Miracle-dz/UniFAR-FTUs huggingface dataset:
Artery and Vein classification for hypertensive retinopathy

Hypertensive Retinopathy (HR) is one of the most common retinal diseases. Hypertension diversely affects different body parts, including the eyes. Sustained hypertension can lead to damage in the retinal vasculature causing vision problems, this condition is termed as Hypertensive Retinopathy (HR). Since HR is a common condition associated with several Cardio-Vascular Diseases (CVD), automated diagnosis of HR can aid the physician when dealing with a large population. Classification of retinal vessels is the first step in characterizing retinal disorders such as HR. In this work, we have proposed an automated support system for classification of arteries and veins (AV) for HR detection. The proposed framework classifies AV using different feature vectors obtained through Radon vessel tracking algorithm. The features are extracted from publicly available standard dataset DRIVE. One of the main advantages of vessel tracking is that it can be further utilized for detecting Artery Vein Ratio (AVR). Classification results are acquired using three different classifiers namely SVM, NN and CNN. Experimental results show that CNN outperforms NN and SVM in AV classification for HR detection.
Enhancing the gain of a HMSIW Based Semicircular Antenna using Antenna-FSS Composite Structure

In the following paper, we have designed a Half Mode Substrate Integrated Waveguide (HMSIW) based Semi-Circular Patch Antenna to operate at 6.5GHz wireless frequency. The parent antenna is such designed that it has an array of metallic vias along the circumference to make the edges behave as Perfect Electrical Conductor (PEC) walls while the radiation occurs from the radial side which acts as a Perfect Magnetic Conductor (PMC) wall because it is devoid of such metallic vias. The antenna exhibits a gain of 6.2 dBi with linear polarization. Its gain is increased using polarization independent Frequency Selective surface (FSS) as superstrate which is fabricated using Arlon AD270 substrate. The gain is found to increase from 6.2dBi to 8.2 dBi at the same 6.5 GHz resonating frequency.
Development of a Biosensor Based on Graphene for Detection of Physiological Signals*

At present, graphene has been widely used in flexible sensors, however, it is still a problem to prepare graphene-based sensors by a simple and low-cost method. In this paper, graphene nanoplatelets (GNPs) are prepared by thermal expansion method, and a capacitance plate is prepared by sealing a GNPs sheet with polyurethane (PU) medical film. Then two capacitor plates are overlapped each other, and the two sides are sealed with a transparent tape to form a GNPs capacitance sensor. In comparison with existing graphene-based sensors, the fabrication method of the GNPs capacitance sensor is simple and low-cost. What is more, the physiological signals can be measured effectively by the GNPs capacitance sensor. The sensor has been used to measure the signal of finger bending motion and pulse wave signal. The results show that the GNPs capacitance sensor is a promising sensor for effective measurement of physiological signals.
Accurate anatomical head segmentations: a data set for biomedical simulations

Detailed computational anatomical models of the entire head are needed for accurate in silico modeling in a variety of transcranial stimulation applications. Models from different subjects help to understand and account for population variability. To this end, we have developed a new library of head models of 20 individuals, segmented from co-aligned multi-modal medical image data. The acquired image modalities allow to accurately model tissues with different material properties, such as electrical conductivity or spatially varying acoustic properties. The usefulness of the models is illustrated for two example applications.
Wearable, Fiber-less, Multi-Channel System for Continuous Wave Functional Near Infrared Spectroscopy Based on Silicon Photomultipliers Detectors and Lock-In Amplification

Development and in-vivo validation of a Continuous Wave (CW) functional Near Infrared Spectroscopy (fNIRS) system is presented. The system is wearable, fiber-less, multi-channel (16×16, 256 channels) and expandable and it relies on silicon photomultipliers (SiPMs) for light detection. SiPMs are inexpensive, low voltage and resilient semiconductor light detectors, whose performances are analogous to photomultiplier tubes (PMTs). The advantage of SiPMs with respect to PMTs is that they allow direct contact with the scalp and avoidance of optical fibers. In fact, the coupling of SiPMs and light emitting diodes (LEDs) allows the transfer of the analog signals to and from the scalp through thin electric cables that greatly increase the system flexibility. Moreover, the optical probes, mechanically resembling electroencephalographic electrodes, are robust against motion artifacts. In order to increase the signal-to-noise-ratio (SNR) of the fNIRS acquisition and to decrease ambient noise contamination, a digital lock-in technique was implemented through LEDs modulation and SiPMs signal processing chain. In-vivo validation proved the system capabilities of detecting functional brain activity in the sensorimotor cortices. When compared to other state-of-the-art wearable fNIRS systems, the single photon sensitivity and dynamic range of SiPMs can exploit the long and variable interoptode distances needed for estimation of brain functional hemodynamics using CW-fNIRS.
Tools and Techniques of Assistive Technology for Hearing Impaired People

Education is the most important part of every human life that is necessary to go ahead in life and help us to exchange information by speaking, writing, or using some other medium. Lack of hearing power or deafness creates difficulties to speak and understand what others are saying. Assistive technology reduces the stress for hearing-impaired people to communicate with normal people. Assistive devices are categorized as Assistive Listening Devices (ALD), Augmentative and Alternative Communication (AAC) and Alert systems. In this research paper, various tools and techniques based on parameters such as alert systems, speech enhancement, SL recognition and learning applications, Telecommunication systems and speech to text are discussed. Most of the available devices for hearing impaired people, are prototypes and not commercially available.
Optimal Trajectories of Brain State Transitions Indicate Motor Function Changes Associated with Aging

Healthy aging is associated with structural and functional changes in sensorimotor systems, leading to a deterioration in motor function. However, most of the previous studies focused on the descriptive measures of alteration, little is known about how structural network facilitate functional dynamics during aging. Based on the structural brain network constructed by diffusion-weighted imaging, we employed recent network control theory to evaluate the control energy necessary to drive the state transition from baseline condition with default mode network (DMN) activation to motor network activation in a large cohort (n = 625; 18–88 years). We found at the whole, the control energy required to activate the motor network declined with aging, in which the motor network contributed most of the control energy. The control energy of nodes within motor network showed both positive and negative age effects, reflecting an aberrant functional integration associated with aging. Interestingly, the control energy of subcortical network most significantly increased with aging, suggesting an altered motor-subcortical circuit, thus requiring more energy for the optimal control. Moreover, the control energy of bilateral putamen showed the largest positive age effect, and this pattern was also supported by the energetic impact of nodes, implying a key role for motor modulation associated with aging. Taken together, our results offer insights of control energy cost necessary for the age-dependent decline in motor task, and provide new clues for brain optimal control of neuromodulation in older adults.
A convolutional neural network based framework for classification of seizure types

Epileptic seizures are caused by a disturbance in the electrical activity of the brain and classified as many different types of epileptic seizures based on the characteristics of EEG and other parameters. Till now research has been conducted to classify EEG as seizure and non-seizures, but the classification of seizure types has not been explored. Thus, in this paper, we have proposed the 8-class classification problem in order to classify different seizure types using convolutional neural networks (CNN). This research study suggests a CNN based framework for classification of epileptic seizure types that include simple partial, complex partial, focal non-specific, generalized non-specific, absence, tonic, and tonic-clonic, and non-seizures. EEG time series was converted into spectrogram stacks and used as input for CNN. To the best of authors knowledge, ours is the very first study that classified the seizures types using the computational algorithm. The four CNN models, namely AlexNet, VGG16, VGG19, and basic CNN model was applied to study the performance of 8-class classification problem. The proposed study showed a classification accuracy of 84.06%, 79.71%, 76.81%, and 82.14% using AlexNet, VGG16, VGG19 and basic CNN models respectively. The experimental results suggest that the proposed framework could be helpful to the neurology community for recognition of seizures types.
Distributed Optimal Power Flow for Electric Power Systems with High Penetration of Distributed Energy Resources

optimization technology is developing to the point of becoming a cost-effective enabler of increased power transfer asset utilization. This paper presents a smart decomposition technique for the traditional optimal power flow (OPF) algorithm to allow distributed optimal power flow (DOPF) calculations without relying on a centralized controller. Hence, it develops a feasible distributed architectures for the electric power industry. The proposed method is implemented using Monte Carlo Tree Search based reinforcement learning (MCTS-RL) algorithm. This reduces computational complexity and allows to avoid difficulties associated with stochastic modeling often used to capture the random nature of distributed energy resources (DER) units and loads. The efficiency of the optimization process is improved when the DOPF reflects the fast response capability of the optimal solution. This contribution provides results for a real-time dispatchable resource and demonstrates the flexibility of RL to adapt to changes of system states, ultimately reducing the generation cost while maintaining the system security constraints.
Designing Configurable Arm Rehabilitation Games: How Do Different Game Elements Affect User Motion Trajectories?

For successful rehabilitation of a patient after a stroke or traumatic brain injury, it is crucial that rehabilitation activities are motivating, provide feedback and have a high rate of repetitions. Advancements in recent technologies provide solutions to address these aspects where needed. Additionally, through the use of gamification, we are able to increase the motivation for participants. However, many of these systems require complex set-ups, which can be a big challenge when conducting rehabilitation in a home-based setting. To address the lack of simple rehabilitation tools for arm function for a home-based application, we previously developed a system, Cellulo for rehabilitation, that is comprised of paper-supported tangible robots that are orchestrated by applications deployed on consumer tablets. These components enable different features that allow for gamification, easy setup, portability, and scalability. To support the configuration of game elements to patients’ level of motor skills and strategies, their motor trajectories need to be classified. In this paper, we investigate the classification of different motor trajectories and how game elements impact these in unimpaired, healthy participants. We show that the manipulation of certain game elements do have an impact on motor trajectories, which might indicate that it is possible to adapt the arm remediation of patients by configuring game elements. These results provide a first step towards providing adaptive rehabilitation based upon patients’ measured trajectories.
Comparison of traditional machine learning methods and Google services in identifying tonality on Russian texts

Classification of text is very important and interesting topic for research due to number of unstructured data and especially in the form of news. The problem arises when classifying news that is written in language other than english. Due to poor support of research done in this field and general language complexity, it is very hard to identify correctly, tone and sentiment of a text. This problems can be avoided by applying various techniques at many stages of this model. Choosing proper technique can be very tricky and would affect efficiency of a model and can save time as well as working time. The aim of this paper is to compare traditional machine learning techniques with Google services by using Translate API and see how well they perform while working with Russian texts. On traditional machine learning techniques after applying pre-processing methods which included stemming, parsing and indexing and by using various different methods of embedding turns our unstructured data into a structured data.
Well-Being Assessment in Hawaii: Creating community-level composite indices in paradise

The purpose of this report is to provide the necessary foundation for the construction of a comparative well-being index for communities in Hawaii. We begin by comparing the composite index and dashboard approaches to describing well-being. We provide guidance on the selection of indicators, their normalization, the weighting of indicators to form a composite index, and the comparison of indices and indicator values across regions. Existing indices are compared to provide context. Available data sources are listed and opportunities to augment current data collection are identified. Specific recommendations are provided regarding the well-being model, data sources, indicator selection, interactive visualization, and communication.
Multimodal Biometric Authentication with Secured Templates — A Review

The advent of internet is highly triggering people towards the digital era. The inexpensive data availability to common people also aids for the abundant data communication. In the meantime, securing data and applications from unauthorized identities is becoming very crucial. Biometric authentication system utilizes physiological and behavioral characteristics of individuals to automate access control and identity verification. These fast, convenient and un-obstructive systems can be categorized as unimodal and multimodal, based on the number of biometric traits or modalities used. Multimodal biometric systems have shown improved success rate of authentication with increased robustness over unimodal biometrics at the cost of facing numerous complications. The unauthorized access to biometric templates which represents distinctiveness of people is one of the major threats to user's privacy and security. Hence, variety of template protection techniques can be practiced for securing biometric templates. This paper gives an overview of multiple biometrics used for authentication. Focusing on challenges in multimodal biometrics practiced through various fusion levels and different algorithms, this paper discusses scope of further research in this field. Majorly, the template security issue of multimodal biometrics is emphasized with various techniques to protect the crucial asset of human identity.
International Conference on Intelligent Computing and its Emerging Applications ( ICEA ) A Phishing Detection System based on Machine Learning

Abstract— As the Internet has become an essential part of human beings’ lives, a growing number of people are enjoying the convenience brought by the Internet, while more are attacks coming from on the dark side of the Internet. Based on some weaknesses of human nature, hackers have designed confusing phishing pages to entice web viewers to proactively expose their privacy, sensitive information. In this article, we propose a URL-based detection system combining the URL of the web page URL and the URL of the web page source code as features, import Levenshtein Distance as the algorithm for calculating the similarity of strings, and supplemented by the machine learning architecture. Due to the great accuracy in small sample numbers and binary classification, we implement Support-vector machine to be the machine learning algorithm model in our system. The system is designed to provide high accuracy and low false positive rate detection results for unknown phishing pages.
A Vision-based System for Breathing Disorder Identification: A Deep Learning Perspective

Recent breakthroughs in computer vision offer an exciting avenue to develop new remote, and non-intrusive patient monitoring techniques. A very challenging topic to address is the automated recognition of breathing disorders during sleep. Due to its complexity, this task has rarely been explored in the literature on real patients using such marker-free approaches. Here, we propose an approach based on deep learning architectures capable of classifying breathing disorders. The classification is performed on depth maps recorded with 3D cameras from 76 patients referred to a sleep laboratory that present a range of breathing disorders. Our system is capable of classifying individual breathing events as normal or abnormal with an accuracy of 61.8%, hence our results show that computer vision and deep learning are viable tools for assessing locally or remotely breathing quality during sleep.
Driver Drowsiness Estimation by Parallel Linked Time-Domain CNN with Novel Temporal Measures on Eye States

SUMMARY Driver drowsiness estimation is one of the important tasks for preventing car accidents. Most of the approaches are binary classi-ﬁcation that classify a driver is signiﬁcantly drowsy or not. Multi-level drowsiness estimation, that detects not only signiﬁcant drowsiness but also moderate drowsiness, is helpful to a safer and more comfortable car sys-tem. Existing approaches are mostly based on conventional temporal measures which extract temporal information related to eye states, and these measures mainly focus on detecting signiﬁcant drowsiness for binary clas-siﬁcation. For multi-level drowsiness estimation, we propose two temporal measures, average eye closed time (AECT) and soft percentage of eyelid closure (Soft PERCLOS). Existing approaches are also based on a time domain convolutional neural network (CNN) as deep neural network models, of which layers are linked sequentially. The network model extracts features mainly focusing on mono-temporal resolution. We found that features focusing on multi-temporal resolution are e ﬀ ective to multi-level drowsiness estimation, and we propose a parallel linked time-domain CNN to extract the multi-temporal features. We collected an own dataset in a real environment and evaluated the proposed methods with the dataset. Compared with existing temporal measures and network models, Our system outperforms the existing approaches on the dataset.
Automatic Detection of General Anesthetic-States using ECG-Derived Autonomic Nervous System Features

Electroencephalogram (EEG)-based prediction systems are used to target anesthetic-states in patients undergoing procedures with general anesthesia (GA). These systems are not widely employed in resource-limited settings because they are cost-prohibitive. Although anesthetic-drugs induce highly-structured, oscillatory neural dynamics that make EEG-based systems a principled approach for anesthetic-state monitoring, anesthetic-drugs also significantly modulate the autonomic nervous system (ANS). Because ANS dynamics can be inferred from electrocardiogram (ECG) features such as heart rate variability, it may be possible to develop an ECG-based system to infer anesthetic-states as a low-cost and practical alternative to EEG-based anesthetic-state prediction systems. In this work, we demonstrate that an ECG-based system using ANS features can be used to discriminate between non-GA and GA states in sevoflurane, with a GA F1 score of 0.834, [95% CI, 0.776, 0.892], and in sevoflurane-plus-ketamine, with a GA F1 score of 0.880 [0.815, 0.954]. With further refinement, ECG-based anesthetic-state systems could be developed as a fully automated system for anesthetic-state monitoring in resource-limited settings.
Energy Efficient Content Based Routing with Cluster Based Scheduling Mechanism

IoT is a distributed network of smart objects that are remotely controlled by the devices across the world. IoT is an insolent technology that growing rapidly. It will make benefits to many applications which make our life comfortable, smoother and secure. IoT devices are constrained in terms of energy, transmission range, storage capacity and processing which affects the performance of network and increases energy consumption. In order to make an energy efficient network our proposed model uses clustering mechanism with sleep scheduling technique. Gathering the data from sources increased data congestion. Routing the aggregated data based on its content over the network. Our proposed model adjusts energy consumption and improve lifetime of network by changing node’s state between active/sleep modes periodically within cluster and make an efficient data transmission based on its content through cluster mechanism. By using MATLAB, stimulated our proposed model and results of simulation improve enrichments of our proposed approach by extend the network’s lifetime and efficient energy utilization. MATLAB is used for performance analysis and from the results, it confirmed that proposed model prolong network’s lifetime.
3D Ultrasound Spine Image Selection Using Convolution Learning-to-Rank Algorithm

3D Ultrasound imaging has become an important means of scoliosis assessment as it is a real-time, cost-effective and radiation-free imaging technique. However, the coronal images from different depths of a 3D ultrasound image have different imaging definitions. So there is a need to select the coronal image that would give the best image definition. Also, manual selection of coronal images is time-consuming and limited to the discretion and capability of the assessor. Therefore, in this paper, we have developed a convolution learning-to-rank algorithm to select the best ultrasound images automatically using raw ultrasound images. The ranking is done based on the curve angle of the spinal cord. Firstly, we approached the image selection problem as a ranking problem; ranked based on probability of an image to be a good image. Here, we use the RankNet, a pairwise learning-to-rank method, to rank the images automatically. Secondly, we replaced the backbone of the RankNet, which is the traditional artificial neural network (ANN), with convolution neural network (CNN) to improve the feature extracting ability for the successive iterations. The experimental result shows that the proposed convolutional RankNet achieves the perfect accuracy of 100% while conventional DenseNet achieved 35% only. This proves that the convolutional RankNet is more suitable to highlight the best quality of ultrasound image from multiple mediocre ones.