Miracle-dz/UniFAR-FTUs huggingface dataset:
Circumferential Horizontal Shear Waves in an Inhomogeneous 1-D Quasi-Crystal Hollow Cylinder

In this paper, equations of wave motion for circumferential horizontal shear (SH) waves in an inhomogeneous 1-D quasi-crystal hollow cylinder are derived. Dispersion relation of SH waves is calculated by employing the Legendre polynomial series. The influence of phonon-phason coupling, inhomogeneity and geometric parameters on dispersion curves is analyzed. Results show that the angular frequency of phonon mode increases, and that of phason mode decreases as phonon-phason coupling constants Ri increase. The numerical results are of reference significance for the design and optimization of quasi-crystal structures.
An Adaptive Scheduling Framework for Distributed Key-Value Stores Using RDMA

Many applications need to cope with Key-Value data, which imposes great pressure on Key-Value storage especially on large-scale workloads. To improve the throughput of Key-Value storage in modern distributed environments, we present an efficient scheduling framework to distribute the workloads onto different nodes. We focus on how to adaptively forward batching requests to a certain number of nodes each disposing a portion of Key-Value items corresponding to a surrogate key. To reduce the overhead of both round-trip notification and the contention derived from skewed workloads, an asynchronous communication method is presented to boost the compaction and coordination in each scheduler. It can be shown that the scheduling framework can fully exploit the high throughput of one-sided writes of modern RDMA networks, such that both the workloads and contention imposed on Key-Value servers can be significantly reduced. We conduct intensive experiments using YCSB benchmark on top of 100-gbps RMDA network. The results show that our proposed method can improve the Key-Value throughput by a factor of two when serving an in-memory Key-Value store with up to 256 work threads.
A Compact Reconfigurable Filtering PIN Diode Switch Based on Hybrid Resonator Topology

This paper presents a reconfigurable band-pass filtering PIN Diode switch. The cylindrical substrate integrated cavity (SIC) and combline SIC resonators with distributed configuration are effectively employed to realize an eight-state compact filtering switch with a third-degree Chebyshev response. The proposed switch operates at 13 GHz with a bandwidth of 400 MHz, return loss of 20 dB, and insolation of better than 38 dB. The synthesized, simulated and measured results agree well, verifying the unique performance and robustness of the proposed solution.
Monolithically Integrated Optoelectronic Transmitter based on Segmented Mach-Zehnder Modulator in EPIC 250 nm BiCMOS Technology

This paper presents a monolithically integrated optoelectronic transmitter based on a segmented Mach-Zehnder modulator. The chip is fabricated in the 250nmSiGe BiCMOS SG25H5 EPIC technology developed at IHP, a silicon-photonic technology combining 0.25 $\mu$m CMOS, high-performance npn HBTs (featuring $\mathrm{f}_{\mathrm{t}}$/fmax of 210/290 GHz), and a full photonic device set for C/O-band. The chip includes a linear electrical driver, a 5 segments Mach-Zehnder modulator, for a total phase shifter length of 2 mm. Electro-optical time-domain measurements demonstrate open eye diagrams up to 44 Gb/s NRZ. The electro-optical bandwidth is 35 GHz, while the power consumption is 500mW, resulting in a power efficiency of 11.3 pJ/bit at 44 Gb/s. The reported transmitter results in a compact and power efficient solution compared to others in a silicon monolithic approach and comparable to hybrid solutions, within the higher 3 dB electro-optical bandwidth that can be found in the literature.
Modeling and Experimental Investigation of a Slotted Piezoelectric Energy Harvester

Harvesting energy from the surrounding environment and human movement has attracted particular attention in recent years. However, how to broaden the effective working range of energy harvesting and increase its output power is a challenging issue. In this paper, a slotted piezoelectric energy harvester is designed. The stress on the piezoelectric ceramic is increased by slotting to improve the conversion of electromechanical energy. The dynamics and electromechanical coupling of the energy harvester are theoretically analyzed. The parameterized finite element model of the slotted piezoelectric energy harvester is developed. The mode analysis and harmonic response analysis are carried out. The effects of parameters such as the length and height of the mass block, the thickness of the elastic substrate, and the length of the harvester on the vibration characteristics and the output voltage of the harvester are studied. A prototype of slotted piezoelectric energy harvester was fabricated and the experiment was carried out. The experimental results verify the effectiveness of the design of the slotted piezoelectric energy harvester. The developed energy harvester can be used to harvester environmental vibration energy and human vibration energy. It has potential applications in charging sensors and portable electronic devices.
An effective classification of Skin Disease using Deep Learning Techniques

According to WHO, Skin Infection is very common but sometimes very serious and affects a large no population all over the world. Monkeypox, Chickenpox, and Measles are the major infectious disease that causes skin infection all over the world. It has been obverse that the cases of Monkeypox have drastically increased as an effect of Covid 19. This infection has spread easily and exponentially that cause serious health issues in many underdeveloped and developing countries. Some time it has been observed that people are not able to properly classify the type of skin infection well in time, which can be a main reason of serious health issues. So, it became important to propose an effective classification of Skin Disease. In this paper the authors have proposed an effective classification of Skin Disease using Deep Learning Techniques. This approach will help in classification of chicken pox, measles, and monkeypox through skin images. The authors have utilized Monkeypox Skin Images Dataset (MSID) dataset to apply the proposed approach. The Loss, Accuracy, Precision, Recall, AUC, and F1 Score parameters have been used to analyze the performance of proposed approaches. The best algorithms with maximum accuracy and other parameters are Xception, EfficientNetV2L, and EfficientNetV2M, and CNN, VGG16, and VGG19 are the least favored algorithms for this research.
Characterization Method for a Class of Thermorheologically Complex Materials

The article presents a method for modeling the response of a class of thermorheologically complex viscoelastic materials. For the type of materials considered, all temperature effects can be correlated through horizontal and vertical shift factors. In addition, the proper evaluation of transient temperature response requires the vertical shift to be represented as a product of two temperature‐dependent functions. It is shown that, in order to obtain complete information, it is necessary to conduct sharp temperature‐drop tests in addition to isothermal creep and recovery experiments. The method and its use are demonstrated for the specific case of Hercules 3502 epoxy resin.
Time Series Prediction of Sea Surface Temperature Using LSTM

Sea Surface Temperature (SST) is a key indicator of the global climate system. It is an essential factor in simulations of atmospheric models, weather predictions, and the study of marine ecosystems. Of these interests, one of the most important is studying changes in sea surface temperatures that result from the anthropogenic forcing of climate, a process known as global warming. An accurate prediction of sea surface temperature is highly beneficial towards understanding climate change, preserving marine ecosystems, etc. This research focuses on processing sea surface temperature data to perform a time series forecasting using a unique long short-term memory neural architecture to predict sea surface temperature in Bay of Bengal region. Given its reasonable high accuracy and low error rate, the assessment metrics show that the recommended neural network for SST prediction can be employed for real-time applications.
AIDNet: Detecting Insulators and Defects from Satellite Remote Sensing Images: An Exploration

The insulator has a diameter of about 200 mm and occupies only 0.4 pixels in the best satellite remote sensing image (SRSI). Detecting insulators and self-explosion defects from SRSI is a challenging task due to the extremely low spatial resolution and mixed pixels issues. Algorithm design that improves the required spatial resolution (rSR) is one way to advance this task. This paper proposes a two-stage detection method combining an adaptive network and a detection network, using multiple filtering and adaptive enhancement methods to achieve end-to-end detection, which can effectively improve the rSR. The idea proposed in this paper can promote the realization of goals that detect insulators and defects from SRSI. The proposed algorithm can realize the detection at relatively poor spatial resolution, and can be applied to small targets detection in general object detection tasks.
Design and Development of Effective IoT-Enabled Multi Health Factor Monitoring System

Since the Internet has become a part of our daily lives, Internet of Things (IoT) has primarily been employed in this period to progress technology. As a result, this paper introduces a reliable and efficient IoT-based healthcare monitoring system with several sensors. the system was developed with multiple sensors like heart rate sensors, ECG sensor and a temperature sensor along with a Wi-Fi module. Sensor’s gathered data will be stored in a cloud based storage i.e. ThinkSpeak. The stored data will be monitored regularly by the doctors and in case of any abnormal reading a short message will be sent to the doctor and the family members. The research has also concentrated on implementing a number of ML algorithms to enhance the performance of the created health monitoring system.
A Data-Driven Joint Chance-Constrained Game for Renewable Energy Aggregators in the Local Market

The local market can promote more self-generated electricity consumed locally. However, the unstable and intermittent generation of renewable energy sources brings uncertainty and overbidding risk to the local market. We proposed a competitive local energy market (LEM) where renewable energy aggregators (REAs) can submit bids instead of acting as price takers. A data-driven joint chance-constrained game of REAs is proposed for the optimal bidding problem under the network constraints and the risk-averse chance constraint. We consider the case that the maximum outputs of REAs are random variables whose probability distributions are unknown, but the decision-maker has access to finite samples. Each player’s strategy set is defined in a data-driven distributionally robust framework with the Wasserstein ambiguity set. The exact reformulation of the distributionally robust chance constraint and the corresponding inner approximation version are derived. We prove the existence of the Nash equilibrium in the proposed distributionally robust chance-constrained game. Finally, an efficient best response seeking algorithm is developed to find the normalized Nash equilibrium, and the convergence proof of the proposed algorithm is also provided. Numerical simulations demonstrate that the proposed game model can effectively increase the revenue of reliable players and reduce the overbidding rate of REAs.
An Overview of Q-learning based Energy Efficient Power Allocation in WBAN(Q-EEPA)

Energy-efficient routing is one of the key techniques to design an efficient sensor network. In Wireless Body Area Networks (WBAN), Transmission Power Control algorithms, interference mitigation algorithms, and efficient routing schemes helps to save energy. The proposed Q-learning-based energy-efficient power allocation (Q-EEPA) provides a high packet delivery ratio with minimum power level, mitigating interference, and minimum hop count. This work considers the design issues in tier 1 and tier 2 of WBAN architecture and gave an overview to solve the issues using the Q-learning algorithm. Q-learning algorithm will allocate the power and routing path during data transmission. Heterogenous activities of a wearer and extensive game theory algorithm act as input to the Q-learning model to choose transmission power level and the next hop in both interference and non-interference scenarios.
Improving Coding Efficiency in All-digital Transmitters

In this paper, coding efficiency (CE) of all-digital transmitters (ADTs) is improved using pulse amplitude modulation (PAM-4), decision threshold optimisation, and delta-sigma core state propagation. Results show a significant decrease in out-of-band noise, with over 10 dB noise reduction, when compared with a similar traditional one-bit ADT. This, not only, provided a more flexible transmitter due to the lower quality analog filter requirements, but also increased the energy efficiency. Such improvements were achieved using similar levels of field programmable gate array (FPGA) resource usage as the traditional one-bit ADT.
Ultrabroadband femtosecond lasers

The exploitation of soliton-like pulse shaping mechanisms and the optimization of phase dispersion in broadband self-modelocked solid state laser oscillators have led to unprecedented advances in ultrafast laser technology. This paper reviews the basic physical mechanisms governing pulse formation and addresses the requirements for optimum performance of these novel ultrashort pulse laser sources. These considerations together with the demonstration of /spl ap/10 fs pulse generation from a Ti:sapphire laser suggest that a family of solid-state laser oscillators with comparable performance can be developed. >
Dual-Semantic Consistency Learning for Visible-Infrared Person Re-Identification

Visible-Infrared person Re-Identification (VI-ReID) conducts comprehensive identity analysis on non-overlapping visible and infrared camera sets for intelligent surveillance systems, which face huge instance variations derived from modality discrepancy. Existing methods employ different kinds of network structure to extract modality-invariant features. Differently, we propose a novel framework, named Dual-Semantic Consistency Learning Network (DSCNet), which attributes modality discrepancy to channel-level semantic inconsistency. DSCNet optimizes channel consistency from two aspects, fine-grained inter-channel semantics, and comprehensive inter-modality semantics. Furthermore, we propose Joint Semantics Metric Learning to simultaneously optimize the distribution of the channel-and-modality feature embeddings. It jointly exploits the correlation between channel-specific and modality-specific semantics in a fine-grained manner. We conduct a series of experiments on the SYSU-MM01 and RegDB datasets, which validates that DSCNet delivers superiority compared with current state-of-the-art methods. On the more challenging SYSU-MM01 dataset, our network can achieve 73.89% Rank-1 accuracy and 69.47% mAP value. Our code is available at https://github.com/bitreidgroup/DSCNet.
A Compact 3-D-Printing-Compatible Dual-Polarized Spherical Resonator Antenna With Improved Bandwidth and Reliability

In this paper, a size-compact and wideband millimeter-wave dual-polarized (DP) spherical resonator antenna is proposed. The dual-polarization behavior of the antenna is attributed to a highly symmetrical geometry of the spherical resonator and orthogonality of the resonant modes inside. The wideband and stable DP radiation performance is enabled by a single quasicruci-form radiation aperture that is essentially a combination of two orthogonal elliptical slots. The configuration as such enhances mechanical strength of the radiation structure and effectively improves the antenna reliability for applications at millimeter-wave frequencies. A Ka-band DP antenna prototype is demonstrated. The antenna prototype is manufactured monolithically by using polymer-based Polyjet 3-D printing and electroless copper plating processes. The RF-measured result of the antenna in one of the polarization orientations is in good agreement with the simulated one, validating wideband and stable radiation performance from 26.5 to 39 GHz (a relative bandwidth of 38.2%) with an in-band reflection coefficient of less than -10 dB and peak realized gains of 7–9 dBi.
Indoor Wireless Localization of Uncooperative Sources Using a Ray Tracing Model

Indoor geolocation of radio frequency (RF) transmitters is challenging due to site-specific multipath effects, especially when the sources are uncooperative. A source being uncooperative means that a-priori the transmit time, location, and power are unknown. It is proposed to use a site-specific ray tracing model to generate realistic indoor RF responses, perform geolocation using multiple methods, and compare their performance. This work will look at two types of geolocation algorithms: (1) time-difference of arrival (TDOA) and (2) received signal strength Indicator (RSSI) fingerprinting or pattern-matching-based methods. An indoor space will be simulated with a grid of fixed receivers and a grid of transmit locations using Remcom's Wireless InSite. Using the output of the Wireless InSite simulation the response from any given transmit location can be generated and used to evaluate geolocation performance.
Design of Video Codec Platform Based on PowerPC+WW602 Architecture

In order to meet the needs of dual-channel high-resolution video encoding and decoding, as well as the need for long-term capture and recording of operating terminal screen information in important control scenarios such as shipborne, vehicle-mounted, and airborne, it is convenient for post-event technical analysis, service quality assessment, and exercises. Deduction and determination of responsibility for operation accidents. The hardware architecture of PowerPC+WW602 is researched, and a platform that supports dual-channel high-definition video encoding and decoding is designed and implemented. The compression algorithm of H.264 encoding standard is used to realize the encoding and decoding of video data, and advanced and mature technology is used to follow the generalization. The video codec platform design based on PowerPC+WW602 architecture can support single-channel or dual-channel video input of high-definition and standard-definition, and realize the codec transmission of two channels of video information. The delay of video capture and display is within 30ms, the picture is clear, and the video capture process is not lost.
An Effective GNSS Fault Detection and Exclusion Algorithm for Tightly Coupled GNSS/INS/Vision Integration via Factor Graph Optimization

Pseudorange measurements from GNSS (Global Navigation Satellite System) receivers are seriously affected by multipath in urban environments, which greatly degrades the positioning accuracy and reliability of GNSS/Inertial Navigation System (INS)/Vision integrated system. Fault Detection and Exclusion (FDE) module is essential to improve the robustness and positioning performance of the system. Recently, GNSS/INS/Vision integration via factor graph optimization (FGO) has attracted extensive attention due to its high accuracy and robustness. As measurements from multiple epochs can be used under FGO framework, it is expected that the detection capability of faulty pseudorange measurements can be improved significantly. Meanwhile, the inclusion of visual measurements could contribute to the capability of FDE of faulty GNSS measurements. In this contribution, we present a parallel GNSS FDE method via FGO, and it calculate the test statistics of each satellite based on the residuals of GNSS measurements in a sliding window. The public GVINS-dataset "urban" were used to evaluate the performance of the parallel GNSS FDE scheme in urban canyons. Experimental results show that compared with the GNSS/INS integration, the 2D positioning accuracy in terms of Root Mean Square Error of the parallel GNSS FDE scheme used for GNSS/INS/Vision integration is improved by 33.5% in urban complex environment. Additionally, compared with the sliding window-based FDE method, for GNSS/INS integration and GNSS/INS/Vision integration, the 2D positioning accuracy is increased by 12.1% and 11.7% respectively.
Robustness to Digital Power Adjustment in Transmit Power Allocation for Poor Conditioned LPWA End Devices

In this study, we investigate robustness to power adjustment using digital settings at low-power wide-area (LPWA) module and a digital step attenuator in transmit power allocation for LPWA end devices under poor conditions, that is, high propagation loss to gateways. Traditionally, performance improvement techniques for end devices under poor conditions have been studied. In conventional techniques, employing analog stepless attenuator is assumed to adjust the transmit power at each end device. However, employing analog stepless attenuator at the end devices is not practical because each end device automatically adjusts its own transmit power. Therefore, we investigated the effect of the digital step attenuator and digital settings at LPWA module on the conventional techniques. Computer simulation results are presented to elucidate the characteristics of the conventional technique with the digital step attenuator and digital settings at LPWA module.
An RFID Sensing Method Based on Magnetic Control Antenna Coupling

Based on magnetic control antenna coupling adjustment, a UHF RFID sensing method is proposed in this paper. The proposed magnetic control antenna coupler consists of a reed switch and radiator coupler. Controlled by a magnet, the reed switch has two states, on and off, and thus the antenna coupler has two states with different resonant frequencies and coupling performance. Coupling this coupler with RFID commercial tag results in the tag working state change. This is mainly reflected in RSS and phase results by the reader. This RFID sensing method uses magnets for wireless passive sensing of various actions. It does not require the design of a specific tag, and sensing functions can be achieved by using commercial tags. Two applications based on the method are shown to reflect its convenience and efficiency. It has good application potential in health assistance, the Internet of things and smart home.
Picosecond microwave pulses generated with a subpicosecond laser-driven semiconductor switch

A subpicosecond laser has been used to trigger a GaAs photoconductive switch driving a dipole antenna. The microwave transient produced was measured with a correlation technique to have a FWHM of less than 3 psec. This technique was also used in a contactless measurement of the 250‐psec lifetime of a GaAs sample.
Gated Recurrent Unit (GRU) in RNN for traffic forecasting based on time-series data

Traffic management has emerged as the top priority in the advancement of smart cities, and traffic prediction has been a crucial area of study in smart transportation systems that emphasize minimizing road congestion. Many new concepts have emerged as a result of vehicle networks (VNs), like mapping of traffic, traffic management, and automobile communication. Machine Learning (ML) is an effective method for discovering hidden insights in ITS without explicitly programming it by learning from data. In this work, Gated Recurrent Unit (GRU), the method in Recurrent Neural Networks is used for the time series traffic analysis and prediction. When compared with previous models, this model has shown a considerable improvement in accuracy. The findings were computed using the widely used forecasting metrics MSE, MAE, and RMSE.