This brief presents an all-digital CMOS time-to-digital converter (TDC) with an integrated smart temperature sensor (STS), effectively reducing circuit complexity and cost. Unlike previous designs employing a single coupling unit, the proposed TDC adopts a two-coupling-unit structure, simplifying the overall architecture while enabling pulse-shrinking time measurement and offset-error cancellation within a single cyclic delay line. The built-in cancellation enhances linearity while minimizing overhead. Notably, the integrated STS requires only one additional coupling unit, ensuring a negligible impact on circuit complexity and cost. Fabricated using the TSMC 0.35-$\mu $m CMOS process, the proposed design demonstrates improved cost efficiency compared to prior works. Experimental results validate the successful measurement of time and temperature, highlighting the advantages of reduced complexity and cost savings.

RNA secondary structural alignment serves as a foundational procedure in identifying conserved structural motifs among RNA sequences, crucially advancing our understanding of novel RNAs via comparative genomic analysis. While various computational strategies for RNA structural alignment exist, they often come with high computational complexity. Specifically, when addressing a set of RNAs with unknown structures, the task of simultaneously predicting their consensus secondary structure and determining the optimal sequence alignment requires an overwhelming computational effort of $$O(L^6)$$ for each RNA pair. Such an extremely high computational complexity makes these methods impractical for large-scale analysis despite their accurate alignment capabilities. In this paper, we introduce REDalign, an innovative approach based on deep learning for RNA secondary structural alignment. By utilizing a residual encoder-decoder network, REDalign can efficiently capture consensus structures and optimize structural alignments. In this learning model, the encoder network leverages a hierarchical pyramid to assimilate high-level structural features. Concurrently, the decoder network, enhanced with residual skip connections, integrates multi-level encoded features to learn detailed feature hierarchies with fewer parameter sets. REDalign significantly reduces computational complexity compared to Sankoff-style algorithms and effectively handles non-nested structures, including pseudoknots, which are challenging for traditional alignment methods. Extensive evaluations demonstrate that REDalign provides superior accuracy and substantial computational efficiency. REDalign presents a significant advancement in RNA secondary structural alignment, balancing high alignment accuracy with lower computational demands. Its ability to handle complex RNA structures, including pseudoknots, makes it an effective tool for large-scale RNA analysis, with potential implications for accelerating discoveries in RNA research and comparative genomics.

BACKGROUND: As the RNA secondary structure is highly related to its stability and functions, the structure prediction is of great value to biological research. The traditional computational prediction for RNA secondary prediction is mainly based on the thermodynamic model with dynamic programming to find the optimal structure. However, the prediction performance based on the traditional approach is unsatisfactory for further research. Besides, the computational complexity of the structure prediction using dynamic programming is [Formula: see text]; it becomes [Formula: see text] for RNA structure with pseudoknots, which is computationally impractical for large-scale analysis. RESULTS: In this paper, we propose REDfold, a novel deep learning-based method for RNA secondary prediction. REDfold utilizes an encoder-decoder network based on CNN to learn the short and long range dependencies among the RNA sequence, and the network is further integrated with symmetric skip connections to efficiently propagate activation information across layers. Moreover, the network output is post-processed with constrained optimization to yield favorable predictions even for RNAs with pseudoknots. Experimental results based on the ncRNA database demonstrate that REDfold achieves better performance in terms of efficiency and accuracy, outperforming the contemporary state-of-the-art methods.

This paper presents an adaptive bilateral matching technique for decoder-side motion vector refinement in video coding. It allows encoder to choose not only the conventional bilateral matching mode with symmetric motion vector difference but also the asymmetric alternatives. To study the efficiency of the proposed technique, the proposed method is integrated in the Versatile Video Coding Test Model 11.0. The experimental result reports an overall of −2.78% luma Bjontegaard Delta rate for the random-access configurations. The compression efficiency on top of the Enhanced Compression Model that beyond the VVC capability are also reported.

This paper overviews the motion vector coding and block merging techniques in the Versatile Video Coding (VVC) standard developed by the Joint Video Experts Team (JVET). In general, inter-prediction techniques in VVC can be classified into two major groups: “whole block-based inter prediction” and “subblock-based inter prediction”. In this paper, we focus on techniques for whole block-based inter prediction. As in its predecessor, High Efficiency Video Coding (HEVC), whole block-based inter prediction in VVC is represented by adaptive motion vector prediction (AMVP) mode or merge mode. Newly introduced features purely for AMVP mode include symmetric motion vector difference and adaptive motion vector resolution. The features purely for merge mode include pairwise average merge, merge with motion vector difference, combined inter-intra prediction and geometric partitioning mode. Coding tools such as history-based motion vector prediction and bidirectional prediction with coding unit weights can be applied on both AMVP mode and merge mode. This paper discusses the design principles and the implementation of the new inter-prediction methods. Using objective metrics, simulation results show that the methods overviewed in the paper can jointly achieve 6.2% and 4.7% BD-rate savings on average with the random access and low-delay configurations, respectively. Significant subjective picture quality improvements of some tools are also reported when comparing the resulting pictures at same bitrates.

Block-based partitioning is one of the fundamental techniques in video coding. Geometric-based block partitioning is a well-studied method to enable better spatial adaptation to the signal properties. This paper introduces the most recent proposal of advanced geometric-based inter prediction (GIP) made to the state-of-the-art are video coding standard - Versatile Video Coding (VVC). Implemented in the latest test model VTM-6.0 to generalize the existing triangle partition mode (TPM) and evaluated with the Joint Video Experts Team (JVET) Common Test Conditions (CTC) sequences, the proposed advanced GIP scheme provides luma BD-rate reduction of 0.56% for random access (RA) and 1.37% for low-delay (LB) test cases with 2% encoder runtime increase and negligible decoder runtime increase. Furthermore, BD-rate reductions up to 2.92% and 3.49% for RA and LB test cases can be achieved in the absence of multiple related VVC inter prediction tools.

This brief presents an all-digital CMOS pulse-shrinking time-to-digital converter (TDC) with temperature-measuring capability for higher circuit value and cost saving. A pulse generator is proposed to generate either a time-added pulse or a temperature-sensing pulse. A cyclic delay line (DL) composed of a temperature-sensing DL and a pulse-shrinking DL becomes area efficient and achieves sufficiently wide dynamic range. A time subtractor that eliminates the effect of the offset error enhances accuracy. The proposed TDC is fabricated with TSMC 0.35-μm CMOS process and has a small area of approximately 0.019 mm2. The effective time resolution is approximately 45 ps, with an integral nonlinearity of -0.5 to 0.85 least significant bit (LSB). The achieved temperature resolution is nearly 0.1 °C/LSB, with an inaccuracy of -1.35 °C to 0.7 °C for a range of 0 °C-90 °C. Experimental results prove that we have proposed the first TDC that successfully measures time and temperature.

This brief presents a cost-efficient all-digital CMOS digital-to-time converter (DTC) that innovatively applies binary-weighted pulse expansion. The DTC consists of a pulse generator (PG) for pulse generation (tp), a binary-weighted pulse-expanding circuit (BWPEC) as a main circuit for digital-to-time conversion, and a time subtractor (TS) for tpremoval. Compared with the original pulse-expanding unit (PEU), a novel PEU with reduced complexity and a linear pulse expansion was developed for binary-weighted operation. The use of the BWPEC with the binary-weighted PEUs exhibits lower circuit complexity and considerably reduces the circuit cost compared to the original unary-weighted pulse-expanding circuit (PEC). A prototype of the 4-bit all-digital DTC was fabricated using a Taiwan Semiconductor Manufacturing Company (TSMC) 0.35-μm CMOS process. The fabricated DTC exhibited an area of 0.02 mm2achieving twofold improvement considerably. The measured resolution was approximately 5 ps, with the integral nonlinearity being -1.2-1 least significant bit (LSB). Without requiring an advanced CMOS process, the pulse variation technique with a simple structure can easily achieve fine resolution. The DTC features cost-efficient pulse expansion with binary weights and low circuit complexity.

MOTIVATION: For many RNA families, the secondary structure is known to be better conserved among the member RNAs compared to the primary sequence. For this reason, it is important to consider the underlying folding structures when aligning RNA sequences, especially for those with relatively low sequence identity. Given a set of RNAs with unknown structures, simultaneous RNA alignment and folding algorithms aim to accurately align the RNAs by jointly predicting their consensus secondary structure and the optimal sequence alignment. Despite the improved accuracy of the resulting alignment, the computational complexity of simultaneous alignment and folding for a pair of RNAs is O(N6), which is too costly to be used for large-scale analysis. RESULTS: In order to address this shortcoming, in this work, we propose a novel network-based scheme for pairwise structural alignment of RNAs. The proposed algorithm, TOPAS, builds on the concept of topological networks that provide structural maps of the RNAs to be aligned. For each RNA sequence, TOPAS first constructs a topological network based on the predicted folding structure, which consists of sequential edges and structural edges weighted by the base-pairing probabilities. The obtained networks can then be efficiently aligned by using probabilistic network alignment techniques, thereby yielding the structural alignment of the RNAs. The computational complexity of our proposed method is significantly lower than that of the Sankoff-style dynamic programming approach, while yielding favorable alignment results. Furthermore, another important advantage of the proposed algorithm is its capability of handling RNAs with pseudoknots while predicting the RNA structural alignment. We demonstrate that TOPAS generally outperforms previous RNA structural alignment methods on RNA benchmarks in terms of both speed and accuracy. AVAILABILITY AND IMPLEMENTATION: Source code of TOPAS and the benchmark data used in this paper are available at https://github.com/bjyoontamu/TOPAS.

MOTIVATION: Non-coding RNAs (ncRNAs) are known to play crucial roles in various biological processes, and there is a pressing need for accurate computational detection methods that could be used to efficiently scan genomes to detect novel ncRNAs. However, unlike coding genes, ncRNAs often lack distinctive sequence features that could be used for recognizing them. Although many ncRNAs are known to have a well conserved secondary structure, which provides useful cues for computational prediction, it has been also shown that a structure-based approach alone may not be sufficient for detecting ncRNAs in a single sequence. Currently, the most effective ncRNA detection methods combine structure-based techniques with a comparative genome analysis approach to improve the prediction performance. RESULTS: In this paper, we propose RNAdetect, a computational method incorporating novel features for accurate detection of ncRNAs in combination with comparative genome analysis. Given a sequence alignment, RNAdetect can accurately detect the presence of functional ncRNAs by incorporating novel predictive features based on the concept of generalized ensemble defect (GED), which assesses the degree of structure conservation across multiple related sequences and the conformation of the individual folding structures to a common consensus structure. Furthermore, n-gram models (NGMs) are used to extract features that can effectively capture sequence homology to known ncRNA families. Utilization of NGMs can enhance the detection of ncRNAs that have sparse folding structures with many unpaired bases. Extensive performance evaluation based on the Rfam database and bacterial genomes demonstrate that RNAdetect can accurately and reliably detect novel ncRNAs, outperforming the current state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: The source code for RNAdetect and the benchmark data used in this paper can be downloaded at https://github.com/bjyoontamu/RNAdetect.

This paper presents an all-digital time-domain smart temperature sensor (TDSTS) with a cost-efficient curvature correction, which can effectively improve the accuracy and cost. Inverter-based TDSTSs are low cost and low complexity but they exhibit a significant curvature, which severely impairs their accuracy. Linearity enhancement through linear interpolation reduces the curvature and results in a twofold improvement in the accuracy. In this paper, the proposed curvature correction not only simplified the scheme to reduce the hardware cost but also successfully eliminated the curvature to achieve the most suitable accuracy. In addition, the correction procedure was also simplified. The TDSTS was implemented in eight Xilinx field-programmable gate arrays for performance verification and occupied 74 slices per sensor. The logic utilization for the proposed curvature-corrected circuit was 17 slices, and its cost was only 23% of the hardware cost. The experimental results indicated that the maximum inaccuracy decreased from 5 °C to 1.8 °C over the temperature range of -20 °C-100 °C, resulting in an approximate threefold improvement in the accuracy. Compared with a previous study, the improvements of 30% and 43% in the accuracy and cost, respectively, successfully validate the proposed technique.

This paper explores the feasibility of automatic 3D segmentation, visualization and quantitative analysis for differentiation activities of mouse embryonic stem cells using time-lapse confocal fluorescence microscopy images. Technical approaches include bilateral filtering, mean-shift segmentation, adaptive thresholding, watershed segmentation, connected component labeling, and video tracking. Our method processes simultaneously two image channels, one for cytoplasm and the other for nuclei. The nucleus images are used to segment 2D and then 3D nuclei and to track each nucleus and calculate velocities of the 3D nucleus. The cytoplasm images are used to help nucleus segmentation and calculate the S/V (surface to volume) ratio of cytoplasm surrounding a nucleus. Volume rendering on the time-lapse fluorescence images generates time-series 3D images for visualizing the dynamic changes of cell velocity and S/V ratios. Using our prototype system, cells with different amount of EGFP fluorescent protein possesses different differentiation activity (velocity and S/V ratio) can be visualized and quantitatively analyzed.

BACKGROUND: Piwi-interacting RNAs (piRNAs) are a new class of small non-coding RNAs that are known to be associated with RNA silencing. The piRNAs play an important role in protecting the genome from invasive transposons in the germline. Recent studies have shown that piRNAs are linked to the genome stability and a variety of human cancers. Due to their clinical importance, there is a pressing need for effective computational methods that can be used for computational identification of piRNAs. However, piRNAs lack conserved structural motifs and show relatively low sequence similarity across different species, which makes accurate computational prediction of piRNAs challenging. RESULTS: In this paper, we propose a novel method, piRNAdetect, for reliable computational prediction of piRNAs in genome sequences. In the proposed method, we first classify piRNA sequences in the training dataset that share similar sequence motifs and extract effective predictive features through the use of n-gram models (NGMs). The extracted NGM-based features are then used to construct a support vector machine that can be used for accurate prediction of novel piRNAs. CONCLUSIONS: We demonstrate the effectiveness of the proposed piRNAdetect algorithm through extensive performance evaluation based on piRNAs in three different species - H. sapiens, R. norvegicus, and M. musculus - obtained from the piRBase and show that piRNAdetect outperforms the current state-of-the-art methods in terms of efficiency and accuracy.

This paper presents an intra line copy (ILC) technique for HEVC screen content coding. It shares the same origin with two other prominent techniques, intra string copy (ISC) and intra block copy (IBC), in applying the notion of string matching to intra-frame coding. This work combines their merits in one scheme with both good compression performance and high regularity. Specifically, it forms a prediction of a coding block by decomposing it into horizontal or vertical lines of pixels and performing line-based predictions based on previously coded data from the current frame. To address the massive amounts of search operations, our fast search algorithm first searches in the horizontal and vertical directions, then checks line vector candidates from spatial and temporal neighbors, and finally references lines having an identical hash value as the prediction line. The resulting line vectors are further predicted adaptively to minimize their coding overhead. Extensive experiments based on SCM-4.0, which includes IBC as an integral component, show that ILC can provide an additional 4%-7% BD-rate savings when the search area extends to the entire frame and 3%-4% improvements with a restricted local search. Compared with ISC, it achieves comparable performance without all its complications from sequential string parsing.

This paper presents a generalized bi-prediction (GBi) technique to extend the notion of the existing weighted bi-prediction to prediction unit (PU) level. GBi allows bi-prediction weights to be transmitted at the coding unit (CU) level and used for the bi-predicted PUs within that CU. The candidate set of weights in the GBi mode includes a total of 7 weights, including 1/2 used for conventional bi-prediction. An index pointing to the entry of a weight value in the candidate weight set is signaled. At most one index per CU is signaled and the corresponding weight values are shared across all the bi-prediction PUs and all color components in that CU. Besides, GBi can also be extended to inter coding tools, such as, merge mode and affine prediction that support bi-prediction. Experimental results show that GBi performs consistently better than the JEM-2.0 anchor in all rate points, with an average Y Bjentegaard delta rate reduction of 1% and no extra decoding burden introduced.

Nowadays, the economic environment is competitive, various companies are all competing for the greatest profit; but create the blue ocean market is not so easy. Therefore, in foreign, many successful businesses focus on human, usage of incentives, empowerment, and educational training to increase the employees' commitment to the organization and to improve efficiency. Enterprise is an organization of human, most of the links cannot be surpassed or replaced. In contrast, the salary is generally low in domestic enterprises. The phenomenon of employees are squeezed is not uncommon. Managers sacrifice human capital in exchange for a slight profit. Take the service industry for example, when employees are treated unfairly, how do they service and satisfy the customers? Therefore, this study takes the customer-oriented market for survey objection. From the perspective of employee, under the business through internal marketing and managers' charismatic leadership, to understand whether the employees can bring good effects to increase organizational commitment. At the same time, whether employees are able to provide a better service to customer through organizational commitment, to improve performance and bring the greater profits for company. This study firstly confirms research variables and the framework by literature reviews, and then uses online questionnaires to collect data. Finally, we use SPSS, AMOS and SEM to test the hypotheses in this study. Under the framework for internal marketing, the results of research are as the followings:

Recently, Intra Line Copy (ILC) and Intra String Copy (ISC) have been introduced as effective means for screen content coding during the development of HEVC Screen Content Coding Extensions. Although conceptually sharing the same theoretical basis, which is essentially string matching, they differ from each other in a number of significant ways. This paper presents detailed comparisons between them with respect to their compression performance, runtime complexity and memory access bandwidth, in order to better understand their merits and faults. Experimental results indicate that they perform comparably to each other, with ILC generally performing better in coding mixed content and ISC better in coding pure screen content. When considering the impact on the memory access bandwidth and coding performance trade-offs, local search for ILC/ISC becomes more favorable than full-frame search in low-delay real-time applications.

With the trend of cloud computing, cloud services are aggressively launched. Along with the evolution of smart terminal and broadband technology, the demand about personal services built on cloud is aware. In 2011 April, Chunghwa Telecom (CHT) launch the Hami+Personal Cloud Service which is the leading product among the domestic industry about personal cloud service. Since the Hami+Personal Cloud service was published, a number of personal information management services and innovative services base on cloud was researched by CHT and published on Hami+Personal Cloud platform. To enhance the competitive ability of Hami+Personal Cloud Service, we continue following up the perception of the users about the Personal Cloud Service. We believe that to optimize the user experience will be directly and effectively to enhance customer satisfaction and product competitiveness. In this article, we will describe the planning and design processes that how the research team optimizes the user experiences about Hami+Personal Cloud service.

This paper presents a mode-dependent distortion model for H.264/SVC coarse grain SNR scalability. It estimates the base-layer and enhancement-layer's distortions with particular consideration of their prediction modes and inter-layer residual prediction. Based on a parametric signal model, the variances of the transformed prediction residual at both layers are first formulated analytically and approximated empirically. The results are then incorporated into the assumption that the transform coefficients are distributed according to the Laplacian distribution to obtain the final distortion estimates. Experimental results confirm its fairly good ability to predict the actual distortions in both the frame and macroblock levels.

To achieve high coding performance for screen content, the intra block copy (IntraBC) performs block matching within a limited area of the reconstructed samples inside the current picture. We further extend its notion from the unit of square coding unit (CU) to non-square prediction unit (PU) partitions. This design is then justified by theoretical and empirical analyses which reveal the same fact that blocks coded by IntraBC mode tend to enable more at smaller partition levels. Besides, the syntax design of the proposed method is fully aligned with that of inter partition modes. Therefore the architecture-wise change in video codec design can be minimized. The experimental results justify the effectiveness of the proposed mode for coding of screen content video. In particular, up to 19.5% rate reduction (with an average of 12.7%) relative to the HM-12.0+RExt-4.1 anchor can be achieved on top of the usage of CU-based IntraBC prediction.

This paper introduces an interframe prediction technique that combines two motion vectors (MVs) derived respectively from template and block matching for overlapped block motion compensation (OBMC). It has a salient feature of not having to signal the template MV, while achieving a prediction performance close to that of bi-prediction. We begin by studying template matching prediction (TMP) from a theoretical perspective. Based on two signal models, the template MV is shown to approximate the pixel true motion around the template centroid, through which we explain why TMP generally outperforms SKIP prediction but is inferior to block-based motion compensation in terms of prediction performance. We then approach the problem of finding another MV to best complement the template MV from both deterministic and statistical viewpoints, the latter leading to the search of its optimal sampling location in the motion field. The result is a search criterion with OBMC window functions forming a geometry-like motion partitioning when the template area is straddled on the top and to the left of a target block. Generalizations to adaptive template design, multihypothesis prediction and motion merging are made to explore the complexity and performance trade-offs. Extensive experiments based on the HM-6.0 software show that the best of them, in terms of compression performance, achieves 1.7-2.0% BD-rate reductions at a cost of 26% and 39% increases in encoding and decoding times, respectively.

This paper introduces a bi-prediction scheme with only a motion overhead as for unidirectional prediction. It combines motion vectors found by template and block matchings with the overlapped block motion compensation (OBMC). An optimal window function is designed based on a model-based framework. Additionally, the concept of adaptive motion merging is incorporated to enable a template-matching-free implementation. Three algorithms featuring different performance and complexity trade-offs are implemented using the TMuC-0.9_HM software and tested with the common test conditions. Relative to the anchor, the best of them achieves an average BD-rate saving of 2.2%, with a minimum of 0.2% and a maximum of 4.7%.

This paper addresses the problem of refining depth information from the received reference and depth images within the MPEG FTV framework. An analytical model is first developed to approximate the per-pixel synthesis distortion (caused by depth-image compression) as a function of depth-error variances, intensity variations, ground-truth depth and virtual camera locations. We then follow the model to detect unreliable depth pixels by inspecting intensity gradients and to refine their values with a candidate-based block disparity search. Additional side information is transmitted to make both operations robust against compression effects. Experimental results show that our scheme offers an average PSNR improvement of 1.2 dB over MPEG FTV and consistently outperforms the state-of-the-art methods. Moreover, it can remove synthesis artifacts to a great extent, producing a result that is very close in appearance to the ground-truth view image.

Sequential pattern mining has been used to predict various aspects of customer buying behavior for a long time. Discovered sequence reveals the chronological relation between items and provides valuable information to aid in developing marketing strategies. Nevertheless, we can hardly know whether the buying is cyclic and how long the interval between the two consecutive items in the sequential pattern is. To solve this problem, in this paper, data mining skills and the fundamentals of statistics are combined to develop a set of algorithms to unearth the cyclic properties of discovered sequential patterns. The algorithms, coupled with the sequential pattern mining process, constitute a thorough scheme to analyze and predict likely consumer behavior. The proposed algorithms are implemented and applied to test against real data collected from a consumer goods company. The experimental results illustrate how the model can be used to predict likely purchases within a certain time frame. Consequently, marketing professionals can execute campaigns to favorably impact customers' behaviors.

The main task of mining sequential patterns is to analyze the transaction database of a company in order to find out the priorities of items that most customers take when consuming. In this article, we propose a new method—the ISP Algorithm. With this method, we can find out not only the order of consumer items of each customer, but also offer the periodic interval of consumer items of each customer. Compared with other previous periodic association rules, the difference is that the period the algorithm provides is not the repeated purchases in a regular time, but the possible repurchases within a certain time frame. The algorithm utilizes the transaction time interval of individual customers and that of all the customers to find out when and who will buy goods, and what items of goods they will buy. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 359–373, 2005.