Unlike Radio Frequency Identification (RFID), emerging Computational RFID (CRFID) integrates the RF front-end and MCU with multiple sensors. CRFIDs need to transmit data within the interrogator range, so when the tags moved rapidly or the contact duration with interrogator is limited, the sensor data collected by CRFID must be transferred to interrogator quickly. In this paper, we focus on throughput optimization for backscatter link, take physical and medium access control (MAC) layers both into consideration, put forward our scheme called ORRIS. On physical layer, we propose Cluster Gather Degree (CGD) indicator, which is the clustering degree of signal in IQ domain. Then CGD is regarded as the criterion to adaptively adjust the rate encoding mode and link frequency, accordingly achieve adaptive rate transmission. On MAC layer, based on the idea of asynchronous transfer, we utilize the the number of clusters in IQ domain to select the optimal Q value as much as possible. So that achieve burst transmission or bulk data transmission. Experiments and analyses on the static and mobile scenarios show that our proposal has significantly better mean throughput than BLINK or CARA, which demonstrate the effectiveness of our scheme.

A retrodirective system utilizing harmonic reradiation from a rectenna is developed and verified for long-range wireless power transfer applications, such as low-power or battery-less devices and lightweight aerial vehicles. The second harmonic generated by the rectifying circuit is used instead of a pilot signal, and thus an oscillator for creating the pilot signal is not required. The proposed retrodirective system consists of a 2.45 GHz transmitter with a two-element phased array antenna, a 4.9 GHz direction-of-arrival (DoA) estimation system, a phase control system, and a rectenna. The rectenna, consisting of a half-wave dipole antenna, receives microwave power from the 2.45 GHz transmitter and reradiates the harmonic toward the 4.9 GHz DoA estimation system. The rectenna characteristics and experimental demonstrations of the proposed retrodirective system are described. From measurement results, the dc output power pattern for the developed retrodirective system is in good agreement with that obtained using manual beam steering. The measured DoA estimation errors are within the range of -2.4° to 4.8°.

In this letter, we investigate the separating redundancy of binary linear codes. Using analytical techniques, we provide a general lower bound on the first separating redundancy of binary linear codes and show the bound is tight for a particular family of binary linear codes, i.e., cycle codes. In other words, the first separating redundancy of cycle codes can be determined. We also derive a deterministic and constructive upper bound on the second separating redundancy of cycle codes, which is shown to be better than the general deterministic and constructive upper bounds for the codes.

A triangle enumerating problem is one of fundamental problems of graph data. Although several triangle enumerating algorithms based on MapReduce have been proposed, they still suffer from generating a lot of intermediate data. In this paper, we propose the efficient MapReduce algorithms to enumerate every triangle in the massive graph based on a vertex partition. Since a triangle is composed of an edge and a wedge, our algorithms check the existence of an edge connecting the end-nodes of each wedge. To generate every triangle from a graph in parallel, we first split a graph into several vertex partitions and group the edges and wedges in the graph for each pair of vertex partitions. Then, we form the triangles appearing in each group. Furthermore, to enhance the performance of our algorithm, we remove the duplicated wedges existing in several groups. Our experimental evaluation shows the performance of our proposed algorithm is better than that of the state-of-the-art algorithm in diverse environments.

Optical orthogonal signature pattern codes (OOSPCs) have attracted great attention due to their important application in the spatial code-division multiple-access network for image transmission. In this paper, we give a construction for OOSPCs based on cyclic codes over Fp. Applying this construction with the Reed-Solomon codes and the generalized Berlekamp-Justesen codes, we obtain two classes of asymptotically optimal OOSPCs.

High Efficiency Video Coding (HEVC) standard is now becoming one of the most widespread video coding standards in the world. As a successor of H.264 standard, it aims to provide a much superior encoding performance. To fulfill this goal, several new notations along with the corresponding computation processes are introduced by this standard. Among those computation processes, the integer motion estimation (IME) is one of bottlenecks due to the complex partitions of the inter prediction units (PU) and the large search window commonly adopted. Many algorithms have been proposed to address this issue and usually put emphasis on a large search window and great computation amount. However, the coding efforts should be related to the scenes. To be more specific, for relatively static videos, a small search window along with a simple search scheme should be adopted to reduce the time cost and power consumption. In view of this, a micro-code-based IME engine is proposed in this paper, which could be applied with search schemes of different complexity. To test the performance, three different search schemes based on this engine are designed and evaluated under HEVC test model (HM) 16.9, achieving a B-D rate increase of 0.55/-0.07/-0.14%. Compared with our previous work, the hardware implementation is optimized to reduce 64.2% of the SRAMs bits and 32.8% of the logic gate count. The final design could support 4K×2K @139/85/37fps videos @500MHz.

Unmanned aerial vehicle mounted base stations (UAV-BSs) can provide wireless cellular service to ground users in a variety of scenarios. The efficient deployment of such UAV-BSs while optimizing the coverage area is one of the key challenges. We investigate the deployment of UAV-BS to maximize the coverage of ground users, and further analyzes the impact of the deployment of UAV-BS on the fairness of ground users. In this paper, we first calculated the location of the UAV-BS according to the QoS requirements of the ground users, and then the fairness of ground users is taken into account by calculating three different fairness indexes. The performance of two genetic algorithms, namely Standard Genetic Algorithm (SGA) and Multi-Population Genetic Algorithm (MPGA) are compared to solve the optimization problem of UAV-BS deployment. The simulations are presented showing that the performance of the two algorithms, and the fairness performance of the ground users is also given.

Many quality metrics have been proposed for the compliance perception to assess haptic device performance and perceived results. Perceived compliance may be influenced by factors such as object properties, experimental conditions and human perceptual habits. In this paper, analysis of softness perception was conducted to find out relevant quality metrics dominating in the compliance perception system and their correlation with perception results, by expressing these metrics by basic physical parameters that characterizing these factors. Based on three psychophysical experiments, just noticeable differences (JNDs) for perceived softness of combination of different stiffness coefficients and damping levels rendered by haptic devices were analyzed. Interaction data during the interaction process were recorded and analyzed. Preliminary experimental results show that the discrimination ability of softness perception changes with the ratio of damping to stiffness when subjects exploring at their habitual speed. Analysis results indicate that quality metrics of Rate-hardness, Extended Rate-hardness and ratio of damping to stiffness have high correlation for perceived results. Further analysis results show that parameters that reflecting object properties (stiffness, damping), experimental conditions (force bandwidth) and human perceptual habits (initial speed, maximum force change rate) lead to the change of these quality metrics, which then bring different perceptual feeling and finally result in the change of discrimination ability. Findings in this paper may provide a better understanding of softness perception and useful guidance in improvement of haptic and teleoperation devices.

A monocone antenna is a type of monopole antenna with wideband characteristics. In this paper, a low-profile and small monocone antenna is proposed, by loading a circular plate and three oblique short elements. The characteristics of the proposed antenna are analyzed via simulation. Consequently, a low-profile and small monocone antenna can be obtained while maintaining the wideband characteristics. The relative bandwidth of the proposed antenna (voltage standing wave ratio (VSWR) ≤ 2) is greater than 158.9%. The frequency band of digital terrestrial television broadcasting and the mobile communication systems (from 470 to 3600MHz) in Japan can be completely covered with VSWR ≤ 2. In addition, the radiation patterns of the proposed antenna are omni-directional. The proposed antenna is prototyped, and the validity of the simulation is verified through measurement.

Extremely compact harmonic tuning circuits for class-F amplifiers are realized using composite right-/left-handed (CRLH) transmission line stubs. The proposed circuits take up only a small fraction of the amplifier circuit area and yet are capable of treating four harmonics up to the 5th with a single stub or double stub configuration. This has become possible by using the negative order resonance modes of the CRLH TL, allowing for flexible and simultaneous control of many harmonics by engineering the dispersion relation of the stub line. The CRLH harmonic tuning stubs for 2-GHz amplifiers were realized using surface mounting chip capacitors, whereas the stub for 4-GHz amplifiers was fabricated based fully on microstrip-line technology. The fabricated 2-GHz and 4-GHz GaN HEMT class-F amplifiers exhibited peak drain efficiency and peak PAE of more than 83% and 74%, respectively.

Edge-preserving smoothing filter smoothes the textures while preserving the information of sharp edges. In image processing, this kind of filter is used as a fundamental process of many applications. In this paper, we propose a new approach for edge-preserving smoothing filter. Our method uses 2D local filter to smooth images and we apply indicator function to restrict the range of filtered pixels for edge-preserving. To define the indicator function, we recalculate the distance between each pixel by using edge information. The nearby pixels in the new domain are used for smoothing. Since our method constrains the pixels used for filtering, its running time is quite fast. We demonstrate the usefulness of our new edge-preserving smoothing method for some applications.

Conventional TLB (Translation Lookaside Buffer) coalescing schemes do not fully exploit the contiguity that a memory allocator provides. The conventional schemes accordingly have certain performance overheads due to page table walks. To address this issue, we propose an efficient scheme, called block contiguity translation (BCT), that accommodates the block size information in a page table considering the Buddy algorithm. By fully exploiting the block-level contiguity, we can reduce the page table walks as certain physical memory is allocated in the contiguous way. Additionally, we present unified per-level page sizes to simplify the design and better utilize the contiguity information. Considering the state-of-the-art schemes as references, the comparative analysis and the performance simulations are conducted. Experiments indicate that the proposed scheme can improve the memory system performance with moderate hardware overheads.

This paper proposes a deep neural network (DNN) based framework to address the problem of vector quantization (VQ) for high-dimensional data. The main challenge of applying DNN to VQ is how to reduce the binary coding error of the auto-encoder when the distribution of the coding units is far from binary. To address this problem, three fine-tuning methods have been adopted: 1) adding Gaussian noise to the input of the coding layer, 2) forcing the output of the coding layer to be binary, 3) adding a non-binary penalty term to the loss function. These fine-tuning methods have been extensively evaluated on quantizing speech magnitude spectra. The results demonstrated that each of the methods is useful for improving the coding performance. When implemented for quantizing 968-dimensional speech spectra using only 18-bit, the DNN-based VQ framework achieved an averaged PESQ of about 2.09, which is far beyond the capability of conventional VQ methods.

Sound event detection is intended to identify the sound events in audio recordings, which has widespread applications in real life. Recently, convolutional recurrent neural network (CRNN) models have achieved state-of-the-art performance in this task due to their capabilities in learning the representative features. However, the CRNN models are of high complexities with millions of parameters to be trained, which limits their usage for the mobile and embedded devices with limited computation resource. Model distillation is effective to distill the knowledge of a complex model to a smaller one, which can be deployed on the devices with limited computational power. In this letter, we propose a novel multi model-based distillation approach for sound event detection by making use of the knowledge from models of multiple teachers which are complementary in detecting sound events. Extensive experimental results demonstrated that our approach achieves a compression ratio about 50 times. In addition, better performance is obtained for the sound event detection task.

To the best of our knowledge, there are a few researches on air-handwriting character-level writer identification only employing acceleration and angular velocity data. In this paper, we propose a deep learning approach to writer identification only using inertial sensor data of air-handwriting. In particular, we separate different representations of degree of freedom (DoF) of air-handwriting to extract local dependency and interrelationship in different CNNs separately. Experiments on a public dataset achieve an average good performance without any extra hand-designed feature extractions.

This letter presents a non-orthogonal multiple access (NOMA) technique for a two-cell multiple-input multiple-output (MIMO) system that exploits the alignments of inter-cell interference channels and signal channels within a cluster in a cell. The proposed technique finds combiner vectors for users that align the inter-cell interference channels and the signal channels simultaneously. This technique utilizes the aligned interference and signal channels to obtain precoder matrices for base stations through which each data stream modulated by NOMA can be transmitted to the intended cluster without interference. In addition, we derive the sufficient condition for transmit and receive antenna configurations in the MIMO NOMA systems to eliminate inter-cell interference and inter-cluster interference simultaneously. Because the proposed technique effectively suppresses the inter-cell interference, it achieves a higher degree of freedom than the existing techniques relying on an avoidance of inter-cell interference, thereby obtaining a better sum rate performance in high SNR regions. Furthermore, we present the hybrid MIMO NOMA technique, which combines the MIMO NOMA technique exploiting channel alignment with the existing techniques boosting the received signal powers. Using the benefits from these techniques, the proposed hybrid technique achieves a good performance within all SNR regions. The simulation results successfully demonstrate the effectiveness of the proposed techniques on the sum rate performance.

In this paper, a new transceiver system for the in-vehicle communication system is proposed to enhance data transmission rate and timing accuracy in TDM-based application. The proposed system utilizes point-to-point (P2P) channel, a closed-loop clock forwarding path, and a transceiver with a repeater and clock delay adjuster. The proposed system with 4 ECU (Electronic Computing Unit) nodes is implemented in 180nm CMOS technology and, when compared with conventional bus-based system, achieved more than 125 times faster data transmission. The maximum data rate was 2.5Gbps at 1.8V power supply and the worst peak-to-peak jitter for the data and clock signals over 5000 data symbols were about 49.6ps and 9.8ps respectively.

Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.

For cloud data center, Virtual Machine (VM) consolidation is an effective way to save energy and improve efficiency. However, inappropriate consolidation of VMs, especially aggressive consolidation, can lead to performance problems, and even more serious Service Level Agreement (SLA) violations. Therefore, it is very important to solve the tradeoff between reduction in energy use and reduction of SLA violation level. In this paper, we propose two Host State Detection algorithms and an improved VM placement algorithm based on our proposed Host State Binary Decision Tree Prediction model for SLA-aware and energy-efficient consolidation of VMs in cloud data centers. We propose two formulas of conditions for host state estimate, and our model uses them to build a Binary Decision Tree manually for host state detection. We extend Cloudsim simulator to evaluate our algorithms by using PlanetLab workload and random workload. The experimental results show that our proposed model can significantly reduce SLA violation rates while keeping energy cost efficient, it can reduce the metric of SLAV by at most 98.12% and the metric of Energy by at most 33.96% for real world workload.

In the beam-type microwave power transmission system, it is required to minimize the interference with communication and the influence on the human body. Retrodirective system that re-radiates a beam in the direction of arrival of a signal is well known as a beam control technique for accurate microwave power transmission. In this paper, we newly propose to apply the retrodirective system to both transmitting and receiving antennas. The leakage to the outside of the system is expected to minimize self-convergently while following the atmospheric fluctuation and the antenna movement by repeating the retrodirective between the transmitting and receiving antenna in this system. We considered this phenomenon theoretically using an infinite array antenna model. Finally, it has been shown by the equivalent circuit simulation that stable transmission can be realized by oscillating the system.