This Letter focuses on deep learning-based monkeys' head swing counting problem. Nowadays, there are very few papers on monkey detection, and even fewer papers on monkeys' head swing counting. This research tries to fill in the gap and try to calculate the head swing frequency of monkeys through deep learning, where we further extend the traditional target detection algorithm. After analyzing object detection results, we localize the monkey's actions over a period. This Letter analyzes the task of counting monkeys' head swings, and proposes the standard that accurately describes a monkey's head swing. Under the guidance of this standard, the monkeys' head swing counting accuracy in 50 test videos reaches 94.23%.

UNcorrectable Bit Errors (UNBEs) are important in considering the reliability of Redundant Array of Inexpensive Disks (RAID). They, however, have been ignored or have not been studied in detail in existing reliability analysis of RAID. In this paper, we present an analytic model to study the reliability of declustered-parity RAID by considering UNBEs. By using the analytic model, the optimistic and the pessimistic estimates of the probability that data loss occurs due to an UNBE during the data reconstruction after a disk failed (we call this DB data loss) are obtained. Then, the optimistic and the pessimistic estimates of the Mean Time To Data Loss (MTTDL) that take into account both DB data loss and the data loss caused by double independent disk failures (we call this DD data loss) are obtained. Furthermore, how the MTTDL depends on the number of units in a parity stripe, rebuild time of a failed disk and write fraction of data access are studied by numerical analysis.

In recent years, the development of visual tracking is getting better and better, but some methods cannot overcome the problem of low accuracy and success rate of tracking. Although there are some trackers will be more accurate, they will cost more time. In order to solve the problem, we propose a reinforced tracker based on Hierarchical Convolutional Features (HCF for short). HOG, color-naming and grayscale features are used with different weights to supplement the convolution features, which can enhance the tracking robustness. At the same time, we improved the model update strategy to save the time costs. This tracker is called RHCF and the code is published on https://github.com/z15846/RHCF. Experiments on the OTB2013 dataset show that our tracker can validly achieve the promotion of the accuracy and success rate.

To secure a wireless sensor and actuator network (WSAN) in cyber-physical systems, trust management framework copes with misbehavior problem of nodes and stimulate nodes to cooperate with each other. The existing trust management frameworks can be classified into reputation-based framework and trust establishment framework. There, however, are still many problems with these existing trust management frameworks, which remain unsolved, such as frangibility under possible attacks. To design a robust trust management framework, we identify the attacks to the existing frameworks, present the countermeasures to them, and propose a hybrid trust management framework (HTMF) to construct trust environment for WSANs in the paper. HTMF includes second-hand information and confidence value into trustworthiness evaluation and integrates the countermeasures into the trust formation. We preform extensive performance evaluations, which show that the proposed HTMF is more robust and reliable than the existing frameworks.

Ontology mapping is important in many areas, such as information integration, semantic web and knowledge management. Thus the effectiveness of ontology mapping needs to be further studied. This paper puts forward a mapping method between different ontology concepts in the same field. Firstly, the algorithms of calculating four individual similarities (the similarities of concept name, property, instance and structure) between two concepts are proposed. The algorithm features of four individual similarities are as follows: a new WordNet-based method is used to compute semantic similarity between concept names; property similarity algorithm is used to form property similarity matrix between concepts, then the matrix will be processed into a numerical similarity; a new vector space model algorithm is proposed to compute the individual similarity of instance; structure parameters are added to structure similarity calculation, structure parameters include the number of properties, instances, sub-concepts, and the hierarchy depth of two concepts. Then similarity of each of ontology concept pairs is represented by a vector. Finally, Support Vector Machine (SVM) is used to accomplish mapping discovery by training and learning the similarity vectors. In this algorithm, Harmony and reliability are used as the weights of the four individual similarities, which increases the accuracy and reliability of the algorithm. Experiments achieve good results and the results show that the proposed method outperforms many other methods of similarity-based algorithms.

In this paper, the problem of channel estimation in orthogonal frequency-division multiplexing systems over fast time-varying channel is investigated by using a Basis Expansion Model (BEM). Regarding the effects of the Gibbs phenomenon in the BEM, we propose a new method to alleviate it and reduce the modeling error. Theoretical analysis and detail comparison results show that the proposed BEM method can provide improved modeling error compared with other BEMs such as CE-BEM and GCE-BEM. In addition, instead of using the frequency-domain Kronecker delta structure, a new clustered pilot structure is proposed to enhance the estimation performance further. The new clustered pilot structure can effectively reduce the inter-carrier interference especially in the case of high Doppler spreads.

This paper reports a high-quality hardware-friendly integer motion estimation (IME) scheme. According to different characteristics of CTU content, the proposed method adopts different adaptive multi-resolution strategies coupled with accurate full-PU modes IME at the finest level. Besides, by using motion vector derivation, IME for the second reference frame is simplified and hardware resource is saved greatly through processing element (PE) sharing. It is shown that the proposed architecture can support the real-time processing of 4K-UHD @60fps, while the BD-rate is just increased by 0.53%.

In this paper, optimal static load balancing in a tree hierarchy network that consists of a set of heterogeneous host computers is considered. It is formulated as a nonlinear optimization problem. We study the effects of the link communication time on the optimal link flow rate (i.e., the rate at which a node forwards jobs to other nodes for remote processing), the optimal node load (i.e., the rate at which jobs are processed at a node), and the optimal mean response time, by parametric analysis. We show that the entire network can be divided into several independent sub-tree networks with respect to the link flow rates and node loads. We find that the communication time of a link has the effects only on the link flow rates and the loads on nodes that are in the same sub-tree network. The increase in the communication time of a link causes the decrease in the link flow rates of its descendant nodes, its ancestor nodes and itself, but causes the increase in the link flow rates of other nodes in the same sub-tree network. It also causes the increase in the loads of its descendant nodes and itself, but causes the decrease in the loads of other nodes in the same sub-tree network. In general, it causes the increase in the mean response time.

A Lévy search strategy based chaotic artificial bee colony algorithm (LABC) is proposed in this paper. The chaotic sequence, global optimal mechanism and Lévy flight mechanism were introduced respectively into the initialization, the employed bee search and the onlooker bee search. The experiments show that the proposed algorithm performed better in convergence speed, global search ability and optimization accuracy than other improved ABC.

Nonnegative Matrix Factorization (NMF) is a promising data-driven matrix decomposition method, and is becoming very active and attractive in machine learning and blind source separation areas. So far NMF algorithm has been widely used in diverse applications, including image processing, anti-collision for Radio Frequency Identification (RFID) systems and audio signal analysis, and so on. However the typical NMF algorithms cannot work well in underdetermined mixture, i.e., the number of observed signals is less than that of source signals. In practical applications, adding suitable constraints fused into NMF algorithm can achieve remarkable decomposition results. As a motivation, this paper proposes to add the minimum volume and minimum correlation constrains (MCV) to the NMF algorithm, which makes the new algorithm named MCV-NMF algorithm suitable for underdetermined scenarios where the source signals satisfy mutual independent assumption. Experimental simulation results validate that the MCV-NMF algorithm has a better performance improvement in solving RFID tag anti-collision problem than that of using the nearest typical NMF method.

This letter presents an analytical study of the reverse link Erlang capacity of 3G/Ad Hoc Integrated networks. In the considered integrated network, 3G networks and Ad Hoc networks operate over the same frequency band and hence cause interference to each other. The reverse link Erlang capacity is analyzed and discussed in two cases: Ad Hoc networks use and do not use power control.

Federated learning is a promising strategy for indoor localization that can reduce the labor cost of constructing a fingerprint dataset in a distributed training manner without privacy disclosure. However, the traffic generated during the whole training process of federated learning is a burden on the up-and-down link, which leads to huge energy consumption for mobile devices. Moreover, the non-independent and identically distributed (Non-IID) problem impairs the global localization performance during the federated learning. This paper proposes a communication-efficient FedAvg method for federated indoor localization which is improved by the layerwise asynchronous aggregation strategy and layerwise swapping training strategy. Energy efficiency can be improved by performing asynchronous aggregation between the model layers to reduce the traffic cost in the training process. Moreover, the impact of the Non-IID problem on the localization performance can be mitigated by performing swapping training on the deep layers. Extensive experimental results show that the proposed methods reduce communication traffic and improve energy efficiency significantly while mitigating the impact of the Non-IID problem on the precision of localization.

A wideband beamformer with mainlobe control is proposed. To make the beamformer robust against pointing errors, inequality rather than equality constraints are used to restrict the mainlobe response, thus more degrees of freedom are saved. The constraints involved are nonconvex, therefore are linearly approximated so that the beamformer can be obtained by iterating a second-order cone program. Moreover, the response variance element is introduced to achieve a frequency invariant beamwidth. The effectiveness of the technique is demonstrated by numerical examples.

This letter presents an analytical study of outage probability of a 3G/Ad Hoc cooperative network. The considered cooperative network can improve the signal quality so as to decrease the outage probability. Meanwhile, it imposes additional interference on other ongoing users. But on the whole, our analytical study and simulation results show that the cooperative network can still effectively overcome outage event and decrease the average outage probability.

Cloud computing is broadly recognized as as the prevalent trend in IT. However, in cloud computing mode, customers lose the direct control of their data and applications hosted by the cloud providers, which leads to the trustworthiness issue of the cloud providers, hindering the widespread use of cloud computing. This paper proposes a trustworthiness verification and audit mechanism on cloud providers called T-YUN. It introduces a trusted third party to cyclically attest the remote clouds, which are instrumented with the trusted chain covering the whole architecture stack. According to the main operations of the clouds, remote verification protocols are also proposed in T-YUN, with a dedicated key management scheme. This paper also implements a proof-of-concept emulator to validate the effectiveness and performance overhead of T-YUN. The experimental results show that T-YUN is effective and the extra overhead incurred by it is acceptable.

A series of Binarized Neural Networks (BNNs) show the accepted accuracy in image classification tasks and achieve the excellent performance on field programmable gate array (FPGA). Nevertheless, we observe existing designs of BNNs are quite time-consuming in change of the target BNN and acceleration of a new BNN. Therefore, this paper presents FCA-BNN, a flexible and configurable accelerator, which employs the layer-level configurable technique to execute seamlessly each layer of target BNN. Initially, to save resource and improve energy efficiency, the hardware-oriented optimal formulas are introduced to design energy-efficient computing array for different sizes of padded-convolution and fully-connected layers. Moreover, to accelerate the target BNNs efficiently, we exploit the analytical model to explore the optimal design parameters for FCA-BNN. Finally, our proposed mapping flow changes the target network by entering order, and accelerates a new network by compiling and loading corresponding instructions, while without loading and generating bitstream. The evaluations on three major structures of BNNs show the differences between inference accuracy of FCA-BNN and that of GPU are just 0.07%, 0.31% and 0.4% for LFC, VGG-like and Cifar-10 AlexNet. Furthermore, our energy-efficiency results achieve the results of existing customized FPGA accelerators by 0.8× for LFC and 2.6× for VGG-like. For Cifar-10 AlexNet, FCA-BNN achieves 188.2× and 60.6× better than CPU and GPU in energy efficiency, respectively. To the best of our knowledge, FCA-BNN is the most efficient design for change of the target BNN and acceleration of a new BNN, while keeps the competitive performance.

In recent years, more emphasis is placed on the performance of massive databases. It is often required not only that database systems provide high throughputs with rapid response times, but also that they are fully available 24-hours-per-day and 7-days-per-week. Requirements for throughput and response time can be satisfied by upgrading the hardware. As a result, databases in the old hardware environment have to be moved to the new one. Moving a database, however, generally requires taking the database off line for a long time, which is unacceptable for numerous applications. In this paper, a very practical and important subject is addressed: how to upgrade the hardware on line, i.e., how to move a database from an old hardware environment to a new one concurrently with users' reading and writing of the database. A technique for this purpose is proposed. We have implemented a prototype based on this technique. Our experiments with the prototype shown that compared with conventional off-line approach, the proposed technique could give a performance improvement by more than 85% in the query-bound environment and 40% in the update-bound environment.

In recent years FPGA has become popular in CNN acceleration, and many CNN-to-FPGA toolchains are proposed to fast deploy CNN on FPGA. However, for these toolchains, updating CNN network means regeneration of RTL code and re-implementation which is time-consuming and may suffer timing-closure problems. So, we propose HBDCA: a toolchain and corresponding accelerator. The CNN on HBDCA is defined by the content of BRAM. The toolchain integrates UpdateMEM utility of Xilinx, which updates content of BRAM without re-synthesis and re-implementation process. The toolchain also integrates TensorFlow Lite which provides high-accuracy quantization. HBDCA supports 8-bits per-channel quantization of weights and 8-bits per-layer quantization of activations. Upgrading CNN on accelerator means the kernel size of CNN may change. Flexible structure of HBDCA supports kernel-level parallelism with three different sizes (3×3, 5×5, 7×7). HBDCA implements four types of parallelism in convolution layer and two types of parallelism in fully-connected layer. In order to reduce access number to memory, both spatial and temporal data-reuse techniques were applied on convolution layer and fully-connect layer. Especially, temporal reuse is adopted at both row and column level of an Input Feature Map of convolution layer. Data can be just read once from BRAM and reused for the following clock. Experiments show by updating BRAM content with single UpdateMEM command, three CNNs with different kernel size (3×3, 5×5, 7×7) are implemented on HBDCA. Compared with traditional design flow, UpdateMEM reduces development time by 7.6X-9.1X for different synthesis or implementation strategy. For similar CNN which is created by toolchain, HBDCA has smaller latency (9.97µs-50.73µs), and eliminates re-implementation when update CNN. For similar CNN which is created by dedicated design, HBDCA also has the smallest latency 9.97µs, the highest accuracy 99.14% and the lowest power 1.391W. For different CNN which is created by similar toolchain which eliminate re-implementation process, HBDCA achieves higher speedup 120.28X.

A novel element is proposed for manipulating two orthogonally-polarized electromagnetic waves, resulting in a polarization-reconfigurable flat transmitarray. This element consists of four identical metallic patterns, including a square frame loaded with short stubs and an internal crossed dipole, which are printed on the two sides of three identical flat dielectric slabs, with no air gap among them. With a linearly-polarized (LP) feeder, the flat transmitarray can transform the LP incident wave into a circular, horizontal or vertical polarization wave in a convenient way. By rotating the LP feeder so that the polarization angle is 0°, 45°, 90° or 135°, the waves of linear horizontal, right-handed circular, linear vertical or left-handed circular polarization can be obtained alternately. Simulations and experiments are conducted to validate the performance. The measured axial ratio bandwidths for RHCP and LHCP transmitarrays are about 7.1% and 5.1%, respectively, the 3dB gain bandwidths are 16.19% and 22.4%, and the peak gains are 25.56dBi and 24.2dBi, respectively.

In this paper, we present an analytic model to study the reliability of some important disk array organizations that have been proposed by others in the literature. These organizations are based on the combination of two options for the data layout, regular RAID-5 and block designs, and three alternatives for sparing, hot sparing, distributed sparing and parity sparing. Uncorrectable bit errors have big effects on reliability but are ignored in traditional reliability analysis of disk arrays. We consider both disk failures and uncorrectable bit errors in the model. The reliability of disk arrays is measured in terms of MTTDL (Mean Time To Data Loss). A unified formula of MTTDL has been derived for these disk array organizations. The MTTDLs of these disk array organizations are also compared using the analytic model. By numerical experiments, we show that the data losses caused by uncorrectable bit errors may dominate the data losses of disk array systems though only the data losses caused by disk failures are traditionally considered. The consideration of uncorrectable bit errors provides a more realistic look at the reliability of the disk array systems.