Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review on Breast Imaging Modalities based on Technical Aspects

Most common invasive cancer in female is Breast cancer and rarely, it will be affected in men also. From the several studies found that the second leading cause of cancer death is breast cancer in women after lung cancer. The Breast cancer occurs when the cells in the breast grow and divide that cell in an uncontrolled way. Due to this cell creating a mass of tissue in the breast itself is called tumor or mass. Signs of breast cancer are seeing changes of skin on a breast, feeling a lump in a breast and size change of breast etc. If the breast cancer/ mass detected at the early stage or at initial stage, then the survival rate of patient will be improved. In this review, several modalities have been introduced to detect the breast cancer. Furthermore some of the output parameters of these modalities have been investigated. Based on various imaging modalities, were selected around 40 articles by using search engines. And studied the selected papers thoroughly and categorize the results based on modalities which have been used. The used Breast cancer imaging techniques are Mammography, Ultrasound, CT scan, MRI and PET scan etc. The modality selection has been completely depends on the patient’s like current state of cancer tissue and density, age of the patient etc.
Preliminary study for intonation classification of imagined speech for brain-computer interface applications

In the current study, we focused on decoding speech prosody from EEG. Prosody (i.e., melody and rhythm of speech) is important during communication as it allows to convey emotion and meaning. However, it has received little attention in the field of brain-computer interfaces. To address this issue, we contrasted the production of two syllables, “ba” and “da”, produced mentally as an affirmation (e.g., “ba.”) or a question (e.g., “ba ? ”) using two different intonations. We focused on spectral features. After classification in the time-frequency domain, we found above chance-level accuracies in specific frequency ranges of the alpha band (7–12 Hz) early on during the production phase. We also obtained above chance-level results on a range of the low-beta band (16–20 Hz) during a late time window. Based on the visual inspection of topographies and the literature, we suggest that the results during the early time window, but not that during the late time window, reflect a genuine difference between imagined affirmation and question production. Future studies should provide more information about neural markers and underlying neuro-cognitive processes to improve the understanding of the imagined intonation production. This would pave the way for the development of speech-based BCI capable of differentiating intonation and prosody in general.
Building Energy Consumption Forecasting based on K-Shape clustering and CNN-LSTM

The management and dispatch of building energy consumption plays a significant role in ensuring energy dispatch, reducing energy consumption, and alleviating the challenges of climate change. Accurate building energy forecasting is the basis for establishing an energy management system. To improve the accuracy of building energy consumption forecasting, this paper proposes ultra-short-term building energy consumption forecasting based on K-Shape clustering and convolution and a long short-term memory network (CNN-LSTM). First, the clustering algorithm is used to cluster the energy consumption of single apartment buildings into multiple apartment building clusters to obtain the energy consumption component features; then a multi-feature time series data set consisting of clustered data of building energy consumption component features and temporal and climatic information is constructed. The data are then fed into a CNN network based on a one-dimensional convolutional layer to extract the nonlinear relationships between different feature variables; the data are then fed into an LSTM network to extract the time-series characteristics of the data in the time dimension. And finally the short-term power consumption forecasting results are output by the fully connected layer. The experimental results show that the proposed method has higher forecasting accuracy compared with other methods.
Real-time indoor air quality monitoring using the Internet of Things

Globalization and industrialization have brought about their reverberation in the form of different simultaneous harmful components that affects environment's equilibrium conditions. The improper management of such materials has resulted in the contamination of essential ecosystem elements that serve as the foundation for human survival. The solution here is evaluating the degree of ferocity and then initiating preventive and eradication measures based on that information. In this work, air quality-based internet of things (IoT) nodes with MQ135 sensors were set to sense the presence and intensity of air pollutants, then transmit the recorded information to a mobile app and web via GSM module or WiFi system for data analysis and display. The MQ135 sensor monitors air quality since it detects the majority of dangerous gases and can correctly quantify their level. The air quality is consistently monitored and will raise an alarm when the air quality drops below a specific range, it indicates that there are significant amounts of dangerous pollutants like CO, CO2, C2H4, NO, NO2, SO2 and CH4 present in the air. The air quality readings taken in PPM are regularly updated to the user through a mobile application to take necessary preventive action.
Review of Recent Intrusion Detection Systems and Intrusion Prevention Systems in IoT Networks

The Internet of Things (IoT) paradigm has gained exponential growth and popularity in recent years. It is a precipitously shifting technology, in which interconnected computing devices and sensors share data and communicate over the Internet, to decipher different problems and deliver new services without human intervention. In fact, IoT is becoming more ubiquitous in our daily lives, connecting physical things to e-services. That is to say; the IoT is the engine that powers home automation, modern health, advanced manufacturing, and smart cities. Thus, IoT holds a lot of promise for enhancing social and corporate life. Despite the rising vogue of IoT equipment, they are weak and vulnerable to many security threats because of the nature of their networks, and inherent hardware limitations that restrict their ability to implement security measures and increase their exposure to vulnerability attacks. Furthermore, the integration and diversification of IoT terminals and applications make IoT more prone to various intrusion attacks. There are many techniques used to preclude IoT from various attacks, Intrusion Detection System (IDS) and Intrusion Preventions System (IPS) are some of them, which can guarantee the security, privacy, and reliability of the IoT. In this paper, we provide a review of several recent and pertinent IDS and IPS proposed for IoT networks, where we highlight the suggested mechanisms and their limitations. This aids to spot the issues that still require to be tackled, well define the mainstream of research direction, and pave the way for novel avenues of research for future researchers.
Analysis of Neural Networks for Object Detection using Image Processing Techniques

Moving real-world object detection is still a difficult task. Whilst recent research data sets increase the number of training sets and test examples to get closer to real world problems, it is another important question apart from accuracy that detectors can process large data sets in reasonable time. Not only the education instances, but the number of classes is significant. Moving object detection requires finding items in a video sequence frame. An object detection mechanism in either frame is needed in - form of monitoring, or when the object first appears in the film. Different history strategies used in the literature have been simulated during moving object detection. In this study we implement a Gaussian mixture analysis and backward propagation using neural network for object detection.
Disease Prediction Using Symptoms based on Machine Learning Algorithms and Natural Language Processing

Health became an important aspect in today's world. People are suffering from various viruses, infections etc. Citizens are getting terrified even if they have some symptoms. This stress is just because of not knowing the disease and its seriousness. Sometimes it becomes too late for a disease to be diagnosed and treated which may become a life threat. Additionally, in some situations patients may not be in a position to visit a doctor at times it may become a serious issue. If a system predicts the disease of a patient, then he/she might get out of the anxiety. This can be done by using some machine learning algorithms like Naïve Bayes, Random Forest and Decision Trees. The ultimate result will be the result of the model with high accuracy. Since communicating will be a difficult, a chatbot is implemented. This is done by using Natural Language Processing (NLP). The final result will be shown on the interface using all three machine learning techniques, and feature extraction will be done depending on their symptoms. Five modules make up the proposed methodology. Preprocessing will be done on the dataset in the first module. The decision tree classifier is used to generate a prediction model in the second module. The Random forest method is used for forecast the illness in the third module, the Naive Bayes technique is utilized in the fourth model, Natural Language Processing is used for chatbot mechanism is the fifth module, with the outputs from all the three models taken into account to find best model.
Advanced tomographic methods in materials research and engineering

1. Introduction 2. Some Mathematical Concepts for Tomographic Reconstruction 3. Visualisation, Processing and Analysis of Tomographic Data 4. Radiation Sources and Interaction of Radiation with Matter 5. Synchrotron X-ray Absorption Tomography 6. Phase Contrast and Holographic Tomography 7. Tomography using magnifying optics 8. Scanning Tomography 9. Three-dimensional X-ray Diffraction 10. Detectors for Synchrotron Tomography 11. Fundamentals of Electron Tomography 12. Applications of Electron Tomography 13. Neutron Absorption Tomography 14. Neutron Phase Contrast and Polarised Neutron Tomography 15. Neutron Refraction and Small-Angle Scattering Tomography Appendix A: Facilities for Tomography Appendix B: Examples on CDROM
Dual-Band Dual-Circularly Polarized Shared-Aperture mmWave Antenna

In this paper, a substrate integration waveguide (SIW) dual-band dual-circularly polarized (CP) antenna is proposed. The antenna is composed of three components i.e., low-frequency feed structure, SIW cavity, and polarization converter. In the high-frequency band, the TM010 mode of the SIW cylindrical cavity is excited by a probe. A slot is etched on the top of the SIW cavity and the linear-circular polarization converter is top-loaded to generate right-handed circularly polarized (RHCP) radiation. In the low-frequency band, the SIW cavity operates as a circular patch. The patch is excited by two branches of the Y-shaped slot to generate left-handed circularly polarized (LHCP) radiation. The simulated results show that the proposed antenna obtains −10 dB impedance bandwidths of 18.65-21.15 GHz and 30.1-31.3 GHz. The 3 dB axial ratio (AR) bandwidths are 18.85-21.15 GHz and 29.8-31.8 GHz with the peak gains of 6 dBi and 3 dBi at the corresponding bandwidths. The isolations of input ports are better than 34 dB and 20 dB, for the dual operating frequencies, respectively.
Machine Learning-Based Condition Monitoring of Solar Photovoltaic Systems: A Review

Due to independency from physical models and low cost of implementation, machine learning-based condition monitoring methods for solar photovoltaic (PV) systems have recently gained attentions from academia and industry. In this paper, a literature review is conducted on machine learning applications in condition monitoring of PV systems. Different types of faults in PV systems and the general categorization of PV condition monitoring are firstly introduced. Machine learning-based PV condition monitoring are discussed in three groups, shallow, hybrid, and deep networks. The future research direction in this area is also provided in the paper.
Comparison of Dual-Active-Bridge-based Topologies for single-phase single-stage EV On-board Chargers

Single-stage on-board chargers for EVs are an attractive solution as they reduce the number of converters stages leading to potential increments in power density and cost reduction. Dual-active-bridge topologies are particularly attractive for these applications as they offer a large voltage conversion range, isolation, and relatively simple controllability. Several DAB-based single-stage AC-DC topologies can be found in the literature; however, a comparison of said topologies has not been yet been presented. Filling the gap, this paper presents and compares the most promising DAB-based topologies used for single-stage OBC applications. For this purpose, a multi-objective optimization with a Pareto-front is used. Apart from the design of the main components, EMI filters are designed for each topology and their size is considered as part of the evaluation.
Combination of Digital Twin and Augmented Reality: A Literature Review

The term digital twin has gained a lot of traction and has seen an increase in interest in recent years in both academia and industry. The concept of a digital twin allows a person to interact with a digital object that is created similarly to a physical object so that the interaction feels the same as interacting with a physical object. Synchronization of real-world behavior in a system known as the Cyber-Physical System. Often data is combined with a 3D Computer-Aided Design model to improve data visualization and make interactions more realistic. One of the tools that can be used in this visualization is Augmented Reality. Research on the combination of AR and digital twin is still a little done, even though the combination of the two technologies can promise interaction with virtual objects in the application of digital twin becomes more realistic so that in the process of monitoring, simulation, maintenance, and decision making can be more accurate. The main focus of this paper is how the framework or model of a combination of digital twin technologies and augmented reality can help with data and information understanding and decision making.
Indoor Positioning: Comparing Different Techniques and Choosing the Best One for a User Authentication Real Scenario

Indoor positioning is a complex issue with many, heterogeneous application cases, each one presenting different requirements and environments. In such a complex ecosystem, an agile taxonomy is needed to be able to select a proper solution for a given scenario, as well as practical recommendations for the most used solutions. Besides providing these tools, we analyse a real-world scenario and its requirements, selecting a practical solution and evaluating it together with its implications and consequences, providing a reference guideline for practical applications of indoor positioning.
Characterization of Conventional and Advanced Current Measurement Techniques Suitable for WBG Semiconductor Devices

With wide bandgap semiconductor devices, conventional current sensors like Rogowski coils and coaxial shunts reach their limits in bandwidth or parasitic inductance. Therefore, novel techniques are continuously being developed, which promise improved characteristics. In order to evaluate the performance of current sensors, it is necessary to compare them in a uniform environment. In this work, different sensors for currents up to 100 A and frequencies of several 100 MHz are characterized in the frequency- and time-domain. The characteristics bandwidth, parasitic inductance and delay are determined by measurement and compared to each other. Among the advanced measurement techniques is a design consisting of coaxially arranged discrete resistors. A prototype is built based on this idea and tested against the other sensors. The prototype outperforms the conventional sensors and is validated in a double pulse test with Gallium nitride devices.
Evaluation of Benchmarking Pre-Trained CNN Model for Autonomous Vehicles Object Detection in Mixed Traffic

In the next few years, the new generation of Autonomous Vehicles (AVs) promises an advanced level of self-driving experiences. One of the most challenging topics in AVs development is the readiness of object detection models in complex urban environments. Mixed traffic is a complex urban environment that contains much uncertainty and is composed of heterogeneous objects. Therefore, this paper evaluates benchmarking the pre-trained CNN model for object detection in a mixed traffic environment. The evaluation is conducted for five modern algorithms and architecture of neural networks, including Faster RCNN, SSD, YOLOv3, YOLOv4, and EfficientDet. Then, we provide a new dataset in the mixed traffic environment under night conditions for more accurate object detection. Moreover, we conduct the simulation by considering the performance parameters that are recall, precision, and F measure. The performance of our dataset is also compared to the MS-COCO dataset. The result shows that the average precision value of Faster RCNN, SSD, YOLOv3, YOLOv4, and EfficientDet is 16.70%, 8.90%, 19.67%, 43.90%, and 55.56% respectively. It shows that YOLOv4 and EfficientDet provide better object detection accuracy than other CNN models.
3D Eye-to-Hand Coordination for Uninstructed Robot Grasp Planning

Robot grasping is widely explored in factories, where most objects are instructed with rigid grasp planning. However, for disordered and cluttered objects, it is rarely applied due to difficult object recognition and robot trajectory constraints, which may bring low-cost performance. Normally, the shape and pose of an object, which can be represented as point cloud are preliminary feedback for robot grasping strategy. Therefore, the point cloud segmentation of disordered and clustered objects is critical for grasping manipulation tasks with hand eye coordination. This paper presents an algorithm for locating and dividing different parts by point cloud image in chaotic environment, to determine the reasonable grasping position of parts and the trajectory planning of manipulator. With the experiment on a eye-to-hand robot platform, the results show that the proposed algorithm is robust to different parts stacked and has a relatively better recognition performance.
Enhancement of Hearing Aid Processing Via Spatial Spectro-Temporal Post-Filtering with a Prototype Eyeglass-Integrated Array

The approach to directional speech enhancement in this paper involves combining adaptive beamforming and real-time capable spectro-temporal post-filtering. Using a prototype listening device with an eyeglass-integrated array, we implement and evaluate our Short-Time Target Cancellation (STTC) processing, which leverages phase differences to compute a spatially-selective (i.e., directional) time-varying spectral gain that can be used either to filter the binaural signals at the ears or as a postfilter for hearing aid processing. In this study, the STTC processing is both compared with, and combined with, an established hearing aid algorithm; the two approaches are compatible and our evaluation results, using instrumental measures and multi-talker mixtures in both anechoic and reverberant settings, indicate that their combination has an additive effect. The approach taken here, using an eyeglass-integrated array to compute a spatially-selective spectro-temporal post-filter, can be used to enhance hearing aid performance in especially challenging acoustic environments.
Experimental Demonstration of a 2.2kW Active-Clamp Converter for High-Current Wide-Voltage-Transfer Ratio Applications

Active-clamp forward converters are typically not applied for converters with power ratings larger than 500 W as power as the power transfer between primary and secondary is discontinuous leading to large magnetic components. This paper, however, proves that silicon carbide (SiC) devices enable this topology as a well-suitable topology for a wide voltage-transfer ratio due to the low switching losses allowing high switching frequencies reducing the size of the magnetic components. A laboratory prototype is designed using a very precise model. It employs SiC MOSFETs of 900 V for the primary and Si synchronous rectifiers of 100 V. The semiconductors are cooled with cost-efficient copper inlays, which effectiveness is demonstrated through a thermal FEM simulation. The developed prototype achieves a maximum efficiency of 95% while maintaining an efficiency above 92% for almost the entire operating region. The high efficiency and the power density of approximately 2 kW/l confirm the proposed concept. Finally, the converter is benchmarked to two LLC resonant converters (Si, SiC) and the prototype is used to verify a highly accurate steady-state model showing errors below 1 %.
Non-Negative Kernel Graphs for Time-Varying Signals Using Visibility Graphs

We present a novel framework to represent sets of time-varying signals as dynamic graphs using the non-negative kernel (NNK) graph construction. We extend the original NNK framework to allow explicit delays as part of the graph construction, so that unlike in NNK, two nodes can be connected with an edge corresponding to a non-zero time delay, if there is higher similarity between the signals after shifting one of them. We also propose to characterize the similarity between signals at different nodes using the node degree and clustering coefficients of their respective visibility graphs. Graph edges that can representing temporal delays, we provide a new perspective that enables us to see the effect of synchronization in graph construction for time-series signals. For both temperature and EEG datasets, we show that our proposed approach can achieve sparse and interpretable graph representations. Furthermore, the proposed method can be useful in characterizing different EEG experiments using sparsity.
A TB-CSP Motor Imagery EEG Recognition Method for Neural-controlled Manipulator System

To develop a manipulator system based on spontaneous EEG signal control, this paper proposes a common spatial pattern method combined with the Bhattacharyya distance of time-frequency signal (TB-CSP) for EEG signal feature extraction. Firstly, for different subjects, the original EEG signal is divided by a specific time window and different frequency band; Secondly, the average Bhattacharyya distance of different frequency bands in a specific time window is calculated to select the optimal time-frequency signal and input it to CSP for feature extraction; Finally, the extracted features are input into SVM and LDA to classify and complete the control of the manipulator. The proposed method achieves 82.6% accuracy on the public dataset BCI IVa and 79.48% accuracy on the self-collected dataset.
Method for Increasing the Speed of Operational Amplifier Based on “Folded” Cascode

Prospective circuits of fast-speed operational amplifiers (Op-Amps) based on a “folded” cascode implemented on silicon bipolar transistors, as well as GaAs JFET and GaAs p-$n$-$p$ BJT transistors are proposed. Computer modeling of Op-Amps in the LTspice environment showed that the Op-Amp's open-loop gain exceeds 60 dB, and their slew rate (SR) reaches hundreds of $\mathbf{V}/\boldsymbol{\mu \mathrm{s}}$. The developed operational amplifiers can be recommended for use in analog front-end, used in detectors of various physical quantities for high energy physics, space tools and medicine.