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Patients with chronic stroke often have difficulty opening their hands and performing grasping movements. Several passive hand orthoses for assisting hand rehabilitation have been developed and demonstrated to be clinically effective. However, current devices have several limitations, such as supporting only a single grasping motion and using an abnormal grasping posture. Therefore, this study developed a three-dimensional (3D)-printed multifunctional hand device (3DP-MFHD) to solve these problems and evaluated the feasibility of using the device during home rehabilitation. Six participants were enrolled, and each of them was provided with the 3DP-MFHD. In addition to a task-oriented training course, the participants were asked to train at home for 4 weeks for at least 5 days per week and 40 min per day. The results revealed that hand grip force increased by 36.1%, lateral pinch force increased by 17.6%, and the Action Research Arm Test score increased by 54.1%. The 3DP-MFHD is a promising means to facilitate hand rehabilitation and improve hand strength and function in patients with chronic stroke. The 3DP-MFHD can be used as part of home rehabilitation.

Patients with chronic stroke often have difficulty opening their hands and performing grasping movements. Several passive hand orthoses for assisting hand rehabilitation have been developed and demonstrated to be clinically effective. However, current devices have several limitations, such as supporting only a single grasping motion and using an abnormal grasping posture. Therefore, this study developed a three-dimensional (3D)-printed multifunctional hand device (3DP-MFHD) to solve these problems and evaluated the feasibility of using the device during home rehabilitation. Six participants were enrolled, and each of them was provided with the 3DP-MFHD. In addition to a task-oriented training course, the participants were asked to train at home for 4 weeks for at least 5 days per week and 40 min per day. The results revealed that hand grip force increased by 36.1%, lateral pinch force increased by 17.6%, and the Action Research Arm Test score increased by 54.1%. The 3DP-MFHD is a promising means to facilitate hand rehabilitation and improve hand strength and function in patients with chronic stroke. The 3DP-MFHD can be used as part of home rehabilitation. [ABSTRACT FROM AUTHOR]

Advanced Chemical-mechanical polishing (CMP) process not only needs to maintain stable run-to-run thickness control but also achieve better within wafer/within chip planarization performance. Furthermore, slurries or other consumable parts, like PAD and Disks selection are also the keys for CMP process optimization. The most difficult thing in CMP process is to have capability to predict and cover the various topologies and layout densities patterned wafers and preventing the hot spots occurrences. In this study, different Neural-Network algorithm with data pre-processing models are implemented to the in-line CMP CLC tuning and dishing/erosion prediction at various topology/pattern density test vehicle pattern wafers. Transfer learning technique is implemented on the original Neural -Network algorithm model, the behavior of real product can be simulated and predicted based on the knowledge of test vehicle database successfully. With the aid of multiple layer erosion/ dishing Neural-Network algorithm model prediction, the potential high risky hot spots can be highlighted at the initial layout design stage, then further shorten the turn-around time of design layout validation. [ABSTRACT FROM AUTHOR]

This article presents a high-sensitivity and high energy efficiency wake-up receiver (WuRx) based on an injection-locked oscillator (ILO) and an envelope detector to provide the capabilities of reliable demodulation for ON–OFF keying (OOK), binary frequency shift keying (BFSK), and differential binary phase shift keying (DBPSK) demodulation schemes. A 54- $\mu \text{W}$ 433-MHz multimode WuRx is implemented in a 0.18- $\mu \text{m}$ CMOS process. For a data rate of 200 kb/s and a bit error rate (BER) <0.1%, the receiver achieves sensitivities of −80/−78/−77 dBm under the OOK/BFSK/DBPSK demodulation schemes, respectively. Furthermore, incorporating an external loop antenna with a reception of an ILO, the receiver features a low-power mode of just $11~\mu \text{W}$ with a sensitivity of at least −71 dBm. [ABSTRACT FROM AUTHOR]

Screen content coding (SCC) is an extension of high efficiency video coding (HEVC), and it is developed to improve the coding efficiency of screen content videos by adopting two new coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the flexible quadtree-based coding tree unit (CTU) partitioning structure and various mode candidates make the fast algorithms of the SCC extremely challenging. To efficiently reduce the computational complexity of SCC, we propose a deep learning-based fast prediction network DeepSCC that contains two parts: DeepSCC-I and DeepSCC-II. Before feeding to DeepSCC, incoming coding units (CUs) are divided into two categories: dynamic CTUs and stationary CTUs. For dynamic CTUs having different content as their collocated CTUs, DeepSCC-I takes raw sample values as the input to make fast predictions. For stationary CTUs having the same content as their collocated CTUs, DeepSCC-II additionally utilizes the optimal mode maps of the stationary CTU to further reduce the computational complexity. Compared with the HEVC-SCC reference software SCM-8.3, the proposed DeepSCC reduces the encoding time by 48.81% on average with a negligible Bjøntegaard delta bitrate increase of 1.18% under all-intra configuration. [ABSTRACT FROM AUTHOR]

The screen content coding (SCC) extension of high efficiency video coding (HEVC) improves coding gain for screen content videos by introducing two new coding modes, namely, intra block copy (IBC) and palette (PLT) modes. However, the coding gain is achieved at the increased cost of computational complexity. In this paper, we propose a decision tree-based framework for fast intra mode decision by investigating various features in the training sets. To avoid the exhaustive mode searching process, a sequential arrangement of decision trees is proposed to check each mode separately by inserting a classifier before checking a mode. As compared with the previous approaches where both IBC and PLT modes are checked for screen content blocks (SCBs), the proposed coding framework is more flexible which facilitates either the IBC or PLT mode to be checked for SCBs such that computational complexity is further reduced. To enhance the accuracy of decision trees, dynamic features are introduced, which reveal the unique intermediate coding information of a coding unit (CU). Then, if all the modes are decided to be skipped for a CU at the last depth level, at least one possible mode is assigned by a CU-type decision tree. Furthermore, a decision tree constraint technique is developed to reduce the rate–distortion performance loss. Compared with the HEVC-SCC reference software SCM-8.3, the proposed algorithm reduces computational complexity by 47.62% on average with a negligible Bjøntegaard delta bitrate (BDBR) increase of 1.42% under all-intra (AI) configurations, which outperforms all the state-of-the-art algorithms in the literature. [ABSTRACT FROM AUTHOR]

Screen content coding (SCC) is an extension of high efficiency video coding by adopting new coding modes to improve the coding efficiency of SCC at the expense of increased complexity. This paper proposes an online-learning approach for fast mode decision and coding unit (CU) size decision in SCC. To make a fast mode decision, the corner point is first extracted as a unique feature in screen content, which is an essential pre-processing step to guide Bayesian decision modeling. Second, the distinct color number in a CU is derived as another unique feature in screen content to build the precise model using online-learning for skipping unnecessary modes. Third, the correlation of the modes among spatial neighboring CUs is analyzed to further eliminate unnecessary mode candidates. Finally, the Bayesian decision rule using online-learning is applied again to make a fast CU size decision. To ensure the accuracy of the Bayesian decision models, new scene change detection is designed to update the models. Results show that the proposed algorithm achieves 36.69% encoding time reduction with 1.08% Bjøntegaard delta bitrate (BDBR) increment under all intra configuration. By integrating into the existing fast SCC approach, the proposed algorithm reduces 48.83% encoding time with a 1.78% increase in BDBR. [ABSTRACT FROM AUTHOR]

Screen content coding (SCC) is the extension to high-efficiency video coding (HEVC) for compressing screen content videos. New coding tools, intrablock copy (IBC), and palette (PLT) modes, are introduced to encode screen content (SC) such as texts and graphics. The IBC mode is used for encoding repeating patterns by performing block matching within the same frame, while the PLT mode is designed for SC with few distinct colors by coding the major colors and their corresponding locations using an index map. However, the use of IBC and PLT modes increases the encoder complexity remarkably though coding efficiency can be improved. Therefore, we propose to have a mode skipping approach to reduce the encoder complexity of SCC by making use of SC characteristics, neighbor coding unit (CU) correlations, and intermediate cost information via random forest (RF). Detailed feature analyses and sample preparation are also described. A novel hyperparameter tuning approach with the consideration of coding bitrate and encoding time is proposed for RFs at each CU size to further boost the encoding process. Experimental results show that our proposed approach can obtain 45.06% average encoding time reduction with only a 1.08% increase in Bjøntegaard delta bitrate. Average encoding time can even be reduced to 58.57% by regulating the hyperparameters. [ABSTRACT FROM AUTHOR]

A Colpitts voltage-controlled oscillator (VCO) is presented for low-power and low-phase-noise applications. The current efficiency of the proposed VCO is improved through ${g} _{{m}}$ -enhanced and current-reuse techniques. In addition, a clover-shaped inductor is employed to mitigate electromagnetic interference (EMI) effects. The VCO is implemented in a 0.18- ${ \mu }\text{m}$ CMOS process and is shown to operate at frequencies of 2.34 to 2.55 GHz with a tuning range of 8.6%. The prototype has a power dissipation of 1.4 mW and achieves a phase noise of −122.85 dBc/Hz at a 1 MHz frequency offset. [ABSTRACT FROM AUTHOR]

A screen content coding (SCC) extension to high efficiency video coding has been developed to incorporate many new coding tools in order to achieve better coding efficiency for videos mixed with camera-captured content and graphics/text/animation. For instance, the Intra Block Copy (IntraBC) mode helps to encode repeating patterns within the same frame while the Palette mode aims at encoding screen content with a few major colors. However, the IntraBC mode brings along high computational complexity due to the exhaustive block matching within the same frame though there are already some constraints and fast approaches applied to the IntraBC mode to reduce its complexity. Thus, we propose a fast intracoding scheme to reduce the complexity of using the IntraBC mode in SCC. Screen content always contains no sensor noise resulting in the characteristics with pixel exactness along both horizontal and vertical directions. These characteristics pave the way for mode skipping and early coding unit (CU) splitting. Besides, early CU pruning and early termination are proposed based on the rate distortion cost to further reduce encoder complexity. Moreover, we also propose reducing the complexity of the IntraBC mode by checking the hash value of each block candidate and the current block during block matching. With our proposed scheme, the encoding time is reduced compared with the SCC while the coding efficiency can still be maintained with a minor increase in the bjontegaard delta bitrate. [ABSTRACT FROM AUTHOR]