This letter presents an efficient hybrid direction of arrival (DOA) estimation scheme for massive uniform linear array. In this scheme, the DOA estimator based on a discrete Fourier transform (DFT) is first applied to acquire coarse initial DOA estimates for single data snapshot. And then, the fine DOA is accurately estimated through using the iterative search estimator within a very small region. It iteratively searches for correct DOA vector by minimizing the objective function using a Taylor series approximation of the DOA vector with the one initially estimated. Since the proposed scheme does not need to perform eigen-decomposition and spectrum search while maintaining better DOA estimates, it also has low complexity and real-time capability. Simulation results are presented to demonstrate the efficiency of the proposed scheme.

The central limit theorem (CLT) claims that the standardized sum of a random sequence converges in distribution to a normal random variable as the length tends to infinity. We prove the existence of a family of counterexamples to the CLT for d-tuplewise independent sequences of length n for all d=2,...,n-1. The proof is based on [n, k, d+1] binary linear codes. Our result implies that d-tuplewise independence is too weak to justify the CLT, even if the size d grows linearly in length n.

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.

This letter presents a comprehensive analytical framework for average pairwise error probability (PEP) of decode-and-forward cooperative network based on various distributed space-time block codes (DSTBCs) with antenna switching (DDF-AS) technique over quasi-static Rayleigh fading channels. Utilizing the analytical framework, exact and asymptotic PEP expressions can be effectively formulated, which are based on the Lauricella multiplicative hypergeometric function, when various DSTBCs are adopted for the DDF-AS system. The derived asymptotic PEP formulas and some numerical results enable us to verify that the DDF-AS scheme outperforms the conventional cooperative schemes in terms of error rate performance. Furthermore, the asymptotic PEP formulas can also provide explicit and useful insights into the full diversity transmission achieved by the DDF-AS system.

Wireless sensor and actuator networks (WSANs) are expected to become key technologies supporting machine-to-machine (M2M) communication in the Internet of things (IoT) era. However, sensors must be able to provide high demand response (DR) levels despite severely limited battery power. Therefore, as part of efforts to achieve a high DR, we are working on research and development related to radio-on-demand sensor and actuator networks (ROD-SANs). ROD-SAN nodes are equipped with wake-up receivers that allow all nodes to stay in sleep mode for a long period of time, and transmit only after the receiver receives a wake-up signal. In addition, sender nodes can direct the receiver nodes to switch communication channels because the wake-up signal also includes information on the channel to use for communication between each other. However, as the number of nodes utilizing the same channel increases, frequent packet collisions occur, thereby degrading response performance. To reduce packet collisions, we propose an own-channel-utilization based channel switching (OCS) scheme, which is a modification of the average-channel-utilization based switching (ACS) as our previous works. The OCS scheme decides whether or not to switch channels based on a probability value that considers not only average-channel utilization of nearby nodes but also own-channel utilization. This approach permits node switching to other channels by considering the overall utilization states of all channels. In this paper, based on simulations, we show that our scheme can improve the delivery ratio by approximately 15% rather than ACS scheme.

We propose an object detector using a sliding window method for an event-driven camera which outputs a subtracted frame (usually a binary value) when changes are detected in captured images. Since sliding window skips unchanged portions of the output, the number of target object area candidates decreases dramatically, which means that our system operates faster and with lower power consumption than a system using a straightforward sliding window approach. Since the event-driven camera output consists of binary precision frames, an all binarized convolutional neural network (ABCNN) can be available, which means that it allows all convolutional layers to share the same binarized convolutional circuit, thereby reducing the area requirement. We implemented our proposed method on the Xilinx Inc. Zedboard and then evaluated it using the PETS 2009 dataset. The results showed that our system outperformed BCNN system from the viewpoint of detection performance, hardware requirement, and computation time. Also, we showed that FPGA is an ideal method for our system than mobile GPU. From these results, our proposed system is more suitable for the embedded systems based on stationary cameras (such as security cameras).

ECN, as a decisive approach for TCP congestion control, has been proposed for many years. However, its deployment on the Internet is much slower than expected. In this paper, we investigate the state of the deployment of ECN (Explicit Congestion Notification) on the Internet from a different viewpoint. We use the data set of web domains published by Alexa as the hosts to be tested. We negotiate an ECN-Capable and a Not ECN-Capable connections with each host and collect all packets belonging to the connections. By analyzing the header fields of the TCP/IP packets, we dig out the deployment rate, connectivity, variation of round-trip time and time to live between the Not ECN-Capable and ECN-Capable connections as well as the rate of IPv6-Capable web servers. Especially, it is clear that the connectivity is different from the domains (regions on the Internet). We hope that the findings acquired from this study would incentivize ISPs and administrators to enable ECN in their network systems.

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.

Real-time monitoring of heart rate (HR) and body temperature (BT) is crucial for the prognosis and the diagnosis of cardiovascular disease and healthcare. Since current monitoring systems are too rigid and bulky, it is not easy to attach them to the human body. Also, their large current consumption limits the working time. In this paper, we develop a wireless sensor patch for HR and BT by integrating sensor chip, wireless communication chip, and electrodes on the flexible boards that is covered with non-toxic, but skin-friendly adhesive patch. Our experimental results reveal that the flexible wireless sensor patch can efficiently detect early diseases by monitoring the HR and BT in real time.

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 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 order to solve the problem in Automatic Identification System (AIS) that the signal in the target slot cannot be correctly received due to partial overlap of signals in adjacent time slots, the paper introduces a new criterion: maximum expected signal power (MESP) and proposes a novel beamforming algorithm based on generalized singular value decomposition (GSVD) and orthogonal projection. The algorithm employs GSVD to estimate the signal subspace, and adopts orthogonal projection to project the received signal onto the orthogonal subspace of the non-target signal. Then, beamforming technique is used to maximize the output power of the target signal on the basis of MESP. Theoretical analysis and simulation results show the effectiveness of the proposed algorithm.

This paper presents an ultra-low-power class-AB bulk-driven operational transconductance amplifier operating in the subthreshold region. Employing the partial positive feedback in current mirrors, the effective transconductance and output voltage swing are enhanced considerably without additional power consumption and layout area. Both traditional and proposed OTAs are designed and simulated for a 180 nm CMOS process. They dissipate an ultra low power of 192 nW. The proposed OTA features not only a DC gain enhancement of 14 dB but also a slew rate improvement of 200%. In addition, the improved gain leads to a 5.3 times wider unity-gain bandwidth than that of the traditional OTA.

Accounting for the exponential increase in security threats, the development of new defense strategies for pervasive environments is acquiring an ever-growing importance. The expected avalanche of heterogeneous IoT devices which will populate our industrial factories and smart houses will increase the complexity of managing security requirements in a comprehensive way. To this aim, cloud-based security services are gaining notable impetus to provide security mechanisms according to Security-as-a-Service (SECaaS) model. However, the deployment of security applications in remote cloud data-centers can introduce several drawbacks in terms of traffic overhead and latency increase. To cope with this, Edge Computing can provide remarkable advantages avoiding long routing detours. On the other hand, the limited capabilities of edge node introduce potential constraints in the overall management. This paper focuses on the provisioning of virtualized security services in resource-constrained edge nodes by leveraging lightweight virtualization technologies. Our analysis aims at shedding light on the feasibility of container-based security solutions, thus providing useful guidelines towards the orchestration of security at the edge. Our experiments show that the overhead introduced by the containerization is very light.

Sectors in the airspace are units of the air traffic control. For airspace traffic data consists of the location of each aircraft with timestamp, we propose an efficient method to identify the sector where each aircraft lies.

This paper proposes a novel low complexity lattice reduction-aided iterative receiver for overloaded MIMO. Novel noise cancellation is proposed that increases an equivalent channel gain with a scalar gain introduced in this paper, which results in the improvement of the signal to noise power ratio (SNR). We theoretically analyze the performance of the proposed receiver that the lattice reduction raises the SNR of the detector output signals as the scalar gain increases, when the Lenstra-Lenstra-Lova's (LLL) algorithm is applied to implement the lattice reduction. Because the SNR improvement causes the scalar gain to increase, the performance is improved by iterating the reception process. Computer simulations confirm the performance. The proposed receiver attains a gain of about 5dB at the BER of 10-4 in a 6×2 overloaded MIMO channel. Computational complexity of the proposed receiver is about 1/50 as much as that of the maximum likelihood detection (MLD).

In recent years, algebraic attacks and fast algebraic attacks have received a lot of attention in the cryptographic community. There are three Boolean functions achieving optimal algebraic immunity based on primitive element of F2n. The support of Boolean functions in [1]-[3] have the same parameter s, which makes us have a large number of Boolean functions with good properties. However, we prove that the Boolean functions are affine equivalence when s takes different values.

Very few existing works about inertial sensor based air-writing focused on writing constraints' effects on recognition performance. We proposed a LSTM-based system and made several quantitative analyses under different constraints settings against CHMM, DTW-AP and CNN. The proposed system shows its advantages in accuracy, real-time performance and flexibility.