COVID-19 Vaccine and Booster Shots Compatibility Analyzer using CMOS-based Monochrome Encoding

The continuous increase in COVID-19 positive cases in the Philippines might further weaken the local healthcare system. As such, an efficient system must be implemented to further improve the immunization efforts of the country. In this paper, a contactless digital electronic device is proposed to assess the vaccine and booster brand compatibility. Using Logisim 2.7.1, the logic diagrams were designed and simulated with the help of truth tables and Boolean functions. Moreover, the finalized logic circuit design was converted into its equivalent complementary metal-oxide semiconductor (CMOS) and stick diagrams to help contextualize how the integrated circuits will be designed. Results have shown that the proposed device was able to accept three inputs of the top three COVID-19 vaccine brands (Sinovac, AstraZeneca, and Pfizer) and assess the compatibility of heterologous vaccinations. With the successful results of the circuit, it can be concluded that this low-power device can be used to manufacture a physical prototype for use in booster vaccination sites. © 2022 IEEE.

FPGA Based Light Weight Encryption of Medical Data for IoMT Devices using ASCON Cipher

Internet of Things applications with various sensors in public network are vulnerable to cyber physical attacks. The technology of IoT in smart health monitoring systems popularly known as Internet of Medical Things (IoMT) devices. The rapid growth of remote telemedicine has witnessed in the post COVID era. Data collected over IoMT devices is sensitive and needs security, hence provided by enhancing a light weight encryption module on IoMT device. An authenticated Encryption with Associated Data is employed on the IoMT device to enhance the security to the medical wellness of patient. This paper presents FPGA-based implementation of ASCON-128, a light weight cipher for data encryption. A LUT6 based substitution box (SBOX) is implemented on FPGA as part of cipher permutation block. The proposed architecture takes 1330 number of LUTs, which is 35% less compared to the best existing design. Moreover, the proposed ASCON architecture has improved the throughput by 45% compared to the best existing design. This paper presents the results pertaining to encryption and decryption of medical data as well as normal images. © 2022 IEEE.

Processor Optimization of an Energy-Efficient NDIR CO2Wireless Sensor Node

CO2 gas sensors are rapidly growing in importance since they can be easily deployed to assess air quality inside buildings, improving people's life. According to medical studies, we know that exposure to high levels of carbon dioxide on a daily basis poses a risk to people's health with adverse effects at different levels, such as on the neurological, cardiovascular, and respiratory systems. Moreover, the COVID-19 pandemic has shown how important it is to monitor air quality and ensure good ventilation to prevent infection through airborne transmission paths. Some studies estimate the likelihood of indoor airborne infection transmission based on continuous CO2 measurements. This work presents the design of a 32-bit microprocessor based on the RISC-V ISA architecture intended for energy-efficient signal processing in wireless sensor nodes. In particular, the processor is optimized for its application in non-dispersive infrared (NDIR) CO2 sensors. We use asynchronous demodulation to extract the information from the sensor. Therefore, we generate digital quadrature signals for demodulation, requiring the computation of trigonometric functions by the RISC-V processor. We try different strategies to optimize the processor design and the demodulation process to minimize energy consumption while measuring CO2 levels. © 2022 IEEE.

BEACON: Scalable Near-Data-Processing Accelerators for Genome Analysis near Memory Pool with the CXL Support

Genome analysis benefits precise medical care, wildlife conservation, pandemic treatment (e.g., COVID-19), and so on. Unfortunately, in genome analysis, the speed of data processing lags far behind the speed of data generation. Thus, hardware acceleration turns out to be necessary. As many applications in genome analysis are memory-bound, Processing-In-Memory (PIM) and Near-Data-Processing (NDP) solutions have been explored to tackle this problem. In particular, the Dual-Inline-Memory-Module (DIMM) based designs are very promising due to their non-invasive feature to the cost-sensitive DRAM dies. However, they have two critical limitations, i.e., performance bottle-necked by communication and the limited potential for memory expansion. In this paper, we address these two limitations by designing novel DIMM based accelerators located near the dis-aggregated memory pool with the support from the Compute Express Link (CXL), aiming to leverage the abundant memory within the memory pool and the high communication bandwidth provided by CXL. We propose BEACON, Scalable Near-Data-Processing Accelerators for Genome Analysis near Memory Pool with the CXL Support. BEAC-ON ad-opts a software-hardware co-design approach to tackle the above two limitations. The BEACON architecture builds the foundation for efficient communication and memory expansion by reducing data movement and leveraging the high communication bandwidth provided by CXL. Based on the BEACON architecture, we propose a memory management framework to enable memory expansion with unmodified CXL-DIMMs and further optimize communication by improving data locality. We also propose algorithm-specific optimizations to further boost the performance of BEACON. In addition, BEACON provides two design choices, i.e., BEACON- D and BEACON-S. BEACON-D and BEACON-S perform the computation within the enhanced CXL-DIMMs and enhanced CXL-Switches, respectively. Experimental results show that compared with state-of-the-art DIMM based NDP accelerators, on average, BEACON-D and BEACON-S improve the performance by 4. 70x and 4. 13x, respectively. © 2022 IEEE.

Design of epidemic detection system in railway station based on FPGA

Since the outbreak of COVID-19 in 2020, wearing masks and displaying green codes in and out of public places has become a habit. The identity verification of the existing railway station is mainly based on face recognition. Due to the outbreak and persistence of the epidemic, people often need to call out the green code on their mobile phones for staff verification, which takes a lot of time. At the same time, the existing face recognition equipment requires the inbound personnel to take off their masks, which also increases the infection risk of the inbound personnel. In order to reduce the above infection risk and speed up people's entry and exit speed, we have designed a system that can identify people wearing masks and judge whether they are confirmed or suspected cases at the same time. Firstly, the system measures the passenger's body temperature through the infrared temperature measurement module, carries out face detection and recognition at the same time, and queries the recognition results in the database to judge whether the passenger is diagnosed or in close contact. When the passenger is normal, it is allowed to pass, otherwise it is not allowed to pass, and updates the relevant data in the cloud database. The system uses Yolo algorithm as the face detection algorithm, and then carries out face recognition through FaceNet network, so as to judge its identity and query the relevant information of the person in the cloud database. After testing, the iterative loss rate of the system is basically below 0.1 and the accuracy is basically stable above 99%. Considering that we need to use it on embedded devices and the amount of calculation operation of deep learning algorithm is large, and FPGA can well build circuits according to the needs of the model because of its reconfigurability, and FPGA can realize hardware acceleration because it can run in parallel, so we finally choose to deploy the model to FPGA to complete face recognition. © 2022 IEEE.

FPGA-Based Deep Learning Models for Analysing Corona Using Chest X-Ray Images

Coronavirus is a large family of viruses that affects humans and damages respiratory functions ranging from cold to more serious diseases such as ARDS and SARS. But the most recently discovered virus causes COVID-19. Isolation at home or hospital depends on one's health history and conditions. The prevailing disease that might get instigated due to the existence of the virus might lead to deterioration in health. Therefore, there is a need for early detection of the virus. Recently, many works are found to be observed with the deployment of techniques for the detection based on chest X-rays. In this work, a solution has been proposed that consists of a sample prototype of an AI-based Flask-driven web application framework that predicts the six different diseases including ARDS, bacteria, COVID-19, SARS, Streptococcus, and virus. Here, each category of X-ray images was placed under scrutiny and conducted training and testing using deep learning algorithms such as CNN, ResNet (with and without dropout), VGG16, and AlexNet to detect the status of X-rays. Recent FPGA design tools are compatible with software models in deep learning methods. FPGAs are suitable for deep learning algorithms to make the design as flexible, innovative, and hardware acceleration perspective. High-performance FPGA hardware is advantageous over GPUs. Looking forward, the device can efficiently integrate with the deep learning modules. FPGAs act as a challenging substitute podium where it bridges the gap between the architectures and power-related designs. FPGA is a better option for the implementation of algorithms. The design attains 121μW power and 89 ms delay. This was implemented in the FPGA environment and observed that it attains a reduced number of gate counts and low power. © 2022 Anupama Namburu et al.

Teaching Computer System Courses with an Online Large-Scale Method

Domain-Specific Architectures (DSAs) and hardware-software co-design are greatly emphasized in the CS community, which demands a significant number of participants with Computer System (CSys) capabilities and skills. Conventional CSys courses in a lecture-lab format are limited in physical resources and inherently difficult to cultivate talents at a large scale. Online teaching is a potential alternative to instantly enlarge the face-to-face class size. Unfortunately, simply putting the lecture contents in CSys courses online lacks 1) personal attention, 2) learner-instructor interactions, and 3) real-hardware experimental environments. To tackle the above challenges, we introduce a four phase online CSys course program and the related teaching methods for a cloud-based teaching platform. The four-phase course program included two basic/required stages and two advanced/optional stages to promote students' knowledge and skill level with appropriate personal attention. We studied if online interaction methods, such as in-class chat and one-on-one online grading interview, can strengthen the connections between teachers and students in both lectures and labs. We created a heterogeneous cloud platform to enable students nationwide to reliably conduct labs or projects on remote programmable hardware. We believe that our proposed course design methodology is beneficial to other CScourses in the post-COVID-19-era. © 2021 IEEE.

Optimized Cohort Creation for Hybrid Online Design-learning During COVID-19

The unprecedented global pandemic COVID-19 significantly disrupted the higher education sector by forcing educators to rethink modes of content delivery. As COVID-19 restrictions slowly lifts, many institutions are operating a hybrid course delivery structure: online lectures and small groups of in-person, hands-on learning sessions. In this paper, a method to model student cohort learning communities is proposed. This model would limit viral spreading through its small and static nature, while promoting a sense of community and identity-building. A similar learning community model was implemented within a 2nd year Integrated Learning Stream pilot program. The goal of this study is to identify the optimal student cohort configuration, based on an anonymized dataset of 81 electrical engineering students' Fall 2020 semester enrollment records. Three very large scale integrated (VLSI) circuit clustering algorithms (Hyperedge Coarsening, Modified Hyperedge Coarsening, and Best Choice) are implemented. The resulting cohorts are evaluated based on cohort members' number of possible interactions external to their cohort. The Best Choice algorithm yielded more uniform cohorts that are less connected with other clusters, showing the cohort model to be a viable method of grouping students to limit cross-cohort transmission. Post-pandemic, the proposed method can be applied in many cohort-based learning use cases. © American Society for Engineering Education, 2021

Design and Implementation of Automatic Line Follower Robot for Assistance of COVID-19 Patients

Coronavirus disease (COVID-19) has caused unprecedented global health problems, and the disease’s spread rate is extremely high. It spreads from infected people (COVID-19 positive) to others via droplets from the mouth or nose when they cough, sneeze, speak, sing, or take deep breaths. Frontline fighters of healthcare organizations such as doctors, nurses, and other medical staff cannot have direct contact with COVID-19 patients in isolation room without personal protective equipment (PPE). Hence, hospital workers have to face different types of problems in distributing foods, medicines, and disposal of waste. An Automatic Line Follower Robot (ALFR) is designed and implemented for COVID-19 patients which is capable of serving infected patients in an isolation room. The main contribution of this paper is to serve essential medicines and foods from the hospital staff and serve it to the patients following the black line. The ALFR also proposes a system which maintains an emergency wireless communication protocol between doctors and patients. It also collects waste from a specified basket and damps it to a proper place. Finally, it can sanitize the isolated room with the help of a disinfectant machine which is assembled in ALFR. ALFR’s performance has significantly improved, and it can successfully complete all tasks. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.