This paper presents an interference suppression and signal restoration technique that can create the clean signals required by automotive frequency-modulated continuous wave radar systems. When a radar signal from another radar system interferes with own transmitted radar signal, the target detection performance is degraded. This is because the beat frequency corresponding to the target cannot be estimated owing to the increase in the noise floor. In this case, advanced weighted-envelope normalization or wavelet denoising can be used to mitigate the effect of the interference; however, these methods can also lead to the loss of the desired signal containing the range and velocity information of the target. Therefore, we propose a method based on an autoregressive model to restore a signal damaged by mutual interference. The method uses signals that are not influenced by the interference to restore the signal. In experiments conducted using two different automotive radar systems, our proposed method is demonstrated to effectively suppress the interference and restore the desired signal. As a result, the noise floor resulting from the mutual interference was lowered and the beat frequency corresponding to the desired target was accurately estimated.

We investigated the low temperature formation of Pd2Si on Si(100) with TiN encapsulating layer formed at 500°C/1 min. Furthermore, the dopant segregation process was performed with ion dose of 1x1015 cm-2 for B+. The uniform Pd2Si was successfully formed with low sheet resistance of 10.4 Ω/sq. Meanwhile, the PtSi formed on Si(100) showed rough surface morphology if the silicidation temperature was 500°C. The estimated Schottky barrier height to hole of 0.20 eV (qφBp) was realized for n-Si(100).

The electric properties of a field-effect transistor not only depend on gate surface sidewall but also on channel orientation when applying channel stain engineering. The change of the gate surface and channel orientation through the rotated FinFETs provides the capability to compare the orientation dependence of performance and reliability. This study characterized the <100> and <110> channels of FinFETs on the same wafer under tensile and compressive stresses by cutting the wafer into rectangular silicon pieces and evaluated their piezoresistance coefficients. The piezoresistance coefficients of the <100> and <110> silicon under tensile and compressive stresses were first evaluated based on the current setup. Tensile stresses enhance the mobilities of both <100> and <110> channels, whereas compressive stresses degrade them. Electrical characterization revealed that the threshold voltage variation and drive current degradation of the {100} surface were significantly higher than those of {110} for positive bias temperature instability and hot carrier injection with equal gate and drain voltage (VG=VD). By contrast, insignificant difference is noted for the subthreshold slope degradation. These findings imply that a higher ratio of bulk defect trapping is generated by gate voltage on the <100> surface than that on the <110> surface.

A high-rate coding scheme that polar codes are concatenated with low density generator matrix (LDGM) codes is proposed in this paper. The scheme, referred to as polar-LDGM (PLG) codes, can boost the convergence speed of polar codes and eliminate the error floor behavior of LDGM codes significantly, while retaining the low encoding and decoding complexity. With a sensibly designed Gaussian approximation (GA), we can accurately predict the theoretical performance of PLG codes. The numerical results show that PLG codes have the potential to approach the capacity limit and avoid error floors effectively. Moreover, the encoding complexity is lower than the existing LDPC coded system. This motives the application of powerful PLG codes to satellite communications in which message transmission must be extremely reliable. Therefore, an adaptive relaying protocol (ARP) based on PLG codes for the relay satellite system is proposed. In ARP, the relay transmission is selectively switched to match the channel conditions, which are determined by an error detector. If no errors are detected, the relay satellite in cooperation with the source satellite only needs to forward a portion of the decoded message to the destination satellite. It is proved that the proposed scheme can remarkably improve the error probability performance. Simulation results illustrate the advantages of the proposed scheme

We have investigated post-metallization annealing (PMA) utilizing TiN gate electrode on the thin ferroelectric undoped HfO2 directly deposited on p-Si(100) by RF magnetron sputtering. By post-deposition annealing (PDA) process at 600°C/30 s in N2, the memory window (MW) in the C-V characteristics was observed in the Al/HfO2/p-Si(100) diodes with 15 to 24-nm-thick HfO2. However, it was not obtained when the thickness of HfO2 was 10 nm. On the other hand, the MW was observed for Pt/TiN/HfO2 (10 nm)/p-Si(100) diodes utilizing PMA process at 600°C/30 s. The MW was 0.5 V when the bias voltage was applied from -3 to 3 V.

Communication reliability and energy efficiency are important issues that have to be carefully considered in WBAN design. Due to the large path loss variation of the WBAN channel, transmission power control, which adaptively adjusts the radio transmit power to suit the channel condition, is considered in this paper. Human motion is one of the dominant factors that affect the channel characteristics in WBAN. Therefore, this paper introduces motion-aware temporal correlation model-based transmission power control that combines human motion classification and transmission power control to provide an effective approach to realizing reliable and energy-efficient WBAN communication. The human motion classification adopted in this study uses only the received signal strength to identify the human motion; no additional tool is required. The knowledge of human motion is then used to accurately estimate the channel condition and suitably select the transmit power. A performance evaluation shows that the proposed method works well both in the low and high WBAN network loads. Compared to using the fixed Tx power of -5dBm, the proposed method had similar packet loss rate but 20-28 and 27-33 percent lower average energy consumption for the low network traffic and high network traffic cases, respectively.

This report focuses on an optimization scheme of advanced MOSFETs for designing CMOS circuits with high power efficiency. For this purpose the physics-based compact model HiSIM2 is applied so that the relationship between device and circuit characteristics can be investigated properly. It is demonstrated that the short-channel effect, which is usually measured by the threshold-voltage shift relative to long-channel MOSFETs, provides a consistent measure for device-performance degradation with reduced channel length. However, performance degradations of CMOS circuits such as the power loss cannot be predicted by the threshold-voltage shift alone. Here, the subthreshold swing is identified as an additional important measure for power-efficient CMOS circuit design. The increase of the subthreshold swing is verified to become obvious when the threshold-voltage shift is larger than 0.15V.

Rotation symmetric Boolean functions which are invariant under the action of cyclic group have been used in many different cryptosystems. This paper presents a new construction of balanced odd-variable rotation symmetric Boolean functions with optimum algebraic immunity. It is checked that, at least for some small variables, such functions have very good behavior against fast algebraic attacks. Compared with some known rotation symmetric Boolean functions with optimum algebraic immunity, the new construction has really better nonlinearity. Further, the algebraic degree of the constructed functions is also high enough.

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.

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.

Facial micro-expression is momentary and subtle facial reactions, and it is still challenging to automatically recognize facial micro-expression with high accuracy in practical applications. Extracting spatiotemporal features from facial image sequences is essential for facial micro-expression recognition. In this paper, we employed 3D Convolutional Neural Networks (3D-CNNs) for self-learning feature extraction to represent facial micro-expression effectively, since the 3D-CNNs could well extract the spatiotemporal features from facial image sequences. Moreover, transfer learning was utilized to deal with the problem of insufficient samples in the facial micro-expression database. We primarily pre-trained the 3D-CNNs on normal facial expression database Oulu-CASIA by supervised learning, then the pre-trained model was effectively transferred to the target domain, which was the facial micro-expression recognition task. The proposed method was evaluated on two available facial micro-expression datasets, i.e. CASME II and SMIC-HS. We obtained the overall accuracy of 97.6% on CASME II, and 97.4% on SMIC, which were 3.4% and 1.6% higher than the 3D-CNNs model without transfer learning, respectively. And the experimental results demonstrated that our method achieved superior performance compared to state-of-the-art methods.

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.

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.

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.

With the continuous refinement of Deep Neural Networks (DNNs), a series of deep and complex networks such as Residual Networks (ResNets) show impressive prediction accuracy in image classification tasks. Unfortunately, the structural complexity and computational cost of residual networks make hardware implementation difficult. In this paper, we present the quantized and reconstructed deep neural network (QR-DNN) technique, which first inserts batch normalization (BN) layers in the network during training, and later removes them to facilitate efficient hardware implementation. Moreover, an accurate and efficient residual network accelerator (RNA) is presented based on QR-DNN with batch-normalization-free structures and weights represented in a logarithmic number system. RNA employs a systolic array architecture to perform shift-and-accumulate operations instead of multiplication operations. QR-DNN is shown to achieve a 1∼2% improvement in accuracy over existing techniques, and RNA over previous best fixed-point accelerators. An FPGA implementation on a Xilinx Zynq XC7Z045 device achieves 804.03 GOPS, 104.15 FPS and 91.41% top-5 accuracy for the ResNet-50 benchmark, and state-of-the-art results are also reported for AlexNet and VGG.

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.

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.

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.

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 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.