Breast cancer is a serious disease across the world, and it is one of the largest causes of cancer death for women. The traditional diagnosis is not only time consuming but also easily affected. Hence, artificial intelligence (AI), especially neural networks, has been widely used to assist to detect cancer. However, in recent years, the computational ability of a neuron has attracted more and more attention. The main computational capacity of a neuron is located in the dendrites. In this paper, a novel neuron model with dendritic nonlinearity (NMDN) is proposed to classify breast cancer in the Wisconsin Breast Cancer Database (WBCD). In NMDN, the dendrites possess nonlinearity when realizing the excitatory synapses, inhibitory synapses, constant-1 synapses and constant-0 synapses instead of being simply weighted. Furthermore, the nonlinear interaction among the synapses on a dendrite is defined as a product of the synaptic inputs. The soma adds all of the products of the branches to produce an output. A back-propagation-based learning algorithm is introduced to train the NMDN. The performance of the NMDN is compared with classic back propagation neural networks (BPNNs). Simulation results indicate that NMDN possesses superior capability in terms of the accuracy, convergence rate, stability and area under the ROC curve (AUC). Moreover, regarding ROC, for continuum values, the existing 0-connections branches after evolving can be eliminated from the dendrite morphology to release computational load, but with no influence on the performance of classification. The results disclose that the computational ability of the neuron has been undervalued, and the proposed NMDN can be an interesting choice for medical researchers in further research.

Current mobile communication terminals are equipped with multiple RF circuits that cover all frequency bands assigned for the communication. In order to make efficient use of frequency spectrum and to reduce circuits in a terminal, a low-loss reconfigurable RF filter is necessary to flexibly change RF frequencies. In this paper, a new reconfigurable bandpass filter (BPF) having eight-frequency (three-bit) selection capability is proposed. It employs branch-line switched type variable resonators that provide low insertion loss. One of the design issues is how to control pass bandwidths among selectable frequencies. In order to analyze the bandwidth variation of the reconfigurable BPF, we calculate the changes of external Q and coupling coefficients. It is shown that the inductive coupling design can achieve less variation of bandwidth for the reconfigurable BPF, compared with commonly used capacitive coupling design. A prototype BPF on a printed circuit board with high dielectric constant substrate has been fabricated and evaluated in 2 GHz bands. It presents performance very close to the design results with respect to insertion loss, center frequency and passband bandwidth. Low insertion loss of less than 1 dB is achieved among the eight frequencies.

This paper shows that the induced peak voltage on the short monopole antenna by the EM field radiated from a small gap discharge when the gap width was experimentally changed from 10 to 360µm was not directly proportional to the discharge voltage between the gap. It was found that the 10mm short monopole antenna induced peak voltage had a peak value between 40 and 60µm gap width.

This paper presents a new way to classify different radiation sources by the parameter of directivity, which is a characteristic parameter of electromagnetic radiation sources. The parameter can be determined from measurements of the electric field intensity radiating in all directions in space. We develop three basic antenna models, which are for 3GHz operation, and set 125,000 groups of cube receiving arrays along the main lobe of their radiation patterns to receive the data of far field electric intensity in groups. Then the Back Propagation (BP) neural network and the Support Vector Machine (SVM) method are adopted to analyze training data set, and build and test the classification model. Owing to the powerful nonlinear simulation ability, the SVM method offers higher classification accuracy than the BP neural network in noise environment. At last, the classification model is comprehensively evaluated in three aspects, which are capability of noise immunity, F1 measure and the normalization method.

String-topology multi-hop network is often selected as an analysis object because it is one of the fundamental network topologies. The purpose of this paper is to establish expression for end-to-end delay for IEEE 802.11 string-topology multi-hop networks. For obtaining the analytical expression, the effects of frame collisions and carrier-sensing effect from other nodes under the non-saturated condition are obtained for each node in the network. For expressing the properties in non-saturated condition, a new parameter, which is frame-existence probability, is defined. The end-to-end delay of a string-topology multi-hop network can be derived as the sum of the transmission delays in the network flow. The analytical predictions agree with simulation results well, which show validity of the obtained analytical expressions.

This paper reports on the trending literature of occlusion handling in the task of online visual tracking. The discussion first explores visual tracking realm and pinpoints the necessity of dedicated attention to the occlusion problem. The findings suggest that although occlusion detection facilitated tracking impressively, it has been largely ignored. The literature further showed that the mainstream of the research is gathered around human tracking and crowd analysis. This is followed by a novel taxonomy of types of occlusion and challenges arising from it, during and after the emergence of an occlusion. The discussion then focuses on an investigation of the approaches to handle the occlusion in the frame-by-frame basis. Literature analysis reveals that researchers examined every aspect of a tracker design that is hypothesized as beneficial in the robust tracking under occlusion. State-of-the-art solutions identified in the literature involved various camera settings, simplifying assumptions, appearance and motion models, target state representations and observation models. The identified clusters are then analyzed and discussed, and their merits and demerits are explained. Finally, areas of potential for future research are presented.

Multiple-way (N-way) asynchronous arbitration is an important issue in asynchronous system design. In this paper, novel implementation methods of N-way asynchronous arbiters are presented. We first present N-way rectangle mesh arbiters using 2-way mutual exclusion elements. Then, N-way token-ring arbiters based on the non-return-to-zero signaling is also presented. The former can issue grant signals with the same percentage for all the arriving request signals while the latency is proportional to the number of inputs. The latter can achieve low latency and low energy arbitration for a heavy workload environment and a large number of inputs. In this paper, we compare their performances using the 28nm FD-SOI process technologies qualitatively and quantitatively.

This paper presents a novel CMOS bias topology serving as not only a bias circuit but also an adaptive linearizer for SiGe HBT power amplifier (PA) IC. The novel bias circuit can well keep the base-to-emitter voltage (Vbe) of RF amplifying HBT constant and adaptively increase the base current (Ib) with the increase of the input power. Therefore, the gain compression and phase distortion performance of the PA is improved. A three-stage 5-GHz band PA IC with the novel bias circuit for WLAN applications is designed and fabricated in IBM 0.35µm SiGe BiCMOS technology. Under 54Mbps OFDM signal at 5.4GHz, the PA IC exhibits a measured small-signal gain of 29dB, an EVM of 0.9% at 17dBm output power and a DC current consumption of 284mA.

Novel multi-band mixers that can receive multiple band signals concurrently are proposed and evaluated. The mixers achieve independent gain control through novel relative power control method of the multiple local oscillator (LO) signals. Linear control is also achieved through multiple LO signal input with total LO power control. Theoretical analysis shows that odd-order nonlinearity components of the multiple LO signals support linear conversion gain control. Dual- and triple-band tests are conducted using typical three MOSFET mixers fabricated by a 0.25 µm SiGe BiCMOS process. Measurements confirm over 40 dB independent control of conversion gain, linear control achieved through LO input power control. The proposed mixers have high input linearity with a 5 dBm output third intercept point. A method is also proposed to reduce interference caused by mixing between multiple LO signals.

This paper provides a method based on electromagnetic (EM) analysis to predict conducted currents in the bulk current injection (BCI) test system for automotive components. The BCI test system is comprised of an injection probe, equipment under test (EUT), line impedance stabilization networks (LISNs), wires and an electric load. All components are modeled in full-wave EM analysis. The EM model of the injection probe enables us to handle multi wires. By using the transmission line theory, the BCI setup model is divided into several parts in order to reduce the calculation time. The proposed method is applied to an actual BCI setup of an automotive component and the simulated common mode currents at the input terminals of EUT have a good accuracy in the frequency range of 1-400MHz. The model separation reduces the calculation time to only several hours.

In deep-submicron era, interconnection delays are not negligible even in high-level synthesis and regular-distributed-register architectures (RDR architectures) have been proposed to cope with this problem. In this paper, we propose a high-level synthesis algorithm using operation chainings which reduces the overall latency targeting RDR architectures. Our algorithm consists of three steps: The first step enumerates candidate operations for chaining. The second step introduces maximal chaining distance (MCD), which gives the maximal allowable inter-island distance on RDR architecture between chaining candidate operations. The last step performs list-scheduling and binding simultaneously based on the results of the two preceding steps. Our algorithm enumerates feasible chaining candidates and selects the best ones for RDR architecture. Experimental results show that our proposed algorithm reduces the latency by up to 40.0% compared to the original approach, and by up to 25.0% compared to a conventional approach. Our algorithm also reduces the number of registers and the number of multiplexers compared to the conventional approaches in some cases.

Recently, high-level synthesis (HLS) techniques for FPGA designs are required in various applications such as computerized stock tradings and reconfigurable network processings. In HLS for FPGA designs, we need to consider module floorplan and reduce multiplexer's cost concurrently. In this paper, we propose a floorplan-driven HLS algorithm for multiplexer reduction targeting FPGA designs. By utilizing distributed-register architectures called HDR, we can easily consider module floorplan in HLS. In order to reduce multiplexer's cost, we propose two novel binding methods called datapath-oriented scheduling/FU binding and datapath-oriented register binding. Experimental results demonstrate that our algorithm can realize FPGA designs which reduce the number of slices by up to 47% and latency by up to 22% compared with conventional approaches while the number of required control steps is almost the same.

Motion estimation (ME) is a key encoding component of almost all modern video coding standards. ME contributes significantly to video coding efficiency, but, it also consumes the most power of any component in a video encoder. In this paper, an ME processor with 3D stacked memory architecture is proposed to reduce memory and core power consumption. First, a memory die is designed and stacked with ME die. By adding face-to-face (F2F) pads and through-silicon-via (TSV) definitions, 2D electronic design automation (EDA) tools can be extended to support the proposed 3D stacking architecture. Moreover, a special memory controller is applied to control data transmission and timing between the memory die and the ME processor die. Finally, a 3D physical design is completed for the entire system. This design includes TSV/F2F placement, floor plan optimization, and power network generation. Compared to 2D technology, the number of input/output (IO) pins is reduced by 77%. After optimizing the floor plan of the processor die and memory die, the routing wire lengths are reduced by 13.4% and 50%, respectively. The stacking static random access memory contributes the most power reduction in this work. The simulation results show that the design can support real-time 720p @ 60fps encoding at 8MHz using less than 65mW in power, which is much better compared to the state-of-the-art ME processor.

In this paper, we propose Affine Combination Lattice Algorithm (ACLA) as a new lattice-based adaptive notch filtering algorithm. The ACLA makes use of the affine combination of Regalia's Simplified Lattice Algorithm (SLA) and Lattice Gradient Algorithm (LGA). It is proved that the ACLA has faster convergence speed than the conventional lattice-based algorithms. We conduct this proof by means of theoretical analysis of the mean update term. Specifically, we show that the mean update term of the ACLA is always larger than that of the conventional algorithms. Simulation examples demonstrate the validity of this analytical result and the utility of the ACLA. In addition, we also derive the step-size bound for the ACLA. Furthermore, we show that this step-size bound is characterized by the gradient of the mean update term.

Delayed correlation has been used to detect orthogonal frequency division multiplexing symbols with cyclic prefix in spectrum sensing. Because of the frequency offset, the outputs of the delayed correlation do not lie only on the real axis of a complex plane. Therefore, the absolute value of the outputs of the delayed correlation is employed. Furthermore, with the use of a filter bank, the number of the outputs of the delayed correlators increases and the averaging over the outputs decreases the noise variance. This paper proposes a new delayed correlation scheme that uses a filter bank and employs the absolute of the outputs of delayed correlation. The proposed scheme improves the probability of detection as the number of the branches of the delayed correlators increases. In the case of 6 branches, the proposed scheme reduces the required sample energy by 1dB the probability of detection of 0.9.

Microwave imaging techniques, particularly for synthetic aperture radar (SAR), produce high-resolution terrain surface images regardless of the weather conditions. Focusing on a feature of complex SAR images, coherent change detection (CCD) approaches have been developed in recent decades that can detect invisible changes in the same regions by applying phase interferometry to pairs of complex SAR images. On the other hand, various techniques of polarimetric SAR (PolSAR) image analysis have been developed, since fully polarimetric data often include valuable information that cannot be obtained from single polarimetric observations. According to this background, various coherent change detection methods based on fully polarimetric data have been proposed. However, the detection accuracies of these methods often degrade in low signal-to-noise ratio (SNR) situations due to the lower signal levels of cross-polarized components compared with those of co-polarized ones. To overcome the problem mentioned above, this paper proposes a novel CCD method by introducing the Pauli decomposition and the weighting of component with their respective SNR. The experimental data obtained in anechoic chamber show that the proposed method significantly enhances the performance of the receiver operation characteristic (ROC) compared with that obtained by a conventional approach.

This work addresses two key topics in the field of energy harvesting and wireless power transfer. The first is the optimum signal design for improved RF-DC conversion efficiency in rectifier circuits by using time varying envelope signals. The second is the design of rectifiers that present reduced sensitivity to input power and output load variations by introducing resistance compression network (RCN) structures.

A cascaded video denoising method based on frame averaging is proposed in this paper. A novel segmentation approach using intensity and structure tensor is used for change compensation, which can effectively suppress noise while preserving the structure of an image. The cascaded framework solves the problem of noise residual caused by single-frame averaging. The classical Wiener filter is used for spatial denoising in changing areas. Our algorithm works in real-time on an FPGA, since it does not involve future frames. Experiments on standard grayscale videos for various noise levels demonstrate that the proposed method is competitive with current state-of-the-art video denoising algorithms on both peak signal-to-noise ratio and structural similarity evaluations, particularly when dealing with large-scale noise.

Using traditional single-layer dictionary learning methods, it is difficult to reveal the complex structures hidden in the hyperspectral images. Motivated by deep learning technique, a deep dictionary learning approach is proposed for hyperspectral image denoising, which consists of hierarchical dictionary learning, feature denoising and fine-tuning. Hierarchical dictionary learning is helpful for uncovering the hidden factors in the spectral dimension, and fine-tuning is beneficial for preserving the spectral structure. Experiments demonstrate the effectiveness of the proposed approach.

In this paper the amplitude probability distribution (APD) measurement method is applied to evaluate noise coupling to an antenna on an evaluation board that uses mixed RF and digital signals of an IC. We analytically investigate noise coupling path to the antenna where the correlation coefficient matches the APD curve of the evaluation board. Moreover, in order to verify the analysis results, the noise coupling path in the board is evaluated by measurements involving In-phase/Quadrature (I/Q) signals as well as electromagnetic simulations. As a result, we demonstrate that APD method is effective in evaluating a degree of noise coupling from an IC to multiple antennas on the board, and confirm that the intensity of noise coupling to each antenna is affected greatly by the board layout patterns.