A novel compress-and-forward (CF) system based on multi-relay network is proposed. In this system, two networks are linked, wherein one is a sensor network connecting the analog source and the relays, and the other is a communication network between the relays and the destination. At several parallel relay nodes, the analog signals are transformed into digital signals after quantization and encoding and then the digital signals are transmitted to the destination. Based on the Chief Executive Officer (CEO) theory, we calculate the minimum transmission rate of every source-relay link and we propose a system model by combining sensor network with communication network according to Shannon channel capacity theory. Furthermore, we obtain the best possible system performance under system power constraint, which is measured by signal-to-noise ratio (SNR) rather than bit error rate (BER). Numerical simulation results show that the proposed CF outperforms the traditional amplify-and-forward (AF) system in the performance versus SNR.

Knowledge graph embedding aims to embed entities and relations of multi-relational data in low dimensional vector spaces. Knowledge graphs are useful for numerous artificial intelligence (AI) applications. However, they (KGs) are far from completeness and hence KG embedding models have quickly gained massive attention. Nevertheless, the state-of-the-art KG embedding models ignore the category specific projection of entities and the impact of entity types in relational aspect. For example, the entity “Washington” could belong to the person or location category depending on its appearance in a specific relation. In a KG, an entity usually holds many type properties. It leads us to a very interesting question: are all the type properties of an entity are meaningful for a specific relation? In this paper, we propose a KG embedding model TPRC that leverages entity-type properties in the relational context. To show the effectiveness of our model, we apply our idea to the TransE, TransR and TransD. Our approach outperforms other state-of-the-art approaches as TransE, TransD, DistMult and ComplEx. Another, important observation is: introducing entity type properties in the relational context can improve the performances of the original translation distance based models.

Biometric template protection techniques have been proposed to address security and privacy issues inherent to biometric-based authentication systems. However, it has been shown that the robustness of most of such techniques against reversibility and linkability attacks are overestimated. Thus, a thorough security analysis of recently proposed template protection schemes has to be carried out. Negative iris recognition is an interesting iris template protection scheme based on the concept of negative databases. In this paper, we present a comprehensive security analysis of this scheme in order to validate its practical usefulness. Although the authors of negative iris recognition claim that their scheme possesses both irreversibility and unlinkability, we demonstrate that more than 75% of the original iris-code bits can be recovered using a single protected template. Moreover, we show that the negative iris recognition scheme is vulnerable to attacks via record multiplicity where an adversary can combine several transformed templates to recover more proportion of the original iris-code. Finally, we demonstrate that the scheme does not possess unlinkability. The experimental results, on the CASIA-IrisV3 Interval public database, support our theory and confirm that the negative iris recognition scheme is susceptible to reversibility, linkability, and record multiplicity attacks.

In this article, we propose a method called “continuous noise masking (cNM)” that allows eliminating residual buzziness in a continuous vocoder, i.e. of which all parameters are continuous and offers a simple and flexible speech analysis and synthesis system. Traditional parametric vocoders generally show a perceptible deterioration in the quality of the synthesized speech due to different processing algorithms. Furthermore, an inaccurate noise resynthesis (e.g. in breathiness or hoarseness) is also considered to be one of the main underlying causes of performance degradation, leading to noisy transients and temporal discontinuity in the synthesized speech. To overcome these issues, a new cNM is developed based on the phase distortion deviation in order to reduce the perceptual effect of the residual noise, allowing a proper reconstruction of noise characteristics, and model better the creaky voice segments that may happen in natural speech. To this end, the cNM is designed to keep only voice components under a condition of the cNM threshold while discarding others. We evaluate the proposed approach and compare with state-of-the-art vocoders using objective and subjective listening tests. Experimental results show that the proposed method can reduce the effect of residual noise and can reach the quality of other sophisticated approaches like STRAIGHT and log domain pulse model (PML).

The packet error rate (PER) performance of multi-hop STBC based cooperative and diversity relaying systems are studied. These systems consist of a source, a destination, and two relay stations in each hop. From in-lab experiments, it is confirmed that the cooperative relaying system has better PER performance than the diversity relaying system with highly correlated channels.

In this prompt report, we present the basic performance evaluation of Intel Optane Data Center Persistent Memory Module (Optane DCPMM), which is the first commercially-available, byte-addressable non-volatile memory modules released in April 2019. Since at the moment of writing only a few reports on its performance were published, this letter is intended to complement other performance studies. Through experiments using our own measurement tools, we obtained that the latency of random read-only access was approximately 374 ns. That of random writeback-involving access was 391 ns. The bandwidths of read-only and writeback-involving access for interleaved memory modules were approximately 38 GB/s and 3 GB/s, respectively.

An electrical capacitance tomography (ECT) method was used to detect fretting wear behavior of electrical connectors. The specimens used in this study were contacts of type-M round two-pin electrical connectors. The experiments consisted of running a series of vibration tests at each frequency combined with one g levels. During each test run, the measured capacitance per pair of electrodes was monitored as a performance characteristic, which is induced by the wear debris generated by the fretting wear of electrical connectors. The fretted surface is examined using scanning electron microscopy (SEM) and energy dispersive spectrometer (EDS) analysis to assess the surface profile, extent of fretting damage and elemental distribution across the contact zone and then compared to the capacitance values. The results exhibit that with the increase of the fretting cycles or the vibration frequency, the characteristic value of the wear debris between the contacts of electrical connector gradually increases and the wear is more serious. Measured capacitance values are consistent with SEM and EDS analysis.

To solve the problem of the self-deployment of heterogeneous directional wireless sensor networks in 3D space, this paper proposes a weighted Voronoi diagram-based self-deployment algorithm (3DV-HDDA) in 3D space. To improve the network coverage ratio of the monitoring area, the 3DV-HDDA algorithm uses the weighted Voronoi diagram to move the sensor nodes and introduces virtual boundary torque to rotate the sensor nodes, so that the sensor nodes can reach the optimal position. This work also includes an improvement algorithm (3DV-HDDA-I) based on the positions of the centralized sensor nodes. The difference between the 3DV-HDDA and the 3DV-HDDA-I algorithms is that in the latter the movement of the node is determined by both the weighted Voronoi graph and virtual force. Simulations show that compared to the virtual force algorithm and the unweighted Voronoi graph-based algorithm, the 3DV-HDDA and 3DV-HDDA-I algorithms effectively improve the network coverage ratio of the monitoring area. Compared to the virtual force algorithm, the 3DV-HDDA algorithm increases the coverage from 75.93% to 91.46% while the 3DV-HDDA-I algorithm increases coverage from 76.27% to 91.31%. When compared to the unweighted Voronoi graph-based algorithm, the 3DV-HDDA algorithm improves the coverage from 80.19% to 91.46% while the 3DV-HDDA-I algorithm improves the coverage from 72.25% to 91.31%. Further, the energy consumption of the proposed algorithms after 60 iterations is smaller than the energy consumption using a virtual force algorithm. Experimental results demonstrate the accuracy and effectiveness of the 3DV-HDDA and the 3DV-HDDA-I algorithms.

Periodic sequences, used as keys in cryptosystems, plays an important role in cryptography. Such periodic sequences should possess high linear complexity to resist B-M algorithm. Sequences constructed by cyclotomic cosets have been widely studied in the past few years. In this paper, the linear complexity of n-periodic cyclotomic sequences of order 2 and 4 over 𝔽p has been calculated, where n and p are two distinct odd primes. The conclusions reveal that the presented sequences have high linear complexity in many cases, which indicates that the sequences can resist the linear attack.

For high definition (HD) videos, the 3D-High Efficiency Video Coding (3D-HEVC) reference algorithm incurs dramatically highly computation loads. Therefore, with the demands for the real-time processing of HD video, a hardware implementation is necessary. In this paper, a reconfigurable architecture is proposed that can support both median filtering preprocessing and mean filtering preprocessing to satisfy different scene depth maps. The architecture sends different instructions to the corresponding processing elements according to different scenarios. Mean filter is used to process near-range images, and median filter is used to process long-range images. The simulation results show that the designed architecture achieves an averaged PSNR of 34.55dB for the tested images. The hardware design for the proposed virtual view synthesis system operates at a maximum clock frequency of 160MHz on the BEE4 platform which is equipped with four Virtex-6 FF1759 LX550T Field-Programmable Gate Array (FPGA) for outputting 720p (1024×768) video at 124fps.

Difference systems of sets (DSSs) introduced by Levenstein are combinatorial structures used to construct comma-free codes for synchronization. In this letter, two classes of optimal DSSs are presented. One class is obtained based on q-ary ideal sequences with d-form property and difference-balanced property. The other class of optimal and perfect DSSs is derived from perfect ternary sequences given by Ipatov in 1995. Compared with known constructions (Zhou, Tang, Optimal and perfect difference systems of sets from q-ary sequences with difference-balanced property, Des. Codes Cryptography, 57(2), 215-223, 2010), the proposed DSSs lead to comma-free codes with nonzero code rate.

Figure-ground (FG) segregation has been considered as a fundamental step towards object recognition. We explored plausible mechanisms that estimate global figure-ground segregation from local image features by investigating the human visual system. Physiological studies have reported border-ownership (BO) selective neurons in V2 which signal the local direction of figure (DOF) along a border; however, how local BO signals contribute to global FG segregation has not been clarified. The BO and FG processing could be independent, dependent on each other, or inseparable. The investigation on the differences and similarities between the BO and FG judgements is important for exploring plausible mechanisms that enable global FG estimation from local clues. We performed psychophysical experiments that included two different tasks each of which focused on the judgement of either BO or FG. The perceptual judgments showed consistency between the BO and FG determination while a longer distance in gaze movement was observed in FG segregation than BO discrimination. These results suggest the involvement of distinct neural mechanism for local BO determination and global FG segregation.

The neural network has been one of the most useful techniques in the area of speech recognition, language translation and image analysis in recent years. Long Short-Term Memory (LSTM), a popular type of recurrent neural networks (RNNs), has been widely implemented on CPUs and GPUs. However, those software implementations offer a poor parallelism while the existing hardware implementations lack in configurability. In order to make up for this gap, a highly configurable 7.62 GOP/s hardware implementation for LSTM is proposed in this paper. To achieve the goal, the work flow is carefully arranged to make the design compact and high-throughput; the structure is carefully organized to make the design configurable; the data buffering and compression strategy is carefully chosen to lower the bandwidth without increasing the complexity of structure; the data type, logistic sigmoid (σ) function and hyperbolic tangent (tanh) function is carefully optimized to balance the hardware cost and accuracy. This work achieves a performance of 7.62 GOP/s @ 238 MHz on XCZU6EG FPGA, which takes only 3K look-up table (LUT). Compared with the implementation on Intel Xeon E5-2620 CPU @ 2.10GHz, this work achieves about 90× speedup for small networks and 25× speed-up for large ones. The consumption of resources is also much less than that of the state-of-the-art works.

We measured eye movements at gaze points while subjects performed calculation tasks and examined the relationship between the eye movements and fatigue and/or internal state of a subject by tasks. It was suggested that fatigue and/or internal state of a subject affected eye movements at gaze points and that we could measure them using eye movements at gaze points in real time.

This paper investigates the secrecy energy efficiency maximization (SEEM) problem in a simultaneous wireless information and power transfer (SWIPT) system, wherein a legitimate user (LU) exploits the power splitting (PS) scheme for simultaneous information decoding (ID) and energy harvesting (EH). To prevent interference from eavesdroppers on the LU, artificial noise (AN) is incorporated into the confidential signal at the transmitter. We maximize the secrecy energy efficiency (SEE) by joining the power of the confidential signal, the AN power, and the PS ratio, while taking into account the minimum secrecy rate requirement of the LU, the required minimum harvested energy, the allowed maximum radio frequency transmission power, and the PS ratio. The formulated SEEM problem involves nonconvex fractional programming and is generally intractable. Our solution is Lagrangian relaxation method than can transform the original problem into a two-layer optimization problem. The outer layer problem is a single variable optimization problem with a Lagrange multiplier, which can be solved easily. Meanwhile, the inner layer one is fractional programming, which can be transformed into a subtractive form solved using the Dinkelbach method. A closed-form solution is derived for the power of the confidential signal. Simulation results verify the efficiency of the proposed SEEM algorithm and prove that AN-aided design is an effective method for improving system SEE.

Media Access Control (MAC) is critical to guarantee different Quality of Service (QoS) requirements for Unmanned Aerial Vehicle (UAV) networks, such as high reliability for safety packets and high throughput for service packets. Meanwhile, due to their ability to provide lower delay and higher data rates, more UAVs are using frequently directional antennas. However, it is challenging to support different QoS in UAV networks with directional antennas, because of the high mobility of UAV which causes serious channel resource loss. In this paper, we propose CU-MAC which is a MAC protocol for Centralized UAV networks with directional antennas. First, we design a mobility prediction based time-frame optimization scheme to provide reliable broadcast service for safety packets. Then, a traffic prediction based channel allocation scheme is proposed to guarantee the priority of video packets which are the most common service packets nowadays. Simulation results show that compared with other representative protocols, CU-MAC achieves higher reliability for safety packets and improves the throughput of service packets, especially video packets.

This paper presents an automated patient-specific ECG classification algorithm, which integrates long short-term memory (LSTM) and convolutional neural networks (CNN). While LSTM extracts the temporal features, such as the heart rate variance (HRV) and beat-to-beat correlation from sequential heartbeats, CNN captures detailed morphological characteristics of the current heartbeat. To further improve the classification performance, adaptive segmentation and re-sampling are applied to align the heartbeats of different patients with various heart rates. In addition, a novel clustering method is proposed to identify the most representative patterns from the common training data. Evaluated on the MIT-BIH arrhythmia database, our algorithm shows the superior accuracy for both ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) recognition. In particular, the sensitivity and positive predictive rate for SVEB increase by more than 8.2% and 8.8%, respectively, compared with the prior works. Since our patient-specific classification does not require manual feature extraction, it is potentially applicable to embedded devices for automatic and accurate arrhythmia monitoring.

Logic encryption is an IC protection technique which inserts extra logic and key inputs to hide a circuit's functionality. An encrypted circuit needs to be activated with a secret key for being functional. SAT attack and Removal attack are two most advanced decryption methods that have shown their effectiveness to break most of the existing logic encryption methods within a few hours. In this paper, we propose SMARTLock, a SAT attack and reMoval Attack-Resistant Tree-based logic Locking method, for resisting them simultaneously. To encrypt a circuit, the method finds large AND and OR functions in it and encrypts them by inserting duplicate tree functions. There are two types of structurally identical tree encryptions that aim to resist SAT attack and Removal attack, respectively. The experimental results show that the proposed method is effective for encrypting a set of benchmarks from ISCAS'85, MCNC, and IWLS. 16 out of 40 benchmarks encrypted by the proposed method with the area overhead of no more than 5% are uncrackable by SAT attack within 5 hours. Additionally, compared to the state-of-the-art logic encryption methods, the proposed method provides better security for most benchmarks.

This paper presents an algorithm to arrange a large number of antenna elements in the limited space of massive MIMO base station antenna without degrading the communication quality under a street-cell line-of-sight environment in mobile communications. The proposed algorithm works by using mathematical optimization in which the objective function is the correlation coefficient between the channel responses of two elements of the base station antenna, according to an algorithm constructed based on the results obtained through basic examinations of the characteristics of the correlation coefficient between channel responses. The channel responses are computed by using the propagation path information obtained by ray-tracing. The arrangements output by the proposed algorithm are mainly evaluated by channel capacity comparison with uniformly spaced arrangements on the vertical plane in single user and multiuser environments. The evaluation results of these arrangements in downlink demonstrate the superiority of the arrangements generated by the proposed algorithm, especially in term of robustness against an increase in the number of users.

A consecutive-k-out-of-n:G system consists of n components which are arranged in a line and the system works if and only if at least k consecutive components work. This paper discusses the optimization problems for a consecutive-k-out-of-n:G system. We first focus on the optimal number of components at the system design phase. Then, we focus on the optimal replacement time at the system operation phase by considering a preventive replacement, which the system is replaced at the planned time or the time of system failure which occurs first. The expected cost rates of two optimization problems are considered as objective functions to be minimized. Finally, we give study cases for the proposed optimization problems and evaluate the feasibility of the policies.