Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hardware Inference Accelerator for Temporal Convolutional Networks

Traditionally, Recurrent Neural Networks (RNNs) are used for time-series prediction. However, recent results have shown that Temporal Convolutional Networks (TCNs) outperform RNNs in terms of accuracy and training time. By using dilated convolutions TCNs are able to capture long term temporal dependencies from time series. The use of existing Convolutional Neural Networks (CNNs) inference accelerators for the computation of dilated convolutions significantly decreases the throughput and causes computation overhead. In order to take into account the one-dimensionality of the convolutions and presence of dilation, this paper proposes a dedicated hardware inference accelerator for TCNs. We use this accelerator to run an adaptation of WaveNet for anomaly detection in an ECG time series dataset. Our results show that it can achieve 6.3 · 10−4 DRAM access/MAC, 0.043 on-chip SRAM access/MAC and 8 ms/inference. This lower number of on-chip and off-chip memory accesses significantly decreases the overall power consumption and increases the throughput.
Epidural anesthesia: New computer technology used to enhance the teaching of a common anesthetic procedure

Abstract : The epidural anesthetic procedure is one of the most commonly used techniques in anesthesia. Its usefulness during labor, general surgery and for post-operative pain control is well documented. The underlying anatomy and technical skills for administering an epidural anesthetic have traditionally been taught using lectures, books, models and human materials. There are also Computer-Assisted Instruction (CAI) CD-ROMS being developed to teach regional anesthesia. Because of rapidly advancing technology, it has recently become possible to use computers to re-create actual human anatomy by extracting data from frozen serial sections, CT and MRI scans. One way this was done was by digitizing this data from a human male and female. The National Library of Medicine originally conceived the Visible Human (VH) Project in 1986. Its initial goal was to acquire CT, MRI, and cryosections from a representative male and female specimen, in approximately one-millimeter intervals. A complete male and female were scanned and digitized but have only been available since 1995. This data set is now available for review and manipulation to licensed individuals and institutions. High-end computers can now take this digital data and re-create the anatomical structures in a three-dimensional format. This new information can be used to generate 3-D computer models of specific regions of the body. We have developed teaching photographs and three-dimensional images to enhance the learning of epidural and spinal anesthetic anatomy and procedures. This includes a description of commonly used equipment such as needles and catheters. Also, sections of the Visible Human lumbosacral region and spinal canal were downloaded and displayed in cross-section, sagittal and parasagittal planes. Structures such as the vertebrae, ligaments, spinal cord and meninges were then labeled in order to train anesthetists in the basic anatomic landmarks.
A New Control Strategy for Harmonic Mitigation Using Open UPQC in Modern LV Networks

The higher penetration of both renewable generation units and nonlinear loads is leading to higher levels of harmonics in microgrids and modern distribution systems. Accordingly, the operation of the sensitive loads in the grid can be affected by the low quality of the power delivered to the customers. Moreover, the higher harmonic levels lead to an increase in losses and lower energy efficiency in the grid. Hence, the harmonics in the grid are regarded as a great challenge by both the customers and Distribution System Operators (DSOs). Accordingly, the researchers have widely focused on minimizing the harmonics of the modern grids using Custom Power Devices (CPDs) in recently published papers. Among the devices incorporated in previous studies, the Open-UPQC is found as an effective and applicable solution for power quality improvement in microgrids and modern distribution systems which can provide benefits for both customers and DSOs. In this paper, a new coordination strategy is proposed for the Open-UPQC to provide the highest harmonic elimination capability besides the voltage improvement in the grid. In order to evaluate the effectiveness of the proposed method, the control strategy is applied to a sample modern distribution system which is formed by adding harmonic-injecting loads to a grid with real data. The results of simulations verified that applying the proposed control strategy to the Open-UPQC provides the effective harmonic reduction in the grid by using the improved coordination of the shunt units.
Background Sound Classification in Speech Audio Segments

Background sound classification is the task of identifying secondary sound sources in the surrounding environment. Real-time speech is always accompanied by a context. This context can be very helpful in enhancing the behavior of a variety of applications. Traditionally, audio classification tasks have mainly focused on speech due to its wide applicability. Recent works have explored environmental scene classification using acoustic features. Availability of different datasets like UrbanSound, ESC50, and AUDIOSET have further aided the process. Previous works have mostly focused on the classification of independently occurring acoustic events. In this work, we explore the classification of background sound in audio recordings containing human speech. We prepare a new dataset YBSS-200 using youtube videos where each sample contains a distinct background sound and an accompanying foreground human voice. We present a convolutional neural network based transfer learning approach using a VGG like Network for classification of context in such acoustic signals. Specific data augmentation techniques were used to improve the classification results.
Health Monitoring and Early Diseases Detection on Dairy Cow Based on Internet of Things and Intelligent System

Diseases are the causes of the decrease dairy cow productivity to produce milk. in normal conditions, dairy cows can produce as much as 12 until 15 liters of milk every day, while dairy cows that are affected by the disease are only able to produce milk as much as 5 until 10 liters every day. The difficulty of early detection and handling of cows that are affected by disease is caused by monitoring the condition of cows that are not carried out at any time, as well as limited knowledge of Breeders about the disease. This study aims to develop a dairy cow health management system, from health monitoring until the detection and handling of cows that have been affected by the disease. We combine monitoring systems and detection systems into one application utilizing Internet of Things and Intelligent System technology. The monitoring system processes the temperature and heart rate data of cows from the sensor, then gives results of a cow's health condition, normal or abnormal. The detection system processes symptom data in cows inputted by Breeders, then gives results of estimates of disease diagnoses, treatment and prevention methods. Experiments shows that the monitoring system can monitor health conditions in dairy cows based on temperature and heart rate with an error rate of 0.6 degrees Celsius and 3.5 Beats perminute. Experiments on the detection system shows that the detection system can diagnose diseases in dairy cows based on physical symptoms with an accuracy rate of 90 percent.
A 26-Gb/s 3-D-Integrated Silicon Photonic Receiver in BiCMOS-55 nm and PIC25G With – 15.2-dBm OMA Sensitivity

This letter presents a 3-D-integrated 26 Gb/s opto-electrical receiver front-end. The electronic integrated circuit (EIC) is fabricated in a BiCMOS-55-nm technology, flipped and placed on top of the photonic integrated circuits (PICs) die through copper pillars. In the receiver chain, a fully differential shunt-feedback TI amplifier (FD-SF TIA) is followed by a limiting amplifiers (LAs) with embedded equalization, output driver and an automatic offset cancelation loop. The whole receiver provides a transimpedance (TI) gain of 76 dBñ with 30-GHz bandwidth. By exploiting the FD-SF TIA with low parasitic capacitance of the Germanium dual heterojunction photo diode (Ge-PD) in the photonic die, the receiver achieves sensitivity of −15.2 dBm optical modulation amplitude (OMA) at Ge-PD and −10-dBm OMA at the single-mode fiber (SMF) optical output with bit error rate of 10−12 and PRBS 15. The sensitivity is aligned with state-of-the-art receivers employing discrete photonics and, to author’s best knowledge, it is the lowest reported among published 25 Gb/s receivers exploiting silicon photonics.
Dual-Mode Microstrip Patch Bandpass Filters With Generalized Frequency Responses

A basic two-pole bandpass filter based on the dual-mode square patch resonator is presented and analyzed to realize generalized frequency responses in terms of two finite transmission zeros (FTZs). The feeding lines of the dual-mode filter are deviated from the center position of the patch resonator to realize different strength of main coupling paths. And then, a novel four-pole patch bandpass filter based on a multilayer dual-mode structure is proposed and analyzed in detail. The input and output ports are designed on the upper substrate layer. The lower dual-mode patch resonator is coupled to the upper one using four coupling apertures in the middle metal layer. The multilayer dual-mode patch filter can be modeled by a dual-extended doublet scheme with source-load coupling. Generalized frequency responses in terms of four FTZs can be achieved. Several design examples using the proposed multilayer dual-mode patch filter are presented to illustrate the high degree of flexibility of FTZs. To verify the proposed structure, two examples were fabricated and measured. The measured results agree well with simulated ones.
Grating Lobes Reduction in Linear Arrays Composed of Subarrays Using PSO

In this paper we present three new scenarios for grating lobes reduction in linear arrays composed of subarrays. In these scenarios particle swarm optimization algorithm is used to solve such non-linear problem. In all of these scenarios amplitude tapering at the subarray level is applied. In the first scenario subarrays have different lengths, in the second scenario they have different lengths and number of elements and in the third scenario they are unequally spaced subarrays. The proposed scenarios are able to disrupt the array periodicity and hence reduce grating lobes level. Arrays with length of 100λ composed of 10 subarrays are synthesized and simulated using the presented scenarios in order to verify their performance. The simulated results show that the side lobe level in the synthesized arrays are below −30 dB.
Existing Approaches to Wide-scale DSM Deployment to Facilitate Transmission Network Flexibility - Results of the Survey in South-East Europe

This paper provides an overview of the demand side management (DSM) practices in South-East Europe. It focuses on the state-of-the-art approaches for DSM provided by large industrial users and distribution networks connected to the transmission network (TN). The main findings of the paper concern the current and envisaged demand-side flexibility potential in the TNs in the region, the preconditions DSM assets need to fulfil to participate in network services, and the presently deployed monitoring, communication and control systems enabling participation of DSM in TN daily operation.
Mobile Five Senses Augmented Reality System: Technology Acceptance Study

The application of the most recent technologies is fundamental to add value to tourism experiences, as well as in other economic sectors. Mobile Five Senses Augmented Reality (M5SAR) system is a mobile guide instrument for cultural, historical, and museum events. In order to realize the proclaimed five senses, the system has two main modules: a (i) mobile application which deals mainly with the senses of sight and hearing, using for that the mobile device camera to recognize and track on-the-fly (museum’s) objects and give related information about them; and a (ii) portable device capable of enhancing the augmented reality (AR) experience to the full five senses through the stimulus of touch, taste, and smell, by associating itself to the users’ smartphone or tablet. This paper briefly presents the system’s architecture but, the main focus is on the analysis of the users’ acceptance for this technology, namely the AR (software) application, and its integration with the (hardware) device to achieve the five senses AR. Results show that social influence, effort expectancy, and facilitating conditions are the key constructs that drive the users to accept and M5SAR’s technology.
Secure Dictionary Learning for Sparse Representation

In this paper, we propose secure dictionary learning for sparse representation based on a random unitary transform. Edge cloud computing is now spreading to many application fields including services that use sparse coding. This situation raises many new privacy concerns. The proposed scheme provides practical MOD and K-SVD schemes that allow computation on encrypted signals. We prove, theoretically, that the proposal has exactly the same dictionary and sparse coefficient estimation performance as sparse dictionary learning for unencrypted signals. It can be directly carried out by using MOD and K-SVD algorithms. Moreover, we apply it to image modeling based on an image patch model. Finally, we demonstrate its excellent performance on synthetic data and natural images.
Alternative Credit Scoring and Classification Employing Machine Learning Techniques on a Big Data Platform

With the bloom of financial technology and innovations aiming to deliver a high standard of financial services, banks and credit service companies, along with other financial institutions, use the most recent technologies available in a variety of ways from addressing the information asymmetry, matching the needs of borrowers and lenders, to facilitating transactions using payment services. In the long list of FinTechs, one of the most attractive platforms is the Peer-to-Peer (P2P) lending which aims to bring the investors and borrowers hand in hand, leaving out the traditional intermediaries like banks. The main purpose of a financial institution as an intermediary is of controlling risk and P2P lending platforms innovate and use new ways of risk assessment. In the era of Big Data, using a diverse source of information from spending behaviors of customers, social media behavior, and geographic information along with traditional methods for credit scoring prove to have new insights for the proper and more accurate credit scoring. In this study, we investigate the machine learning techniques on big data platforms, analyzing the credit scoring methods. It has been concluded that on a HDFS (Hadoop Distributed File System) environment, Logistic Regression performs better than Decision Tree and Random Forest for credit scoring and classification considering performance metrics such as accuracy, precision and recall, and the overall run time of algorithms. Logistic Regression also performs better in time in a single node HDFS configuration compared to a non-HDFS configuration.
Stress Detection Through Electrodermal Activity (EDA) and Electrocardiogram (ECG) Analysis in Car Drivers

The stress in a driver, happening during unforeseen events or taxing situations, is linked to a subject’s sympathetic system response. We present a system which detects the stress presence in car drivers through the analysis of an endosomatic Electrodermal Activity (EDA) signal, namely, Skin Potential Response (SPR), coupled with the analysis of the Electrocardiogram (ECG) signal. To log these signals we utilize a device which records the SPR from each hand of the driver, and the ECG from the chest. In the case of the SPR signal, since the hands movement injects motion artifacts, we also utilize an algorithm that dynamically selects the smoother signal coming from the two hands, and is thus able to output a clean SPR signal. Statistical features are then derived from the ECG and SPR signals, allowing their classification using a Supervised Machine Learning Algorithm. Various subjects were tested in an environment set in a company which develops professional driving simulators, both in hardware and software, and consisted in a motorized platform, a cockpit and a 180° projection screen. The test encompassed driving through a highway, with some unforeseen events happening at some positions. In the end we get a Balanced Accuracy in stress detection of 77.59 % for the considered events.
Two Design Schemes for LT Codes Degree Distribution by Combining Degree Distribution

With the rapid development of communication technology and the Internet, digital fountain codes have become a hot topic in many fields. Degree distribution is a key factor affecting the performance of fountain codes. Traditional single degree distribution, such as Ideal Soliton Distribution, Robust Soliton Distribution, and Binary Exponential Distribution cannot satisfy the request of increasing multimedia applications due to their obvious performance defects. So designing a superior performance degree distribution is a challenging task. In this paper, we ought to investigate this challenging issue by presenting two new schemes of degree distribution based on Poisson distribution, Ideal Soliton Distribution and Moved-Robust Soliton Distribution with single-scale coefficient and double-scale coefficient. The simulation results show that the two schemes we proposed have greatly improved in terms of bit error rate, decoding overhead and the ratio of decoding success compared with the known single degree distribution and initial single-scale coefficient combined degree distribution.
RETOS PARA ENFERMERÍA EN EL CUIDADO DE PERSONAS CON DOLOR: UNA FORMA DE HUMANIZACIÓN CHALLENGES FOR NURSING CARE OF PEOPLE WITH PAIN: A FORM OF HUMANIZATION

Introduction: Disease and pain have accompanied man through its evolution. It is not possible erase his memory and isolates the social and historical context. Nursing accompanies its evolutionary process. Advances in management, involving nurses in the care of patients suffering pain. The aim of this study is: Visualizing the pain of people and generate a reflection to understand the pain and suffering. Formulate challenges that motivate nurses to continually educate themselves on this issue. Materials and Methods: Reflection article based on an extensive analysis of the literature on pain -related publications. Review the history and evolution of the concept of pain through time. Reflection and formulation challenges for nursing based on clinical experience of care for patients with pain. Results: To review the history of the evolution of the concept of pain helps to understand the presence of this in human daily life. Imposes challenges that result in nursing actions to address it, and continually motivates study to alleviate it. Discussion: The academic curriculum of the training institutions for nurses and nursing assistant should strengthen: the humanization of the care offered. Nursing care currently lacks sensitivity and humanity. Conclusions: It is not possible avoid the pain, but can be alleviated. Nursing, professional and technological level has challenges of continuous improvement and best practice in the care of these patients. In this new century, the challenges are: the humanization of care and training and formation in pain.
Robust Assessment of ECG Signal Quality for Wearable Devices

With the fast development of mobile health technology, wearable device provides continuous monitoring in a low-cost and convenient way, which is a promising solution for personalized medicine. However, the signal quality issue limits its wide adoption. In our study, we proposed a robust electrocardiogram (ECG) signal quality assessment algorithm. The algorithm has been validated in real clinical scenario with excellent performance.
Electric Water Heater Control Through Informed Fitted Q-Iteration

The scheduling of Thermostatically Controlled Loads (TCLs) in a residential demand response (DR) context requires scalability as TCLs come in many forms. Model-free control algorithms, such as Fitted Q-iteration (FQI), can provide such scalability. They accommodate the variety in e.g. electric water heaters or heat pumps available on the market and thermal characteristics of residential buildings. However, these approaches require a significant amount of data. In the building simulation literature, Model Predictive Control (MPC) with grey-box models provides a technique that connects domain knowledge with experimental data, which results in algorithms with a higher sample efficiency. This work proposes to combine grey-box MPC and FQI in the Informed FQI method and shows how to use MPC to provide domain knowledge to the model-free FQI algorithm. The new approach is compared to FQI and MPC with a linear RC-model in terms of cost and user comfort.
List Selection and Decision Fusion Scheme for Belief Propagation List Decoding of Polar Codes

To the best of our knowledge, Belief Propagation List (BPL) decoding achieves the best error-correction performance within the class of BP based polar code decoding algorithms. It regards different permutations of the polar code factor graph as a range of available "decoding lists" for individually executing the BP algorithm. Then, a minimum Euclidean distance criterion is employed to combine these individual decisions. However, we demonstrated that simply increasing the number of lists involved in the BPL algorithm does not always improve its performance. Instead, we exploit the extrinsic information transfer (EXIT) chart technology to verify the efficiency of a decoding list. Based on this analysis, an efficient list selection scheme is proposed. In the same spirit, in order to mitigate the impact of relatively inferior decoding lists on other selected decoding lists, new decision fusion schemes are also designed. Benefiting from these improvements of BPL algorithm, performance gains are evidenced in our simulation results.
Human and Vehicle Attenuations for Millimeter-wave Channels

To satisfy the high data rate and low latency needs for the future 5G New Radio wireless communications, one of the possible solutions is to operate the transmission at the millimeter wave (mm-wave) spectrum. In this paper, we setup an instrument-based measurement system to measure the quantified degradations of the human body and vehicle at the considered mm-wave spectrums.
Motivational Design Techniques to Increase Adherence to a Telemonitoring Therapy A Study with Adolescent Pectus Patients

The most common surgery for pectus patients is associated with severe acute and persistent postoperative pain. It is, therefore, important that patients are adequately monitored after their surgery to prevent the chronification of pain. This paper provides a deeper understanding of young adolescents when they are asked to use a telemonitoring platform for their postoperative follow-up. Motivational design techniques, such as gamification mechanics, were integrated into 17 alternative designs and evaluated via a mixed-method approach with eight pectus patients and nine parents. The quantitative scoring of all 17 designs on a seven-point Likert scale showed that participants appreciated the designs and were motivated to adhere to their telemonitoring therapy when adequately supported by the platform. A thematic analysis of in-depth interviews conducted in the context of their homes confirmed that pectus patients had the will to be adherent, were invested in their own well-being, appreciated technical feedback, valued their privacy, and requested a professional platform to support them in their daily telemonitoring activities.