IEEE Consumer Technology Society

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      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • IEEE Consumer Technology Society Board of Governors

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      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • IEEE Consumer Technology Society Officers and Committee Chairs

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      Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • Deep Controllable Backlight Dimming for HDR Displays

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      Authors: Lvyin Duan;Demetris Marnerides;Alan Chalmers;Zhichun Lei;Kurt Debattista;
      Pages: 191 - 199
      Abstract: High dynamic range (HDR) displays with dual-panels are one type of displays that can provide HDR content. These are composed of a white backlight panel and a colour LCD panel. Local dimming algorithms are used to control the backlight panel in order to reproduce content with high dynamic range and contrast at a high fidelity. However, existing local dimming algorithms usually process low dynamic range (LDR) images, which are not suitable for processing HDR images. In addition, these methods use hand-crafted features to estimate the backlight values, which may not be suitable for many kind of images. In this work, a novel deep learning based local dimming method is proposed for rendering HDR images on dual-panel HDR displays. The method uses a Convolutional Neural Network (CNN) to directly predict backlight values, using as input the HDR image that is to be displayed. The model is designed and trained via a controllable power parameter that allows a user to trade off between power and quality. The proposed method is evaluated against seven other methods on a test set of 105 HDR images, using a variety of quantitative quality metrics. Results demonstrate improved display quality and better power consumption when using the proposed method compared to the best alternatives.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • An Innovative Architecture of Full-Digital Microphone Arrays Over A²B
           Network for Consumer Electronics

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      Authors: D. Pinardi;N. Rocchi;A. Toscani;M. Binelli;G. Chiorboli;A. Farina;L. Cattani;
      Pages: 200 - 208
      Abstract: Microphone arrays of various sizes and shapes are currently employed in consumer electronics devices such as speakerphones, smart TVs, smartphones, and headphones. In this paper, a full-digital, planar microphone array is presented. It makes use of digital Micro Electro-Mechanical Systems (MEMS) microphones, connected through the Automotive Audio Bus (A2B). A clock propagation model for A2B networks, developed in a previous work, was employed to estimate the effects of jitter and delay on microphone arrays. It will be shown that A2B allows for a robust data transmission, while ensuring deterministic latency and channels synchronization, thus overcoming the signal integrity issues which usually affect MEMS capsules. The microphone positioning is also discussed since it greatly affects the spatial accuracy of beamforming. Numerical simulations were performed on four regular geometries to identify the optimal layout in terms of number of capsules and beamforming directivity. An A2B planar array with equilateral triangle geometry and four microphones, three in the vertices and one in the center, was built. Experimental measurements were performed, obtaining an excellent matching with numerical simulations. Finally, the concept of an array of arrays (meta-array) is presented, designed by combining several triangular units and analyzed through numerical simulations.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • S2Net: Shadow Mask-Based Semantic-Aware Network for
           Single-Image Shadow Removal

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      Authors: Qiqi Bao;Yunmeng Liu;Bowen Gang;Wenming Yang;Qingmin Liao;
      Pages: 209 - 220
      Abstract: Existing shadow removal methods often struggle with two problems: color inconsistencies in shadow areas and artifacts along shadow boundaries. To address these two problems, we propose a novel shadow mask-based semantic-aware network (S2Net) that uses shadow masks as guidance for shadow removal. The color inconsistency problem is solved in two steps. First, we use a series of semantic-guided dilated residual (SDR) blocks to transfer statistical information from non-shadow areas to shadow areas. The shadow mask-based semantic transformation (SST) operation in SDR enables the network to remove shadows while keeping non-shadow areas intact. Then, we design a refinement block by incorporating semantic knowledge of shadow masks and applying the learned modulated convolution kernels to get traceless and consistent output. To remove artifacts along shadow boundaries, we propose a newly designed boundary loss. The boundary loss encourages spatial coherence around shadow boundaries. By including the boundary loss as part of the loss function, a significant portion of artifacts along shadow boundaries can be removed. Extensive experiments on the ISTD, ISTD+, SRD and SBU datasets show our S2Net outperforms existing shadow removal methods.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • Design and ASIC-Implementation of Hardware-Efficient Cooperative
           Spectrum-Sensor for Data Fusion-Based Cognitive Radio Network

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      Authors: Rohit B. Chaurasiya;Rahul Shrestha;
      Pages: 221 - 235
      Abstract: This paper presents hardware-friendly algorithm for Gini-index (GI) cooperative-spectrum-sensing (CSS) algorithm for the data-fusion based cooperative cognitive-radio network. It simplifies the complex computations of sample-covariance-matrix (SCM) elements and test-statistics value of the conventional GI-based CSS algorithm. It delivers excellent detection performance under the realistic scenario of non-uniform dynamical noise and signal-power. Based on GI-based CSS algorithm, three different VLSI architectures are proposed for the cooperative spectrum sensor (CSR): CSR-VLAR1, CSR-VLAR2, and CSR-VLAR3. Here, CSR-VLAR1 is the first-time reported CSR-architecture for the conventional GI-based CSS algorithm. Subsequently, CSR-VLAR2 represents hardware-architecture of the proposed hardware-friendly GI-based CSS algorithm. Eventually, additional architectural optimization has been applied to CSR-VLAR2 that is transformed into the most hardware-efficient VLSI-architecture of CSR, referred as CSR-VLAR3, which is ASIC chip-fabricated in UMC 130 nm-CMOS technology node. Furthermore, both CSR-VLAR1 and CSR-VLAR2 are synthesized and post-layout simulated in the same technology node. Our ASIC-chip of CSR-VLAR3 occupies 0.35 mm2 of core-area and consumes 8.31 mW of total power at 88.8 MHz of maximum clock frequency, when the supply voltage is 1.2 V. Our CSR ASIC-chip has been functionally verified with the aid of real-world signals, using USRPs and FPGAs based test-setup of cooperative cognitive-radio network. Measured results of our design are compared with reported implementations where the proposed CSR is $4.52times $ hardware-efficient and $2.8times $ power-efficient than the state-of-the-art CSR-implementations. Thus, our work addresses the key challenge of designing hardware-efficient CSR that delivers excellent de-ection performance in the real-world scenario.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • Joint Regression Network and Window Function-Based Piecewise Neural
           Network for Cuffless Continuous Blood Pressure Estimation Only Using
           Single Photoplethesmogram

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      Authors: Zijie Qiu;Danni Chen;Bingo Wing-Kuen Ling;Qing Liu;Wenli Li;
      Pages: 236 - 260
      Abstract: The blood pressure (BP) is generally measured using a cuff based sphygmomanometer. However, it is inconvenient to be used. Recently, an alternative solution only using the photoplethesmograms (PPGs) was proposed. In this case, the continuous BP estimation could be performed. First, the features were extracted from the PPGs. Then, a regression network was employed to estimate the BP values. Nevertheless, the accuracy of this approach was not so high. In order to improve the estimation accuracy, this paper proposes to cascade a two layer piecewise neural network to the output of the existing regression network to correct the estimation error. In particular, the overall system is a three layer network. The first layer of the network is the existing regression network. It generates the initial estimated BP values. The second layer of the network consists of the window functions. It segments the range of the BP values into various regions for the further processing. The final layer of the network performs the estimation correction. The performance of our proposed network is evaluated via two practical datasets and three common regression networks including the three layer artificial neural network (ANN) based regression network, the random forest (RF) based regression network and the support vector regression (SVR) based network. For the first dataset, our proposed method with the RF model and the piecewise neural network achieves the systolic BP (SBP) estimation error and the diastolic BP (DBP) estimation error at $3.01{pm }2.22$ mmHg with the correlation coefficient at 0.926 and $4.43{pm }3.37$ mmHg with the correlation coefficient at 0.935, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $5.34{pm }4.08$ mmHg with the correlation coefficient at 0.740 and $5.89{pm }4.98$ mmHg with the correlation coefficient at 0.840, respectively. For the second dataset, our proposed method with the RF model and the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $7.91{pm }8.06$ mmHg with the correlation coefficient at 0.876 and $3.47{pm }5.59$ mmHg with the correlation coefficient at 0.859, respectively. On the other hand, the conventional RF model without the piecewise neural network achieves the SBP estimation error and the DBP estimation error at $9.77{pm }9.01$ mmHg with the correlation coefficient at 0.805 and $7.08{pm }5.55$ mmHg with the correlation coefficient at 0.612, respectively. It can be seen that our proposed network yields the estimated BP values highly correlated to the reference BP values. Also, our proposed method yields the higher accuracies compared to the existing networks. This demonstrates the effectiveness of our proposed network.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • Accurate Current Sharing and Voltage Regulation in Hybrid Wind/Solar
           Systems: An Adaptive Dynamic Programming Approach

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      Authors: Rui Wang;Dazhong Ma;Ming-Jia Li;Qiuye Sun;Huaguang Zhang;Peng Wang;
      Pages: 261 - 272
      Abstract: Renewable energy is an advisable choice to reduce fuel consumption and $rm CO_{2}$ emission. Therein, wind energy and solar energy are the most promising contributors to reach this goal. Although the hybrid wind/solar system has been widely studied, the real-time current sharing based on their maximum capacities is rarely achieved in terms of seconds. Based on this, this paper proposes an accurate current sharing and voltage regulation approach in hybrid wind/solar systems, which is based on distributed adaptive dynamic programming approach. Firstly, the equivalent wind/solar model is built, which is an indispensable preprocessing to achieve the complementary between wind energy and solar energy. Therein, the wind energy and solar energy can output relative current according to their respective capacity ratio, which ensure the maximum utilization ratio of renewable energy source. Furthermore, current sharing and voltage regulation problem is switched into optimal control problem. Under this effect, each source agent aims to obtain the optimal control variable and achieve accurate current sharing/voltage regulation. Moreover, an adaptive dynamic programming approach based on Bellman principle is proposed. It can achieve accurate current sharing and voltage regulation. Finally, the simulation results are provided to illustrate the performance of the proposed adaptive dynamic programming approach.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • Time-Shift Modeling-Based Hear-Through System for In-Ear Headphones

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      Authors: Chong-Rui Huang;Cheng-Yuan Chang;Sen M. Kuo;
      Pages: 273 - 280
      Abstract: The hear-through (HT) technique was developed actively to compensate for passive isolation by enhancing the perception of ambient sound by wearers of headphones. The passive material in headphones reduces the high-frequency components of sound above 500 Hz. The HT algorithm compensates for the loss of ambient sound by generating an artificial sound, derived using a relative transfer function (RTF) between a microphone and the user’s ears. Conventionally, HT performance depends on the direction of arrival (DOA) of the ambient sound. This work develops a time-shift scheme for identifying the RTF and an active equalization algorithm to calculate accurately the compensating sound. A shaping filter is also developed to prevent acoustical interference by the artificial sound at low frequency. Finally, the proposed approach is integrated into in-ear headphones and its HT performance is compared with that of a commercial product to verify the effectiveness of the proposed algorithm.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • Dual Locality-Based Flash Translation Layer for NAND Flash-Based Consumer
           Electronics

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      Authors: Yuhan Luo;Mingwei Lin;Yubiao Pan;Zeshui Xu;
      Pages: 281 - 290
      Abstract: NAND flash memory shows prominent performance, so it has been used as storage devices of consumer electronics, such as the smart phones and tablet personal computers. As the storage management software of NAND flash memory, the page-level flash translation layer (PLFTL) owns very high I/O access performance for consumer electronics. As an improved version of PLFTL, the demand-based PLFTL selectively keeps active mapping entries in the DRAM (Dynamic Random Access Memory) and the demand-based PLFTL mainly considers the temporal locality of workloads. However, the spatial locality also appears in many workloads. To exploit the temporal locality and spatial locality of workloads, a novel dual locality-based FTL (DL-FTL) is proposed in this paper. DL-FTL uses the sequential cache mapping state table (S-CMST) and sequential physical address cache mapping table (SPA-CMT) to process the sequential requests. To decrease the update counts of translation pages, the mapping entries that are evicted from S-CMST will be written back to NAND flash memory using a batch update strategy. The experimental results show that our proposed DL-FTL raises the cache hit ratio by up to 66.39% and reduces the system response time by up to 21.64% on average, compared with the demand-based PLFTL.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • Secured Convolutional Layer IP Core in Convolutional Neural Network Using
           Facial Biometric

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      Authors: Anirban Sengupta;Rahul Chaurasia;
      Pages: 291 - 306
      Abstract: This paper presents a novel methodology to design a secured custom reusable intellectual property (IP) core for the convolutional layer of convolutional neural network (CNN). Since the reusable IP cores used in system-on-chips (SoCs) of consumer electronics (CE) systems are susceptible to the hardware threat of IP counterfeiting. Therefore, this paper also presents the security of the proposed convolutional layer reusable IP core against the threat of IP counterfeiting using facial biometrics. This enables the integration of secured reusable IP cores in the SoCs of CE systems, thereby ensuring the safety of end consumers. In the proposed approach, the convolutional layer IP core is designed through high-level synthesis (HLS) process and secured by embedding secret biometric security information into the design during register allocation phase of the HLS process. The qualitative and quantitative analysis of the proposed approach exhibits significantly lower probability of coincidence (Pc) (up to 47% less) and higher tamper tolerance (1.93E+25) than recent approaches. Further, it offers robust security with zero design overhead.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
  • Crowd Counting by Using Top-k Relations: A Mixed Ground-Truth CNN
           Framework

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      Authors: Li Dong;Haijun Zhang;Kai Yang;Dongliang Zhou;Jianyang Shi;Jianghong Ma;
      Pages: 307 - 316
      Abstract: Crowd counting has important applications in the environments of smart cities, such as intelligent surveillance. In this paper, we propose a novel convolutional neural network (CNN) framework for crowd counting with mixed ground-truth, called top- $k$ relation-based network (TKRNet). Specifically, the estimated density maps generated in a coarse-to-fine manner are treated as coarse locations for crowds so as to assist our TKRNet to regress the scattered point-annotated ground truth. Moreover, an adaptive top- $k$ relation module (ATRM) is proposed to enhance feature representations by leveraging the top- $k$ dependencies between the pixels with an adaptive filtering mechanism. Specifically, we first compute the similarity between two pixels so as to select the top- $k$ relations for each position. Then, a weight normalization operation with an adaptive filtering mechanism is proposed to make the ATRM adaptively eliminate the influence from the low correlation positions in the top- $k$ relations. Finally, a weight attention mechanism is introduced to make the ATRM pay more attention to the positions with high weights in the top- $k$ relations. Extensive experimental results demonstrate the effectiveness of our proposed TKRNet on several public datasets in comparison to state-of-the-art methods.
      PubDate: Aug. 2022
      Issue No: Vol. 68, No. 3 (2022)
       
 
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