Miracle-dz/UniFAR-FTUs huggingface dataset:
Control of a Smart Active Flexible Needle for Therapeutic procedures

Minimally Invasive Surgery (MIS) is more efficient than open surgery on the grounds that the recuperation and hospitalization time of MIS is significantly lesser than that of ordinary surgical methods. Till date physicians perform MIS procedures using conventional flexible passive needles. From the literature survey, it is observed that self-actuating needle promises enhancement in target reaching accuracy thereby widening the needle steerable methodology. Considering this into account, here, we aim to develop and control a shape memory alloy actuated smart needle by state feedback controller. We started from a previously proposed passive flexible needle in the literature to the proposed smart active needle. Since only one cause of idle movement of passive needle is the bevelled-tip. The main objective of the proposed controller for this smart needle is to remain within the desired plane of interest inside the tissue region. The three control inputs, one primary and two secondary are deployed to the proposed state feedback controller. Numerical simulations are presented to evaluate the proposed control technique on the developed smart active needle.
The OH ground-state masers in W3(OH) – II. Polarization and multifrequency results

We present observational data on OH masers in W3(OH) with the emphasis on the three ground-state lines, other than 1665 MHz, and on the polarization of the masers. Polarization studies indicate that the magnetic field orientation varies systematically over the field of view, and the large number of Zeeman pairs detected has allowed us to construct a map of the magnetic field in the source. In several parts of the region, the masers lie in arcs where the major axis of the maser usually lies parallel to the arc; these may delineate propagating shocks. A spectacular arc of maser emission occurs in the far south of the region at both 1665 and 1667 MHz. Such co-propagation of maser frequencies is very rare and exists only for pairs of lines where one line is 1665 MHz.
Ordinal Data Stream Collection with Condensed Local Differential Privacy

Continuous collection of users' ordinal data is essential to numerous industrial applications, such as disease surveillance, road traffic analysis, and computation offloading. However, the data collector is not fully-trusted, so that the necessitate privacy protection on the sensitive ordinate data streams of users becomes critical. Local Differential Privacy (LDP) is typically used to resolve this problem with high efficiency for real-time data analysis. However, existing LDP methods are mainly designed for one-time data collection, and they bring low utility in some time slots for continuous collection, due to the data sparsity problem that some users' data are empty in most of time slots. Therefore, we propose a Condensed LDP (CLDP)-based scheme, which provides real-time statistics with the protection of each user's time-series data and the high utility in each time slot. First, our scheme optimizes the utility by allocating each user's privacy budget in the time dimension. The privacy budgets originally used in empty time slots are saved and allocated to non-empty time slots for every user, so as to increase the utility in each time slot. Then, CLDP is used to further counteract the negative impact of data sparsity. Finally, privacy analysis is given to show the privacy protection of data streams, and sufficient experiments on real datasets are conducted to demonstrate the effectiveness of the proposed scheme.
SequenceShield: A Robust and Accurate DDoS Detection Method via Serializing the Traffic with Direction Information

Distributed Denial of Services (DDoS) attacks continue to be one of the most challenging threats to the Internet. Previous methods try to reveal the pattern of attacks by using artificially selected statistical features or header fields of network packets. However, they suffer from low accuracy and are not robust in the face of evasion attacks (clever attackers can change some specific header fields of the packet or slow down the packet rate to avoid being detected). To achieve robust and accurate DDoS detection, we propose a method named SequenceShield. Sequence Shield is based on our observation that the direction information of each network packet can reveal the behavior pattern of DDoS attack traffic. Specifically, we innovatively serialize the network traffic between two endpoints into a sequence structure named interaction sequence (IS) with direction information. Then the converted IS is sent to a gated recurrent unit (GRU) network to learn the behavior pattern of attacks. SequenceShield only uses each packet's direction and protocol information, which makes our proposed method robust enough in the face of evasion attacks. Moreover, experimental results on two well-known datasets indicate that the classification performance of SequenceShield outperforms the state-of-the-art DDoS detection methods.
A Hybrid BiLSTM-ATT Model for Improved Accuracy Sentiment Analysis

Sentiment analysis is an important and challenging task in the field of natural language processing. Researchers have used vocabulary-based methods and machine learning methods to conduct research on sentiment analysis tasks. In recent years, deep learning has achieved great success in the field of sentiment analysis. However, for complex textual data, using a single model is often insufficient. Inspired by deep learning models, in this study, we proposed a hybrid model using BiLSTM and attention mechanism, called BiLSTM-ATT model, to solve the problem of sentiment analysis. First, we adopted the GloVe method to train the initialized word embeddings. GloVe converts textual information into word vectors, which can calculate the distance between words. Next, we used a convolutional neural network that can extract local features, and a BiLSTM that can extract long-range semantic features of text with bidirectional extraction of long-term dependencies. Finally, the attention mechanism was used to improve the performance of the model by calculating the weight of the data. Experimental results demonstrate that our proposed hybrid BiLSTM-ATT model outperforms traditional deep learning methods in Accuracy, Recall, and Fl-Score. Our method was compared with deep learning methods on the IMDB movie review dataset.
Stable Causal Feature Selection based on Direct Causal Effect Estimation

Feature selection is an important technique and tool for selecting relevant and critical features from the high-dimensional data in many tasks of data mining and machine learning. However, existing algorithms mainly rely on the associations between features and response variables such that they lack stability and may result in wrong conclusions. To improve the interpretability and stability of feature selection algorithms, we propose a stable causal feature selection algorithm based on the direct causal effect estimation, which achieves interpretable feature selection by considering the causal relationship between features and response variables. Specifically, we develop a theorem based on the graphical causal model to estimate the direct causal effects for supporting the proposed stable causal feature selection algorithm. Moreover, the causal feature selection algorithm is applied for a real-world application, i.e. earthquake prediction by utilising a neural network model, BiLSTM. Abundant experiments carried out on synthetic and real-world data sets demonstrate that the proposed stable causal feature selection algorithm has better performance than the baselines.
Learning-based Dwell Time Prediction for Vehicular Micro Clouds

Vehicular Micro Clouds (VMCs) are an emerging development in the domain of vehicular networks posed to provide local services to users without the need for external infrastructure. This can significantly improve the user experience, in particular due to the low latencies that such systems can achieve. Due to the distributed nature of such a VMC, effective local coordination is important while using minimal communication resources. To this end, it is important to know, how long vehicles will be participating in, and contributing to a VMC. In this work, we investigate, how previous, heuristic-based approaches can be improved by incorporating local, learning-based techniques. Our analysis indicates a potential improvement of the accuracy of the prediction, and resulted in an improved simulation environment within which the learning-based approach can be deployed.
Modeling and analysis of automatic transmission engagement dynamics-linear case

A control-oriented model of a typical four-speed automatic transmission is developed by using the bond graph modeling method. The planetary gear set model utilizes the Karnopp friction model for hydraulic and one-way clutches, in order to provide a favorable computing efficiency. The full gear set model is reduced for various phases of the park/ reverse and park/drive engagements. The reduced gear set models and linearized torque converter model are used as a basis for an algebraic analysis of the engagement dynamics. The analysis is originally conducted for the basic case of fully applied brake, and it is then extended by an analysis of the influence of wheel dynamics in the brake-off case. The analysis results are verified by computer simulations and experiments.
Gain Enhancement of Novel Rectangular Microstrip Patch Antenna using RT-Duroid substrate and FR-4 substrate on S-band

The aim of this work is to construct an antenna with improved gain using novel RT-Duroid substrate. The RT-Duroid rectangular patch antenna is developed to improve antenna gain by utilising FEKO software and gathering a database of 40 samples with a pretest power of 80%, an alpha error level of 0.05, and a 95% confidence interval. The Novel antenna is suited for frequencies ranging from 2 to 3 GHz. According to the findings of this investigation, the RT Duroid design looks to be superior to the FR-4 patch antenna. RT DUROID patch antenna design is having more gain (71 %) and directivity (58 %) with comparison of FR-4 patch antenna design.
Large MIMO Channel Estimation Study Based on Independent Component Analysis

MIMO systems, which consist of multiple transmitting and receiving antennas, are used as a method for effective use of frequency bandwidth. In general, signal separation in MIMO requires a known channel matrix. However, using Independent Components Analysis, signal separation is possible even when the channel matrix is unknown. On the other hand, if the channel matrix is known, MIMO technology may be efficiently utilized by precoding in the reverse direction line of TDD. In this study, we examined whether ICA can be used for signal separation and channel information estimation. We report that signal detection and channel estimation in large-scale MIMO can be performed without any problems.
Accelerating Adversarial Attack using Process-in-Memory Architecture

Recent research has demonstrated that machine learning algorithms are vulnerable to adversarial attacks, in which small but carefully crafted input perturbations can lead to algorithm failure. It has been demonstrated that certain adversarial attack algorithms are capable of producing these types of perturbations. These attack methods are inapplicable when the attack must be generated in near real time. The use of a hardware accelerator, such as a Process-in-Memory (PIM) archi-tecture, is a potential method for addressing this issue. The PIM architecture is regarded as a superior option for data-intensive applications such as solving optimization problems and Deep Neural Networks (DNN) due to its capacity for ultra-low-latency parallel processing. However, implementing an adversarial attack algorithm directly on the PIM platform is inefficient due to the PIM architecture's complexity and overhead costs. To address this issue, we utilize a novel adversarial attack scheme based on the PIM that leverages Look-up-Table (LUT)-based processing. The proposed LUT-based PIM architecture is capable of being dynamically programmed to execute the operations necessary for an adversarial attack algorithm. Our simulations reveal that the proposed method is capable of achieving an ultra-low operating delay and energy-efficiency performance.
SiC-capped nanotip arrays for field emission with ultralow turn-on field

Silicon nanotips with tip diameter and height measuring 1 nm and 1 μm, respectively, and density in the range of 109–3×1011 cm−2, were fabricated monolithically from silicon wafers by electron cyclotron resonance plasma etching technique at a temperature of 200 °C. Field emission current densities of 3.0 mA/cm2 at an applied field of ∼1.0 V/μm was obtained from these silicon nanotips. High-resolution transmission electron microscope and Auger electron spectroscopy analyses concluded that the nanotips are composed of monolithic silicon and nanometer-size SiC cap at the top. A 0.35 V/μm turn-on field to draw a 10 μA/cm2 current density was demonstrated, which is much lower than other reported materials. The excellent field emission property demonstrated by these nanotips, which were fabricated by a process integrable to the existing silicon device technology at low temperatures, is a step forward in achieving low-power field emission displays and vacuum electronic devices.
Solving the point cloud registration based on deep learning

Point cloud registration is used to create a complete environment map with multiple pieces of scans. It is used mostly to guide autonomous robots when the surrounding is complicated. However, the problem is hard to solve so deep learning networks are often utilized. Most neural networks proposed need manual annotation and selection of patches which is time-consuming and makes the model only work for one particular scenario. In this work, a new method is studied in which a self-supervised learning technique is incorporated into the network. The input to this network is one raw point cloud without any labels or annotations, and as a result, the need for annotating or selecting patches is removed. A key point sampling process is also implemented in this network, which filters non-relevant points and further improves the performance. Based on our experiments, this self-supervised model has the capability of performing better than those requiring manual processing of the input.
SSA and BPNN Based Efficient Situation Prediction Model for Cyber Security

Establishing an effective situation prediction model for cyber security can know the active situation of future network malicious events in advance, which plays a vital role in cyber security protection. However, traditional models cannot achieve sufficient prediction accuracy when predicting cyber situations. To solve this problem, the initial location information of the sparrow population is optimized, and a sparrow search algorithm based on the Tent map is proposed. Then, the BP neural network is optimized using the improved sparrow search algorithm. Finally, a situation prediction model based on the sparrow search algorithm and BP neural network is proposed, namely T-SSA-BPNN. The simulation results show that the convergence speed and global search ability of the prediction model are improved. It can effectively predict the network security situation with high accuracy.
A Survey of Random Noise Suppression Methods for Seismic Data based on Deep Learning

Deep learning has increasingly made their way into the seismic exploration industry as a result of the ongoing development of artificial intelligence. The deep learning approach is brought to the field of seismic data denoising with the goal of addressing the issue that standard denoising methods and denoising models cannot effectively suppress random noise with good quality. Deep learning employs multi-layer convolution to extract deep-level data features, and it creates complex denoising models through adaptive learning of linear approximation to achieve end-to-end mapping between noisy seismic data and denoised seismic data, which introduces a novel concept for seismic data denoising. In this paper, the research of depth learning in random noise suppression of seismic data is introduced, analyzed and summarized with the deep learning network framework as the classification standard.
Efficiently Executing Sparse Matrix-Matrix Multiplication on General Purpose Digital Single Processor

Sparse Matrix-Matrix Multiplication (SpMM) is a kernel for large-scale scientific computing and deep learning. Previous SpMM optimization research focused on CPU or GPU architecture, with little attention paid to the General Purpose Digital Single Processor (GPDSP). Earlier work shows that reducing irregular memory access and increasing data reuse can improve the performance of SpMM. In this paper, we propose an effective SpMM vectorization method for GPDSP Matrix2000b. According to the memory hierarchy of Matrix2000b, we designed an adaptive sparse matrix tilling scheme and combined it with row-major compute in order to improve data reuse. Then we implemented a multi-level parallel algorithm that includes thread-level parallelism, data-level parallelism and instruction-level parallelism based on the multi-level computing components of Matrix2000b to vectorization and eliminate irregular memory access. We also designed a three-level double buffer workflow based on the memory hierarchy to overlap the computation and memory access. When memory theoretical bandwidth is 42.6 G B/s, experimental evaluation reveals that the average performance can reach 34.81 GFlops/s and the maximum performance can reach 65.47 GFlops/s.
EEG-Based Multioutput Classification of Sleep Stage and Apnea Using Deep Learning

Sleep is closely related to physical and mental health and quality of life, and accurately evaluating sleep quality remains a major research topic in related fields. Conventional methods of sleep quality evaluation involve the use of polysomnography (PSG), which continuously records physiological changes during sleep. With the recorded data, sleep quality, and sleep stage or sleep-related disorders can be diagnosed via manual inspection by trained experts. However, the practice is time-consuming, labor-intensive and yields high inter-and intra-rater variability. To overcome such disadvantages, machine learning or deep learning-based automated techniques have been recently proposed. Most of the existing methods, however, can only classify sleep stage or the presence of apnea, not both. This study proposes a novel method that allows simultaneous classification of both sleep stage and apnea, through multioutput classification using a single deep learning model to comprehensively evaluate sleep quality. PSG recordings from a total of 98 subjects from the ISRUC-Sleep dataset subgroup 1 were analyzed in this study, and the proposed model was trained to classify sleep stage and apnea using three EEG channels. The proposed model showed an accuracy and a micro average f1 score of 0.99 for the classification of apnea, and an accuracy and a micro average f1 score of 0.69 for the classification of the sleep stages. The proposed model provides a new paradigm in sleep deep learning research by enabling comprehensive evaluation of sleep quality, hence could help sleep experts to make clinical decisions.
Order-sorting filter transmittance measured with an array detector

The simultaneous measurement of the spectrally and spatially variant transmittance of a linear variable order-sorting filter in a manner that closely resembles its conditions of actual use is described. The transmittance of a prototype order-sorting filter was measured in the 400- to 880-nm wavelength region by illuminating it with the output beam of a spectrophotometer while the filter was attached to the front of a 30 x 32 pixel silicon array detector. The filter was designed to be used in the output beam of a grating spectrometer to prevent the dispersal of higher diffracted orders onto an array detector. Areas of the filter that were spatially matched to the corresponding detector pixel column had measured peak transmittances of about 90 percent that were uniform to within +/- 1.5 percent along a given column. Transmittances for incident wavelengths shorter than the desired bandpass, corresponding to the order overlap region, were measured in the 0.003 range. Line spread function measurements made with the array detector indicated no significant beam spreading caused by inserting the filter into the beam.
Unsupervised K-means for Energy Conservation in IoT Networks

The K-means clustering algorithm is one potential solution that prolongs the lifetime of non-uniformly distributed wireless networks. However, this algorithm is sensitive to initialization, which, if unsuitable, can lead to inadequate clustering. This paper proposes the implementation of Unsupervised K-means (U-K-means), a free initialization version of K-means, as a new clustering technique for topology management in wireless sensor networks. The simulation results demonstrate that U-K-means outperforms the traditional K-means algorithm-based solutions in terms of consumed energy and network durability.
Image-based Apple Disease Detection Based on Residual Neural Network and Transfer Learning

Apple planting is an important part of China's agricultural economy. With the progress of computer technology, China, as the first apple planting country in the world, also needs to change to a digital and intelligent working mode. As apples are easy to be attacked by various diseases and pests during planting, and the early pathological changes mainly occur in leaves, especially mottle leaf disease, gray spot disease, rust and cedar rust. How to find these diseases in time and quickly make accurate classification is the key to subsequent treatment. In this paper, Based on the ResNet50 network architecture, the attention mechanism is added to design the new work model needed to improve the accuracy of disease identification. Aiming at the problem of small sample size of apple tree disease image dataset, we uses the idea of transfer learning to upgrade the network model training process. The network model is trained according to the set training strategy to increase the convergence speed and the working accuracy of the network model. Compared with other traditional classification methods, it is proved that the detection method based on ResNet50 network model in convolutional neural network is more effective.
Sentiment Analysis on Amazon Alexa Reviews Using NLP & Classification

many firms and manufacturers, knowledge on public opinion is critical. They frequently want to know what consumers and the general public think of their products and services in real time. However, personally reading every post on the website and extracting important viewpoint information is not feasible. The frequency of data is very high to do it manually. Sentiment analysis enables for efficient and cost-effective data processing on a wide scale. We investigated the application of sentiment analysis in business to better understand its strengths and limits in order to learn more about it. Using Python and Machine Learning Algorithms, this project created a representation to forecast the response of a remark given the observation declaration using a dataset of Amazon Alexa reviews.
A Comparative Analysis of the Multipath Fading Channel Models in the Case of the Hyper-Rayleigh Fading

The proposed research performs a comparative analysis of the $\eta-\mu$ and $\kappa-\mu$ shadowed channel models in terms of the amount of fading (AoF) for the case of hyper-Rayleigh regime. The AoF was derived and novel limitations for the parameters were obtained for the models. A connection between the statistical parameters of the models was determined for an equicluster scenario. By numeric simulation of the problem of the energy-based frequency white space detection it was shown that the predictive ability of the models differs by units of percent, and the difference increases with the gain of the signal-to-noise ratio.