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This paper presents a class of reduced footprint inline microstrip bandpass filters capable of covering bandwidth up to 20% as well as good flexibility in establishing various cross-couplings for creating transmission zeros. The resonating elements are quarter-wave stepped-impedance resonators (SIRs), which have a wide upper stopband in nature. To achieve size miniaturization, the low-impedance segment of each SIR is implemented as a thick-trace ring and configured in a spiral form, and the high-impedance section is deformed to accommodate the low-impedance section. An interlaced coupling structure is proposed to enhance the coupling limited by the downsized coupled segments of adjacent resonators. In addition, by properly routing the associated shorted stubs, the structure can easily establish cross-coupling between nonadjacent resonators. Thus, compact SIR filters in an inline arrangement can be achieved to have either narrow or wide passbands with multiple transmission zeros. Two such circuits with sharp transition bands and upper stopband extension are synthesized, fabricated, and tested. The measured results show good agreement with simulated data. The circuit areas together with the performances of the experimental filters are compared with those in existing literature.

We propose a coupled system that can produce nonreciprocal channels of light. The system consists of a metal layer sandwiched between two magnetophotonic crystals (MPCs). A resonance mode called Tamm magnetoplasmon polariton (TMP) around the interfaces between the metal and the MPCs can be achieved in this structure. It is found that nonreciprocal light-tunneling channels can be obtained through the coupling of two nonsymmetric TMPs. The nonreciprocal wavelength channels can be adjusted, depending on the incidence direction of the light, whereas the nonreciprocal direction channels can be adjusted, depending on the wavelength of the incident light. Results are demonstrated through electromagnetic-field simulations based on a finite-element solver.

Objective: Breast cancer is the most common female cancer in the world, and it poses a huge threat to women's health. There is currently promising research concerning its early diagnosis using deep learning methodologies. However, some commonly used Convolutional Neural Network (CNN) and their variations, such as AlexNet, VGGNet, GoogleNet and so on, are prone to overfitting in breast cancer classification, due to both small-scale breast pathology image datasets and overconfident softmax-cross-entropy loss. To alleviate the overfitting issue for better classification accuracy, we propose a novel framework for breast pathology classification, called the AlexNet-BC model. The model is pre-trained using the ImageNet dataset and fine-tuned using an augmented dataset. We also devise an improved cross-entropy loss function to penalize overconfident low-entropy output distributions and make the predictions suitable for uniform distributions. The proposed approach is then validated through a series of comparative experiments on BreaKHis, IDC and UCSB datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods at different magnifications. Its strong robustness and generalization capabilities make it suitable for histopathology clinical computer-aided diagnosis systems. [ABSTRACT FROM AUTHOR]

Network embedding aims to learn the low-dimensional node representations for networks, which has attracted an increasing amount of attention in recent years. Most existing efforts in this field attempt to embed the network based on node similarity, which generally relies on edge existence statistics of the network. Instead of relying on the global edge existence statistics for every node pair, in this article, we utilize the information between a pair of nodes in a local way and propose a model, called node pair information preserving network embedding (NINE), based on adversarial networks. The main idea lies in preserving the node pair information (NI) by means of adversarial networks. The architecture of the proposed NINE model consists of three main components, namely: 1) NI embedder; 2) NI generator; and 3) NI discriminator. In the NI embedder, to avoid the complicated similarity calculation for a pair of nodes, the original NI vector calculated from the direct neighbor information of the two nodes is adopted as features, and the edge existence information is taken as labels to learn the embedded NI vector in a supervised learning manner. The second component is the NI generator, which takes the original node representation vectors of a node pair as input and outputs the generated NI vector. In order to make the generated NI vector follow the same distribution of the corresponding embedded NI vector, the generative adversarial network (GAN) is adopted, resulting in the third component, called the NI discriminator. Extensive experiments are conducted on seven real-world datasets in three downstream tasks, namely: 1) network reconstruction; 2) link prediction; and 3) node classification. Comparison results with seven state-of-the-art models demonstrate the effectiveness, efficiency, and rationality of our model. [ABSTRACT FROM AUTHOR]

Full-wave analysis of finite but electrically large frequency selective surfaces (FSSs) substrates for the radar cross section (RCS) estimation is too time-consuming and unbearable for practical applications. Such FSS substrates can be either absorbing or reflective types and are widely used as radomes of antennas for stealth applications, where the electromagnetic (EM) backscattering is of much concern. This large EM problem is simplified by applying Floquet mode decomposition on the scattering fields, where the Kirchhoff approximation is employed to find the backscattering fields for RCS estimation. The resulted formula allows one to explore the scattering mechanism for RCS enhancement or reduction. Besides, the approximation is used to resemble the FSS radome by equivalent substrates. Thus, the transmission and reflection coefficients of the equivalent dielectric substrates are similar to those from FSS elements under an infinite array assumption. Afterward, the resulting EM problems of antenna radiations and scattering can be easily analyzed using the equivalent substrates of EM materials. Numerical examples of planar FSSs to form radomes demonstrate the feasibility of numerical simulations’ proposed work. The presented work can also be directly applied for fast analysis of EM shielding effectiveness by FSS substrates. [ABSTRACT FROM AUTHOR]

A passive and active wideband reconfigurable power divider (PD)/power combiner (PC) is proposed and realized in 40-nm CMOS for fifth-generation (5G) millimeter-wave (mmW) beamforming systems. Based on the conventional Wilkinson PD/PC, the synthesis two-stage LC ladder is used to replace the quarter-wavelength transmission line to save the die area and achieve a wideband design for the passive PD/PC. Besides, the single-pole-double-throw (SPDT) switch function is added to realize the reconfigurable function. In PD mode, the measured $\vert S_{21}\vert $ is −4.76/−4.53 dB at 28/38 GHz, and the 3-dB bandwidth (BW) is 22.28 GHz. In switch mode, the measured $\vert S_{21}\vert $ is −1.83/−2.10 dB at 28/38 GHz, and the 3-dB BW is 26.93 GHz. Compared with the other reconfigurable PDs/PCs and on-chip PDs, the proposed passive PD/PC demonstrates the widest 3-dB BW and compact area. Besides, we propose an active reconfigurable PD/PC. The bidirectional amplifiers (BDAs), based on modified distributed amplifier (DA) architecture, compensate for the loss of the passive elements. The T-type network inductors, equivalent to magnetically coupled resonator (MCR) circuit, replace the artificial transmission lines to avoid the extra dc dissipation on the termination resistor. By controlling switches, the reconfigurable function and bidirectional mechanism can be implemented. Compared with the other active reconfigurable PDs/PCs, our proposed PD/PC demonstrates a broader BW, lower dc power with a compact die area. [ABSTRACT FROM AUTHOR]

A new gold (Au) nanoparticle (NP)/tungsten trioxide (WO3)-based chemoresistive formaldehyde gas sensor is fabricated and reported. The WO3 thin film and Au NPs are prepared by radio frequency (RF) sputtering and rapid thermal evaporation (RTE) approaches, respectively. The utilized Au NPs successfully elevate the surface area/volume (${S}_{A}/{V}$) ratio and improve corresponding sensing performance. Experimentally, the proposed Au NP/WO3 sensor shows a very high sensing response of 1303.5 and a response (recovery) time of 16 s (16 s) under introduced 20 ppm formaldehyde (HCHO)/air gas at 225 °C. This value is remarkably higher than those of previous reports. In addition, a very low detection content of 40 ppb HCHO/air is obtained, revealing that a widespread detection range of formaldehyde concentration can be acquired for the proposed sensor. To expand the application ability in wireless sensing, a gray algorithm GM(1, 1) model incorporating the first-order differential approach is used to substantially diminish the transmission data, increase the transmission efficiency, and improve the accuracy of the response data. The formaldehyde sensor in this study exhibits advantages including a simple structure, ease of manufacturing, relatively lower cost, practical smart sensing ability, and wireless transmission applications. [ABSTRACT FROM AUTHOR]

Machine-learning (ML) can be employed to enhance the positioning accuracy of visible-light-positioning (VLP) system. To diminish the training time and complexity, the whole area is usually divided into several positioning unit cells. Most literatures only focus on the positioning performance within an unit cell, and assume the unit cell can be repeatedly duplicated to cover the whole area. In this work, we propose and demonstrate a positioning unit cell model duplication scheme, named as spatial sequence adaptation (SSA) process. We also propose and demonstrate a residual concatenation neural network (RCNN) and transfer learning (TL) to refine the model of the target positioning unit cell. A practical test-bed with vertical distance of 2.8 m consisting of two unit cells with dimensions of about 1.55 m × 2 m per cell is constructed. The client side is an autonomous mobile robot (AMR) for acquiring continuous training and testing data. Our experimental results reveal that high precision positioning in the duplicated unit cell duplication can be achieved. [ABSTRACT FROM AUTHOR]

Electrical properties and microstructure of phosphorus (P) implanted p-type Si substrates were evaluated by four-point probe (4PP), Differential Hall Effect Metrology (DHEM), secondary ion mass spectrometry (SIMS) and transmission electron microscopy (TEM) techniques, after RTA 750–950°C and CO2 laser 120–140W annealing. When RTA temperature was increased, defect concentration at a-Si/c-Si interface gradually disappeared. For CO2 laser annealing, defects were found to aggregate at the end of range (EOR) region. Sheet resistance (Rs) values decreased significantly with increasing RTA temperature due to defects disappearing at EOR. Rs did not change appreciably for the CO2 laser annealing case. The dopant activation ratio was less than 50% below RTA temperature of 850 °C, and it increased to 70% at 950 °C. For CO2 laser annealing, the activation ratio could reach to over 80% regardless of the laser power. The highest active concentration and the lowest resistivity or mobility were found to be within the top 20 nm region of the surface. Although RTA annealing conditions influenced dopant diffusion and the resulting electrical property depth profiles significantly, depth profiles did not change much with changes in CO2 laser power level. It was demonstrated that DHEM data in conjunction with SIMS and TEM data can be successfully employed to evaluate, in detail, the electrical properties of ultra-shallow junctions for metal oxide semiconductor field effect transistor (MOSFET) applications. [ABSTRACT FROM AUTHOR]

Optical wireless communication (OWC) has emerged as a complementary or alternative technology to the radio-frequency (RF) communication. OWC based on image sensor, which is also called optical camera communication (OCC) has attracted much attention from industrial and academic societies. Here, we discuss several recent OCC technologies. We propose and demonstrate a light emitting diode (LED) light panel and rolling shutter image sensor based OCC system using frame-averaging background removal (FABR) technique, Z-score normalization, and neural network (NN). Here, a driver circuit for the LED display panel based on a bipolar-junction-transistor (BJT) and a metal-oxide-semiconductor field-effect-transistor (MOSFET) is also discussed. It can provide low enough driving frequency from a few Hz to kHz and high bias current for the LED light panel. Experimental results show that the proposed scheme can mitigate the inter-symbol interference (ISI) observed in the rolling shutter pattern produced by the high noise-ratio (NR) of the display contents. [ABSTRACT FROM AUTHOR]

In this study, p-type Pi-gate (PG) poly-Si nanowire channel junctionless accumulation-mode (JAM) field-effect transistors (FETs) were successfully fabricated and their reliability was investigated. The reliability of these PG JAM FETs was found to be dependent on the effective channel doping concentration (Nch,eff). Through a negative gate bias stress (NGBS) test, we found that degradation in the average subthreshold swing (A.S.S.) and shift in the threshold voltage (VTH) increases as the Nch,eff of the PG JAM FETs decreases. Furthermore, the PG JAM FETs with a lower Nch,eff show the more severe rate of deterioration in the transconductance (Gm) and ON current (ION). By increasing Nch,eff to reduce the electric field (E-field) on the gate oxide and tune the carrier transport mechanism in the poly-Si nanowire channel, a better immunity against the NGBS test in the p-type PG JAM FETs can be achieved under a gate overdrive voltage (VGOD = VG – VTH = −3.5 V) to perform the NGBS test. [ABSTRACT FROM AUTHOR]