Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling and characterization of HBT limits

In this paper, we present an overview of electrothermal runaway and mixed-mode degradation in HBTs. Both these effects limit the bias up to which an HBT can be used in circuit design. For electrothermal runaway, we discuss the DC case, with and without externally applied base resistance, and also the “pulsed SOA” case, which is most relevant to RF applications. For mixed-mode degradation, we focus on the deceleration which occurs at very long time scales, and which, if accounted for, leads to a strongly enhanced lifetime prediction. Finally, we make some remarks about the physical explanation for mixed-mode degradation. All this work is illustrated with measurements on NPN transistors from NXP's QUBiC GEN8 BiCMOS process.
Exploiting Correlation With Wideband CQI and Making Differential Feedback Overhead Flexible in 4G/5G OFDM Systems

Differential channel quality indicator (CQI) and wideband CQI are key components of the reduced feedback schemes employed in the 5G New Radio (NR) and 4G Long Term Evolution (LTE) standards. They enable a base station (BS) to acquire channel state information that is essential for rate adaptation and frequency-domain scheduling without overwhelming the uplink. We present a novel throughput-optimal rate adaptation rule that exploits the correlation between the differential and wideband CQIs to improve throughput without any additional feedback. It also shows that the prevalent conventional method that adds the two CQIs is sub-optimal. We then propose a novel flexible-overhead differential CQI feedback scheme, in which the number of bits for differential CQI can be different across the subbands. This provides a new flexibility to the BS to control the feedback overhead. It differs from the current rigid parameterization, in which a user always feeds back a 2-bit differential CQI for each subband. In various single-user and multi-user deployment scenarios involving small-scale fading, large-scale shadowing, and co-channel interference, the proposed approach achieves nearly the same throughput as the feedback scheme employed in 5G and LTE, but with much less overhead.
A Fast Decomposition Method to Solve a Security-Constrained Optimal Power Flow (SCOPF) Problem Through Constraint Handling

This paper presents a decomposition methodology using constraint handling rules to improve the computation time of a security-constrained optimal power flow (SCOPF) problem. In order to evaluate methodology performance, tests over small (500 buses), medium (4,918 buses), and large scale (11,615 buses) transmission networks were carried out. The methodology consisted in the decomposition of the SCOPF problem into a base case problem and contingency sub-problems using constraint handling rules to solve the complete problem in an iterative fashion. The first stage involved solving an OPF problem using a base case network. The second stage dealt with the modification of the initial base case by updating some of the constraint limits according to the evaluation of potentially relevant contingencies. The entire algorithm resorted to parallel computing tools. The methodology, along with active power re-dispatch through droop control and PV/PQ switching in post-contingency scenarios, successfully solved the tested networks with the set of proposed constraints.
Face Anti-Spoofing via Adversarial Cross-Modality Translation

Face Presentation Attack Detection (PAD) approaches based on multi-modal data have been attracted increasingly by the research community. However, they require multi-modal face data consistently involved in both the training and testing phases. It would severely limit the applicability due to the most Face Anti-spoofing (FAS) systems are only equipped with Visible (VIS) imaging devices, i.e., RGB cameras. Therefore, how to use other modality (i.e., Near-Infrared (NIR)) to assist the performance improvement of VIS-based PAD is significant for FAS. In this work, we first discuss the big gap of performances among different modalities even though the same backbone network is applied. Then, we propose a novel Cross-modal Auxiliary (CMA) framework for the VIS-based FAS task. The main trait of CMA is that the performance can be greatly improved with the help of other modality while no other modality is required in the testing stage. The proposed CMA consists of a Modality Translation Network (MT-Net) and a Modality Assistance Network (MA-Net). The former aims to close the visible gap between different modalities via a generative model that maps inputs from one modality (i.e., RGB) to another (i.e., NIR). The latter focuses on how to use the translated modality (i.e., target modality) and RGB modality (i.e., source modality) together to train a discriminative PAD model. Extensive experiments are conducted to demonstrate that the proposed framework can push the state-of-the-art (SOTA) performances on both multi-modal datasets (i.e., CASIA-SURF, CeFA, and WMCA) and RGB-based datasets (i.e., OULU-NPU, and SiW).
Comments on “Random Distance Method for Generating Unimodal and Multimodal Cancelable Biometric Features”

This article points out the fallacies in the theory and its implementation proposed by Kaur and Khanna. They have set out a cancelable biometric-based template protection method to address the security and privacy concerns emerging from the use of biometric systems. There are three major issues associated with the method proposed in their study. The first issue relates to the mathematical fallacy in the proof of the random distance method. The second issue concerns the claim of dimension-reduction by 50%, despite the fact that RDM does not preserve inter- and intra-user variations. The third issue is in salting the feature vectors using the OR operation between the feature vectors and random grid (RG), which is incorrect. As it will result in revealing partial information only and will not increase entropy. Furthermore, they have stated that their approach results in noninvertibility using the median filtering. However, its implementation is flawed.
Inhibition of Ca3(PO4)2, CaCO3, and CaSO4 Precipitation for Industrial Recycling Water

In an attempt to control Ca3(PO4)2, CaCO3, and CaSO4 deposits in industrial recycling water systems, an acrylic acid (AA)–allylpolyethoxy carboxylate (APEC) copolymer was examined as a nonphosphorus inhibitor. The synthesized AA–APEC copolymer was characterized by FT-IR. The performance of AA–APEC on inhibition of Ca3(PO4)2, CaCO3, and CaSO4 precipitation was compared with that of current commercial inhibitors. It was shown that AA–APEC exhibited excellent ability to control inorganic minerals, with approximately 82.88% CaSO4 inhibition and 99.89% Ca3(PO4)2 inhibition at levels of 3 and 6 mg/L AA–APEC, respectively. AA–APEC also displayed ability to prevent the formation of CaCO3 scales. Transmission electron microscopy (TEM) images indicated that the outstanding performance of AA–APEC on Ca3(PO4)2 inhibition resulted from a decrease in size of Ca3(PO4)2 solid particles thereby dispersing these particles throughout a fluid, while CaCO3 inhibition was attributed to the formation of ribbon-shaped structures.
A CP-free STBC-MIMO OFDM communication system with ISI suppression capability for underwater multipath channel

Because of a progress in marine development for seabed natural resource, deep sea exploring becomes essential. Then, wireless underwater communication has become important to communicate with distanced terminals. In case that the signal propagation channel contains reflection objects such as sea surface and structures in the sea, plural delayed signals are generated and cause the Inter Symbol Interference (ISI) between transmitting symbols. To avoid the ISI degradation, acoustic OFDM communication system inserts gap interval between successive OFDM symbols such as Cyclic Prefix (CP). However, the insertion of CP lowers data transmission bandwidth. This study evaluates an ISI suppression performance for underwater communication system using Space Time Block Code (STBC) with MIMO OFDM system [1]–[2] with no CP insertion. Previously we have reported computer simulation results as [3], and this paper describes improved signal processing method and Ocean experiment results. Simulated Bit Error Rate (BER) performance vs maximum Multipath Delay L. Multipath condition is one direct wave and additional L delayed waves with linearly degraded amplitude and random phase. In case of SNR (dB) more than 20 dB, 70 points delay (corresponding to 11ms) for 128 points OFDM symbol (20ms length) is successfully suppressed, which corresponds to 55% ISI condition. We uused two transmitting and 4 receiving transducers. The distance between TX and RX is roughly 6.0m. 3.3 ms multipath delay is observed with 20ms OFDM symbol. Which corresponds to 16.5% Inter-Symbol-Interference (ISI) condition. Measured constellations for simple OFDM with no CP and 2×4 STBC-MIMO OFDM with no CP. And corresponding measured BERs are 0.0401 and 0.0023, respectively. Because of ISI, the OFDM with no CP suffers the ISI and the BER is degraded, while CP-free 2×4 STBC- MIMO OFDM has shown improved BER because of ISI suppression capability. In this simulation, time series distortion was observed. For time series distortion, BER was improved when AIF canceled. And corresponding measured BER is 0.0017. the BER of CP-free STBC-MIMO OFDM (AIF can cell), which we used to deal with distortion, was the smallest.
Towards a Prototype Based Explainable JavaScript Vulnerability Prediction Model

Security has become a central and unavoidable aspect of today's software development. Practitioners and researchers have proposed many code analysis tools and techniques to mitigate security risks. These tools apply static and dynamic analysis or, more recently, machine learning. Machine learning models can achieve impressive results in finding and forecasting possible security issues in programs. However, most of the current approaches fall short of developer demands in two areas at least: explainability and granularity of predictions. In this paper, we propose a novel and simple yet, promising approach to identify potentially vulnerable source code in JavaScript programs. The model improves the state–of–the–art in terms of explainability and prediction granularity as it gives results at the level of individual source code lines, which is fine-grained enough for developers to take immediate actions. Additionally, the model explains each predicted line (i.e., provides the most similar vulnerable line from the training set) using a prototype-based approach. In a study of 186 real–world and confirmed JavaScript vulnerability fixes of 91 projects, the approach could flag 60% of the known vulnerable lines on average by marking only 10% of the code-base, but in particular cases, the model identified 100% of the vulnerable code lines while flagging only 8.72% of the code–base.
Pre-trained VGG-16 with CNN Architecture to classify X-Rays images into Normal or Pneumonia

Chest radiograph (x-ray) are primarily used to diagnose pneumonia However, x-ray examination is a difficult operation, even for a qualified radiologist and there is a need to improve the accuracy of the diagnosis. In this paper, we used chest x-ray images in the Visual Geometry Group (VGG16) concept from Convolutional neural network (CNN) to classify pneumonia or normal that could help doctors in their decision-making phase. In order to refine the training samples in a balanced way, data augmentation methods are used. This method is a supervised learning technique that predicts the outcome, based on the accuracy of the data set used by the network. VGG 16 model were utilized and calibrate the models of deep learning to achieve greater precision in training and validation. Model has successfully implemented in python using Keras library. The model was capable of achieving a 95.67 percent test precision and a 96 AUC score on the dataset with 12.64 test loss and 50 percent accuracy on test.
Lightweight Attended Multi-Scale Residual Network for Single Image Super-Resolution

Recently, deep convolutional neural networks (CNN) have been widely applied in the single image super-resolution (SISR) task and achieved significant progress in reconstruction performance. However, most of the existing CNN-based SR models are impractical to real-world applicants due to numerous parameters and heavy computation. To tackle this issue, we propose a lightweight attended multi-scale residual network (LAMRN) in this work. Specially, we present an attended multi-scale residual block (AMSRB) to extract multi-scale features, where we embed the efficient channel attention block (ECA) to enhance the discrimination of features. Besides, we introduce a double-attention fusion (DAF) block to fuse the low-level and high-level features efficiently. We use spatial attention and channel attention to obtain guidance from the low-level and high-level features, which is used to guide the feature fusion. Extensive experimental results demonstrate that our LAMRN achieves competitive performance against the state-of-the-art methods with similar parameters and computational operations.
Twisted Spatiotemporal Optical Vortex Random Fields

We present twisted spatiotemporal optical vortex (STOV) beams, which are partially coherent light sources that possess a coherent optical vortex and a random twist coupling their space and time dimensions. These beams have controllable partial coherence and transverse orbital angular momentum (OAM), which distinguishes them from the more common spatial vortex and twisted beams (known to carry longitudinal OAM) in the literature and should ultimately make them useful in applications such as optical communications and optical tweezing. We present the mathematical analysis of twisted STOV beams, deriving the mutual coherence function and linear and angular momentum densities. We simulate the synthesis of twisted STOV beams and investigate their free-space propagation characteristics. We discuss how to physically generate twisted STOV fields and lastly conclude with a summary and brief discussion of future research.
Distributed Deep Convolutional Compression for Massive MIMO CSI Feedback

Massive multiple-input multiple-output (MIMO) systems require downlink channel state information (CSI) at the base station (BS) to achieve spatial diversity and multiplexing gains. In a frequency division duplex (FDD) multiuser massive MIMO network, each user needs to compress and feedback its downlink CSI to the BS. The CSI overhead scales with the number of antennas, users and subcarriers, and becomes a major bottleneck for the overall spectral efficiency. In this paper, we propose a deep learning (DL)-based CSI compression scheme, called DeepCMC, composed of convolutional layers followed by quantization and entropy coding blocks. In comparison with previous DL-based CSI reduction structures, DeepCMC proposes a novel fully-convolutional neural network (NN) architecture, with residual layers at the decoder, and incorporates quantization and entropy coding blocks into its design. DeepCMC is trained to minimize a weighted rate-distortion cost, which enables a trade-off between the CSI quality and its feedback overhead. Simulation results demonstrate that DeepCMC outperforms the state of the art CSI compression schemes in terms of the reconstruction quality of CSI for the same compression rate. We also propose a distributed version of DeepCMC for a multi-user MIMO scenario to encode and reconstruct the CSI from multiple users in a distributed manner. Distributed DeepCMC not only utilizes the inherent CSI structures of a single MIMO user for compression, but also benefits from the correlations among the channel matrices of nearby users to further improve the performance in comparison with DeepCMC. We also propose a reduced-complexity training method for distributed DeepCMC, allowing to scale it to multiple users, and suggest a cluster-based distributed DeepCMC approach for practical implementation.
An Effective Machine Learning Scheme to Analyze and Predict the Concentration of Persistent Pollutants in the Great Lakes

Persistent organic pollutants (POPs) are highly toxic and difficult to degrade in the natural ecology, which has a severe negative impact on the ecological environment. Quantifying changes in the concentrations of persistent organic pollutants in the Great Lakes is challenging work. Machine learning (ML) methods are potent predictors that have recently achieved impressive performance on time series tasks. ARIMA model, Linear Regression methods, XGBoost algorithm, and Long Short-Term Memory (LSTM) are commonly used for estimating time-series changes. Traditionally Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) have been standard criteria to measure the error between the actual value and predicted value; however, Euclidean distance (ED) cannot effectively calculate the similarity between two-time series. We proposed an alternative criterion called Penalty Dynamic Time Wrapping (Penalty-DTW) based on Dynamic Time Wrapping (DTW). It can accurately measure the difference between the actual value and the predicted value. We study the benefits of Penalty-DTW vs. ED under the above ML algorithms. Further, considering the machine learning algorithm’s uncertainty, we proposed combining LSTM and deep ensemble methods to quantify algorithms uncertainty and make a confident prediction. We find improved LSTM model outperformed other predictive power models by comparing pollutant concentration prediction. The prediction results show that the concentration of pollutants has a stable downward trend in recent years. Simultaneously, we found that pollutants’ concentration correlates with seasons, which positively guides environmental pollution control in the Great Lakes.
Iterative Semidefinite Relaxation for Geolocation of Uncooperative Radars Using Doppler Frequency Measurements

In this article, we propose a novel method utilizing iterative semidefinite relaxation for geolocation of stationary uncooperative radars. In our scenario, the geolocation is to be performed in a receiver located on a moving platform. The proposed method uses frequency measurements that are Doppler-shifted due to the platform motion. First, constructed nonconvex maximum likelihood cost function for position estimation is relaxed to a convex optimization problem by applying linearization to range variables. Then, at each iteration of the method, carrier frequency and position of the radar are estimated jointly. Conducted experiments show that a few iterations are enough for convergence to accurate estimates. The proposed method is computationally less expensive compared to traditional techniques, which require extensive grid search procedures in either position or carrier frequency parameter space. In the experiments, the performance of the proposed method is compared to the state-of-the-art techniques and the Cramer–Rao lower bound (CRLB). It is observed that the proposed method attains the CRLB at low noise levels while still providing accurate solutions at high noise cases. Furthermore, it is also seen that platform-radar geometry has an impact on the proposed method's performance. If the radar lies in the convex hull of the receiver path, the proposed method performs significantly better due to improvement in the linearization of range values.
High-order Superdirectivity of Line Acoustic Vector Sensor Arrays

In this paper, the closed form expression of spatial coherence of the noise between the combined vector sensor components in the isotropic noise field is derived, and a superdirective beamformer based on the Gram-Schmidt orthogonal transform is proposed. One such combined directional sensor is composed of a single-degree-of-freedom particle velocity sensor and an omnidirectional acoustic pressure sensor, and its spatial response is directional. The solution to the spatial coherence is solved to constitute the noise covariance matrix of the line array in the endfire direction. Compared to the traditional pressure sensor arrays, the vector sensor arrays can pick up signals at the desired direction and increase the array gain by 4dB - 6dB in a wide frequency band with low side lobes and no left-right ambiguity, which are useful to suppress the unwanted noise and interferences from other sides. The proposed superdirective method is superior to the conventional beamforming and the trade-off can be made between directivity index and robustness. The performance of high-order superdirectivity of the line vector sensor array is analyzed, which is useful to the underwater hydrophone array design.
Market Profile with Convolutional Neural Networks: Learning the Structure of Price Activities

Many studies tried to apply machine learning (ML) methods to forecast the financial time series during the past decade. Moreover, the emergence of deep learning has led to another researches with results that significantly outperform previous models. However, most of the deep learning researches used the raw financial time series price data which consists of open, close, high and low (OCHL) as learning features. According to statistics, Long Short Term Memory networks (LSTM) is the first choice to deal with the forecasting problems with OCHL datasets due to its feedback connection networks, resulting higher performances for price series prediction. Meanwhile, Convolutional Neural Networks (CNN) has increased its popularity since it outperforms traditional ML models in classification problems. In this paper, there are three types of future trend that are the ultimate targets to be discovered. Nevertheless, OCHL features may be too sensitive to learn the large future trend in financial time series. This study proposes a novel approach: Convolutional Neural Networks with Market Profiles (CNN-MPs) which includes (1) adapting Market Profile to covert financial time series data to grey-scale image method, (2) generating two types of learning images: stacked and sequential profile that can keep the interaction between continuous profiles, and (3) learning the structure of price activities with CNN. Market Profile is a concept that has been widely used in the financial decision-making by comparing the current price with the market fair value. In addition, the trend is well established at the accepting movement of fair value which can be confirmed from the structure of profiles. In experiments, one of the popular commodities, corn was selected to evaluate the proposed method. And the experimental results show that proposed sequential profile method obtained 17% higher accuracy and more profitability than LSTM networks and other methods. Therefore, the proposed CNN-MPs method can effectively discover the trend of corn providing those who need import corn with a reference.
Leveraging Similarity Metrics to In-Situ Discover Planktonic Interspecies Variations or Mutations

Planktons are vital to our planet's ecosystems. Mapping and monitoring planktonic-distribution in the ocean is of great importance in understanding these ecosystems as well as gaining insights on the general health of our planet. The task of identifying different planktonic species and their concentrations is a critical yet challenging part of this endeavor. In this paper, we explore the utilization of one-shot classifiers on in-situ captured planktonic images. The similarity-based classification method used by the one-shot classification algorithms gives rise to various research opportunities outside of the typical classification paradigm. Exploring the development of a proficient plankton image classification framework that can determine how different or how similar two plankton specimens are, can inform us if they belong to the same species, if they are different variations of the same species or if we have encountered a completely unseen classes of plankton. This similarity is valuable information that assists in further mapping the diversity of the planktonic species. We further extend the Siamese one-shot classifier with few-shot classifier to improve the model performance. Empirical evaluations of the new extension yield promising results.