System-on-Chip Solution for Patients Biometric: A Compressive Sensing-Based Approach

IEEE The ever-increasing demand for biometric solutions for the internet of thing (IoT)-based connected health applications is mainly driven by the need to tackle fraud issues, along with the imperative to improve patient privacy, safety and personalized medical assistance. However, the advantages offered by the IoT platforms come with the burden of big data and its associated challenges in terms of computing complexity, bandwidth availability and power consumption. This paper proposes a solution to tackle both privacy issues and big data transmission by incorporating the theory of compressive sensing (CS) and a simple, yet, efficient identification mechanism using the electrocardiogram (ECG) signal as a biometric trait. Moreover, the paper presents the hardware implementation of the proposed solution on a system on chip (SoC) platform with an optimized architecture to further reduce hardware resource usage. First, we investigate the feasibility of compressing the ECG data while maintaining a high identification quality. The obtained results show a 98.88% identification rate using only a compression ratio of 30%. Furthermore, the proposed system has been implemented on a Zynq SoC using heterogeneous software/hardware solution, which is able to accelerate the software implementation by a factor of 7.73 with a power consumption of 2.318 W

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

Zynq SoC based acceleration of the lattice Boltzmann method

Cerebral aneurysm is a life‐threatening condition. It is a weakness in a blood vessel that may enlarge and bleed into the surrounding area. In order to understand the surrounding environmental conditions during the interventions or surgical procedures, a simulation of blood flow in cerebral arteries is needed. One of the effective simulation approaches is to use the lattice Boltzmann (LB) method. Due to the computational complexity of the algorithm, the simulation is usually performed on high performance computers. In this paper, efficient hardware architectures of the LB method on a Zynq system‐on‐chip (SoC) are designed and implemented. The proposed architectures have first been simulated in Vivado HLS environment and later implemented on a ZedBoard using the software‐defined SoC (SDSoC) development environment. In addition, a set of evaluations of different hardware architectures of the LB implementation is discussed in this paper. The experimental results show that the proposed implementation is able to accelerate the processing speed by a factor of 52 compared to a dual‐core ARM processor‐based software implementation

Energy-efficient and real-time wearable for wellbeing-monitoring IoT system based on SoC-FPGA

Wearable devices used for personal monitoring applications have been improved over the last decades. However, these devices are limited in terms of size, processing capability and power consumption. This paper proposes an efficient hardware/software embedded system for monitoring bio-signals in real time, including a heart rate calculator using PPG and an emotion classifier from EEG. The system is suitable for outpatient clinic applications requiring data transfers to external medical staff. The proposed solution contributes with an effective alternative to the traditional approach of processing bio-signals offline by proposing a SoC-FPGA based system that is able to fully process the signals locally at the node. Two sub-systems were developed targeting a Zynq 7010 device and integrating custom hardware IP cores that accelerate the processing of the most complex tasks. The PPG sub-system implements an autocorrelation peak detection algorithm to calculate heart rate values. The EEG sub-system consists of a KNN emotion classifier of preprocessed EEG features. This work overcomes the processing limitations of microcontrollers and general-purpose units, presenting a scalable and autonomous wearable solution with high processing capability and real-time response.info:eu-repo/semantics/publishedVersio

Implantation sur circuit SoC-FPGA d'un système de chiffrement/déchiffrement AES-128 bits en utilisant deux approches de différents niveaux d'abstraction

RÉSUMÉ : La sécurité des données est une priorité absolue dans le monde technologique. Pour garantir la sécurité et la confidentialité des données, l'usage des systèmes de chiffrement/déchiffrement devient une nécessité dans plusieurs domaines. Dans ce mémoire nous présentons une architecture simple de système de chiffrement avancé à 128 bits en mode compteur (AES-CTR-128 bit), implantée sur une carte PYNQ-Z2 pour chiffrer/déchiffrer des signaux d'électrocardiogramme ECG (ElectroCardioGram) de la base de données MIT-BIH. Le système n'utilise que 13% des ressources matérielles du circuit Xilinx ZYNQ XC7Z020. Il consomme une puissance de 43 mW et opère à une fréquence maximale de 109.43 MHz, qui correspond à un débit maximal de 14 Gbps. Le temps d'exécution de chiffrement et de déchiffrement d'un fichier de valeurs séparée par des virgules CSV (Comma Separated Value) par rapport d'un fichier texte TXT (Text) est environ deux fois plus court dans les deux plateformes utilisant deux approches ayant des niveaux d'abstraction différents. La première utilise la programmation bas-niveaux via la plateforme Xilinx Vitis alors que la seconde utilise l'outil Jupyter/Python. L'architecture matérielle proposée est environ quatre fois plus rapide que l'implantation logicielle et il y a une légère différence au niveau du temps d'exécution pour l'implantation de notre architecture sur les deux plateformes présentées (Vivado/Vitis ou Jupyter/Python). Nous avons aussi testé notre architecture matérielle avec d'autres types de données tels que les signaux audio et des images. Nous avons utilisé la plateforme Jupyter/Python pour sa simplicité de manipulation. Le chiffrement/déchiffrement d'un signal audio d'une durée de 7 secondes et d'une fréquence d'échantillonnage de 8 kHz est réduit respectivement à 4.6 ms et 4.87 ms, par rapport à 16.18 ms et 15.8 ms pour le chiffrement/déchiffrement d'un signal audio par l'implantation logicielle. De même pour l'image couleur et l'image en niveau de gris. Ainsi que le temps de chiffrement d'une image couleur prend entre trois à quatre fois le temps de chiffrement d'une image en niveau de gris dans les deux implantations logicielle et matérielle. L'architecture matérielle présentée peut être utilisée dans un large éventail d'applications embarquées. Les résultats présentés ont montré que l'architecture proposée a surpassé toutes les autres implantations existantes sur FPGA. -- Mot(s) clé(s) en français : Cryptographie, AES, Circuit FPGA, Signal ECG, Circuit ZYNQ, Chiffrement/Déchiffrement, Cryptage/Décryptage. -- ABSTRACT : Data security is a top priority in the technological world. To ensure data security and privacy, the use of encryption/decryption systems becomes a necessity in several areas. In this dissertation, we present a simple architecture of advanced 128-bit counter mode encryption systems (AES-CTR-128 bit), implemented on a PYNQ-Z2 board to encrypt/decrypt electrocardiogram (ECG) signals from the MIT-BIH database. The system uses only 13% of the hardware resources of the Xilinx ZYNQ XC7Z020 chip. It consumes 43 mW of power and operates at a maximum frequency of 109.43 MHz, which corresponds to a maximum through of 14 Gbps. The execution time of encryption and decryption of the comma-separated value (CSV) file compared to the text file (TXT) is about twice as short in both platforms using two approaches with different abstraction levels. The first use low-level programming via the Xilinx Vitis platform while the second uses the Jupyter/Python tool. The proposed hardware architecture is about four times faster than the software implementation and there is a slight difference in execution time for the implementation of our architecture on the two platforms presented (Vivado/Vitis or Jupyter/Python). We also tested our hardware architecture with other types of data such as audio signals and images. We used the Jupyter/Python platform for its simplicity of handling. The encryption/decryption of an audio signal with a duration of 7 seconds and a sampling rate of 8 kHz are reduced to 4.6 ms and 4.87 ms, respectively, compared to 16.18 ms and 15.8 ms for the encryption/decryption of an audio signal by the software implementation. The same applies to the color and grayscale image. Thus, the encryption time of a color image takes between three and four times the encryption time of a grayscale image in both software and hardware implementations. The presented hardware architecture can be used in a wide range of embedded applications. The presented results showed that the proposed architecture outperformed all other existing FPGA-based implementations. -- Mot(s) clé(s) en anglais : Cryptography, AES, FPGA circuit, ECG signal, ZYNQ circuit, Encryption/Decryption

HW/SW Co-design and Prototyping Approach for Embedded Smart Camera: ADAS Case Study

In 1968, Volkswagen integrated an electronic circuit as a new control fuel injection system, called the “Little Black Box”, it is considered as the first embedded system in the automotive industry. Currently, automobile constructors integrate several embedded systems into any of their new model vehicles. Behind these automobile’s electronics systems, a sophisticated Hardware/Software (HW/SW) architecture, which is based on heterogeneous components, and multiple CPUs is built. At present, they are more oriented toward visionbased systems using tiny embedded smart camera. This visionbased system in real time aspects represents one of the most challenging issues, especially in the domain of automobile’s applications. On the design side, one of the optimal solutions adopted by embedded systems designer for system performance, is to associate CPUs and hardware accelerators in the same design, in order to reduce the computational burden on the CPU and to speed-up the data processing. In this paper, we present a hardware platform-based design approach for fast embedded smart Advanced Driver Assistant System (ADAS) design and prototyping, as an alternative for the pure time-consuming simulation technique. Based on a Multi-CPU/FPGA platform, we introduced a new methodology/flow to design the different HW and SW parts of the ADAS system. Then, we shared our experience in designing and prototyping a HW/SW vision based on smart embedded system as an ADAS that helps to increase the safety of car’s drivers. We presented a real HW/SW prototype of the vision ADAS based on a Zynq FPGA. The system detects the fatigue/drowsiness state of the driver by monitoring the eyes closure and generates a real time alert. A new HW Skin Segmentation step to locate the eyes/face is proposed. Our new approach migrates the skin segmentation step from processing system (SW) to programmable logic (HW) taking the advantage of High-Level Synthesis (HLS) tool flow to accelerate the implementation, and the prototyping of the Vision based ADAS on a hardware platform

Recent Advances in Embedded Computing, Intelligence and Applications

The latest proliferation of Internet of Things deployments and edge computing combined with artificial intelligence has led to new exciting application scenarios, where embedded digital devices are essential enablers. Moreover, new powerful and efficient devices are appearing to cope with workloads formerly reserved for the cloud, such as deep learning. These devices allow processing close to where data are generated, avoiding bottlenecks due to communication limitations. The efficient integration of hardware, software and artificial intelligence capabilities deployed in real sensing contexts empowers the edge intelligence paradigm, which will ultimately contribute to the fostering of the offloading processing functionalities to the edge. In this Special Issue, researchers have contributed nine peer-reviewed papers covering a wide range of topics in the area of edge intelligence. Among them are hardware-accelerated implementations of deep neural networks, IoT platforms for extreme edge computing, neuro-evolvable and neuromorphic machine learning, and embedded recommender systems

The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study

Background: This paper examines the development of the Connected Health research landscape with a view on providing a historical perspective on existing Connected Health research. Connected Health has become a rapidly growing research field as our healthcare system is facing pressured to become more proactive and patient centred. Objective: We aimed to identify the extent and coverage of the current body of knowledge in Connected Health. With this, we want to identify which topics have drawn the attention of Connected health researchers, and if there are gaps or interdisciplinary opportunities for further research. Methods: We used a systematic mapping study that combines scientific contributions from research on medicine, business, computer science and engineering. We analyse the papers with seven classification criteria, publication source, publication year, research types, empirical types, contribution types research topic and the condition studied in the paper. Results: Altogether, our search resulted in 208 papers which were analysed by a multidisciplinary group of researchers. Our results indicate a slow start for Connected Health research but a more recent steady upswing since 2013. The majority of papers proposed healthcare solutions (37%) or evaluated Connected Health approaches (23%). Case studies (28%) and experiments (26%) were the most popular forms of scientific validation employed. Diabetes, cancer, multiple sclerosis, and heart conditions are among the most prevalent conditions studied. Conclusions: We conclude that Connected Health research seems to be an established field of research, which has been growing strongly during the last five years. There seems to be more focus on technology driven research with a strong contribution from medicine, but business aspects of Connected health are not as much studied