This paper proposes a distributed delay-compensated observer for a wireless sensor network with delay. Each node of the sensor network aggregates data from the other nodes and sends the aggregated data to the neighbor nodes. In this communication, each node also compensates communication delays among the neighbor nodes. Therefore, all of the nodes can synchronize their sensor measurements using scalable and local communication in real-time. All of the nodes estimate the state variables of a system simultaneously. The observer in each node is similar to the delay-compensated observer with multi-sensor delays proposed by Watanabe et al. Convergence rates for the proposed observer can be arbitrarily designed regardless of the communication delays. The effectiveness of the proposed method is verified by a numerical simulation.

In order to realize intelligent robot system, it is required to process large amount of data input from complex and different kinds of sensors in a short time. FPGA is expected to improve process performance of robots due to better performance per power consumption than high performance CPU, but it has lower development productivity than software. In this paper, we discuss automatic generation of FPGA components for robots. A design tool, developed for easy integration of FPGA into robots, is proposed. The tool named cReComp can automatically convert circuit written in Verilog HDL into a software component compliant to a robot software framework ROS (Robot Operation System), which is the standard in robot development. To evaluate its productivity, we conducted a subject experiment. As a result, we confirmed that the automatic generation is effective to ease the development of FPGA components for robots.

Low Power Wide Area Network (LPWAN) is designed for low-bandwidth, low-power, long-distance, large-scale connected IoT applications and realistic for networking in an emergency or restricted situation, so it has been proposed as an attractive communication technology to handle unexpected situations that occur during and/or after a disaster. However, the traditional LPWAN with its default protocol will reduce the communication efficiency in disaster situation because a large number of users will send and receive emergency information result in communication jams and soaring error rates. In this paper, we proposed a LPWAN based decentralized network structure as an extension of our previous Disaster Information Sharing System (DISS). Our network structure is powered by Named Node Networking (3N) which is based on the Information-Centric Networking (ICN). This network structure optimizes the excessive useless packet forwarding and path optimization problems with node name routing (NNR). To verify our proposal, we conduct a field experiment to evaluate the efficiency of packet path forwarding between 3N+LPWA structure and ICN+LPWA structure. Experimental results confirm that the load of the entire data transmission network is significantly reduced after NNR optimized the transmission path.

Inter-channel crosstalk is one of the crucial issues in multichannel optical systems. Conventional studies assume that the crosstalk and the main signals have identical format. The present study, in contrast, considers different signal formats for the main and crosstalk lights, and shows that bit error degradation is different depending on the modulation format. Statistical properties of the crosstalk are also investigated. The result quantitatively confirms that a crosstalk light whose signal distribution is closer to a Gaussian profile causes larger degradation.

Sharing perceptual data (e.g., camera and LiDAR data) with other vehicles enhances the traffic safety of autonomous vehicles because it helps vehicles locate other vehicles and pedestrians in their blind spots. Such safety applications require high throughput and short delay, which cannot be achieved by conventional microwave vehicular communication systems. Therefore, millimeter-wave (mmWave) communications are considered to be a key technology for sharing perceptual data because of their wide bandwidth. One of the challenges of data sharing in mmWave communications is broadcasting because narrow-beam directional antennas are used to obtain high gain. Because many vehicles should share their perceptual data to others within a short time frame in order to enlarge the areas that can be perceived based on shared perceptual data, an efficient scheduling for concurrent transmission that improves spatial reuse is required for perceptual data sharing. This paper proposes a data sharing algorithm that employs a graph-based concurrent transmission scheduling. The proposed algorithm realizes concurrent transmission to improve spatial reuse by designing a rule that is utilized to determine if the two pairs of transmitters and receivers interfere with each other by considering the radio propagation characteristics of narrow-beam antennas. A prioritization method that considers the geographical information in perceptual data is also designed to enlarge perceivable areas in situations where data sharing time is limited and not all data can be shared. Simulation results demonstrate that the proposed algorithm doubles the area of the cooperatively perceivable region compared with a conventional algorithm that does not consider mmWave communications because the proposed algorithm achieves high-throughput transmission by improving spatial reuse. The prioritization also enlarges the perceivable region by a maximum of 20%.

This paper proposes an enhanced affinity graph (EA-graph) for image segmentation. Firstly, the original image is over-segmented to obtain several sets of superpixels with different scales, and the color and texture features of the superpixels are extracted. Then, the similarity relationship between neighborhood superpixels is used to construct the local affinity graph. Meanwhile, the global affinity graph is obtained by sparse reconstruction among all superpixels. The local affinity graph and global affinity graph are superimposed to obtain an enhanced affinity graph for eliminating the influences of noise and isolated regions in the image. Finally, a bipartite graph is introduced to express the affiliation between pixels and superpixels, and segmentation is performed using a spectral clustering algorithm. Experimental results on the Berkeley segmentation database demonstrate that our method achieves significantly better performance compared to state-of-the-art algorithms.

A low complexity channel estimation scheme using data-dependent superimposed training (DDST) is proposed in this paper, where the pilots are inserted in more than one block, rather than the single block of the original DDST. Comparing with the original DDST (which improves the performance of channel estimation at the cost of huge computational overheads), the proposed DDST scheme improves the performance of channel estimation with only a slight increase in the consumption of computation resources. The optimal precoder is designed to minimize the data distortion caused by the rank-deficient precoding. The optimal pilots and placement are also provided to improve the performance of channel estimation. In addition, the impact of power allocation between the data and pilots on symbol detection is analyzed, the optimal power allocation scheme is derived to maximize the effective signal-to-noise ratio at the receiver. Simulation results are presented to show the computational advantage of the proposed scheme, and the advantages of the optimal pilots and power allocation scheme.

Saliency quality assessment aims at estimating the objective quality of a saliency map without access to the ground-truth. Existing works typically evaluate saliency quality by utilizing information from saliency maps to assess its compactness and closedness while ignoring the information from image content which can be used to assess the consistence and completeness of foreground. In this letter, we propose a novel multi-information fusion network to capture the information from both the saliency map and image content. The key idea is to introduce a siamese module to collect information from foreground and background, aiming to assess the consistence and completeness of foreground and the difference between foreground and background. Experiments demonstrate that by incorporating image content information, the performance of the proposed method is significantly boosted. Furthermore, we validate our method on two applications: saliency detection and segmentation. Our method is utilized to choose optimal saliency map from a set of candidate saliency maps, and the selected saliency map is feeded into an segmentation algorithm to generate a segmentation map. Experimental results verify the effectiveness of our method.

Digital phase-locked loops (DPLLs) have been designed in a number of ways to correctly generate pulse signals in various systems. However, the existing DPLLs have poor acquisition performance or are prone to the divergence phenomenon when modeling and/or round-off errors exist and the noise statistics are incorrect. In this paper, we propose a novel DPLL whose phase estimator is designed in hybrid form that utilizes the advantages of Monte Carlo estimation, which is robust to nonlinear effects such as measurement quantization, and a finite memory estimator, which is robust against incorrect noise information and system model mismatch. The robustness of the proposed hybrid Monte Carlo/finite memory DPLL is demonstrated by comparing its phase estimation performance via a numerical example.

In this paper, we investigate different power allocation optimization problems with interferences for distributed antenna systems (DAS) with and without D2D communication, respectively. The first objective problem is maximizing spectral efficiency (SE) of the DAS with D2D communication under the constraints of the minimum SE requirements of user equipment (UE) and D2D pair, maximum transmit power of each remote access unit (RAU) and maximum transmit power of D2D transmitter. We transform this non-convex objective function into a difference of convex functions (D.C.) then using the concave-convex procedure (CCCP) algorithm to solve the optimization problem. The second objective is maximizing energy efficiency (EE) of the DAS with D2D communication under the same constraints. We first exploit fractional programming theory to obtain the equivalent objective function of the second problem with subtract form, and then transform it into a D.C. problem and use CCCP algorithm to obtain the optimal power allocation. In each part, we summarize the corresponding optimal power allocation algorithms and also use similar method to obtain optimal solutions of the same optimization problems in DAS. Simulation results are provided to demonstrate the effectiveness of the designed power allocation algorithms and illustrate the SE and EE of the DAS by using D2D communication are much better than DAS without D2D communication.

In content oriented networks (CON), routers in a network are generally equipped with local cache storages and store incoming contents temporarily. Efficient utilization of total cache storage in networks is one of the most important technical issues in CON, as it can reduce content server load, content download latency and network traffic. Performance of networked cache is reported to strongly depend on both cache decision and content request routing. In this paper, we evaluate several combinations of these two strategies. Especially for routing, we take up off-path cache routing, Breadcrumbs, as one of the content request routing proposals. Our performance evaluation results show that off-path cache routing, Breadcrumbs, suffers low performance with cache decisions which generally has high performance with shortest path routing (SPR), and obtains excellent performance with TERC (Transparent En-Route Cache) which is well-known to have low performance with widely used SPR. Our detailed evaluation results in two network environments, emerging CONs and conventional IP, show these insights hold in both of these two network environments.

A novel image enhancement method for vein recognition is introduced. Inspired by observation that the intensity of the vein vessel changes rapidly during the smoothing process compared to that of background (i.e., skin tissue) due to its thin and long shape, we propose to exploit the smoothing speed as a restoration weight for the vein image enhancement. Experimental results based on the CASIA multispectral palm vein database demonstrate that the proposed method is effective to improve the performance of vein recognition.

Intrusion detection system (IDS) is a device or software to monitor a network system for malicious activity. In terms of detection results, there could be two types of false, namely, the false positive (FP) which incorrectly detects normal traffic as abnormal, and the false negative (FN) which incorrectly judges malicious traffic as normal. To protect the network system, we expect that FN should be minimized as low as possible. However, since there is a trade-off between FP and FN when IDS detects malicious traffic, it is difficult to reduce the both metrics simultaneously. In this paper, we propose a sequential classifiers combination method to reduce the effect of the trade-off. The single classifier suffers a high FN rate in general, therefore additional classifiers are sequentially combined in order to detect more positives (reduce more FN). Since each classifier can reduce FN and does not generate much FP in our approach, we can achieve a reduction of FN at the final output. In evaluations, we use NSL-KDD dataset, which is an updated version of KDD Cup'99 dataset. WEKA is utilized as a classification tool in experiment, and the results show that the proposed approach can reduce FN while improving the sensitivity and accuracy.

In this paper, we propose a method to construct a scalable sensor data stream delivery system that guarantees the specified delivery quality of service (i.e., total reachability to destinations), even when delivery server resources (nodes) are in a heterogeneous churn situation. A number of P2P-based methods have been proposed for constructing a scalable and efficient sensor data stream system that accommodates different delivery cycles by distributing communication loads of the nodes. However, no existing method can guarantee delivery quality of service when the nodes on the system have a heterogeneous churn rate. As an extension of existing methods, which assign relay nodes based on the distributed hashing of the time-to-deliver, our method specifies the number of replication nodes, based on the churn rate of each node and on the relevant delivery paths. Through simulations, we confirmed that our proposed method can guarantee the required reachability, while avoiding any increase in unnecessary resource assignment costs.

A new fuzzy level set method (FLSM) based on the global search capability of quantum particle swarm optimization (QPSO) is proposed to improve the stability and precision of image segmentation, and reduce the sensitivity of initialization. The new combination of QPSO-FLSM algorithm iteratively optimizes initial contours using the QPSO method and fuzzy c-means clustering, and then utilizes level set method (LSM) to segment images. The new algorithm exploits the global search capability of QPSO to obtain a stable cluster center and a pre-segmentation contour closer to the region of interest during the iteration. In the implementation of the new method in segmenting liver tumors, brain tissues, and lightning images, the fitness function of the objective function of QPSO-FLSM algorithm is optimized by 10% in comparison to the original FLSM algorithm. The achieved initial contours from the QPSO-FLSM algorithm are also more stable than that from the FLSM. The QPSO-FLSM resulted in improved final image segmentation.

In this paper, we propose a method for automatic virtual resource allocation by using a multi-target classification-based scheme (MTCAS). In our method, an Infrastructure Provider (InP) bundles its CPU, memory, storage, and bandwidth resources as Network Elements (NEs) and categorizes them into several types in accordance to their function, capabilities, location, energy consumption, price, etc. MTCAS is used by the InP to optimally allocate a set of NEs to a Virtual Network Operator (VNO). Such NEs will be subject to some constraints, such as the avoidance of resource over-allocation and the satisfaction of multiple Quality of Service (QoS) metrics. In order to achieve a comparable or higher prediction accuracy by using less training time than the available ensemble-based multi-target classification (MTC) algorithms, we propose a majority-voting based ensemble algorithm (MVEN) for MTCAS. We numerically evaluate the performance of MTCAS by using the MVEN and available MTC algorithms with synthetic training datasets. The results indicate that the MVEN algorithm requires 70% less training time but achieves the same accuracy as the related ensemble based MTC algorithms. The results also demonstrate that increasing the amount of training data increases the efficacy ofMTCAS, thus reducing CPU and memory allocation by about 33% and 51%, respectively.

A 1-Transistor 4-Magnetic Tunnel Junction (1T-4MTJ) memory cell has been proposed for field type of Magnetic Random Access Memory (MRAM). Proposed 1T-4MTJ memory cell array is achieved 44% higher density than that of conventional 1T-1MTJ thanks to the common access transistor structure in a 4-bit memory cell. A self-reference sensing scheme which can read out with write-back in four clock cycles has been also proposed. Furthermore, we add to estimate with considering sense amplifier variation and show 1T-4MTJ cell configuration is the best solution in IoT applications. A 1-Mbit MRAM test chip is designed and fabricated successfully using 130-nm CMOS process. By applying 1T-4MTJ high density cell and partially embedded wordline driver peripheral into the cell array, the 1-Mbit macro size is 4.04 mm2 which is 35.7% smaller than the conventional one. Measured data shows that the read access is 55 ns at 1.5 V typical supply voltage and 25C. Combining with conventional high-speed 1T-1MTJ caches and proposed high-density 1T-4MTJ user memories is an effective on-chip hierarchical non-volatile memory solution, being implemented for low-power MCUs and SoCs of IoT applications.

We have proposed and demonstrated a mode selective active-MMI (multimode interferometer) laser diode as a mode selective light source so far. This laser diode features; 1) lasing at a selected space mode, and 2) high modulation bandwidth. Based on these, it is expected to enable high speed interconnection into future personal and mobile devices. In this paper, we explain the mode selection, and the high speed modulation principles. Then, we present our recent results concerning high speed frequency response of the fundamental and first order space modes.

A spectrum-based fault localization technique (SBFL), which identifies fault location(s) in a buggy program by comparing the execution statistics of the program spectra of passed executions and failed executions, is a popular automatic debugging technique. However, the usefulness of SBFL is mainly affected by the following two factors: accuracy and fault understanding in reality. To solve this issue, we propose a SBFL framework to support fault understanding. In the framework, we firstly localize a suspicious fault module to start debugging and then generate a weighted fault propagation graph (WFPG) for the hypothesis fault module, which weights the suspiciousness for the nodes to further perform block-level fault localization. In order to evaluate the proposed framework, we conduct a controlled experiment to compare two different module-level SBFL approaches and validate the effectiveness of WFPG. According to our preliminary experiments, the results are promising.

Many autocorrelation-based frequency estimation algorithms have been proposed. However, some of them cannot construct a strict linear prediction (LP) property among the adjacent autocorrelation lags, which affects the estimators' performance. To improve the precision of frequency estimation, two novel autocorrelation based frequency estimation methods of the real sinusoid signal in additive white Gaussian noise (AWGN) are proposed in this paper. Firstly, a simple method is introduced to transform the real sinusoid signal into the noncircular signal. Secondly, the autocorrelation of the noncircular signal is analyzed and a strict LP property is constructed among the adjacent autocorrelation lags of the noncircular signal. Thirdly, the least squares (LS) and reformed Pisarenko harmonic decomposer (RPHD) frameworks are employed to improve estimation accuracy. The simulation results match well with the theoretical values. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.