Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Consensus Measurement of Individual-level Decentralization in Blockchains

Decentralization is widely recognized as a crucial characteristic of blockchains that enables them to resist malicious attacks such as the 51% attack and the takeover attack. Prior research has primarily examined decentralization in blockchains employing the same consensus protocol or at the level of block producers. This paper presents the first individual-level measurement study comparing the decentralization of blockchains employing different consensus protocols. To facilitate cross-consensus evaluation, we present a two-level comparison framework and a new metric. We apply the proposed methods to Ethereum and Steem, two representative blockchains for which decentralization has garnered considerable interest. Our findings dive deeper into the level of decentralization, suggest the existence of centralization risk at the individual level in Steem, and provide novel insights into the cross-consensus comparison of decentralization in blockchains.
Sensorless Field Oriented Control of AC Induction Motor Using PI, PD & PID Controllers

With the Field-Oriented Control (FOC) vector approach, the control of the induction motor behaves similar to that of a separately excited dc motor. The torque and flux components in the d-q rotating reference frame can be independently controlled with the help of unit vectors. The main focus of the present work is to show the effective utilization and feasibility of field-oriented control of AC induction motors using PI, PD, and PID controllers without using any physical sensors and using observers. The speed, torque, and position in this scheme are estimated, analyzed, and simulated with the help of a motor control block-set in MATLAB/Simulink for a (10HP, squirrel cage induction motor) to a VSI using the SVPWM for effective modulation.
COMPUTATIONAL COMPLEXITY IN THE ANALYSIS OF LARGE ANTENNAS

The main factors affecting the overall effectiveness of any numerical procedure for the analysis of large antennas (or scatterers), i.e., the problem size and the computational cost, are introduced and briefly discussed. It is shown how the size can be rigorously defined and extimated and the corresponding minimum, ideal, computational cost is determined. Then, the possibility of developping algorithms approaching the ideal limits is examined, and possible ways to achieve the goal are enlighted.
Analysis Of PPG Signal Correlation With Cardiac and Respiratory Signals In Humans Using Spectral Analysis Techniques

Non-invasive diagnostics is a growing discipline, and ophthalmic devices that usage photons as detecting essentials are becoming progressively significant and pertinent in this context. The reason for this is that they may be used for research purposes with little to no financial outlay, thanks to their simple structure, ease of use, and low cost in compared to equipment like EEG, MRI, and FMRI. Photoplethysmography (PPG) sensors, which are able to measure blood volumetric changes in the subcutaneous vessels, are used in the present work with sensitive temperature sensors to track respiratory activity. A peak at around 0.12 Hz is also visible in a detailed analysis of the PPG signal's frequency spectrum (fb). Low frequency rhythms, also known as the relaxation rhythm, are said to be accentuated/made more prominent at 0.13Hz (fl) in combination with the respirational ultimate throughout deep inhalation. FFT graphs signal power over the whole frequency spectrum. As the PPG FFT comprises many peaks, Power spectral density has been employed to avoid misperception. Normally, the cardiac uttermost is the utmost pronounced PPG top. Breathing frequency peaks are weaker. Other than these two peaks, a peak of 0.14 or less may be identified, depending on the subject's mental and physical health.
Mining Attributed Input Grammars and their Applications in Fuzzing

Undetected errors in software systems are a common cause of vulnerabilities and security holes. Grammar Fuzzing is an effective method for testing these systems, but it has limitations such as lack of knowledge about the semantics of the program and difficulty obtaining grammar for these systems. To address these limitations, we propose an approach to automatically mine grammars, and enhance it with semantic rules and contextual constraints to create attribute grammars. These attribute grammars can then be used for fuzzing. Our preliminary results show that this automated extraction process is feasible, as we successfully applied it to an expression parser and were able to extract an attribute grammar representing the parser’s functionality.
Brain Tumor Segmentation using MRI Images by Optimized U-Net

Segmenting tumor in the brain is a challenging process undertaken by the surgeon to assess and locate the tumor location in the MRI images. To overcome this constraint, an improved U-Net architecture for use in the BraTS20 and BraTS21 challenge’s brain tumor segmentation problem is proposed. The accuracy has been improved by modifying the loss function. Comprehensive ablation research to investigate Deep Supervision loss, Cross-Entropy, Decoder Attention, and Residual Connections to determine the best model architecture and learning schedule is performed. Multiple convolutional channels have been experimented with, and post-processing techniques to find the ideal spot for the U-Net encoder’s depth have also been undertaken. The proposed technique outperforms every U-Net variant and produces superior outcomes while incurring a minimal loss.
Removal of Artifacts In EEG Signals Using Sign Based LMS Adaptive Filtering Techniques

The electroencephalogram (EEG) has been used for many years to help doctors diagnose and assess conditions affecting the brain. An electroencephalogram is a specialized diagnostic tool for monitoring brain activity electrically. Human brain anomalies and responses to stimuli can be deduced from EEG recordings. The presence of extraneous signals, known as artefacts, can cause distortion in EEG recordings. Since these aberrations introduce spikes that can be mistaken for neurological rhythms and since the properties of practically all biomedical signals vary with time, it is challenging to evaluate the EEG. Therefore, in order to guarantee an accurate analysis and diagnosis, noise and unwanted signals must be removed from the EEG. The identification of artefacts, or any potential deviation that has a source other than the brain, is a critical issue in this field. Artifacts can originate from a variety of sources, including the undesired movement of the eyes and eyelids, breathing, bodily movements, power line interference, muscular, cardiac activity, etc. The computational complexity is the primary subject of extensive research into adaptive filtering algorithms. The goal is to create a variety of novel ANC's for denoising EEG signals while taking into account computing complexity, Signal-to-Noise Ratio, Mis-adjustment, and convergence difficulties. This is done by taking into account three types of adaptive algorithms: The Least Mean Square (LMS) Adaptive algorithm and its several weight update variations.
Realization of SWAP Gate using Electro-Optic Phenomenon-based Mach-Zehnder Interferometer

In this paper, the SWAP gate is realized using the electro-optic (EO) phenomenon in the Mach-Zehnder Interferometer (MZI). In the structuring of the SWAP gate, two MZI is being used, and output is obtained from the bar port of both MZI. The SWAP gate output is obtained from MATLAB by using the mathematical description of MZI. The proposed layout of the SWAP gate is designed using titanium diffused lithium niobate (Ti:LiNb03) waveguide and simulated using the beam propagation method (BPM) at 1.33 μm wavelength.
Enhancing the Bandwidth of a Vibro-impact Cantilever Beam Triboelectric Harvester Using Repulsive Magnetic Nonlinearity

Most energy harvesters are linear, with a restricted frequency bandwidth and poor performance, which prompted the development of nonlinear harvesters with a broader frequency bandwidth. Herein, magnetic nonlinearity is integrated with triboelectric Vibro-impact energy harvesting structures to expand the operational bandwidth and enhance harvesting efficiency at low frequencies. The harvester comprises one cantilever beam that has a magnet attached to its end and is positioned in such a way that the magnet faces another fixed magnet with the same polarity to create a repulsive magnetic force. The beam's tip magnet bottom surface acts as an upper electrode of the triboelectric layer, and when the structure vibrates, it will impact another lower electrode leading to the generation of an electrical signal. The system will vibrate in either a monostable or bistable range by controlling the spacing of the magnets. A single-degree-of-freedom model simulates the dynamic behavior and the generated voltage signals. The harvester's dynamic behavior is evaluated at various separation distances between the two magnets. As a result, higher bandwidth was achieved under the influence of magnetic nonlinearity. Impact-driven triboelectric energy harvesters with induced magnetic nonlinearity can effectively improve efficiency by broadening frequency bandwidth at low frequencies.
Towards an MRI-Compatible Flexible Endoscopic Robot for Transsphenoidal Neurosurgery

This paper presents the design, modeling, and testing of a magnetic resonance imaging (MRI)-compatible flexible endoscopic robot for MRI-guided transsphenoidal neurosurgery. The robot consists of a straight rigid shaft, a tendon-driven steerable tip with two degrees of freedom, and a low-profile hydraulic tendon-driven system. The small diameter (about 3 mm) of the robot will allow the robot to pass through narrow nasal cavities and navigate in congested skull base. By filling the steerable tip with deionized water, the MRI contrast of the robot can be improved, therefore enabling intra-operative tracking of the robot movement. In this paper, we will present the design and manufacturing of the robot system, kinematic modeling of the steerable tip, and experimental studies to verify our model and demonstrate the MRI-compatibility and intra-operative tracking of the robot.
An Order Statistics Filtering-based Real-time Voice Activity Detection Algorithm

In this paper,we propose an effective real-time voice activity detection algorithm.It makes use of the subband spectral entropy as the speech/noise discrimination feature.The speech spectrum is divided into several subbands at first. Then,the spectral entropy of each subband is estimated.We apply order statistics filters(OSF)to a sequence of the subband entropies to obtain the spectral entropy of each frame.The speech/noise classification is based on the spectral entropy.The experimental results show that the proposed algorithm can distinguish speech from noise effectively and improve the performance of automatic speech recognition(ASR)system significantly.It is proved to be robust under various noisy environments and SNR conditions.Moreover,the proposed algorithm is of low computational complexity which is suitable for embedded ASR system application.
Quantifying and Improving Resilience in the Informal Social Networks of Organizations

Organizations spend a considerable amount of time designing their organizational structure. These structures generally reflect the priorities and functions of the organization and deserve attention. However, research has reinforced the power of informal networks to affect performance and communication. The ability of a person to leverage their weak ties to build a coalition, find a key piece of information, or signal a warning has appreciable impacts on an organization’s ability to perform. This paper explores an informal structure and offers methods to re-enforce its resilience. In this unique network, the nodes represent people and their edges represent the overlap of tenure throughout their career. We define the resilience of this organization by exploring its error tolerance and offer a procedure that leverages the ClusterRank centrality for improving resilience through targeted internal assignments and the strengthening of informal ties. Interventions demonstrate promising results with appreciable shifts in the critical threshold with minimal adjustments. By utilizing these tools, organizations can better understand and address possible deficiencies in their informal structure’s resilience and remain better postured to respond to perturbations.
Targeted Bitstream Fault Fuzzing Accelerating BiFI on Large Designs

Fault injection attacks are a powerful instrument in an attacker’s toolbox to extract secret keys from cryptographic primitives. Generally, detailed information about the implementation and platform is needed to conduct such an attack in a meaningful fashion. With Bitstream Fault Injection (BiFI), Swierczynski et al. demonstrated that even without any prior knowledge, an adversary could use bitstream faults to disclose the secret key of cryptographic implementations on FPGAs. With a brute-force strategy, an extensive set of faulty bitstreams is generated by manipulating the FPGA’s LUTs, some of which enable the adversary to attack the design successfully. The drawback of BiFI is that its runtime scales with the design size because of aforementioned brute-force approach. Hence, it can be prohibitively slow, e.g., months, for large state-of-the-art FPGAs.In this work, we present Targeted Bitstream Fault Fuzzing (TBFF), which accelerates BiFI by identifying candidates of vulnerable LUTs using automated netlist reverse-engineering algorithms. Hence, the goal of TBFF is to combine the best of both worlds: TBFF automatically identifies small but crucial structures that are part of most cryptographic primitives, such as counters, done signals, or SBoxes. Introducing faults in these structures often instantly results in faulty behavior that can be exploited to recover the secret key. As a result, instead of brute-forcing, only a few targeted bitstream manipulations are needed to recover the secret key with the marginal overhead of identifying the relevant areas. Extrapolating this result for large-scale designs, TBFF can be conducted in minutes compared to months using the previous brute-force approach. In various case studies, we demonstrate the efficacy of our attack by attacking several AES designs.
Compact Circular Polarized Ant-formed Rectangular slot mm-wave Antenna for 5G Satellite Communication

In this paper, a compact circular polarized Antformed rectangular slot antenna is presented. The proposed antenna consists of an Ant-shaped rectangular patch rotated 0°. To achieve an Axial ratio below 3 dB and to improve the AR bandwidth the proposed antenna is rotated at 45° and 135° respectively. In both directions, the axial ratio falls below 3dB which forms circular polarization. To validate this design, the antenna is designed using a simulation software HFSS with the overall dimensions of 13 x13x1.6 mm3. The simulated impedance bandwidth (IMBW) for | Sll |< -10dB of the proposed antenna is 5.75% and the simulated axial ratio bandwidth (ARBW) <3dB is 7.07% and peak gain is from 5. 4dB to 4.1dB within 3dB ARBW. This proposed circular polarized antenna is suitable for 5G non-geostationary satellite communications and falls under the ka band.
Deep Fake BERT: Efficient Online Fake News Detection System

The newscast system has shifted from conventional print to online media platforms in the current computing era. As a result, online media platforms enable us to absorb information more quickly and with fewer editorial constraints, and false information is disseminated at an extraordinary rate and on a massive scale. Many practical algorithms for identifying fake News have recently been created, which use unidirectional text sequence analysis. News and social context-level information were encoded using sequential neural networks. As a result, a bidirectional training strategy is capable of enhancing classification. This paper proposed Deep Fake BERT, a new model for identifying bogus News in online media. The model uses a BERT-based deep learning technique by integrating multiple simultaneous modules into a single-layer DCNN with various kernel filter sizes and strides. This combination can handle ambiguity, the most challenging aspect of natural language comprehension. This approach used classification methods such as Naive Bayes, Feed Forward Neural Networks, and LSTM, and prediction results were compared. Based on the comparison, the proposed model yields a classification accuracy is 99.25% to the existing methods.
Lung Nodule Diagnosis for Cancer from X-Ray Images using Convolutional Neural Networks and Image Processing

The Low Dose based Computing Tomography (LDCT) for screening of lung cancer has been applied now greatly because of its performance and effectiveness. Many Radiologist evaluate these LDCT images are facing difficulty to handle the challenges like omission of micro nodules, several manual repetitions, shortcomings in consistency, etc. Hence there is a need for an efficient methodology to help radiologist to accurately detect the infected nodules thereby improving efficiency in cost effective manner. Multiple new deep learning models are contributed to this area to demonstrate the detection of benign lung nodules. However, there is no proven or completely recognized clinical practice for achieving this. The proposed work aims at developing and accessing Deep Learning algorithm to identify nodules (Pulmonary) on LDCT, prevalently investigate the nodules by taking Lung Nodule Analysis (LUNA) public dataset to find Benign lung nodules showing early symptoms seen in the lungs of a patient. These lung nodules are detected automatically from the features extracted from the lung region segments using CNN algorithm. The work resulted in the detection of all the benign nodules with significant false positive detection rate reduction.
Multimodal Efficient Bioscrypt Authentication using MATLAB

Multimodal biometric systems are able to overcome some of these shortcomings, as mono-model biometric systems present a number of security issues and often offer unacceptable error rates. By combining two or more biometric systems into one identification system, multimodal biometrics improve the accuracy of authentication. However, the characteristics of a single biometric system should be statistically independent of the features of different biometrics systems. This article proposes a multimodal biometric system that can recognize fingerprints, faces and iris patterns. The system is applied to a point level that is consistent with different means of normalization and fusion. Compatibility scores are generated when query and database images are matched. The Fusion module combines the normalized and weighted sum scores to determine compatibility scores. The cumulative rule is used to combine these individual adjusted scores and their weights into a total score. Weights associated with each biometric attribute indicate how important that attribute is to the user, this system establishes an identity that is more trustworthy than individual biometric systems that establish identities by analyzing individual fingerprints. In a multimodal biometric system, multiple biometric properties are combined to enhance authentication performance and to reduce fraudulent access. The designed scheme exceeds single biometric systems in terms of reliability and accuracy.
Interface Management for Telecommunication System Design through OPM and DSM-based Approaches

Telecommunication systems require precise interface management and analysis as interactions play a key role in realizing the core functionality of such systems. This paper models and analyzes the interface relationships of different components of an industrial Reconfigurable Transmitarray Antenna system through Design Structure Matrix (DSM), which is a universal tool for precise interface management in various engineering disciplines. The novelty lies in the introduction of new types of interface relationships for telecommunication systems such as Radio Frequency, Electrical, Analog, and Digital, in an extension of typical interaction definitions in complex systems (Spatial, Structural, Energy, Material, and Informational). The approach presented in this paper aims to utilize and enhance systems engineering methods such as Object-Process Methodology (OPM) and DSM. While these tools have not been widely used to represent the nature of telecommunication systems, we demonstrate their potential to improve interface management process. The study of such a system led us to focus more attention on the signal flow as an interaction type. This type of focus is certainly less obvious in mechanical systems.
Prediction and Analysis of Sentiments of Reddit Users towards the Climate Change Crisis

Climate crisis is one of the most talked about issues in the world today. In spite of a global agreement on the necessity to protect the earth from global warming, people still lack awareness on the graveness of the situation. Social media platforms such as Twitter, Facebook, Instagram, Reddit and others offer immense opportunities for people to become vocal and participate with their opinions and thoughts on these practical challenges by exchanging information and talking about them. By looking at their attitudes and the issues they discuss, it is possible to determine in this research how users of Reddit, one of the most well-known and popular social media platforms in the world, feel about climate change. Retrieved comments and posts are classified into two sentiment classes: Positive and Negative. To understand the sentiments, we find sentiment targets by comparing two neural networks CNN and RNN and using the more accurate model to predict sentiments of the comments in the test dataset and analyse the nature of climate change discussion over time. Although the computational maximal accuracy for the two models is comparable, it was discovered that the CNN model scored marginally better than the RNN in terms of average precision, average accuracy, and average loss. The examination of Reddit users’ opinions demonstrates that the general attitude is negative, particularly when people acknowledge extreme weather events that have the potential to impact the public wellbeing framework.
Stock Market Prediction using Machine Learning Technique

The stock exchange has grown to be one of most important events in today's financial world. The current state of the stock market has a significant impact on the global economy. People from many walks of life, whether they come from business or academic backgrounds, have been drawn to the stock market with great success. The stock market's nonlinear character has made research on it among the most important and popular topics worldwide. People choose to make investments in the stock market based on their predictions or knowledge from earlier studies. In terms of forecasting, people frequently seek out instruments or strategies that would reduce their risks and maximize their earnings; as a result, stock price forecasting assumes a significant position in the always competitive stock market industry. Adopting conventional methods like fundamental and technical analysis doesn't seem to guarantee the predictability's consistency and accuracy. As a result, machine learning technologies have emerged as the most recent trend in stock market forecasting, with predictions based on current market values because of training on earlier values. In order to forecast the present trend of the stock market, this article focuses upon Machine Leaning, Analysis and LSTM (Long Short Term Memory) technology.