Named Data Networking (NDN) uses name to indicate content mechanism to divide content, and uses content names for routing and addressing. However, the traditional network devices that support the TCP/IP protocol stack and location-centric communication mechanisms cannot support functions such as in-network storage and multicast distribution of NDN effectively. The performance of NDN routers designed for specific functional platforms is limited, and it is difficult to deploy on a large scale, so the NDN network can only be implemented by software. With the development of data plane languages such as Programmable Protocol-Independent Packet Processors (P4), the practical deployment of NDN becomes achievable. To ensure efficient data distribution in the network, this paper proposes a protocol-independent multicast method according to each binary bit. The P4 language is used to define a bit vector in the data packet intrinsic metadata field, which is used to mark the requested port. When the requested content is returned, the routing node will check which port has requested the content according to the bit vector recorded in the register, and multicast the Data packet. The experimental results show that bitwise multicast technology can eliminate the number of flow tables distributed compared with the dynamic multicast group technology, and reduce the content response delay by 57% compared to unicast transmission technology.

IEEE 802.11ah is a specification being developed for sub-1GHz license-exempt operation and is intended to provide Low Power Wide Area (LPWA) communication services and support Internet of Things (IoT) features such as large-scale networks and extended transmission range. However, these features also make the 802.11ah networks highly susceptible to channel contention and hidden node problem (HNP). To address the problems, the 11ah Task Group proposed a Restricted Access Window (RAW) mechanism. It shows outstanding performance in alleviating channel contention, but its effect on solving HNP is unsatisfactory. In this paper, we propose a simple and effective hidden node grouping algorithm (HNGA) based on IEEE 802.11ah RAW. The algorithm collects hidden node information by taking advantage of the 802.11 association process and then performs two-stage uniform grouping to prevent hidden node collisions (HNCs). Performance of the proposed algorithm is evaluated in comparison with other existing schemes in a hidden node situation. The results show that our proposed algorithm eliminates most of hidden node pairs inside a RAW group with low overhead penalty, thereby improving the performance of the network. Moreover, the algorithm is immune to HNCs caused by cross slot boundary transmissions.

Current trends in modern out-of-order processors involve implementing deeper pipelines and a large instruction window to achieve high performance, which lead to the penalty of the branch misprediction recovery being a critical factor in overall processor performance. Multi path execution is proposed to reduce this penalty by executing both paths following a branch, simultaneously. However, there are some drawbacks in this mechanism, such as design complexity caused by processing both paths after a branch and performance degradation due to hardware resource competition between two paths. In this paper, we propose a new recovery mechanism, called Recovery Critical Misprediction (RCM), to reduce the penalty of branch misprediction recovery. The mechanism uses a small trace cache to save the decoded instructions from the alternative path following a branch. Then, during the subsequent predictions, the trace cache is accessed. If there is a hit, the processor forks the second path of this branch at the renamed stage so that the design complexity in the fetch stage and decode stage is alleviated. The most contribution of this paper is that our proposed mechanism employs critical path prediction to identify the branches that will be most harmful if mispredicted. Only the critical branch can save its alternative path into the trace cache, which not only increases the usefulness of a limited size of trace cache but also avoids the performance degradation caused by the forked non-critical branch. Experimental results employing SPECint 2000 benchmark show that a processor with our proposed RCM improves IPC value by 10.05% compared with a conventional processor.

Exploring the structural information as prior to facial images is a key issue of face super-resolution (SR). Although deep convolutional neural networks (CNNs) own powerful representation ability, how to accurately use facial structural information remains challenges. In this paper, we proposed a new residual fusion network to utilize the multi-scale structural information for face SR. Different from the existing methods of increasing network depth, the bottleneck attention module is introduced to extract fine facial structural features by exploring correlation from feature maps. Finally, hierarchical scales of structural information is fused for generating a high-resolution (HR) facial image. Experimental results show the proposed network outperforms some existing state-of-the-art CNNs based face SR algorithms.

To understand the brightness uniformity for the driver of the LED array display, automatic electronic measurement equipment and its testing scheme will be proposed in this paper. The driving performance and dynamic characteristics will be investigated by using the proposed current-based bias voltage regulator. A complete testing procedure will be provided to assess the performance for the LED array display driver.

NAND flash memory has been widely used in storage systems. Aiming to design an efficient buffer policy for NAND flash memory, a life-aware buffer management algorithm named LAB-LRU is proposed, which manages the buffer by three LRU lists. A life value is defined for every page and the active pages with higher life value can stay longer in the buffer. The definition of life value considers the effect of access frequency, recency and the cost of flash read and write operations. A series of trace-driven simulations are carried out and the experimental results show that the proposed LAB-LRU algorithm outperforms the previous best-known algorithms significantly in terms of the buffer hit ratio, the numbers of flash write and read operations and overall runtime.

We propose a novel probing scheme capable of discovering shared bottlenecks among multiple paths between two multihomed hosts simultaneously, without any specific help from the network routers, and a subsequent grouping approach for partitioning these paths into groups. Simulation results show that the probing and grouping have an excellent performance under different network conditions.

In multimedia communication, due to the limited computational capability of the personal information machine, a coder with low computational complexity is needed to integrate services from several media sources. This paper presents two efficient candidate schemes to simplify the most computationally demanding operation, the excitation codebook search procedure. For fast adaptive codebook search, we propose an algorithm that uses residual signals to predict the candidate gain-vectors of the adaptive codebook. For the fixed codebook, we propose a fast search algorithm using an energy function to predict the candidate pulses, and we redesign the codebook structure to twin multi-track positions architecture. Overall simulation results indicate that the average perceptual evaluation of speech quality (PESQ) score is degraded slightly, by 0.049, and our proposed methods can reduce total computational complexity by about 67% relative to the original G.723.1 encoder computation load, and with perceptually negligible degradation. Objective and subjective evaluations verify that the more efficient candidate schemes we propose can provide speech quality comparable to that using the original coder approach.

In this paper, the spurious modes and quality-factor (Q) values of the one-port dual-mode AlN lamb-wave resonators at 500-1000 MHz were studied by theoretical analysis and experimental verification. Through finite element analysis, we found that optimizing the width of the lateral reflection boundary at both ends of the resonator to reach the quarter wavelength (λ/4), which can improve its spectral purity and shift its resonant frequency. The designed resonators were micro-fabricated by using lithography processes on a 6-inch wafer. The measured results show that the spurious mode can be converted and dissipated, splitting into several longitudinal modes by optimizing the width of the lateral reflection boundary, which are consistent well with the theoretical analysis. Similarly, optimizing the interdigital transducer (IDT) width and number of IDT fingers can also suppress the resonator's spurious modes. In addition, it is found that there is no significant difference in the Qs value for the two modes of the dual-mode resonator with the narrow anchor and full anchor. The acoustic wave leaked from the anchor into the substrate produces a small displacement, and the energy is limited in the resonator. Compared to the resonator with Au IDTs, the resonator with Al IDTs can achieve a higher Q value due to its lower thermo-elastic damping loss. The measured results show the optimized dual-mode lamb-wave resonator can obtain Qs value of 2946.3 and 2881.4 at 730.6 MHz and 859.5 MHz, Qp values of 632.5 and 1407.6, effective electromechanical coupling coefficient (k2eff) of 0.73% and 0.11% respectively, and has excellent spectral purity simultaneously.

This paper presents a fully digital modulation calibration technique for channel mismatch of TIADC at any frequency. By pre-inputting a test signal in TIADC, the mismatch errors are estimated and stored, and the stored values will be extracted for compensation when the input signal is at special frequency which can be detected by a threshold judgement module, thus solving the problem that the traditional modulation calibration algorithm cannot calibrate the signal at special frequency. Then, by adjusting the operation order among the error estimation coefficient, modulation function and input signal in the calibration loop, further, the order of correlation and modulation in the error estimation module, the complexity of the proposed calibration algorithm is greatly reduced and it will not increase with the number of channels of TIADC. What's more, the hardware consumption of filters in calibration algorithm is greatly reduced by introducing a CSD (Canonical Signed Digit) coding technique based on Horner's rule and sub-expression sharing. Applied to a four-channel 14bit 560MHz TIADC system, with input signal at 75.6MHz, the FPGA verification results show that, after calibration, the spurious-free dynamic range (SFDR) improves from 33.47dB to 99.81dB and signal-to-noise distortion ratio (SNDR) increases from 30.15dB to 81.89dB.

Many optimisation algorithms improve the algorithm from the perspective of population structure. However, most improvement methods simply add hierarchical structure to the original population structure, which fails to fundamentally change its structure. In this paper, we propose an umbrellalike hierarchical artificial bee colony algorithm (UHABC). For the first time, a historical information layer is added to the artificial bee colony algorithm (ABC), and this information layer is allowed to interact with other layers to generate information. To verify the effectiveness of the proposed algorithm, we compare it with the original artificial bee colony algorithm and five representative meta-heuristic algorithms on the IEEE CEC2017. The experimental results and statistical analysis show that the umbrellalike mechanism effectively improves the performance of ABC.

Visual object tracking is always a challenging task in computer vision. During the tracking, the shape and appearance of the target may change greatly, and because of the lack of sufficient training samples, most of the online learning tracking algorithms will have performance bottlenecks. In this paper, an improved real-time algorithm based on deep learning features is proposed, which combines multi-feature fusion, multi-scale estimation, adaptive updating of target model and re-detection after target loss. The effectiveness and advantages of the proposed algorithm are proved by a large number of comparative experiments with other excellent algorithms on large benchmark datasets.

Convolutional Neural Networks (CNNs) have recently demonstrated outstanding performance in image retrieval tasks. Local convolutional features extracted by CNNs, in particular, show exceptional capability in discrimination. Recent research in this field has concentrated on pooling methods that incorporate local features into global features and assess the global similarity of two images. However, the pooling methods sacrifice the image's local region information and spatial relationships, which are precisely known as the keys to the robustness against occlusion and viewpoint changes. In this paper, instead of pooling methods, we propose an alternative method based on local similarity, determined by directly using local convolutional features. Specifically, we first define three forms of local similarity tensors (LSTs), which take into account information about local regions as well as spatial relationships between them. We then construct a similarity CNN model (SCNN) based on LSTs to assess the similarity between the query and gallery images. The ideal configuration of our method is sought through thorough experiments from three perspectives: local region size, local region content, and spatial relationships between local regions. The experimental results on a modified open dataset (where query images are limited to occluded ones) confirm that the proposed method outperforms the pooling methods because of robustness enhancement. Furthermore, testing on three public retrieval datasets shows that combining LSTs with conventional pooling methods achieves the best results.

In this paper, we investigate the sum rate (SR) maximization problem for downlink cooperative non-orthogonal multiple access (C-NOMA) system under in-phase and quadrature-phase (IQ) imbalance at the base station (BS) and destination. The BS communicates with users by a half-duplex amplified-and-forward (HD-AF) relay under imperfect IQ imbalance. The sum rate maximization problem is formulated as a non-convex optimization with the quality of service (QoS) constraint for each user. We first use the variable substitution method to transform the non-convex SR maximization problem into an equivalent problem. Then, a joint power and rate allocation algorithm is proposed based on successive convex approximation (SCA) to maximize the SR of the systems. Simulation results verify that the algorithm can improve the SR of the C-NOMA compared with the cooperative orthogonal multiple access (C-OMA) scheme.

In industry, automatic speech recognition has come to be a competitive feature for embedded products with poor hardware resources. In this work, we propose a tiny end-to-end speech recognition model that is lightweight and easily deployable on edge platforms. First, instead of sophisticated network structures, such as recurrent neural networks, transformers, etc., the model we propose mainly uses convolutional neural networks as its backbone. This ensures that our model is supported by most software development kits for embedded devices. Second, we adopt the basic unit of MobileNet-v3, which performs well in computer vision tasks, and integrate the features of the hidden layer at different scales, thus compressing the number of parameters of the model to less than 1 M and achieving an accuracy greater than that of some traditional models. Third, in order to further reduce the CPU computation, we directly extract acoustic representations from 1-dimensional speech waveforms and use a self-supervised learning approach to encourage the convergence of the model. Finally, to solve some problems where hardware resources are relatively weak, we use a prefix beam search decoder to dynamically extend the search path with an optimized pruning strategy and an additional initialism language model to capture the probability of between-words in advance and thus avoid premature pruning of correct words. In our experiments, according to a number of evaluation categories, our end-to-end model outperformed several tiny speech recognition models used for embedded devices in related work.

This paper proposes the new series highly integrated intelligent power module (IPM), which is developed to provide a ultra-compact, high performance and reliable motor drive system. Details of the key design technologies of the IPM is given and practical application issues such as electrical characteristics, system operation performance and power dissipation are discussed. Layout placement and routing have been optimized in order to reduce and balance the parasitic impedances. By implementing an innovative direct bonding copper (DBC) ceramic substrate, which can effectively dissipate heat, the IPM delivers a fully integrated power stages including two three-phase inverters, power factor correction (PFC) and rectifier in an ultra-compact 75.5mm × 30mm package, offering up to a 17.3 percent smaller space than traditional motor drive scheme.

Social relationships (e.g., couples, opponents) are the foundational part of society. Social relation atmosphere describes the overall interaction environment between social relationships. Discovering social relation atmosphere can help machines better comprehend human behaviors and improve the performance of social intelligent applications. Most existing research mainly focuses on investigating social relationships, while ignoring the social relation atmosphere. Due to the complexity of the expressions in video data and the uncertainty of the social relation atmosphere, it is even difficult to define and evaluate. In this paper, we innovatively analyze the social relation atmosphere in video data. We introduce a Relevant Visual Concept (RVC) from the social relationship recognition task to facilitate social relation atmosphere recognition, because social relationships contain useful information about human interactions and surrounding environments, which are crucial clues for social relation atmosphere recognition. Our approach consists of two main steps: (1) we first generate a group of visual concepts that preserve the inherent social relationship information by utilizing a 3D explanation module; (2) the extracted relevant visual concepts are used to supplement the social relation atmosphere recognition. In addition, we present a new dataset based on the existing Video Social Relation Dataset. Each video is annotated with four kinds of social relation atmosphere attributes and one social relationship. We evaluate the proposed method on our dataset. Experiments with various 3D ConvNets and fusion methods demonstrate that the proposed method can effectively improve recognition accuracy compared to end-to-end ConvNets. The visualization results also indicate that essential information in social relationships can be discovered and used to enhance social relation atmosphere recognition.

This paper introduces an inverter-based true random number generator (I-TRNG). It uses a single CMOS inverter to amplify thermal noise multiple times. An adaptive calibration mechanism based on clock tuning provides robust operation across a wide range of supply voltage 0.5∼1.1V and temperature -40∼140°C. An 8-bit Von-Neumann post-processing circuit (VN8W) is implemented for maximum raw entropy extraction. In a 130nm CMOS technology, the I-TRNG entropy source only occupies 635μm2 and consumes 0.016pJ/raw-bit at 0.6V. The I-TRNG occupies 13406μm2, including the entropy source, adaptive calibration circuit, and post-processing circuit. The minimum energy consumption of the I-TRNG is 1.38pJ/bit at 0.5V, while passing all NIST 800-22 and 800-90B tests. Moreover, an equivalent 15-year life at 0.7V, 25°C is confirmed by an accelerated NBTI aging test.

Homotopy algorithm provides a very powerful approach to select the best regularization term for the l1-norm minimization problem, but it is lack of provision for handling singularities. The singularity problem might be frequently encountered in practical implementations if the measurement matrix contains duplicate columns, approximate columns or columns with linear dependent in kernel space. The existing method for handling Homotopy singularities introduces a high-dimensional random ridge term into the measurement matrix, which has at least two shortcomings: 1) it is very difficult to choose a proper ridge term that applies to several different measurement matrices; and 2) the high-dimensional ridge term may accumulatively degrade the recovery performance for large-scale applications. To get around these shortcomings, a modified ridge-adding method is proposed to deal with the singularity problem, which introduces a low-dimensional random ridge vector into the l1-norm minimization problem directly. Our method provides a much simpler implementation, and it can alleviate the degradation caused by the ridge term because the dimension of ridge term in the proposed method is much smaller than the original one. Moreover, the proposed method can be further extended to handle the SVMpath initialization singularities. Theoretical analysis and experimental results validate the performance of the proposed method.

There exist two intrinsic issues in multiple-shot person re-identification: (1) large differences in camera view, illumination, and non-rigid deformation of posture that make the intra-class variance even larger than the inter-class variance; (2) only a few training data that are available for learning tasks in a realistic re-identification scenario. In our previous work, we proposed a local distance comparison framework to deal with the first issue. In this paper, to deal with the second issue (i.e., to derive a reliable distance metric from limited training data), we propose an adaptive learning method to learn an adaptive distance metric, which integrates prior knowledge learned from a large existing auxiliary dataset and task-specific information extracted from a much smaller training dataset. Experimental results on several public benchmark datasets show that combined with the local distance comparison framework, our adaptive learning method is superior to conventional approaches.