HfO2 Gate Dielectrics for Future Generation of CMOS Device Application

Introduction High- k gate dielectrics have been extensivelystudied as alternates to conventional gate oxide( Si O2 ) due to the aggressive downscaling of Si O2thickness in CMOS devices,and hence the exces-sive gate leakage.Hf O2 has emerged as one...     

Hybrid Model to Detect Zero Quantized DCT Coefficients in H.264

In H.264 coding, there are a large number of discrete cosine transform (DCT) coefficients of the prediction residue which are quantized to zeros. Therefore, it is desired to design a method which can early detect zero quantized DCT coefficients (ZQDCT) before implementing DCT and quantization (Q) and thus reduce redundant computations for H.264 coding. To achieve this, a hybrid model is proposed in this paper in order to predict ZQDCT coefficients. First, the Gaussian distribution is applied to study the integer DCT coefficients in H.264 and hence an adaptive scheme with multiple thresholds is derived to realize different types of DCT and Q implementations. Then the adaptive scheme is further optimized by considering a more efficient condition to sufficiently detect all-zero DCT blocks. As a result, a hybrid model is developed. Compared with other methods in the literature, the proposed hybrid model is able to detect more ZQDCT coefficients and hence reduce more computations for H.264 encoding. It is shown by experimental results that the proposed hybrid model can achieve the best performance in reducing computations and obtain almost the same rate-distortion (R-D) performance as the original encoder in the H.264 reference software JM9.5     

Differential Retinal Protein Expression in Primary and Secondary Retinal Ganglion Cell Degeneration Identified by Integrated SWATH and Target-Based Proteomics

To investigate the retinal proteins associated with primary and secondary retinal ganglion cell (RGC) degeneration and explore their molecular pathways, SWATH label-free and target-based mass spectrometry was employed to identify the proteomes in various retinal locations in response to localized optic nerve injury. Unilateral partial optic nerve transection (pONT) was performed on adult Wistar rats and their retinas were harvested 2 weeks later. To confirm the separation of primary and secondary RGC degeneration, immunohistochemistry of RNA binding protein with multiple splicing (RBPMS) and glial fibrillary acidic protein (GFAP) was performed on retinal whole-mounts. Retinal proteomes in the temporal and nasal quadrants were evaluated with high resolution hybrid quadrupole time-of-flight mass spectrometry (QTOF-MS), and SWATH-based acquisition, and their expression was compared to the corresponding retinal quadrant in contralateral control eyes and further validated by multiple reaction monitoring mass spectrometry (MRM-MS). A total of 3641 proteins (FDR < 1%) were identified using QTOF-MS. The raw data are available via ProteomeXchange with the identifier PXD026783. Bioinformatics data analysis showed that there were 37 upregulated and 25 downregulated proteins in the temporal quadrant, whereas 20 and five proteins were upregulated and downregulated, respectively, in the nasal quadrant, respectively (n = 4, p < 0.05; fold change ≥ 1.4-fold or ≤0.7). Six proteins were regulated in both the temporal and the nasal quadrants, including CLU, GFAP, GNG5, IRF2BPL, L1CAM, and CPLX1. Linear regression analysis indicated a strong association between the data obtained by means of SWATH-MS and MRM-MS (temporal, R² = 0.97; nasal, R² = 0.96). Gene ontology analysis revealed statistically significant changes in the biological processes and cellular components of primary RGC degeneration. The majority of the significant changes in structural, signaling, and cell death proteins were associated with the loss of RGCs in the area of primary RGC degeneration. The combined use of SWATH-MS and MRM-MS methods detects and quantifies regional changes of retinal protein expressions after localized injury. Future investigation with this integrated approach will significantly increase the understanding of diverse processes of progressive RGC degeneration from a proteomic prospective.     

Highly efficient electrophosphorescent polymer light-emitting devices

We report a high performance polymer electroluminescent device based on a bi-layer structure consisting of a hole transporting layer (poly(vinylcarbazole)) and an electron transporting layer poly(9,9-bis(octyl)-fluorene-2,7-diyl) (BOc-PF) doped with platinum(II)-2,8,12,17-tetraethyl-3,7,13,18-tetramethylporphyrin (PtOX). The devices show red electrophosphorescence with a peak emission at 656 nm and a full width at half maximum of 18 nm, consistent with exclusive emission from the PtOX dopants. BOc-PF emission is not observed at any bias. The required doping levels for these phosphorescence-based polymer light-emitting diodes (PLEDs) are significantly lower than for other reported phosphorescence-based PLEDs or organic light-emitting diodes (OLEDs). A doping level of 1% or more give an LED with exclusive PtOX emission, whereas related PLEDs or OLEDs doped with phosphorescent dopants require doping levels of >5% to achieve exclusive dye dopant emission. The device external efficiency was enhanced from 1% to 2.3% when doped with PtOX. The lower doping level in BOc-PF/PtOX based PLEDs decreases triplet–triplet annihilation in these devices, leading to quantum efficiency that is only weakly dependent on current density. The luminescence transient decay time for this device is 500 μs.     

Reinforcement learning models for scheduling in wireless networks

The dynamicity of available resources and network conditions, such as channel capacity and traffic characteristics, have posed major challenges to scheduling in wireless networks. Reinforcement learning (RL) enables wireless nodes to observe their respective operating environment, learn, and make optimal or near-optimal scheduling decisions. Learning, which is the main intrinsic characteristic of RL, enables wireless nodes to adapt to most forms of dynamicity in the operating environment as time goes by. This paper presents an extensive review on the application of the traditional and enhanced RL approaches to various types of scheduling schemes, namely packet, sleep-wake and task schedulers, in wireless networks, as well as the advantages and performance enhancements brought about by RL. Additionally, it presents how various challenges associated with scheduling schemes have been approached using RL. Finally, we discuss various open issues related to RL-based scheduling schemes in wireless networks in order to explore new research directions in this area. Discussions in this paper are presented in a tutorial manner in order to establish a foundation for further research in this field.     

Determination of fault-tolerant fabric-cutting schedules in a just-in-time apparel manufacturing environment

In apparel manufacturing, accurate upstream fabric-cutting planning is crucial for the smoothness of downstream sewing operations. Effective and reliable fabric-cutting schedules are difficult to obtain because the apparel manufacturing environment is fuzzy and dynamic. In this paper, genetic algorithms and fuzzy-set theory are used to generate fault-tolerant fabric-cutting schedules in a just-in-time production environment. The proposed method is demonstrated by two cases with production data collected from a Hong Kong-owned garment production plant in China. Results of the two cases preliminarily show that the genetically improved fault-tolerant schedules effectively satisfy the demand for downstream production units, guarantee consistent and reliable system performance, and also reduce production costs through reduced operator idle time. More cases will be conducted in order to further validate the effectiveness of the proposed method.     

H.264/SVC mode decision based on optimal stopping theory

Fast mode decision algorithms have been widely used in the video encoder implementation to reduce encoding complexity yet without much sacrifice in the coding performance. Optimal stopping theory, which addresses early termination for a generic class of decision problems, is adopted in this paper to achieve fast mode decision for the H.264/Scalable Video Coding standard. A constrained model is developed with optimal stopping, and the solutions to this model are employed to initialize the candidate mode list and predict the early termination. Comprehensive simulation results are conducted to demonstrate that the proposed method strikes a good balance between low encoding complexity and high coding efficiency.     

Adaptive quantization-parameter clip scheme for smooth quality in H.264/AVC

In this paper, we investigate the issues over the smooth quality and the smooth bit rate during rate control (RC) in H.264/AVC. An adaptive quantization-parameter (Q(p)) clip scheme is proposed to optimize the quality smoothness while keeping the bit-rate fluctuation at an acceptable level. First, the frame complexity variation is studied by defining a complexity ratio between two nearby frames. Second, the range of the generated bits is analyzed to prevent the encoder buffer from overflow and underflow. Third, based on the safe range of the generated bits, an optimal Q(p) clip range is developed to reduce the quality fluctuation. Experimental results demonstrate that the proposed Q(p) clip scheme can achieve excellent performance in quality smoothness and buffer regulation.     

Optimization of software components selection for component-based software system development

During the last two decades, there has been a growing interest in component-based software system (CBSS) development both in academia and in industry. In CBSS development, it is common to identify software modules first. Once they are determined, we need to select appropriate software components for each software module. However, very few research works so far have addressed the theoretical aspect especially in the optimization of software component selection for CBSS development. Previous studies of CBSS development seldom considered the influence of software components on coupling and cohesion of software modules. In this paper, the formulation of an optimization model of software components selection for CBSS development is described. The model has two objectives: maximizing the functional performance of the CBSS and maximizing the cohesion and minimizing the coupling of software modules. A genetic algorithm (GA) is introduced to solve the optimization model for determining the optimal selection of software components for CBSS development. An example of developing a financial system for small- and medium-size enterprises is used to illustrate the proposed methodology.