Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarimetric Calibration for a Ground-based Synthetic Aperture Radar System

We propose a polarimetric calibration procedure required to produce accurate polarimetric characteristic of the illuminating scene by GB-SAR. The correction for polarimetric distortions of the GB-SAR system employs only three types of calibration targets, i.e., trihedral, dihedral and 22.5°-rotated dihedral corner reflectors. Azimuth and frequency dependencies on polarimetric responses of a trihedral corner reflector were evaluated by numerical simulation, and we found little difference within 300 MHz bandwidth and of 2 m synthetic aperture length. The proposed polarimetric calibration method was verified by experiment. An amplitude error as low as 0.5 dB and phase error within 3° was achieved by the GB-SAR system at 17 GHz, showing excellent agreement between the theoretical and calibrated polarimetric signatures for a trihedral corner reflector. Finally, we investigate the effects of polarimetric calibration by Pauli decomposition, which indicates desirable polarimetric performance after polarimetric calibration.
Fast and Accurate Antenna Array Pattern Synthesis Using Iterative FFT via Least-square Active Element Pattern Expansion (LS-AEPE)

A new method incorporating the least-squares active element pattern expansion (LS-AEPE) into the iterative Fourier transform (IFT) procedure is introduced to synthesize the shaped beam pattern for the linear array with the strong scattering environment. The synthesized result, involving the mutual coupling and platform effect, is more accurate than traditional IFT, and the procedure takes very litter time. A 16-element array surrounded with many irregular scatters is given to validate the accuracy and efficiency of the introduced method.
Eye-blink based Personal Authentication Using Time-series Directional Features and Waveform Features

In this study, we propose the personal authentication method using characteristics of eye blink and investigate effective features for authentication. The time-series gradient directional features and the waveform features extracted from the time-series gradient intensities are adopted as the features for recognition. The degree of similarity between registration and recognition features is obtained by the dynamic time warping (DTW) and Euclidean distance. From the experimental results, individual differences appeared in features obtained in the iris peripheral region and during opening eyes.
Printing Process of Electrically Conductive Silver on Heat Transfer Polymer Substrates for Wearable Electronics Applications

Most of textile substrates are porous because they are fabricated from yarns by weaving, or knitting. Hence direct printing of electrically conductive ink to form solid patterns on textile fabrics is a challenge. One of the conductive circuitry forming methods includes the usage of the heat transfer polymer (HTP) substrate. In this study, we investigate the described fabrication process which includes the printing of electrically conductive silver ink on the HTP substrate for wearable electronics applications. The printing process on the HTP substrate using screen printing was empirically studied using our newly developed roll-to-roll pilot line for flexible hybrid electronics. The investigated materials included $60\mu \mathrm{m}$ thick HTP substrates, and a shear thinning silver ink. The test vehicles includes lenticular patterns from 200 to $700\mu \mathrm{m}$. The results of the study show that the different line-widths towards the resistivity values of the same length printed silver patterns follow a power function equation. The study also finds that the average electrical resistance values of the printed silver on the HTP substrate are 17.3% lower than those of on the polyethylene terephthalate (PET) substrate.
Ultra Low-Power Encryption/Decryption Core for Lightweight IoT Applications

The fact that security is not the main functionality of an IoT device means that even lesser portion of its computing power is available for security. Security measures implemented in traditional computers such as cryptography, present a challenge from this context when applied in IoT devices. Different algorithms have been presented in the literature that meet IoT requirements and yet maintain the needed security level. Most of the studies have focused on popular algorithms, especially AES finalists. However, more IoT oriented algorithms have been presented to provide better performance in terms of power and capacity, those are known as lightweight cryptographic algorithms. PRESENT as one of the lightweight cryptographic algorithms is the main focus of this study. The algorithm has been implemented using MATLAB, ASIC and FPGA flows. Lowest achievable power consumption is 120.9 µW at throughput of 20.64 Mbps for the ASIC. For the FPGA, best achievable results was for 16 s-box design utilizing a power of 225.95 mW and 352 LUTs.
Efficient Optical Spatial First-Order Differentiator Based on Graphene-Based Metalines and Evolutionary Algorithms

We propose a novel optical spatial differentiator to perform the differentiation computation in terahertz region based on the graphene metalines, which consist of graphene layers with different widths and chemical potentials. The numerical simulation results show that when beam waist size w>1. 9λ, the metalines perform the first-order differentiation in the reflection spectrum with efficiency>97%, which can be theoretically demonstrated by using transfer matrix method. In order to further improve the performance of the differentiator, evolutionary algorithm, such as genetic algorithm, is used to inversely design the structure parameters and chemical potentials of graphene metalines. The optimization results show that some performance metrics of the differentiator, for example normalized root-mean-square deviation, are better than the previous structures. Obviously, the proposed graphene metalines combined with inverse design technology can achieve a high-performance optical spatial differentiator in terahertz region and provide a new way to design the photonics devices.
Piezoelectric Ultra-Stretchable Strain Sensor with Excellent Linearity and Unique Self-Healing Ability

Biomedical monitoring is of great importance to society, as this area directly impacts our daily lives. Wearable strain sensors are essential for the realization of applications in the broad fields of remote healthcare monitoring, soft robots, immersive gaming, among many others. These flexible sensors should be comfortably adhered to skin and capable of monitoring human motions with high accuracy, as well as exhibiting excellent durability. However, it is challenging to develop electronic materials that possess the properties of skin—compliant, elastic, stretchable, and self-healable. Here we show a soft electronic material composed of poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAAMPSA), polyaniline (PANI) and phytic acid (PA) that exhibits ultrahigh stretchability (~1935%), excellent repeatable self-healing ability without intervention (repeating conductivity healing efficiency >98%), and exceptional linearity without calibration. The PAAMPSA/PA and doped PANI emeraldine salt, under ambient conditions, construct a dynamic network of polymer chains crosslinked by hydrogen bonds and electrostatic interactions which enables ultrahigh stretchability and autonomous repeatable self-healing. Sensitive strain-responsive geometric and piezoresistive mechanisms of the material owing to the homogenous and viscoelastic nature provide the sensor with excellent linear responses to omnidirectional tensile strain and bending deformations. It is shown that this electronic material is also scalable and simple to process in an environmentally-friendly manner, paving the way for the next generation flexible electronics.
Improving code-switching speech recognition with data augmentation and system combination

We focused on a study of comprehensive approaches to an improved code-switching speech recognition, using data augmentation and system combination methods. For data augmentation, we not only use speech speed perturbation based method, but we also attempt to add diversified room impulse response based reverberate noise, as well as music, babble, and white noise based additive noise. It is found we still can achieve significant performance improvement with such noise-corrupted data augmentation methods, though our SEAME code-switching data belongs to a clean corpus. In addition to data augmentation methods, we also adopt Minimum Bayesian risk-based lattice combination method to further improve our recognition results. We achieve significant word error rate (WER) reduction on lattice combination with/without recurrent neural network language model based lattice rescoring. Compared with our previous efforts [6], we achieve up to 2.29% and 5.61% absolute WER reduction on the two dev sets respectively, while 4.83% and 8.04% absolute WER reduction after system combination.
Ant-Taxi to Pie-Passenger: Optimizing Routes and Time for Distributed Taxi Ride Sharing

Sustainability of local co-operative taxi networks in the face of competition from corporate behemoths is a problem worthy of investigation. Lack of infrastructure background support and venture capital demands alternative strategies for survival. A viable solution requires novel modeling approach and computational framework. Such framework needs to be fully distributed and therefore is sub-optimal. We propose an intra-city taxi service that leverages individual rides and local network for ride management. This, in turn, demands a bunch of decision variables and objectives to be optimized. Some of these objectives are conflicting and therefore the problem requires a multi objective optimization formulation. We propose to exploit the flexible structure and multi-agent like behavior of Ant colony optimization to tackle the diversity of objectives in the ride share problem. We consider the scenario of ride share only since that is the hardest part in the taxi business. The implementation is multi-modular and provides an alternative approach to commonly used centralized taxi network. We define novel performance metrics and present results to supplement the modeling approach. Our results show that spatio-temporal diversity is the biggest road block to wide scale ride sharing. Nonetheless, our solution ensures up to 77% acceptance in shared rides.
Robust Active Vibration Controller Design for a Class of Underactuated Nonlinear Systems

This paper proposes a robust active vibration controller design method for nonlinear mechanical systems with multiple disturbances. The main idea of the feedback law is to convert a nonlinear system into an aseismatic desired system using the energy shaping technique. The degree of freedom for the design of nonlinear controllers is clarified. The controller uses only relative information, which is easily measured by sensors. The control law derivation does not require a solution of the Hamilton-Jacobi equations. The proposed parameter design method can ensure the robustness against the uncertainty of the damping coefficient. The effectiveness of the proposed method is confirmed using an example and simulations.
Dimensional Emotion Recognition from Speech Using Modulation Spectral Features and Recurrent Neural Networks

Dimensional emotion recognition (DER) from speech is used to track the dynamics of emotions for robots to naturally interact with humans. The DER system needs to obtain frame-level feature sequences by selecting the appropriate acoustic features and duration. Moreover, these sequences should reflect the dynamic characteristics of the utterance. Temporal modulation cues are good at capturing the dynamic characteristics for speech perception and understanding. In this paper, we propose a DER system using modulation spectral features (MSFs) and recurrent neural networks (RNNs). The MSFs are obtained from temporal modulation cues, which are produced from auditory front-ends by auditory filtering of speech signals and modulation filtering of the temporal envelope in a cascade manner. Then, the MSFs are fed into RNNs to capture the dynamic change of emotions from the sequences. Our experiments of predicting valence and arousal involving the RECOLA database demonstrated that the proposed system significantly outperforms the baseline systems, improving arousal predictions by 17% and valence predictions by 29.5%.
Modeling, Simulation and Optimization of Power Plant Energy Sustainability for IoT Enabled Smart Cities Empowered With Deep Extreme Learning Machine

A smart city is a sustainable and effective metropolitan hub, that offers its residents high excellence of life through appropriate resource management. Energy management is among the most challenging problems in such metropolitan areas due to the difficulty and key role of energy systems. To optimize the benefit from the available megawatt-hours, it is important to predict the maximum electrical power output of a baseload power plant. This paper explores the method of a deep extreme learning machine to create a predictive model that can predict a combined cycle power plant’s hourly full-load electrical output. An intelligent energy management solution can be achieved by properly monitoring and controlling these resources through the internet of things (IoT). The universe of artificial intelligence has produced many strides through deep learning algorithms and these methods were used for data analysis. Nonetheless, for further accuracy, deep extreme learning machine (DELM) is another candidate to be investigated for analyses of the data sequence. By using the DELM approach, a high level of reliability with a minimum error rate is achieved. The approach shows better results compared to previous investigations since previous studies could not meet the findings up to the mark and unable to predict power plant electrical energy output efficiently. During the investigation, it is shown that the proposed approach has the highest accuracy rate of 98.6% with 70% of training (33488 samples), 30% of test and validation (14352 examples). Simulation results validate the prediction effectiveness of the proposed scheme.
Synthesis, Characterization, and Study of NO2 Gas Sensing Behavior of CSA Doped PANi-Ta2O5 Nanocomposite

Polyaniline in its emeraldine salt form was obtained by chemical oxidative polymerization of aniline monomer. PANi thus obtained was mixed with tantalum pentoxide (Ta2O5) nano powder in 50 wt% to get PANi-Ta2O5 nanocomposite. The nanocomposite was further mixed with Camphor Sulphonic Acid (CSA) with concentrations of 20wt%, 30wt% and 40wt% respectively for solid-state doping. All the samples were characterized by FESEM, TEM, XRD, FTIR and UV-Vis spectroscopy. Thin film sensor device was developed taking each of the samples as the sensing material to study the gas sensing properties towards NO2 gas at room temperature. The CSA doped nanocomposite possessed highly aggregated globular morphology that led to high surface area. Doping of CSA notably favored the gas sensing process. PANi-Ta2O5-CSA40wt% exhibited the best performance with sensor response above 80% towards 500 ppm NO2, a faster response time of 55 seconds and a recovery time of 160 seconds. The device showed good repeatability and high selectivity to NO2. The sensor response was studied under 38%, 57% and 65% relative humidity conditions respectively and it was found to remain stable under different humidity levels.
Dipole Antenna Array Fed by a SIW Based Circular Resonator for Generating Plane Spiral Orbital Angular Momentum Wave

Recently plane spiral orbital angular momentum (PS-OAM) wave has attracted much attention in wireless communications and remote sensing. In this paper, a dipole antenna array fed by a half-mode substrate integrated waveguide (HMSIW) based circular resonator cavity is proposed to generate the PS-OAM waves. Compared with the current feeding network for the circular antenna array, this feeding design is very simple. The full-wave simulations of this proposed antenna shows that this structure do generate the PS-OAM waves. The main lobe direction of the beam is concentrated at θ = 90°, and the 3-dB angular width is about 60°. The antenna efficiency is 90.18%. In main lobe plane, the phase of the beam change approximately linearly with azimuthal angle, and the purity of PS-OAM is over 83%. Besides the PS-OAM dipole array antenna, other types of phased arrays antenna might also be inspired by this feeding approach.
Ripple: An Efficient Runtime Reconfigurable P4 Data Plane for Multicore Systems

The P4 language was originally proposed as a means of providing programmable network data plane functions with the potential to implement, understand, and interpret custom protocols. However, the recompilation process and the re-instantiation of network functions on a target device introduces measurable downtime for network services. In this work, we present Ripple, an efficient, runtime reconfigurable P4 engine on multicore systems to eliminate the recompilation process. The Ripple framework adopts the "generic and static" philosophy, and uses a highly optimized universal binary executable to avoid the lengthy recompilation and reconfiguration process, while supporting packet processing at line rates. We leverage advanced I/O and vectorization techniques to take advantage of packet level parallelism and processor architectural features, thus significantly improve the performance of P4 data plane at runtime. Our in-depth evaluation on multicore architecture shows that Ripple reaches comparable throughput with one of the state-of-art frameworks that requires recompilation and reconfiguration.
Innovative EMI Shielding Solutions on Advanced SiP Module for 5G Application

Recently, the high growing semiconductor is the Internet of Things (IoT) and fifth generation (5G) connectivity application. Based on 5G, the different working frequency between sub 6GHz and millimeter wave (mmWave) frequency range on 28GHz and 39GHz, both systems provide good networking coverage in the city. The mmWave antenna is designed by array antenna to beamforming the Radio frequency signal to the networking devices to achieve high speed data transmission requirement. The system in package (SiP) could integrate various heterogeneous components such as antenna in package (AiP) to get small form factor and high electrical performance. The electromagnetic interference (EMI) is very critical which can damage electronic devices and equipment. To meet the high frequency of electrical performancents, the partition EMI shielding has provided the signal isolation between different function blocks such as digital, analog and RF antenna devices in SiP module. In this paper, a test vehicle is designed to evaluate the shielding effectiveness (SE) by vector network analyzer (VNA). The different partition EMI shielding method and study will proceed and measure under mmWave frequency of 22GHz to 40GHz. The reliability verification of EMI shielding on advanced SiP module will utilize standard JEDEC test condition including Temperature Cycle Test (TCT), High Temperature Storage (HTS), unbias-high accelerated stress test (u-HAST) results to verify the package structure. Finally, this paper will demonstrate the advanced SiP with EMI shielding solutions for future 5G Connectivity devices application.
Development of a Chinese Depressed Speech Corpus Based on The Disturbed Effect of Self-Processing

Depression has long been recognized as one of the leading causes of disability and burden worldwide. In psychology, it is well known that the self is not only the cognitive subject, but also the core of personality. And the high incidence of suicide and pervasive hopelessness in depressed individuals suggested that the self might be abnormal among them. In order to expand the application of the depression detection, we employ classical scientific psychology paradigms on abnormalities of self-related processing in depressed individuals to develop a Chinese depressed speech corpus. Eleven depressed individuals and ten healthy subjects, who are gender-balanced and age-balanced, were recruited to participate in this study. Currently we have preliminarily collected 6 and 2.5 hours of speech data respectively, with the results of preliminary analysis indicating that there exist abnormalities in the depressed speech. The study results will provide a new perspective and strategy for further study on the building and application of Chinese speech corpus in depression.
A Comparison of Surgical Cavity 3D Reconstruction Methods

In robot-assisted minimally invasive surgery (RMIS), surgeons are tasked with navigating and operating in dynamic 3D cavities. 3D spatial information can specifically indicate resection targets or delicate structures to avoid, and is critical to many surgical tasks. While preoperative imaging can provide information about relative anatomical or spatial locations of interest, these data are only a snapshot of a dynamically changing environment; natural movement due to breathing, deformation or natural heartbeat require the operator to make intraoperative, on-the-fly decisions. Online monocular RGB visual feedback may assist, but lacks the spatial information critical to precise surgical operations. In reality, surgeons must negotiate static, offline preoperative data with reduced dimensionality or sparse imaging to perform intricate, life-critical tasks. Real-time dense 3D reconstruction can alleviate these issues. In this comparative work, three approaches towards dense 3D reconstruction from laparoscopic imaging are investigated. For this, a new laparoscopic surgical scene dataset is introduced, and was captured using a pre-calibrated stereo camera. This camera recorded a surgical scene from various viewpoints in time, and a ground truth dense surgical scene was recorded with a high-precision 3D scanner. Three different reconstruction algorithms were then implemented and compared, including simultaneous localization and mapping (SLAM), visual odometry (VO), and structure from motion (SFM). A successful method will enable real-time registration of preoperative imaging and segmentation, thus resulting in safer and more robust surgical operations. This work also enables future research in vision-based force estimation and other research areas in RMIS.
Interactions Between Specific Human and Omnidirectional Mobile Robot Using Deep Learning Approach: SSD-FN-KCF

To fulfill the tasks of human-robot interaction (HRI), how to detect the specific human (SH) becomes paramount. In this paper, the deep learning approach by the integration of Single-Shot Detection, FaceNet, and Kernelized Correlation Filter (SSD-FN-KCF) is developed. From the outset, the SSD is employed to detect the human up to <inline-formula> <tex-math notation="LaTeX">$8m$ </tex-math></inline-formula> using the RGB-D camera with <inline-formula> <tex-math notation="LaTeX">$320\times 240$ </tex-math></inline-formula> resolution. Afterward the omnidirectional mobile robot (ODMR) is driven to the neighborhood of <inline-formula> <tex-math notation="LaTeX">$2.5\sim 3.0m $ </tex-math></inline-formula> such that the depth image can accurately estimate the detected human’s pose. Subsequently, the ODMR is commanded to the vicinity of <inline-formula> <tex-math notation="LaTeX">$1.0m$ </tex-math></inline-formula> and the orientation inside −60~60° with respect to the optical axis to identify whether he/she is the SH by the FaceNet. To reduce the computation time of the FaceNet and extend the SH’s tracking, the KCF is employed to achieve the task of HRI (e.g., human following). Based on the image processing result, the required pose for searching or tracking (specific) human is accomplished by the image-based adaptive finite-time hierarchical constraint control. Finally, the experiment with the SH, who is far from and on the backside of the ODMR, validates the effectiveness and robustness of the proposed approach.