Miracle-dz/UniFAR-FTUs huggingface dataset:
±CIM SRAM for Signed In-Memory Broad-Purpose Computing From DSP to Neural Processing

This work introduces the ±CIM SRAM macro having the unique capability of performing in-memory multiply-and-accumulate computation with signed inputs and signed weights. This uniquely enables the execution of a broad set of workloads, ranging from storage, subsequent signal processing, and pre-conditioning or feature extraction to final convolutional neural network (CNN) computations. The ability to handle arbitrary input/weight sign in any operand within the same array and the same access cycle enables true end-to-end data locality, preserving the inherent benefits of in-memory computing along the entire signal chain. The proposed broad-purpose computing SRAM is based on a commercial 8T dual-port bitcell, and its simplicity allows the enhanced periphery to be pitch-matched with the array, making it amenable for automated design via memory compilers. The ±CIM pipelined architecture allows concurrent read/write and compute operations, avoiding the traditional memory unavailability in compute mode for improved throughput and easier system integration. A 40-nm test chip demonstrating the ±CIM architecture with adjustable input/weight precision exhibits an energy efficiency up to 41 TOPS/W, at an area (energy) overhead of 38% (25%) and negligible performance overhead compared to a compiled SRAM baseline. The sub-LSB computation mean-squared error associated with mismatch (0.38 LSB) and temporal noise (0.62 LSB) confirms the inherent robustness of the architecture. When used for neural network tasks (LeNet-5 and VGG), the accuracy drop is kept between 0.3% and 3.4%, compared to a double-precision software implementation. As an example of digital signal processing (DSP) workload, a frequency-domain feature extractor for voice activity detection keeps the accuracy drop lower than 3.8%.
Research on Voice Print Recognition of Electrical Faults Based on Attention-MFCC Algorithm

The sound of electrical equipment during operation contains a lot of information about the status of power equipment. In order to extract status information to identify the operating status of power equipment, a voiceprint recognition algorithm for electrical equipment was proposed in this paper. Gas Insulated Switchgear (GIS) were selected as research objects. The sound of GIS with different working conditions were captured. After preprocessing like framing and windowing, Mel frequency cepstral coefficient (MFCC) were used for audio feature extraction. For the sound characteristics of power equipment, the feature extraction method uses mid-time MFCC coefficients. Meanwhile, attention mechanism was introduced to improve the recognition performance of the algorithm. Then, Deep Neural Network (DNN) was used as the feature recognition algorithm. The research results show that this algorithm can effectively identify the operating status of power equipment. Based on the MFCC acoustic model, the F1 scores of GIS under different fault conditions generally exceed 90%. Compared with the original MFCC algorithm, the Attention-MFCC algorithm are more in line with the characteristics of smooth changes in the sound energy of the power equipment.
Survey on Space-air-ground Integrated Networks in 6G

With 6G receiving more and more attention, the space-air-ground integrated networks (SAGIN) have become the key research content of the next generation communication. This paper investigates the current development status of SAGIN, discusses the key technologies of SAGIN. In addition, the prospect and conception of the application of SAGIN in 6G are presented
Price Prediction and Benefit Analysis on PV Power Generation Projects in the Carbon Trading Market

Under the grid parity policy, the development of photovoltaic power generation projects is facing economic problems. Participating in the carbon trading market can not only reduce carbon emissions, but also help photovoltaic power generation projects obtain more benefit. In order to study the impact of the carbon trading market on photovoltaic power generation projects under the grid parity policy, two perspectives can be proceeded, namely the benefit perspective and the cost perspective. In the study of the carbon trading market, an improved Long Short-Term memory model is used to minimize the impact of the randomness of carbon trading price on photovoltaic power generation projects. The results show that, improved Long Short-Term memory model greatly reduce prediction errors; no matter from the perspective of benefit or cost, the carbon trading market can help the development of photovoltaic power generation projects under the grid parity policy.
Preliminary pulse and temperature testing

The COVID-19 virus is currently the deadliest threat to mankind. The signs of the sickness include a change in the body temperature, a decrease in body oxygen levels, and a decrease in heart rate. Because of the pandemic crisis created by COVID-19, it has become essential to examine every individual's health so that it does not influence others in public areas as well as in their personal spaces. The primary goal of our project is to use the cloud to automate the temperature and pulse testing of every individual in public locations and preserve the database for future reference. To assist with the current scenario, we created a system that would automatically measure the individual's temperature using a temperature sensor and pulse using a pulse oximeter sensor without any human intervention. If the measured values are meeting essential threshold levels, the individual's hands are automatically sanitized and then they are allowed to enter inside public places.
Deep Learning Technique for COVID 19 Prediction using CT Scan Images

Corona Disease Virus (COVID-19) is a rapidly spreading contagious viral disease that causes respiratory contaminations and is currently generating a worldwide medical crisis. It has caused a massive influence on people's lives, general well-being, and the global economy. Henceforth, it is critical to straightaway analyze the positive cases in order to keep the illness from spreading further and to regard infected patients as fast as could really be expected. Both patients and specialists will be benefitted by the early recognizable capability of outrageous COVID-19 by utilizing chest CT to examine biomedical images. RT-PCR (switch record polymerase chain response) based tests help to identify COVID-19, which has numerous limits. In this work, different CNN based Classifier model methodologies are utilized to follow the presence of COVID-19 from chest CT filter images of patients. In true indicative situations, a profound CNN-based methodology could be amazingly valuable in accomplishing quick COVID-19 testing. By utilizing irregularity data obtained from sifted images, image expansion enhances the number of profitable models for creating the CNN model. The proposed model has a grouping exactness of 95% for CT examines utilizing this strategy. With picture expansion, CT check pictures have an affectability of 94.78%and a particularity of 95.98%. The trial results were contrasted with ResNet-18, ResNet-50, and VGG-16 models, with freely available datasets containing CT images.
Hyperspectral Imagery Spatial Super-Resolution Using Generative Adversarial Network

Hyperspectral imagery contains both spatial structure information and abundant spectral features of imaged objects. However, due to sensor limitations, abundant spectral information always comes at the sacrifice of low spatial resolution, which brings about difficulties with object analysis and identification. The super-resolution (SR) of HSIs, restored by the traditional interpolation algorithms or the network models trained with the mean-square-error-based loss function, tends to produce over-smoothed images. In this paper, we propose a novel Hyperspectral imagery Spatial Super-Resolution algorithm based on a Generative Adversarial Network (HSSRGAN). The generator network in HSSRGAN consists of two interacting part, i.e., a spatial feature enhanced network (SFEN) and a spectral refined network (SRN), while the discriminator network is employed to predict the probability that the authentic HR image is comparatively more similar than the forged generated image. Concretely, SFEN with the special dense residual blocks is designed to fully extract and enhance more deep hierarchical spatial features of hyperspectral imagery, while SRN is constructed to capture spectral interrelationships and refine the spatial context information so as to increase spatial resolution and alleviate spectral distortion. Moreover, SFEN and SRN are trained by the least-absolute-deviation based loss function to investigate spatial context and the spectral-angle-mapper based loss function to refine spectral information. We validate two versions of our proposed algorithm, 3D-HSSRGAN and 2D-HSSRGAN, on Pavia Centre dataset and Cuprite dataset. Experimental results show that the presented approach is superior to several existing state-of-the-art works.
All-Optical In-Phase/Quadrature Microwave Mixer for Antenna Remoting Applications

A new microwave photonic signal processing structure for realising in-phase/quadrature (I/Q) mixing is presented. It is based on a dual-drive Mach Zehnder modulator (DDMZM) and an optical phase modulator inside a Sagnac loop interferometer. Two output IF signals with a quadrature phase difference are obtained by using two photodetectors to detect the upper and lower sidebands of the optical signal at the Sagnac loop output. A quadrature phase difference in the two IF signals is realised by biasing the DDMZM at the quadrature point. This has the advantage of very little quadrature phase errors as a standard modulator bias controller can accurately lock the DDMZM bias point. The optical phase modulator driven by an RF signal can be placed in a remote location for antenna remoting. Theoretical analysis and simulation show using the proposed I/Q mixer for image rejection can overcome the modulation index dependent image rejection ratio problem in the reported I/Q mixers based on a cascaded modulator structure. The Sagnac loop based I/Q mixer is experimentally verified with results show only ±1° quadrature phase error and more than 50 dB image rejection ratio over the operating frequency range of 6 to 20 GHz.
Driving Range Estimation of Electric Vehicles using Deep Learning

Electric vehicles (EV) are gaining popularity due to their reduced pollution, fewer emissions, and energy savings but has big challenges like driver's range anxiety, that slows down the penetration of electric vehicles. This work focuses on analyzing the impact of different factors affecting the driving range of EVs and developing a deep learning-based optimized system for range prediction using neural networks. An electric vehicle model is developed in MATLAB Simulink to analyze the impact of various factors on the driving range and validation of the range prediction model. A comparative study on different neural network models is done using the Keras framework to select the best suited regression model to predict the driving range based on prediction error. The analysis reveals the impact of various factors like driver behaviour, exploitation environment, battery parameters, and auxiliary loads on the driving range. For the test data, the bidirectional long short-term memory shows the minimum error compared to other sequential models with a mean square error of 0.029 km in the prediction of driving range. The work can be further extended by integrating to driver assistance systems for best route selection, charge scheduling, trip planning etc.
Automating the Detection of Cyberstalking

Cyberstalking and cyberbullying are serious issues that affect the online lifestyle of the victim or an organization and have a bad impact on a person's behavior. Social media can be considered as goldmines to collect personal details of a person, it can provide stalkers with all our details and this information can be misused in a lot of ways. This work aims to research ways to define, identify a cyberstalker as well as propose an algorithm to identify the potential cyberstalkers so that it alerts the victim to take precautions. This process is automated with Machine Learning and Natural Language Processing techniques. The project aims to propose an automated solution for identifying a stalker online.
Extreme learning machine based short-term wind power prediction framework with adaptive variational mode decomposition

a novel extreme learning machine(ELM) based short-term wind power forecasting framework with adaptive variational mode decomposition(VMD) is put forward. First, adaptive VMD approach is developed to decompose the raw historical wind power sequence into multiple intrinsic mode functions(IMFs) to get rid of the random noise and extract the trend and harmonics. Then ELM model, which is respectively trained by using every IMF of the wind power training set as input, is employed to predict wind power. What is more, the prediction results are reconstructed to achieve higher prediction accuracy. Finally, compared with the LSTM, SVR, ELM and ARIMA model, the proposed model gets the optimum performance that be desired in short-term wind power forecasting.
Towards Interpretable and Reliable Reading Comprehension: A Pipeline Model with Unanswerability Prediction

Multi-hop QA with annotated supporting facts, which is the task of reading comprehension (RC) considering the interpretability of the answer, has been extensively studied. In this study, we define an interpretable reading comprehension (IRC) model as a pipeline model with the capability of predicting unanswerable queries. The IRC model justifies the answer prediction by establishing consistency between the predicted supporting facts and the actual rationale for interpretability. The IRC model detects unanswerable questions, instead of outputting the answer forcibly based on the insufficient information, to ensure the reliability of the answer. We also propose an end-to-end training method for the pipeline RC model. To evaluate the interpretability and the reliability, we conducted the experiments considering unanswerability in a multi-hop question for a given passage. We show that our end-to-end trainable pipeline model outperformed a non-interpretable model on our modified HotpotQA dataset. Experimental results also show that the IRC model achieves comparable results to the previous non-interpretable models in spite of the trade-off between prediction performance and interpretability.
Genetic disruption of the kindlin-3/β1-integrin interaction results in defective β1-integrin dependent platelet adhesion and granula secretion

The affinity state of integrins is regulated by two cytoplasmic integrin binding proteins: talin and kindlin. Loss of either of them results in inactive integrins in various cell types. To test whether a direct interaction between kindlin and the integrin is required for integrin activation and β1 integrin mediated signaling we analyzed platelets from mice expressing a kindlin-binding deficient β1 integrin (β1TTAA). Our data clearly show that integrin inside-out signaling depends on a direct integrin-kindlin interaction as β1TTAA integrins remain inactive upon platelet activation by soluble agonists. Further we show that single platelet adhesion to injured vessel walls is strongly reduced in β1 null and β1 (TTAA) platelets suggesting an important role of β1 integrins for initial platelet adhesion. In line with this observation, we show that β1 integrin-mediated signals direct granula secretion.
Influence of the Binder on Lithium Ion Battery Electrode Tortuosity and Performance

The electrochemical performance of porous graphite anodes in lithium ion battery applications is limited by the lithium ion concentration gradients in the liquid electrolyte, especially at high current densities and for thick coatings during battery charging. Beside the electrolyte transport parameters, the porosity and the tortuosity of the coating are key parameters that determine the electrode’s suitability for high power applications. Here, we investigate the tortuosity of graphite anodes using two water as well as three n-methyl-2-pyrrolidone based binder systems by analysis of symmetric cell impedance measurements, demonstrating that tortuosities ranging from ∼ 3–10 are obtained for graphite anodes of similar thickness ( ∼ 100 μ m), porosities ( ∼ 50%) and areal capacity ( ∼ 3.4 mAh/cm 2 ). Furthermore, selected electrodes with tortuosities of 3.1, 4.3, and 10.2 were cycled in cells with reference electrode at charging C-rates from 0.1-20 1/h, illustrating the clear correlation between electrode tortuosity and its rate capability. Understanding and predicting rate limitations in lithium ion batter- ies with porous electrodes requires profound knowledge of the electrolyte transport parameters (i.e., transference number, diffu- sion coefﬁcient, conductivity) and the thermodynamic factor, but also the porosity and the tortuosity of the electrodes. The tortuosity of the electrode is particularly critical because the effective electrolyte conductivity and the effective diffusion coefﬁcient in the electrolyte directly scale with the inverse of the tortuosity, so that care has to be taken to develop electrodes with minimized tortuosity. Using ex- perimental approaches 1–4 or 3D tomography, 5,6 it was that of tortuosity. For a active material, tortuosity are obtained for the graphite anodes under study although the binder volume fraction is minor (here between 0.8 and 5% for binder mass fractions of 1.5 wt% to 10 wt%) compared to the void volume fraction of the electrodes (here 51 ± 5%). The pronounced effect of the small volume fraction binder phase was previously shown for inhomogeneous binder distributions prepared by different drying procedures. 9 More detailed investigations of the electrode preparation steps and their inﬂuence on electrode tortuosity will be subject of a future work.
An improved CRNN for Vietnamese Identity Card Information Recognition

This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity (CID) card. First, we apply Mask-RCNN to segment and align the CID card from the background. Next, we present two approaches to detect the CID card’s text lines using traditional image processing techniques compared to the EAST detector. Finally, we introduce a new end-to-end Convolutional Recurrent Neural Network (CRNN) model based on a combination of Connectionist Temporal Classification (CTC) and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions together. The length of the CTC’s output label sequence is applied to the attention-based decoder prediction to make the final label sequence. This process helps to decrease irregular alignments and speed up the label sequence estimation during training and inference, instead of only relying on a data-driven attention-based encoder-decoder to estimate the label sequence in long sentences. We may directly learn the proposed model from a sequence of words without detailed annotations. We evaluate the proposed system using a real collected Vietnamese CID card dataset and find that our method provides a 4.28% in WER and outperforms the common techniques.
Calotropis gigantea fibers: A potential reinforcement for polymer matrices

ABSTRACT Natural fibers are identified as one of the effective alternatives for reinforcing the polymer matrices on account of their sustainability and renewable characteristics by replacing the synthetic fibers. This study is intended to apprehend the properties of the fibers derived from the stem of Calotropis gigantea plant. The functional groups of biopolymers were recognized by Fourier transform infrared spectrum. The crystalline nature of the cellulose that represents the mechanical strength and integrity of the fibers was found from the X-ray diffraction, whereas the thermal behavior was studied by thermogravimetric analysis. Scanning electron microscope was used to study the morphology of the fibers. The results of these analytical studies have shown that the crystallinity index of the fibers was 56.08% and the fibers were able to withstand a temperature of about 220°C proving that the fibers can be used as effective reinforcements for polymer matrices similar to the commonly used bio-fibers.
An Automated Brain Image Analysis System for Brain Cancer using Shearlets

In this paper, an Automated Brain Image Analysis (ABIA) system that classifies the Magnetic Resonance Imaging (MRI) of human brain is presented. The classification of MRI images into normal or low grade or high grade plays a vital role for the early diagnosis. The Non-Subsampled Shearlet Transform (NSST) that captures more visual information than conventional wavelet transforms is employed for feature extraction. As the feature space of NSST is very high, a statistical t-test is applied to select the dominant directional sub-bands at each level of NSST decomposition based on sub-band energies. A combination of features that includes Gray Level Co-occurrence Matrix (GLCM) based features, Histograms of Positive Shearlet Coefficients (HPSC), and Histograms of Negative Shearlet Coefficients (HNSC) are estimated. The combined feature set is utilized in the classification phase where a hybrid approach is designed with three classifiers; k-Nearest Neighbor (kNN), Naive Bayes (NB) and Support Vector Machine (SVM) classifiers. The output of individual trained classifiers for a testing input is hybridized to take a final decision. The quantitative results of ABIA system on Repository of Molecular Brain Neoplasia Data (REMBRANDT) database show the overall improved performance in comparison with a single classifier model with accuracy of 99% for normal/abnormal classification and 98% for low and high risk classification.
Blockchain: Secured Solution for Signature Transfer in Distributed Intrusion Detection System

Exchange of data in networks necessitates provision of security and confidentiality. Most networks compromised by intruders are those where the exchange of data is at high risk. The main objective of this paper is to present a solution for secure exchange of attack signatures between the nodes of a distributed network. Malicious activities are monitored and detected by the Intrusion Detection System (IDS) that operates with nodes connected to a distributed network. The IDS operates in two phases, where the first phase consists of detection of anomaly attacks using an ensemble of classifiers such as Random forest, Convolutional neural network, and XGBoost along with genetic algorithm to improve the performance of IDS. The novel attacks detected in this phase are converted into signatures and exchanged further through the network using the blockchain framework in the second phase. This phase uses the cryptosystem as part of the blockchain to store data and secure it at a higher level. The blockchain is implemented using the Hyperledger Fabric v1.0 and v2.0, to create a prototype for secure signature transfer. It exchanges signatures in a much more secured manner using the blockchain architecture when implemented with version 2.0 of Hyperledger Fabric. The performance of the proposed blockchain system is evaluated on UNSW NB15 dataset. Blockchain performance has been evaluated in terms of execution time, average latency, throughput and transaction processing time. Experimental evidence of the proposed IDS system demonstrates improved performance with accuracy, detection rate and false alarm rate (FAR) as key parameters used. Accuracy and detection rate increase by 2% and 3% respectively whereas FAR reduces by 1.7%.