Review: Recent Directions in ECG-FPGA Researches

لقد شهدت السنوات القليلة الماضية اهتماماً متزايداً نحو استخدام مصفوفة البوابات المنطقية القابلة للبرمجة FPGA في التطبيقات المختلفة. لقد أدى التقدم الحاصل في مرونة التعامل مع الموارد بالاضافة الى الزيادة في سرعة الاداء وانخفاض الثمن للـ FPGA وكذلك الاستهلاك القليل للطاقة الى هذا الاهتمام المتزايد بالـ FPGA. ان استخدام الـ FPGA في مجالات الطب والصحة يهدف بشكل عام الى استبدال اجهزة المراقبة الطبية كبيرة الحجم وغالية الثمن باخرى أصغر حجماً مع امكانية تصميمها لكي تكون اجهزة محمولة اعتماداً على مرونة التصميم التي يوفرها الـ FPGA. إنصب الاهتمام في العديد من البحوث الحالية على استخدام نظام FPGA لمعالجة الجوانب المتعلقة بإشارة تخطيط القلب وذلك لتوفير التحسينات في الاداء وزيادة السرعة بالاضافة الى أيجاد وإقتراح افكار جديدة لمثل هذه التطبيقات. ان هذا البحث يوفر نظرة عامة عن الاتجاهات الحالية في انظمة ECG-FPGA.The last few years witnessed an increased interest in utilizing field programmable gate array (FPGA) for a variety of applications. This utilizing derived mostly by the advances in the FPGA flexible resource configuration, increased speed, relatively low cost and low energy consumption. The introduction of FPGA in medicine and health care field aim generally to replace costly and usually bigger medical monitoring and diagnostic equipment with much smaller and possibly portable systems based on FPGA that make use of the design flexibility of FPGA. Many recent researches focus on FPGA systems to deal with the well-known yet very important electrocardiogram (ECG) signal aspects to provide acceleration and improvement in the performance as well as finding and proposing new ideas for such implementations. The recent directions in ECG-FPGA are introduced in this paper

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of the medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies ranging from emerging memristive devices, to established Field Programmable Gate Arrays (FPGAs), and mature Complementary Metal Oxide Semiconductor (CMOS) technology can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. After providing the required background, we unify the sparsely distributed research on neural network and neuromorphic hardware implementations as applied to the healthcare domain. In addition, we benchmark various hardware platforms by performing a biomedical electromyography (EMG) signal processing task and drawing comparisons among them in terms of inference delay and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that different accelerators and neuromorphic processors introduce to healthcare and biomedical domains. This paper can serve a large audience, ranging from nanoelectronics researchers, to biomedical and healthcare practitioners in grasping the fundamental interplay between hardware, algorithms, and clinical adoption of these tools, as we shed light on the future of deep networks and spiking neuromorphic processing systems as proponents for driving biomedical circuits and systems forward.Comment: Submitted to IEEE Transactions on Biomedical Circuits and Systems (21 pages, 10 figures, 5 tables

Open electronics for medical devices: State-of-art and unique advantages

A wide range of medical devices have significant electronic components. Compared to open-source medical software, open (and open-source) electronic hardware has been less published in peer-reviewed literature. In this review, we explore the developments, significance, and advantages of using open platform electronic hardware for medical devices. Open hardware electronics platforms offer not just shorter development times, reduced costs, and customization; they also offer a key potential advantage which current commercial medical devices lack—seamless data sharing for machine learning and artificial intelligence. We explore how various electronic platforms such as microcontrollers, single board computers, field programmable gate arrays, development boards, and integrated circuits have been used by researchers to design medical devices. Researchers interested in designing low cost, customizable, and innovative medical devices can find references to various easily available electronic components as well as design methodologies to integrate those components for a successful design

High-performance AES-128 algorithm implementation by FPGA-based SoC for 5G communications

none4siIn this research work, a fast and lightweight AES-128 cypher based on the Xilinx ZCU102 FPGA board is presented, suitable for 5G communications. In particular, both encryption and decryption algorithms have been developed using a pipelined approach, so enabling the simultaneous processing of the rounds on multiple data packets at each clock cycle. Both the encryption and decryption systems support an operative frequency up to 220 MHz, reaching 28.16 Gbit/s maximum data throughput; besides, the encryption and decryption phases last both only ten clock periods. To guarantee the interoperability of the developed encryption/decryption system with the other sections of the 5G communication apparatus, synchronization and control signals have been integrated. The encryption system uses only 1631 CLBs, whereas the decryption one only 3464 CLBs, ascribable, mainly, to the Inverse Mix Columns step. The developed cypher shows higher efficiency (8.63 Mbps/slice) than similar solutions present in literature.openP.Visconti, R. Velazquez, S. Capoccia, R. de FazioVisconti, P.; Velazquez, R.; Capoccia, S.; de Fazio, R

High-performance AES-128 algorithm implementation by FPGA-based SoC for 5G communications

In this research work, a fast and lightweight AES-128 cypher based on the Xilinx ZCU102 FPGA board is presented, suitable for 5G communications. In particular, both encryption and decryption algorithms have been developed using a pipelined approach, so enabling the simultaneous processing of the rounds on multiple data packets at each clock cycle. Both the encryption and decryption systems support an operative frequency up to 220 MHz, reaching 28.16 Gbit/s maximum data throughput; besides, the encryption and decryption phases last both only ten clock periods. To guarantee the interoperability of the developed encryption/decryption system with the other sections of the 5G communication apparatus, synchronization and control signals have been integrated. The encryption system uses only 1631 CLBs, whereas the decryption one only 3464 CLBs, ascribable, mainly, to the Inverse Mix Columns step. The developed cypher shows higher efficiency (8.63 Mbps/slice) than similar solutions present in literature

Simulation and implementation of novel deep learning hardware architectures for resource constrained devices

Corey Lammie designed mixed signal memristive-complementary metal–oxide–semiconductor (CMOS) and field programmable gate arrays (FPGA) hardware architectures, which were used to reduce the power and resource requirements of Deep Learning (DL) systems; both during inference and training. Disruptive design methodologies, such as those explored in this thesis, can be used to facilitate the design of next-generation DL systems

Enhancing Random Forest Classification with NLP in DAMEH: A system for DAta Management in EHealth Domain

The use of pervasive IoT devices in Smart Cities, have increased the Volume of data produced in many and many field. Interesting and very useful applications grow up in number in E-health domain, where smart devices are used in order to manage huge amount of data, in highly distributed environments, in order to provide smart services able to collect data to fill medical records of patients. The problem here is to gather data, to produce records and to analyze medical records depending on their contents. Since data gathering involve very different devices (not only wearable medical sensors, but also environmental smart devices, like weather, pollution and other sensors) it is very difficult to classify data depending their contents, in order to enable better management of patients. Data from smart devices couple with medical records written in natural language: we describe here an architecture that is able to determine best features for classification, depending on existent medical records. The architecture is based on pre-filtering phase based on Natural Language Processing, that is able to enhance Machine learning classification based on Random Forests. We carried on experiments on about 5000 medical records from real (anonymized) case studies from various health-care organizations in Italy. We show accuracy of the presented approach in terms of Accuracy-Rejection curves

Low-power Wearable Healthcare Sensors

Advances in technology have produced a range of on-body sensors and smartwatches that can be used to monitor a wearer’s health with the objective to keep the user healthy. However, the real potential of such devices not only lies in monitoring but also in interactive communication with expert-system-based cloud services to offer personalized and real-time healthcare advice that will enable the user to manage their health and, over time, to reduce expensive hospital admissions. To meet this goal, the research challenges for the next generation of wearable healthcare devices include the need to offer a wide range of sensing, computing, communication, and human–computer interaction methods, all within a tiny device with limited resources and electrical power. This Special Issue presents a collection of six papers on a wide range of research developments that highlight the specific challenges in creating the next generation of low-power wearable healthcare sensors

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers