Miracle-dz/UniFAR-FTUs huggingface dataset:
Condensin II subunit NCAPH2 associates with shelterin protein TRF1 and is required for telomere stability

Condensin II subunits are known to be expressed and localized to interphase nuclei of eukaryotic cells. Although some studies have shown that condensin II likely exerts axial compaction forces, organizes chromosome territories, and has possible transcriptional modulatory functions, the full range of condensin II interphase activities are not known. In particular, it is not known if condensin II interphase activities are generally genome‐wide or if they have additional local activities unique to specific chromosomal structures such as telomeres. Here, we find that NCAPH2 interacts with TRF1 and these two proteins co‐localize at telomeres. Depletion of NCAPH2 leads to ATR‐dependent accumulation of 53BP1 and γH2AX DNA damage foci, including damage specific to telomeres. Furthermore, depletion of NCAPH2 results in a fragile telomere phenotype and apparent sister‐telomere fusions only days after NCAPH2 depletion. Taken together these observations suggest that NCAPH2 promotes telomere stability, possibly through a direct interaction with the TRF1 shelterin component, and prevents telomere dysfunction resulting from impaired DNA replication. Because proper telomere function is essential for chromosome integrity these observations reveal a previously unappreciated function for NCAPH2 in ensuring genome and telomere stability.
ML Based Model for Detection of Brain Tumor

Tumors are hazardous for human life present in any part of the body particularly present in the brain is dangerous to life. It is good to predict in the initial stages and treated to save the life. This paper presents a comprehensive solution for brain tumor prediction using deep learning and transfer learning techniques implemented in python with Tensor flow, Keras libraries and flask framework. The workflow encompasses data augmentation, model creation, training, testing and validation. The dataset, consisting of brain MRI images subjected to data augmentation to improve model performance. The pre-trained image dataset is employed as the backbone for feature extraction, followed by a custom dense layer for prediction of the tumor. The model is trained and evaluated over 15 epochs achieving a remarkable accuracy of approximately 94.94 in training and in testing the accuracy ranges around 90.0%. The model is trained to accept single MRI image from the image database for the prediction of the tumor.
Automated Image Captioning and Speech Synthesis

An innovative system that creates natural language captions for supplied photos and transforms them to voice for increased accessibility is Automated Image Captioning and voice Synthesis. The suggested system extracts important information from the image and generates a relevant and coherent caption using cutting-edge technologies of deep learning like Convolutional Neural Networks & Recurrent Neural Networks. Text-to-speech synthesis techniques are then used to translate the generated captions to speech, allowing visually challenged users to access the material. The suggested approach was tested using typical benchmark datasets and yielded encouraging results. Because the system can create natural language captions and convert them to voice, it May be used in diverse applications, consisting of media accessibility & assistive technology.
Social Engineering Solutions for Document Generation Using Key-Logger Security Mechanism and QR Code

Social engineering, in the context of computer security, refers to cheat people into reveal personal information or other personal data. The merits of electronic documentation are countless, save up your time, money, resources and increased security of documents. User is able to manage documents digitally such that information can be shared, builds, retrieved, systemize and stored efficiently and appropriately. As user requires obtaining some government certificates for general needs or every year, the current process of obtaining a certificate or document is lengthy. The proposed system can generate and retrieve certificates and documents through e-documents. Security techniques used are QR code, key-logging Encryption and Decryption using AES algorithm. QR code is 2D matrix code that is designed by keeping two point under consideration, i.e. it stores large amount of data as compared to 1D barcodes and are fast decoded. Security is mainly focused on key-logging where the key-logger hardware or software is used to capture the client's keyboard strokes to intercept the password. One Time Password(OTP) system is a secure implementation and designed to for providing a stricter set of policies depart from natural human habits of choosing their own passwords does not endure by functionalities of an organization, leaving unsafe from different malicious attacks. Proposed system performs better than the manual processing and to improve user driven visualization to improve security. The outcome is the authorized digital document.
Electromagnetic Metamaterial Absorbers for Soil Property Evaluation

In this paper, we proposed a sensor probe utilizing an electromagnetic metamaterial absorber. This device comprises a tri-layer structure with periodically arranged copper split-ring resonators, a resin layer, and a magnesium layer, forming a complete electromagnetic wave absorber. Magnesium in contact with the soil dissolves in response to soil properties, and wireless measurement of the reflected response due to dissolution enables measurement of soil properties. Simulation and reflection measurement results revealed that this device can absorb 70% of incident electromagnetic waves. Furthermore, we examined the relationship between the reflection spectrum and the polarization direction, as well as the correlation between the time of device placement on soil and the reflection spectrum. The results showed that the device exhibited a low dependency on polarization direction and underwent significant changes in the reflection spectrum with the dissolution of magnesium. Therefore, this device shows a promising potential for a practical application in soil property evaluation.
Predictive Precision: LSTM-Based Analytics for Real-time Stock Market Visualization

Artificial intelligence (AI) is widely implemented in finance for stock price prediction using deep learning techniques. However, stock data’s high volatility and signal-to-noise ratio can make it difficult for neural networks (NN) to make accurate predictions. Nevertheless, even 70% accuracy can provide a good return value, leading to the increasing adoption of AI. In this study, we utilized long short-term memory (LSTM), a time series forecasting method, to predict the future stock market prices of Twitter (TWTR) and Facebook (FB) on NYSE and evaluated the performance of the model. Additionally, sentiment analysis, which involves understanding information from sentiment rich sources such as news and social media, plays a crucial role in predicting changes in stock market prices. With the abundance of data in modern times, manual data examination is no longer practical, necessitating AI and data mining techniques. The impact of emotion classification on stock market prices was also investigated in this study. The deep learning method involved using an LSTM neural network to learn the features instead of relying on human feature engineering. The NLP model used in this study combines LSTM, an intriguing branch of machine learning that uses past data to predict future values, with real-time visualization for sentiment analysis.
Depth-Resolved Physiological Response of Retina to Transcorneal Electrical Stimulation Measured With Optical Coherence Tomography

Transcorneal electrical stimulation (TES) has become an effective strategy to modulate retinal neural activities and partially restore visual function in ophthalmic diseases. However, the exact responses in different retinal layers still need to be clarified. This paper’s goal was to evaluate the depth-resolved retinal physiological responses evoked by TES by using optical coherence tomography (OCT). A custom-built spectral-domain OCT system was used to record the intrinsic optical signals (IOSs) in different retinal layers. TES and flickers were used to stimulate the retina electrically and visually. Tetrodotoxin was used to inhibit the retinal neural activity for confirming the origin of TES-induced IOSs. We found both positive and negative IOSs could be evoked by TES in three segmented retinal layers, especially in the inner retina and subretinal space. The TES-induced IOSs correlated with the TES intensity. After tetrodotoxin injection, the IOSs evoked by TES were significantly declined, peculiarly in the inner retina. The IOSs elicited by flickers kept increasing during the stimulation, while those evoked by TES kept at a stable level. In conclusion, TES could elicit IOSs that originated from retinal neural activity in all segmented layers. The TES-induced IOSs were highly synchronized to the electrical field in the retina.
Grip-Vision: Enhancing Object Handling with Computer Vision-Based Grip Classification

The hand is the primary functional part of the human body. Due to various traumatic injuries or accidents, people are suffering from upper extremity amputations. This paper presents a detailed description of the system which is to be integrated with the artificial hand for amputees. This computer vision-based approach is aimed at classifying objects based on the grip required for lifting them using the canny edge detection method and feature descriptors like BRISK. Experimentation is done on household objects like bottles, cups, and mobile phones with the help of state of art Machine Learning models and classifiers like Random Forest, SVM, Decision Tree, and KNN. The trained model can classify the objects based on their required grips with the highest accuracy scores of both the KNN classifier and Random Forest classifier that is 91.86% and 90.27% respectively with the proper output of the classified grip.
A 60-GHz Wideband L-probed Magneto-Electric Dipole Antenna

In this work, a wideband millimeter-wave L probed magneto-electric (ME) dipole antenna is proposed for a 60-GHz unlicensed band. The proposed Antenna emits a unidirectional radiation pattern with wide bandwidth owing to its complementary nature. The proposed ME dipole antenna had the simulated $S_{11}$ less than $-10 \mathrm{~dB}$ with $25.57 \%$ bandwidth covering from 54.46GHz to 71.72GHz and peak gain of 12.33dBi with $3-\mathrm{dB}$ gain bandwidth of $31.9 \%$ covering 54.33GHz to 75GHz.
Fingerprint Template Ageing Revisited - It’s the Quality, Stupid!

Several studies conducted on time-span separated fingerprint datasets revealed recognition performance degradation as compared to commonly achieved accuracies. The reported accuracy reduction is mainly caused by a shift of the genuine score distributions towards the impostor ones. This fact raised discussions about the reasons behind eventual template ageing effects in fingerprint recognition. Analysing the only publicly available time-separated fingerprint dataset (CASIA Fingerprint Subject Ageing Version 1.0), it is demonstrated in the current study that (i) advances in recognition technology mitigate the earlier observed decrease in recognition accuracy and (ii) advances in fingerprint quality assessment now lead to the conclusion that it is indeed fingerprint quality which causes the earlier observed recognition impact. Current results on this dataset do not suggest the existence of a fingerprint template ageing effect.
Fast and Accurate Temporal Data Classification Using Nearest Weighted Centroid

Classification is an important task in the field of machine learning and data mining due to its wide applications. Among many algorithms to classify temporal data, the k-nearest neighbor with the dynamic time warping (DTW) is performant. However, it is very time consuming as it needs to compare time series query with all training samples. Since the computational complexity of the DTW is quadratic, these comparisons pose some problems for large data, either it is not applicable or it takes a lot of time to classify the data. To obtain fast accurate results by reducing the data size, classification based on nearest centroid could be one solution. In this paper, to speed up the nearest neighbor classification, and specially to make it applicable for huge datasets, we propose a fast accurate k-nearest weighted centroid classifier. Using generalized $k$-means-based clustering within each class, we obtain a small number of weighted centroids per class. A wide range of datasets is used to evaluate the efficiency of the proposed classifier, which outperforms the alternative ones.
Dopaminergic Modulation of Synaptic Plasticity, Its Role in Neuropsychiatric Disorders, and Its Computational Modeling

Neuromodulators modify intrinsic characteristics of the nervous system in order to reconfigure the functional properties of neural circuits. This reconfiguration is crucial for the flexibility of the nervous system to respond on an input-modulated basis. Such a functional rearrangement is realized by modification of intrinsic properties of the neural circuits including synaptic interactions. Dopamine is an important neuromodulator involved in motivation and stimulus-reward learning process, and adjusts synaptic dynamics in multiple time scales through different pathways. The modification of synaptic plasticity by dopamine underlies the change in synaptic transmission and integration mechanisms, which affects intrinsic properties of the neural system including membrane excitability, probability of neurotransmitters release, receptors’ response to neurotransmitters, protein trafficking, and gene transcription. Dopamine also plays a central role in behavioral control, whereas its malfunction can cause cognitive disorders. Impaired dopamine signaling is implicated in several neuropsychiatric disorders such as Parkinson’s disease, drug addiction, schizophrenia, attention-deficit/hyperactivity disorder, obsessive-compulsive disorder and Tourette’s syndrome. Therefore, dopamine plays a crucial role in the nervous system, where its proper modulation of neural circuits may enhance plasticity-related procedures, but disturbances in dopamine signaling might be involved in numerous neuropsychiatric disorders. In recent years, several computational models are proposed to formulate the involvement of dopamine in synaptic plasticity or neuropsychiatric disorders and address their connection based on the experimental findings.
Automated Detection of Exudates in Retinal Images

Diabetic Retinopathy (DR) is a major cause of blindness which is caused by damage to the small blood vessels of the retina. Early detection of Diabetic Retinopathy is important to prevent vision loss. Main two stages of diabetic retinopathy are non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). Microaneurysms, hemorrhages, cotton-wool spots and exudates are symptoms of non proliferative diabetic retinopathy which is the early stage of diabetic retinopathy. Of these symptoms exudates detection is more important for the early detection of diabetic retinopathy. In this paper, we are proposing a method for the automated detection of exudates by using retinal images. We show that different morphological operations could be incorporated in order to detect exudates quite accurately. In the process of exudates detection optic disk detection is an important part. Otherwise, optic disk could be wrongly identified as exudates. We have used different morphological operations for the exudates detection from the retinal images and the developed algorithm has a sensitivity and specificity 94.59% and 88.46% respectively. This is considerably a higher success rate with the existing systems.
Point Clouds Attribute Compression Using Data-Adaptive Intra prediction

In recent years, 3D sensing and capture technologies have made constant progress, leading to point clouds with higher resolution and fidelity. Since most applications demand compact storage and fast transmission, the issue of how to compress point clouds efficiently becomes an intractable problem. While previous GFT-based solutions use the transform tool to decorrelate attributes directly, ignoring the overall attribute’s data spatial redundancy, Graph Fourier Transform (GFT) has shown good performance on point cloud attribute compression. So, motivated by coding tools in traditional image and video coding, we propose a block-based data-adaptive intra prediction tool before graph transform processing to further reduce the redundancy. We adopt uniform quantizing and context-based arithmetic coding to get the final bitstream. Experimental results on different datasets demonstrate that our method improves the compression efficiency of other GFT-based schemes and has much better BD-BR performance than the state-of-the-art Region-Adaptive Hierarchical Transform (RAHT) approach on most specified point cloud contents.
New-Automotive -Autonomous Driving Challenges For The Microelectronic Components

Key focus of the European Research in Horizon 2020 is ‘digital transformation’. This means the strategic and disruptive evolution towards a digital society. The connection between IoT (Internet of Things), big data and AI (artificial intelligence) and autonomous systems like robotic, automation, machine learning and self-driving impact the innovation in products, processes and business models. Generally, many subjects and challenges have to be addressed in this frame. This are safety, security, privacy, sensors and actuators, the development of software within the scope of life cycle issues, system integration, connected vehicles, vehicle sensors with external sources (V2X also called CAR2X), standardization, situation understanding, cognition and decision-making. Furthermore, insurances as well as the General Data Protection Regulation (GDPR) will influence the field of autonomous driving.
Advanced AODV Technique to Improve Communications in a 4G Telecommunication Network and Related Issues

The major purpose of this research study is to improve network performance in 4G groups and people (bandwidth or throughput—the amount of data or bytes that need to transmit in a given length of time), as well as signal strength in hubs (nodes), and to address concerns about handoff and connection loss. The Advanced Ad-hoc On-demand Distance Vector (Adv_AODV) technique is used to improve bandwidth performance and signal loss (dBm) by comparing with the TCP (Transfer Control Protocol). The TCP compared with Adv_AODV based MANET and VANET. Information is transferred and different nodes are established. The algorithm's precision was determined. The sample size for this research study N = 20 with and two group such of TCP and MANET and VANET. The Adv_AODV method achieves higher bandwidth performance and signal strength. The degree of significance in the t-Test analysis is significant with [P = 0.0452], with a significance value of 0.05 and confidence interval of 95% with G-power of 80%. The Adv_AODV routing protocol, as well as MANET and VANET, where nodes (hubs) regularly shift their location inside the network. Unique moving endpoints (hubs) and other interconnected nodes (hubs) engage with one another and exchange important information over a different transmission medium.
Proposing adaptive PN sequence length scheme for testing non-destructive structure using DS-UWB

The ultra wideband (UWB) technology has many advantages in positioning, measurement systems, however powers of UWB signals rapidly reduces while traveling in propagation environments, especially in concrete or soil propagation environment. Therefore, we proposes an adaptive pseudo random (PN) sequence length searching method of direct sequence ultra wideband (DS-UWB) transmission system for reducing signal processing time while remaining the quality of detection. The proposed scheme measures the depth of non-destructive structure using variable PN sequence length, and then based on the calculated distance errors, the minimum PN sequence length is determined. Simulation results show that both processing time and accuracy could be improved by the proposed scheme.
Probe-Based Antenna Measurements at Sub-THz Frequencies

In this work, we integrate a probe platform and an over-the-air chamber to realize a probe-based antenna measurement system at sub-THz frequencies. Only one-time probing is needed to measure the S parameters and 3D radiation pattern. This probing operation is suitable for antenna-in-package modules where the feed point is at the opposite side of antenna. In addition, a sub-THz reflector is carefully designed for compact antenna test range so that the far field condition can be fulfilled in a small chamber.
A Wideband, Low-Profile, and 1-Bit Beam-Scanning Reflectarray at X-Band

A 1-bit reconfigurable reflectarray with wide-angle scanning range from 9.15 GHz to 10.9 GHz is proposed in this paper. To allow wideband coverage while maintaining low profile, the unit cell loaded by one p-i-n diode was designed to optimally increase element bandwidth, i.e., the bandwidth for reflection-phase difference within 180° ± 20° between the diode’s on- and off-states. To achieve greater degree of structural optimization and improvement for aperture efficiency, p-i-n diodes and the biasing network were deployed on the opposite side of phasing elements. A $10 \times 10$ reconfigurable reflectarray was prototyped and can experimentally scan its main beam from +40° to −40° at frequencies of 9.15, 10, and 10.9 GHz with peak gain of 14.97 dBi. The measured 1-dB bandwidth is 10.8%.
Three-Port S-Parameter based characterization of integrated bridged-T-Coils

This paper introduces a bridged-t-coil characterisation tool based on several levels of equivalent circuits and input impedance measurements gathered from S-parameter analysis. The input impedances of the models for different circuitry, termination and frequencies are calculated based on symbolic variables and equated to the simulated input impedances of the bridged-t-coil for the same test set-up resulting in a linear equation system. By calculating the values of the components of the equivalent circuits it is possible to analyse and design an integrated bridged-t-coil in a systematic manner. Therefore, the proposed models allow an acceleration of the design flow as critical parameters can be extracted so that the layout can be tuned and optimized regarding the gained information. As a proof-of-concept an integrated bridged-t-coil is designed in a 65nm CMOS technology and the S-parameters of the proposed models are compared to the S-parameters of the EM-simulated three-port. The deviation of the S-parameters in the considered frequency band is below 3%.
ZipNet: ZFNet-level Accuracy with 48× Fewer Parameters

With the introduction of Convolutional Neural Networks, models for image classification achieve higher classification accuracy. Based on the pattern of the design of CNN architectures, increasing the number of layers equates to a higher classification accuracy, but also increases the number of parameters and model size. This negatively affects the model training time, processing time, and memory requirement. We develop ZipNet, a CNN architecture with a higher classification accuracy than ZFNet, the winner of ILSVRC 2013, but with 48.5× smaller model size and 48.7× fewer parameters. The classification accuracy of ZipNet is higher than the performance of ZFNet and SqueezeNet on all configurations of the Caltech-256 dataset with varying number of training examples.