Miracle-dz/UniFAR-FTUs huggingface dataset:
Algorithm optimization of Er-doped Superfluorescent Fiber Source Feedback Control Module

Feedback control module, as an important unit that exists in the Er-doped superfluorescent fiber source’s (ED-SFS’s) circuit system, shows high significance to the output characteristics of ED-SFS. In this paper, we put forward an optimized feedback control module scheme utilizing the incremental differential-ahead proportion-integral-derivation (IDA-PID) control algorithm. We theoretically analyze this control algorithm and compare it with the other two PID control algorithms. The experimental results show that, with the IDA-PID algorithm, the output power stability of ED-SFS can be reduced from 2.13% to 1.65%, and the mean wavelength thermal stability can be reduced from 261ppm to 148ppm over one hundred degrees Celsius (-40°C~+60°).
A Point Cloud Distortion Removing and Mapping Algorithm based on Lidar and IMU UKF Fusion

When the lidar motion is rapid, point cloud distortion is an important factor affecting the accuracy of lidar odometry. In this paper, an effective lidar and IMU fusion based point cloud distortion removing and mapping algorithm is proposed. UKF is used to fuse the angular velocity, linear acceleration of the IMU and the pose of the lidar. It can get the angular velocity bias, linear acceleration bias of the IMU and the linear velocity of the lidar. Then the pose of the lidar at each time in a scanning period is obtained, therefore the distortion of the point cloud can be removed. UKF can also estimate the pose of the lidar, which can be used as the initial value of the point cloud registration algorithm in lidar odometry, therefore the computation time can be reduced. The rapid rotation and rapid translation motion experiments show that the proposed method can deal with the rapid motion of lidar, and achieve high efficiency and precision mapping.
Calibration of the Loop Probe for the Near-Field Measurement

Accurate near-field measurements of either deterministic or stochastic electromagnetic fields require a relevant process that removes the influence of the probes, transmission lines and measurement circuits on the measurement results. The main part of the experimental work presented in this paper is related to a calibration procedure of a test setup consisting of a microstrip test structure and the scanning magnetic loop probe. The calibration characteristic is obtained through comparing measured and simulated results of the same structure. The characteristic is then used to convert measured voltage into the magnetic field across and along the microstrip line at the specific height above it. A good agreement between measured data with calibration and simulated results is obtained.
Performance analysis of FSS Element Based on Substrate Integrated Waveguide Cavity Technology

In this paper, a frequency selective surface (FSS) element has been presented based on substrate integrated waveguide (SIW) technology for improved EM performance. The SIW cavity results in the generation of additional resonance, which combines with the resonance of FSS shows a relatively wider bandwidth (BW). Furthermore, the FSS based on SIW exhibits a sharp roll-off performance with flat transmission characteristics. The FSS element is designed to operate in Ku-band near 15.0 GHz for satellite application. Two design examples have been reported for the dual band and broadband response. The estimated results have proven the superiority of the SIW cavity FSS over its conventional form.
Design and characterization of innovative 3D printed embedded strain gauges

In this paper a low-cost load cell with four embedded strain gauges has been developed using Fused Deposition Modeling (FDM) technology. Two commercial materials, conductive and non-conductive, have been used to manufacture the sensor in a single and non-stop printing cycle. Afterwards, each strain gauge has been characterized by evaluating the resistance change in relation to the applied force and as a consequence, it has been found that they are sensible to deformations due to compression and tension. Hence, it has been proved that with this manufacturing approach it is possible to integrate sensitive elements such as strain gauges into nonsensitive elements to create low-cost sensors using FDM, one of the most common 3D printing technology.
A Set-Membership Filter with Box Bounding for Nonlinear Biased Dynamic Systems

This paper investigates a set-membership filter with box bounding for some nonlinear dynamic systems with sensor biases. This tracking problem is difficult since the uncertainty of both noises and biases. First, the sensor bias is augmented as part of the state. It is proved that when the parameter is bounded in a box, the remainder bound of a typical measurement function can be obtained analytically in real time. Next, both the state prediction and set-membership filter update can be equivalently transformed to solve linear optimization problems with lower computational complexity. Finally, a numerical example in the context of multiple sensor tracking system with sensor biases demonstrates the effectiveness of the box bounding state filter.
Study on Grounding Ways of Secondary Cable in UHVDC Converter Substation

The ground potential will rise abnormally when impulse current injects into grounding grid if a converter substation is struck by lightning or there exist short-circuit faults. Transient ground potential rise (TGPR) could couple to the cable core wire through conductive and near-field coupling ways and it will invade the secondary system eventually. Taking those two coupling ways into consideration, this paper builds a simulation model describing the technisim on how TGPR couples to the secondary cables in an UHVDC converter substation and searches for the best grounding way for suppressing disturbance voltage in secondary cables’ cores caused by TGPR. Through simulation and test, for a single cable harness with armor double-ended grounded, we propose that cable’s shielding sheath should be grounded only in protecting cabin. For a four-segment cable, we find the best grounding way is that for the harness segment between protecting cabin and terminal box, we should guarantee armor double-ended in both sides while shielding sheath be grounded only in protecting cabin, for the segment between terminal box and operating cabinet, we should guarantee armor double-ended in both ends while shielding sheath be grounded only in terminal box, what’s more, the armor and sheath among operating cabinets should be grounded only in phase B’s cabinet.
Quasi-one-dimensional Terahertz Phononic Band Gap in Phospholipid Tails

Terahertz quantized vibrational modes and quasi-one-dimensional terahertz phononic band gap in hydrocarbon tails of phospholipids are revealed through vibration calculation based on the ab initio density functional theory (DFT). We obtain the correlation between vibrational modes and frequencies. The phononic band gap in linear hydrocarbon chains provides a perspective in the interpretation of biochemical phenomena related to biological membranes, such as the photosynthetic energy transfer and the electron transport chain in the inner mitochondrial membrane.
Design and Implementation of a Low-Power, Embedded CNN Accelerator on a Low-end FPGA

in this paper, an optimized hardware for Convolutional Neural Networks with the purpose of implementation on embedded vision systems is presented. This design method is meant to be implemented with minimum resource consumption on a low-end hardware platform. We propose an architecture on a Z-turn evaluation board featuring a Xilinx Zynq-7000 system on chip (SoC). All computations in this architecture are optimized as 8-bit. Also, the accelerator has a frequency of 160 MHz and power consumption of 1.77 watts which leads to a performance of 40.96GOP/s, using only 134 computing units and 601 KB of internal memory. So we can claim that the acceptable speed and low power and low area consumption of this architecture make it an ideal choice for portable and embedded CNN applications.
Advantages of Virtual Reality in the Teaching and Training of Radiation Protection during Interventions in Harsh Environments

Human interventions in radioactive environments have high stakes. They are often time-sensitive and radiation exposure must be minimised for the safety of personnel. Existing sites were not developed with remote decommissioning in mind, therefore human intervention remains the preferred approach for dexterous manual labour over robotic systems.For ageing sites, knowledge transfer after retirement is an increasingly relevant problem for maintenance and decommissioning tasks, where new workers lack the in-depth “on the ground” experience of the installation.Virtual Reality provides workers the agency to explore an accurate representation of the area, enabling them to gain experience without undue radiation exposure.This paper explores and discusses the teaching and training applications of a Virtual Reality environment with integrated radiation dose maps, and looks at where the system may be developed further.
Resilient Event-Triggered Controller Synthesis of Load Frequency Control for Multi-Area Power Systems under Periodic DoS Jamming Attacks

This paper concentrates on the controller synthesis problem for load frequency control (LFC) of multi-area power systems under resilient event-triggering communication scheme (RETCS) and periodic denial-of-service (DoS) jamming attacks. Firstly, a periodic RETCS under the given DoS attacks is designed, which can eliminate impact of the periodic DoS jamming attacks effectively. Second, a state error dependent switched LFC model for multi-area power systems is given. Then, a co-design method for RETCS and LFC gains is developed. Finally, the effectiveness of the proposed method is demonstrated by a two-area power system.
Scalable Modeling and Synthesis of On-Chip Spiral Inductors

In this paper, an efficient synthesis for integrated spiral inductors based on particle swarm optimization is presented. The synthesis/optimization process aims to determine the inductor layout parameters that maximize the quality factor while considering the imposed restrictions (project specifications). The spiral inductors are modeled using a double-π scalable model, in which the design parameters of inductor are taken as input. All the elements of the scalable model are calculated from the scalable expressions and process parameters. Particle swarm optimization is used to explore the design space and resolution of the optimization problem. Three design examples of inductors with inductance values of 3 nH, 5.5 nH and 8 nH, and with operating frequency of 2 GHz, 3.5 GHz and 5 GHz are used to evaluate the synthesis process. The obtained results confirm the validity of the adopted methodology and the PSO efficiency in determining the most adequate inductor structure to the project specifications.
A Delay-Aware Implementation Scheme for Cost-Effective Implication-Based Concurrent Error Detection

Implications have been shown to be beneficial for both concurrent error detection and diagnosis. To reduce the incurred hardware cost, one critical issue is selection of a minimum number of appropriate implications. Although the previous work developed several implication selection algorithms, the critical path delay may still be high. This is because the factors related to the critical path delay have not been well studied and considered during implication selection. In this paper, we investigate these factors and develop a new delay-aware implication selection algorithm. A buffer insertion algorithm is also developed such that the minimum number of buffers are inserted to further reduce the delay. This algorithm is integrated with the implication selection algorithm as a delay-aware implementation scheme. Experimental results on 18 ISCAS'85 and ITC'99 benchmark circuits show that 29.02% delay overhead reduction is achieved on average with only additional 0.34% implications selected.
Importance of speaker specific speech features for emotion recognition

The recognition of emotions is an inherent ability possessed by humans, which has long intrigued many researchers. Primarily due to the possibility of its successful emulation and integration in independent systems. Further, speech, being a mixture of utterances conveying a state of mind, proves to be a suitable candidate from which emotionality can be inferred, due to its many feature variations. This is corroborated by human beings themselves using this modality for extraction of emotionality clues. Another important aspect has to do with communicational register adaptation and the skill to discern different emotions in different speakers. Sure enough, the same emotional utterance may be interpreted divergently for two different people, meaning emotionality specific information is present in a speaker’s personal register. As a demo, we propose a real-time automatic emotion recognition system from speech, based on the use of the well established VGG-like convolutional neural network speaker recognition model VGGVox, trained with over 100,000 utterances from the VoxCeleb1 dataset on speaker recognition, for emotional feature extraction and feeding to state-of-the-art classifiers for accurate recognition of emotional states. Positive supporting results have been captivating enough to spark interest in the technique.
A Novel ECG Heartbeat Classification Approach Based on Compressive Sensing in Wearable Health Monitoring System

Compressive sensing has been widely used in wearable health monitoring system for arrhythmia classification of electrocardiogram (ECG) signals since it has the ability to process signal with low energy consumption. However, the ECG data to be classified is not only enormous in amount, but also complex in structure. Therefore, solutions are needed to deal with such huge and diverse datasets. In this paper, the authors develop a novel ECG classification scheme using deep learning in wearable health monitoring system. The proposed method first compresses and reconstructs the ECG signals based on compressive sensing, and then develops a deep learning approach to perform heartbeat classification over the recovered ECG signals. Simulation experiments have shown that the proposed scheme can achieve classification accuracy of up to 97.06% with low energy consumption in sensor node as compared to benchmarking methods.
Segmentation of Brain Tumor from MRI Images using Fast Marching Method

The brain tumor is caused when there is uncontrolled growth of cells in the brain. There are two types of tumor, Malignant and Benign. With malignant being the cancerous type and spreads to other regions. Segmentation of brain tumor for further medical analysis is very important. With the recent advancements in the field of medical image analysis and computer vision, the process of segmentation is implemented using computer-aided systems. In this paper, a gradient-based Fast Marching method is used for segmenting the brain tumor. The segmented and extracted images were compared with the ground truth image and dice similarity was calculated for the evaluation of the segmentation algorithm. The images were preprocessed for the removal of noise and contrast-enhanced for better interpretation of the image. PSNR, MSE, SSIM were the parameters calculated for the analysis of image quality.
AI Routers & Network Mind: A Hybrid Machine Learning Paradigm for Packet Routing

With the increasing complexity of network topologies and architectures, adding intelligence to the network control plane through Artificial Intelligence and Machine Learning (AI&ML) is becoming a trend in network development. For large-scale geo-distributed systems, determining how to appropriately introduce intelligence in networking is the key to high-efficiency operation. In this treatise, we explore two deployment paradigms (centralized vs. distributed) for AI-based networking. To achieve the best results, we propose a hybrid ML paradigm that combines a distributed intelligence, based on units called "AI routers," with a centralized intelligence, called the "network mind", to support different network services. In the proposed paradigm, we deploy centralized AI control for connection-oriented tunneling-based routing protocols (such as multiprotocol label switching and segment routing) to guarantee a high QoS, whereas for hop-by-hop IP routing, we shift the intelligent control responsibility to each AI router to ease the overhead imposed by centralized control and use the network mind to improve the global convergence.
A High-accuracy Target Tracking Method and Its Application in Acoustic Engineering

Target tracking within the azimuth history diagram is usually challenged by the random marine noise in passive sonar. In this paper, we propose a target tracking technology based on principal component analysis (PCA), which can enhance the useful signal information by using signal-to-noise separation technology. Considering that PCA may lead to azimuth mismatch errors, we further improve the accurate performance of the proposed method by using error corrections. The simulations are conducted by a set of simulation data and the results demonstrate that the new method can provide a higher accuracy and reliability compared with the conventional ones.
Grid Tied Solid Oxide Fuel Cell Power Generation System for Peak Load Management

Solid Oxide Fuel Cell (SOFC) based Transportation system have been reported as the future of human society. The SOFC based systems are pollution free and it converts fuel to electrical energy at high efficiency. The use of these fuel cell electric vehicle as stationary power production unit has added to the future prospects of such systems. This will help in creating a network of decentralized small scale power production units in the grid. Such power production units can be used effectively to meet the peak power demands in the grid.This paper proposes a grid tied Solid Oxide Fuel Cell (SOFC) system. The mathematical modelling of fuel cell given literature has been analysed and a Solid Oxide Fuel Cell (SOFC) model has been simulated. The SOFC has been further connected to a boost converter and an inverter to create a grid connected power generation system. Synchronous reference frame (SRF) theory has been used to syncronise the output of the proposed system to the grid. The proposed system has also been simulated using MATLAB with its Simpower system Blockset. The performance of the proposed system is found to be satisfactory.
Time-Varying Cross-Range in Wideband Sonar Imaging

The detection of targets in the received wideband sonar images with a time-varying cross-range parameter is considered. The sonar images are assumed to be sparse, and their reconstruction is possible by using the compressive sensing theory. The Alltop and the Bjorck sequences are used as the transmitted waveforms. In the real-world scenarios, the Doppler shift changes during the transmission, changing the cross-range of the received image as well. These changes make targeting of points more complex or impossible, even in ideal cases (i.e. when the set of measurements is not reduced, or when the signal is strictly sparse). A combination of a simple compressive sensing method and the polynomial Fourier transform as a basis for the estimation of the cross-range parameters is used for a successful localization of target points. The performance for the considered sequences is validated on numerical examples and simulated data.
Acoustical Factors Affecting Hearing Aid Performance

This book is a compilation of papers presented at a conference organized by the editors in June 1978. Twentyseven persons participated in the preparation of 17 chapters designed "to bring to a wider audience a progress report on the 'state of the art' in the acoustic areas of hearing aid performance. " The first of four sections reviews physical factors governing sound reverberation and speech perception by normal and hearing-impaired listeners in closed space. This is followed with reports of data showing variations in acoustic pressures recorded from manikin-worn aids as a function of (1) signal azimuths, (2) directional characteristics and location of receiving microphones, and (3) acoustic characteristics of the test environment. The second section focuses on the acoustic shaping characteristics of the normal and simulated external ear and on earmolds and microphones. The historic development and physical description of the Zwislocki coupler is outlined, followed by a report on
Design of CPW-Fed Triple-Band Two-Port MIMO Antenna with U- Shaped Slot Isolation Structure for High Isolation

A novel U-shaped slot isolation structure is placed between a two-port CPW-Fed rectangular patch antenna to reduce the mutual coupling between them. The isolation structure not only increases the minimum isolation by 5 dB but also, coverts a wideband antenna (2.22–4.86 GHz) into a dual-band (2.1–3.03 GHz and 3.45–4.97 GHz) and also helps in achieving a higher-band (7.15–7.94 GHz) due to better impedance matching. The center-to-center distance between the patch antennas is 0.35 λ0. The isolation structure consists of eight etched U-shaped slots from the metal strip of dimensions 19×41 mm2. The proposed MIMO antenna is useful in 2.4 GHz WLAN, 2.5 GHz WiMAX, as a receiver for all IEEE C-Bands and satellite communication. The prototype has been fabricated on an FR-4 substrate of dimension 90×50×0.8 mm3 having an antenna area of 0.22 $\lambda _0^2$ with a maximum measured gain of 3.5 dB across the operating bands.
High-Sensitivity Sensing for Relative Humidity and Temperature Based on an Optoelectronic Oscillator Using a Polyvinyl Alcohol-Fiber Bragg Grating-Fabry Perot Filter

In this paper, a relative humidity (RH) and temperature sensing scheme with high sensitivity based on an optoelectronic oscillator (OEO) is proposed and experimentally demonstrated. A polyvinyl alcohol-Fiber Bragg grating-Fabry Perot (PVA-FBG-FP) filter, in which the PVA film acts as an RH-strain transducer, is employed in the OEO to implement a single passband microwave photonic filter (MPF). In the OEO loop, the oscillating frequency is determined by the center frequency of the PVA-FBG-FP filter and the laser source. When the external RH or temperature changes, the central wavelength of PVA-FBG-FP filter is shifted, leading to the change in the center frequency of the MPF. By simply monitoring the variation of oscillating frequency using an electrical spectrum analyzer or a digital signal processor, the measurements for the RH and temperature can be achieved. In the experiment, the sensitivities of the RH and temperature are measured to be as high as 508.3 MHz/%RH and 1.8118 GHz/°C, respectively.
ANN-based Internal Model Control strategy applied in the WWTP industry

Wastewater Treatment Plants (WWTPs) are industries where highly complex and non-linear processes are performed to reduce the pollutant concentrations of residual waters. However, some nitrogen and phosphorus derived pollutants are generated in these processes. As a consequence, certain control strategies have been developed to maintain these pollutants under certain limits. Benchmark Simulation Model No.1 (BSM1), a framework emulating the behaviour of a general purpose WWTP, considers a default controller strategy based on Proportional Integral (PI) controllers. Nevertheless, these controllers are based on linearised models of the WWTP behaviour. For that reason, this work proposes a new control approach based on Internal Model Controllers (IMC) adopting Artificial Neural Networks (ANNs), which are able to model the real plant behaviour without performing linearisation. Results show that the proposed IMC is improving the default controller performance around a 16% and a 53% in terms of the Integral Absolute Error (IAE) and the Integral Square Error (ISE), respectively.