Bandwidth Tunable Optical Bandpass Filter Based on Parity-Time Symmetry

A chip-scale tunable optical filter is indispensable to meeting the demand for reconfigurability in wavelength division multiplexing systems, channel routing, and switching, etc. Here, we propose a new scheme of bandwidth tunable band-pass filters based on a parity-time (PT) symmetric coupled microresonator system. Large bandwidth tunability is realized on the basis of the tuning of the relative resonant frequency between coupled rings and by making use of the concept of the exception point (EP) in the PT symmetric systems. Theoretical investigations show that the bandwidth tuning range depends on the intrinsic loss of the microresonators, as well as on the loss contrast between the two cavities. Our proof-of-concept device confirms the tunability and shows a bandwidth tuning range from 21 GHz to 49 GHz, with an extinction ratio larger than 15 dB. The discrepancy between theory and experiment is due to the non-optimized design of the coupling coefficients, as well as to fabrication errors. Our design based on PT symmetry shows a distinct route towards the realization of tunable band-pass filters, providing new ways to explore non-Hermitian light manipulation in conventional integrated devices.

Optimal design of CMOS current mode instrumentation amplifier using bio-inspired method for biomedical applications

Analog integrated circuits for biomedical applications require good performance. This paper presents an instrumentation amplifier (IA) design based on three complementary metal oxide semiconductor (CMOS) conveyors with an active resistor. This circuit offers the possibility to control the gain by voltage and current. We have designed the IA to minimize the parasitic resistance (Rx) with large bandwidth and high common mode rejection ratio (CMRR) using the artificial bee colony algorithm (ABC). The topology is simulated using 0.35µm CMOS technology parameters. The optimization problem is represented by an objective function that will be implemented using MATLAB script. The results were approved by the simulation using the advanced design system (ADS) tool. The simulation results were compared to the characteristics of some other instrumentation amplifiers exsisting in the literature. The circuit has a higher CMRR than other topologies.

A three-dimensional broadband underwater acoustic concentrator

We propose a three-dimensional (3D) omnidirectional underwater acoustic concentrator based on the concept of acoustic prison, which can realize a substantial enhancement of underwater sound signals in broadband ranges. This device mainly employs the non-resonant multiple reflection characteristics of the semi-enclosed geometric space, so it has a wide working frequency bandwidth. Compared with the previous reported concentrators based on transform acoustics mechanism, the structure is more simple, and most importantly, it can realize omnidirectional signal enhancement in 3D space. Moreover, the working frequency band of this acoustic concentrator depends on the size of the concentrator, so it can be changed directly through a size scaling, which is convenient for engineering applications. In general, the designed underwater acoustic concentrator has the advantages of simple structure, scalability and large bandwidth of working frequency, and high signal gain. It has potential application values in underwater target detection and other aspects.

Fundamentals of Internet of Things (IoT): Applications, Challenges, Future Trends

One of the jargons in Information Technology is the Internet of Things (IoT). The future is the Internet of Things, which will transform real-world objects into intelligent objects. IoT aims to introduce plug-and-play technology providing the ultimate user, ease of operation, remote access control, and configurable. This paper presents IoT technology from a bird’s eye view covering its applications in all fields, various challenges faced, and future prospects/trends. IoT applications require higher data rates, large bandwidth, increased capacity, low latency, and high throughput..

Research on the Teaching Model of Animation Professional Class Based on AR/VR Technology and 5G Network

The use of emerging technologies is becoming more common in today’s world, and as media integration deepens, so does the use of virtual reality technology. As an emerging science and technology, VR/AR technology plays a critical role in society’s development and progress, and its application in current education and teaching has become an indispensable link. The production of high-quality animation professionals is the society’s and the animation industry’s urgent need for talent, the precondition for the prosperity of cultural and creative industries, and the foundation of national soft power. To bring more technical changes to education and provide theoretical support for the realization of human-computer interaction technology, this paper mainly elaborates on the concept and application fields of VR/AR technology and discusses the specific applications of VR/AR technology according to its different educational stages. The main point of the presentation is to demonstrate how, in the era of 5G networks, large bandwidth, ultralow latency, massive connectivity, and ultrahigh reliability provide a strong network foundation for widespread VR/AR application. We will investigate the paucity of teaching methods, teaching modes, and teaching platforms as a result of the application of 5G-based VR/AR technology in the teaching field of animation majors, as well as the design of teaching methods, teaching modes, and experimental training platforms based on 5G-based VR/AR technology in animation majors in conjunction with the training plan of engineering students at our university. We will delve deeper into the integration of VR/AR technology and higher education teaching, laying the groundwork for “AI+” education. VR/AR technology can be used in the classroom to present information to students in a more three-dimensional and intuitive way, which not only increases student interest in learning but also improves the learning experience and cultivates core literacy.

Data Augmentation Algorithm Based on Generative Antagonism Networks (GAN) Model for Optical Transmission Networks (OTN)

OTN (Optical Transmission Networks) is one of the mainstream infrastructures over the ground-transmission networks, with the characteristics of large bandwidth, low delay, and high reliability. To ensure a stable working of OTN, it is necessary to preform high-level accurate functions of data traffic analysis, alarm prediction, and fault location. However, there is a serious problem for the implementation of these functions, caused by the shortage of available data but a rather-large amount of dirty data existed in OTN. In the view of current pretreatment, the extracted amount of effective data is very less, not enough to support machine learning. To solve this problem, this paper proposes a data augmentation algorithm based on deep learning. Specifically, Data Augmentation for Optical Transmission Networks under Multi-condition constraint (MVOTNDA) is designed based on GAN Mode with the demonstration of variable-length data augmentation method. Experimental results show that MVOTNDA has better performances than the traditional data augmentation algorithms.

Stack-based grating for wideband polarization splitter in terahertz

A novel wideband terahertz polarization beam splitter with special diffraction orders working at terahertz band is described in this paper. The polarizer can achieve high diffraction efficiency and uniformity in the 2.50 - 2.56 THz band. Based on rigorous coupled-wave analysis (RCWA) and simulated annealing algorithm, we proposed an efficient algorithm to optimize the polarizer. After calculations, 98.45% single-port high-efficiency reflection for transverse electric (TE) polarization and 42.33%/42.57% highly uniform dual-port beam splitting for transverse magnetic (TM) polarization were finally obtained. In addition, through RCWA and simplified modal method, the electromagnetic field distributions of TE and TM polarizations are shown visually and described quantitatively. Moreover, the results displayed in Sec. 3 prove that the grating possesses the characteristics of relatively large bandwidth and insensitivity to the incident angle. Therefore, the novel scheme in this paper has great reference value for the research of terahertz modulation devices and the integration of terahertz communication systems.

A Broadband High-speed Programmable Multi-modulus Divider Based on CMOS Process

This paper introduces a design of a high-speed programmable multi-modulus divider (MMD) based on 65nm CMOS process. The design adopts the cascade structure of 7 level 2/3 frequency dividers, and expands the frequency division range by adjusting the number of cascade stages, so as to achieve a continuous frequency division ratio of 16 to 255. Among them, the first level 2/3 frequency divider adopts the D flip-flop design of CML (current mode logic) structure, the second level 2/3 frequency divider adopts the D flip-flop design of E-TSPC (extended true-single-phase-clock) structure. The whole circuit realizes the working frequency range of 13∼18GHz high frequency and large bandwidth. This design has completed layout drawing and parasitic parameter extraction simulation. The simulation results show that the operating frequency range of the circuit can reach 13∼18GHz. When the input signal is 18GHz and the frequency division ratio is 255, the phase noise is about -135dBc/Hz@1kHz. It has the advantages of high frequency, large bandwidth, and low phase noise.

A Miniaturized Planar Monopole Antenna Based on a Coupling Structure for Compact Mobile Internet of Things (IoT) and Electric Vehicles (EVs) Device Applications in 5G, LTE, WLAN, WiMAX, Sirius/XM Radio, V2X, and DSRC Wireless Systems

A miniaturized internal antenna with monopole structure is constituted, including three radiating strips of a compact prototype and a back-coupling pad to improve the impedance matching, which achieves a wide bandwidth of 2.972 GHz between the operating frequencies of 2315–5285 MHz, and is introduced and researched. There is an urgent need for a complete frequency-continuous and large bandwidth design in the current wireless communication design to achieve a multimode, multifrequency, physical phenomenon design with large bandwidth and continuous operating frequency. The recommended antenna provides a broadband operation in an electric vehicles (EVs) and Internet of Things (IoT) devices application embedded in the wireless communication standard for 5G, LTE, V2X, WLAN, WiMAX, Sirius/XM Radio, V2X, and DSRC to support the multiband application. This design is embedded side edge of overall placement in the device and is integrated applicable to the trend of heterogeneous wireless multiple access networks in electric vehicle and Internet of Things system devices, which covered the 5G with supporting the band of n7/n38/n40/n53/n77/n78/n79/n90, the 4G with supporting the band of 7/38/40/41/42/43/48/67, the V2X and DSRC for the operating frequencies between 2500 and 5000 MHz, the Sirius/XM Radio for the operating frequencies of 2320–2345 MHz, the ISM band of WiFi and BT covering the band of 2450–2483.5 and 5150–5350 MHz, and the WiMAX also supporting the band of 2300–2690 and 3400–3690 MHz. Moreover, the compact antenna manufactured an FR4 material with the antenna area of 5 × 10 × 0.8 mm3 and the ground area of 33.5 × 10 × 0.8 mm3. The proposed design better benefits a narrow space on the PCB with a low profile and is easy to make with a circuit board design.

Optical coherent dot-product chip for sophisticated deep learning regression

Optical implementations of neural networks (ONNs) herald the next-generation high-speed and energy-efficient deep learning computing by harnessing the technical advantages of large bandwidth and high parallelism of optics. However, due to the problems of the incomplete numerical domain, limited hardware scale, or inadequate numerical accuracy, the majority of existing ONNs were studied for basic classification tasks. Given that regression is a fundamental form of deep learning and accounts for a large part of current artificial intelligence applications, it is necessary to master deep learning regression for further development and deployment of ONNs. Here, we demonstrate a silicon-based optical coherent dot-product chip (OCDC) capable of completing deep learning regression tasks. The OCDC adopts optical fields to carry out operations in the complete real-value domain instead of in only the positive domain. Via reusing, a single chip conducts matrix multiplications and convolutions in neural networks of any complexity. Also, hardware deviations are compensated via in-situ backpropagation control provided the simplicity of chip architecture. Therefore, the OCDC meets the requirements for sophisticated regression tasks and we successfully demonstrate a representative neural network, the AUTOMAP (a cutting-edge neural network model for image reconstruction). The quality of reconstructed images by the OCDC and a 32-bit digital computer is comparable. To the best of our knowledge, there is no precedent of performing such state-of-the-art regression tasks on ONN chips. It is anticipated that the OCDC can promote the novel accomplishment of ONNs in modern AI applications including autonomous driving, natural language processing, and scientific study.