Several new memories are being studied as candidates of future DRAM that seems difficult to be scaled. However, the read signal in these new memories needs to be amplified in a single-end manner with reference signal supplied if they are aimed for being applied to the high-density main memory. This scheme, which is fortunately not necessary in DRAM's 1/2Vdd pre-charge sense amp, can become a serious bottleneck in the new memory development, because the device electrical parameters in these new memory cells are prone to large cell-to-cell variations without exception. Furthermore, the extent to which the parameter fluctuates in data “1” is generally not the same as in data “0”. In these situations, a new sensing scheme is proposed that can minimize the sensing error rate for high-density single-end emerging memories like STT-MRAM, ReRAM and PCRAM. The scheme is based on averaging multiple dummy cell pairs that are written “1” and “0” in a weighted manner according to the fluctuation unbalance between “1” and “0”. A detailed analysis shows that this scheme is effective in designing 128Mb 1T1MTJ STT-MRAM with the results that the required TMR ratio of an MTJ can be relaxed from 130% to 90% for the fluctuation of 6% sigma-to-average ratio of MTJ resistance in a 16 pair-dummy cell averaging case by using this technology when compared with the arithmetic averaging method.

In this paper, we propose the novel low voltage CMOS current mode reference circuit. It reduces the minimum supply voltage by consisting the subthreshold two stage operational amplifier (OPAMP) which is regarded as the combination of the proportional to absolute temperature (PTAT) and the complementary to absolute temperature (CTAT) current generators. It makes possible to implement without extra OPAMP. This proposed circuit has been designed and evaluated by SPICE simulation using TSMC 65nm CMOS process with 3.3V (2.5V over-drive) transistor option. From simulation results, the line sensitivity is as good as 0.196%/V under the condition that the range of supply voltage (VDD) is wide as 0.6V to 3.0V. The temperature coefficient is 71ppm/ under the condition that the temperature range is from -40 to 125 and VDD=0.6V. The power supply rejection ratio (PSRR) is -47.7dB when VDD=0.6V and the noise frequency is 100Hz. According to comparing the proposed circuit with prior current mode circuits, we could confirm the performance of the proposed circuit is better than that of prior circuits.

The Collaborative Filtering (CF) algorithms work fairly well in personalized recommendation except in sparse data environment. To deal with the sparsity problem, researchers either take into account auxiliary information extracted from additional data resources, or set the missing ratings with default values, e.g., video popularity. Nevertheless, the former often costs high and incurs difficulty in knowledge transference whereas the latter degrades the accuracy and coverage of recommendation results. To our best knowledge, few literatures take advantage of users' preference on video popularity to tackle this problem. In this paper, we intend to enhance the performance of recommendation algorithm via the inference of the users' popularity preferences (PPs), especially in a sparse data environment. We propose a scheme to aggregate users' PPs and a Collaborative Filtering based algorithm to make the inference of PP feasible and effective from a small number of watching records. We modify a k-Nearest-Neighbor recommendation algorithm and a Matrix Factorization algorithm via introducing the inferred PP. Experiments on a large-scale commercial dataset show that the modified algorithm outperforms the original CF algorithms on both the recommendation accuracy and coverage. The significance of improvement is significant especially with the data sparsity.

Considering that different people are different in their linguistic preference and in order to determine the consensus state when using Computing with Words (CWW) for supporting consensus decision making, this paper first proposes an interval composite scale based 2-tuple linguistic model, which realizes the process of translation from word to interval numerical and the process of retranslation from interval numerical to word. Second, this paper proposes an interval composite scale based personalized individual semantics model (ICS-PISM), which can provide different linguistic representation models for different decision-makers. Finally, this paper proposes a consensus decision making model with ICS-PISM, which includes a semantic translation and retranslation phase during decision process and determines the consensus state of the whole decision process. These models proposed take into full consideration that human language contains vague expressions and usually real-world preferences are uncertain, and provide efficient computation models to support consensus decision making.

A method for accurate estimation of personalized video preference using multiple users' viewing behavior is presented in this paper. The proposed method uses three kinds of features: a video, user's viewing behavior and evaluation scores for the video given by a target user. First, the proposed method applies Supervised Multiview Spectral Embedding (SMSE) to obtain lower-dimensional video features suitable for the following correlation analysis. Next, supervised Multi-View Canonical Correlation Analysis (sMVCCA) is applied to integrate the three kinds of features. Then we can get optimal projections to obtain new visual features, “canonical video features” reflecting the target user's individual preference for a video based on sMVCCA. Furthermore, in our method, we use not only the target user's viewing behavior but also other users' viewing behavior for obtaining the optimal canonical video features of the target user. This unique approach is the biggest contribution of this paper. Finally, by integrating these canonical video features, Support Vector Ordinal Regression with Implicit Constraints (SVORIM) is trained in our method. Consequently, the target user's preference for a video can be estimated by using the trained SVORIM. Experimental results show the effectiveness of our method.

The sound power level is a physical quantity indispensable for evaluating the amount of sound energy radiated from electrical and mechanical apparatuses. The precise determination of the sound power level requires the qualification of the measurement environment, such as a hemi-anechoic room, by estimating the deviation of the sound pressure level from the inverse-square law. In this respect, Annex A of ISO 3745 specifies the procedure for room qualification and defines a tolerance limit for the directivity of the sound source, which is used for the qualification. However, it is impractical to prepare a special loudspeaker only for room qualification. Thus, we developed a simulation method to investigate the influence of the sound source directivity on the measured deviation of the sound pressure level from the inverse-square law by introducing a quantitative index for the influence of the directivity. In this study, type 4202 reference sound source by Brüel & Kjær was used as a directional sound source because it has been widely used as a reference standard for the measurement of sound power levels. We experimentally obtained the directivity of the sound source by measuring the sound pressure level over the measurement surface. Moreover, the proposed method was applied to the qualification of several hemi-anechoic rooms, and we discussed the availability of a directional sound source for the process. Analytical results showed that available reference sound sources may be used for the evaluation of hemi-anechoic rooms depending on the sound energy absorption coefficient of the inner wall, the directionality of the microphone traverse, and the size of the space to be qualified. In other words, the results revealed that a reference sound source that is once quantified by the proposed method can be used for qualifying hemi-anechoic rooms.

Multi-camera videos with abundant information and high flexibility are useful in a wide range of applications, such as surveillance systems, web lectures, news broadcasting, concerts and sports viewing. Viewers can enjoy an enhanced viewing experience by choosing their own viewpoint through viewing interfaces. However, some viewers may feel annoyed by the need for continual manual viewpoint selection, especially when the number of selectable viewpoints is relatively large. In order to solve this issue, we propose an automatic viewpoint navigation method designed especially for sports. This method focuses on a viewer's personal preference for viewpoint selection, instead of common and professional editing rules. We assume that different trajectory distributions of viewing objects cause a difference in the viewpoint selection according to personal preference. We learn the relationship between the viewer's personal viewpoint-selection tendency and the spatio-temporal game context represented by the objects trajectories. We compare three methods based on Gaussian mixture model, SVM with a general histogram and SVM with a bag-of-words to seek the best learning scheme for this relationship. The performance of the proposed methods are evaluated by assessing the degree of similarity between the selected viewpoints and the viewers' edited records.

Scalable high efficiency video coding (SHVC) can provide variable video quality according to terminal devices. However, the computational complexity of SHVC is increased by introducing new techniques based on high efficiency video coding (HEVC). In this paper, a hardware oriented low complexity algorithm is proposed. The hardware oriented proposals have two key points. Firstly, the coding unit depth is determined by analyzing the boundary correlation between coding units before encoding process starts. Secondly, the redundant calculation of R-D optimization is reduced by adaptively using the information of the neighboring coding units and the co-located units in the base layer. The simulation results show that the proposed algorithm can achieve over 62% computation complexity reduction compared to the original SHM11.0. Compared with other related work, over 11% time saving have been achieved without PSNR loss. Furthermore, the proposed algorithm is hardware friendly which can be implemented in a small area.

Collaborative filtering with only implicit feedbacks has become a quite common scenario (e.g. purchase history, click-through log, and page visitation). This kind of feedback data only has a small portion of positive instances reflecting the user's interaction. Such characteristics pose great challenges to dealing with implicit recommendation problems. In this letter, we take full advantage of matrix factorization and relative preference to make the recommendation model more scalable and flexible. In addition, we propose to take into consideration the concept of covisitation which captures the underlying relationships between items or users. To this end, we propose the algorithm Integrated Collaborative Filtering for Implicit Feedback incorporating Covisitation (ICFIF-C) to integrate matrix factorization and collaborative ranking incorporating the covisitation of users and items simultaneously to model recommendation with implicit feedback. The experimental results show that the proposed model outperforms state-of-the-art algorithms on three standard datasets.

Video quality generally suffers from packet losses caused by an unreliable channel when video is transmitted over an error-prone wireless channel. This quality degradation is the main reason that a video compression encoder uses error-resilient coding to deal with the high packet-loss probability. The use of adequate error resilience can mitigate the effects of channel errors, but the coding efficiency for bit reduction will be decreased. On the other hand, H.264/AVC uses multiple reference frame (MRF) motion compensation for a higher coding efficiency. However, an increase in the number of reference frames in the H.264/AVC encoder has been recently observed, making the received video quality worse in the presence of transmission errors if the cyclic intra-refresh is used as the error-resilience method. This is because the reference-block selection in the MRF chooses blocks on the basis of the rate distortion optimization, irrespective of the intra-refresh coding. In this paper, a new error-resilient reference selection method is proposed to provide error resilience for MRF based motion compensation. The proposed error-resilient reference selection method achieves an average PSNR enhancement up to 0.5 to 2dB in 10% packet-loss-ratio environments. Therefore, the proposed method can be valuable in most MRF-based interactive video encoding system, which can be used for video broadcasting and mobile video conferencing over an erroneous network.

This paper presents an ultra-low power and temperature-independent voltage detector with a post-fabrication programming method, and presents a theoretical analysis and measurement results. The voltage detector is composed of a programmable voltage detector and a glitch-free voltage detector to realize both programmable and glitch-free operation. The programmable voltage detector enables the programmable detection voltages in the range from 0.52V to 0.85V in steps of less than 49mV. The glitch-free voltage detector enables glitch-free operation when the supply voltage is near 0V. A multiple voltage copier (MVC) in the programmable voltage detector is newly proposed to eliminate the tradeoff between the temperature dependence and power consumption. The design consideration and a theoretical analysis of the MVC are introduced to clarify the relationship between the current in the MVC and the accuracy of the duplication. From the analysis, the tradeoff between the duplication error and the current of MVC is introduced. The proposed voltage detector is fabricated in a 250nm CMOS process. The measurement results show that the power consumption is 248pW and the temperature coefficient is 0.11mV/°C.

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly in inferring a new user's consumption preference. Our model can also learn meaningful consumption-specific topics automatically.

This paper presents human-centered video feature selection via mRMR-SCMMCCA (minimum Redundancy and Maximum Relevance-Specific Correlation Maximization Multiset Canonical Correlation Analysis) algorithm for preference extraction. The proposed method derives SCMMCCA, which simultaneously maximizes two kinds of correlations, correlation between video features and users' viewing behavior features and correlation between video features and their corresponding rating scores. By monitoring the derived correlations, the selection of the optimal video features that represent users' individual preference becomes feasible.

In this work, we propose general, practical and accurate models to analyze the performance of multi-cache systems, in which a cache forwards its miss stream (i.e., requests which have not found the target item) to other caches. We extend a miss stream modeling technique originally known as Melazzi's approximation, which provides a simple but accurate approximate analysis for caches with cascade configurations. We consider several practical replication strategies, which have been commonly adopted in the context of ICN, taking into account the effects of temporal locality. Also, we capture the existing state correlations between neighboring caches by exploiting the cache eviction time. Our proposed models to handle traffic patterns allow us to go beyond the standard Poisson approximation under Independent Reference Model. Our results, validated against simulations, provide interesting insights into the performance of multi-cache systems with different replication strategies.

The most commonly used scattering parameters (S parameters) are normalized to a real reference resistance, typically 50Ω. In some cases, the use of S parameters normalized to some complex reference impedance is essential or convenient. But there are different definitions of complex-referenced S parameters that are incompatible with each other and serve different purposes. To make matters worse, different simulators implement different ones and which ones are implemented is rarely properly documented. What are possible scenarios in which using the right one matters? This tutorial-style paper is meant as an informal and not overly technical exposition of some such confusing aspects of S parameters, for those who have a basic familiarity with the ordinary, real-referenced S parameters.

We address the problem of entity identification on a microblog with special attention to indirect reference cases in which entities are not referred to by their names. Most studies on identifying entities referred to them by their full/partial name or abbreviation, while there are many indirectly mentioned entities in microblogs, which are difficult to identify in short text such as microblogs. We therefore tackled indirect reference cases by developing features that are particularly important for certain types of indirect references and modeling dependency among referred entities by a Conditional Random Field (CRF) model. In addition, we model non-sequential order dependency while keeping the inference tractable by dynamically building dependency among entities. The experimental results suggest that our features were effective for indirect references, and our CRF model with adaptive dependency was robust even when there were multiple mentions in a microblog and achieved the same high performance as that with the fully connected CRF model.

This paper describes, for the first time, the circuit design considerations and measurements of core building blocks that support a 1.9-GHz-band (Band I) BiFET MMIC three-power-mode power amplifier (PA) for WCDMA handset applications. The blocks are a reference voltage (Vref) generator, a control logic circuit, and ESD protection circuits. Our proposed Vref-generator, based on a current-mirror topology, can successfully suppress Vref variation against threshold voltage (Vth) dispersion in the FET as well as current gain dispersion in the HBT. On-wafer measurements over several wafer lots show that the standard deviation of Vref is as small as 18 mV over a Vth dispersion range from -0.6 V to -1.0 V. As a result, the measured quiescent current dispersion in the HPM is also suppressed to less than 5.4 mA, despite the fact that the average quiescent current is relatively high, at 81.3 mA. Several simulations reveal that small decoupling capacitances of approximately 1 pF added to the gate control lines of RF switch FETs ensure stable operation of the control logic even if an undesired RF coupling is present between an RF signal path and the gate lines. An empirical and useful design approach for ESD protection using HBT base-collector diodes allows easy and precise estimation of the HBM ESD robustness. With the above building blocks, a 3 mm × 3 mm PA was designed and fabricated by an in-house BiFET process. Measurements conducted under the conditions of a 3.4-V supply voltage and a 1.95-GHz WCDMA modulated signal are as follows. The PA delivers a 28.3-dBm output power (Pout), a 28.2-dB power gain (Gp), and 40% PAE while restricting the ACLR1 to less than -42 dBc in the HPM. In the MPM, 17.4 dBm of Pout, 15.9 dB of Gp, and 25.3% of PAE are obtained, while in the LPM, the PA delivers 7 dBm of Pout, 11.7 dB of Gp, and 13.9% of PAE. The HBM ESD robustness is 2 kV.

A CMOS bandgap reference circuit without resistors, which can successfully operate under 1V supply voltage is proposed. The improvement is realized by the technique of the voltage divider and a new current source. The most attractive merit is that the proposed circuit breaks the bottleneck of low supply voltage design caused by the constant bandgap voltage value (1.25V). Moreover, the temperature coefficient of the reference voltage Vref is improved by compensating the temperature dependence caused by the current source. The simulation results using a standard CMOS 0.18 um process show that the value of Vref can be achieved around 0.5 V with a minimum supply voltage of 0.85 V. Meanwhile, the temperature coefficient of the output voltage is only 3.5ppm/°C from 0 °C to 70 °C.

In this paper, we investigate a preference-aware multicast mechanism in active array aided LTE (Long Term Evolution) networks. An active antenna system can direct vertical beams in different horizontal and vertical directions, so the amount of energy delivered is more concentrated on the target users. The active array provides each multicast group with an individual beam with specific downtilt delivering shared video to all users in the group. For the multicast system, the objective of our proposed resource allocation scheme is to maximize the total throughput, subject to the constraints of power, subcarrier and antenna downtilt, as well as horizontal angles and the vertical half power bandwidth. To solve the problem, individual beams are steered for multicast groups. Furthermore, a novel subcarrier assignment scheme is proposed to enhance the spectrum resource utilization, and the optimal power allocation is obtained by virtue of Lagrangian method. Simulation results demonstrate the throughput and the spectral efficiency enhancement of our proposed scheme over other conditional schemes.

Predicting the routing paths between any given pair of Autonomous Systems (ASes) is very useful in network diagnosis, traffic engineering, and protocol analysis. Existing methods address this problem by resolving the best path with a snapshot of BGP (Border Gateway Protocol) routing tables. However, due to route deficiencies, routing policy changes, and other causes, the best path changes over time. Consequently, existing methods for path prediction fail to capture route dynamics. To predict AS-level paths in dynamic scenarios (e.g. network failures), we propose a per-neighbor path ranking model based on how long the paths have been used, and apply this routing model to extract each AS's route choice configurations for the paths observed in BGP data. With route choice configurations to multiple paths, we are able to predict the path in case of multiple network scenarios. We further build the model with strict policies to ensure our model's routing convergence; formally prove that it converges; and discuss the path prediction capturing routing dynamics by disabling links. By evaluating the consistency between our model's routing and the actually observed paths, we show that our model outperforms the state-of-the-art work [4].