The Circuits and Systems Society (CASS) places a strong emphasis on advancing education within its domain. Various educational programs and resources have contributed to the professional development of CASS members for more than 20 years now. A major transformation within the Society was started in 2021 with a clear vision to become a premier educational resource within circuits and systems areas of interest. This article provides a comprehensive review of flagship educational programs and past initiatives. Insights from the lessons learned and future trends in the education ecosystem guided society’s strategies and frameworks for improving and developing new educational products and services. The benefits of learning with the CASS program are presented, highlighting the Society’s deep commitment to serving the community at large in educational pursuit for years to come.

Due to the lack of enough physical or suck central pattern generator (SCPG) development, premature infants require assistance in improving their sucking skills as one of the first coordinated muscular activities in infants. Hence, we need to quantitatively measure their sucking abilities for future studies on their sucking interventions. Here, we present a new device that can measure both intraoral pressure (IP) and expression pressure (EP) as ororhithmic behavior parameters of non-nutritive sucking skills in infants. Our device is low-cost, easy-to-use, and accurate, which makes it appropriate for extensive studies. To showcase one of the applications of our device, we collected weekly data from 137 premature infants from 29 week-old to 36 week-old. Around half of the infants in our study needed intensive care even after they were 36 week-old. We call them full attainment of oral feeding (FAOF) infants. We then used the Non-nutritive sucking (NNS) features of EP and IP signals of infants recorded by our device to predict FAOF infants' sucking conditions. We found that our pipeline can predict FAOF infants several weeks before discharge from the hospital. Thus, this application of our device presents a robust and inexpensive alternative to monitor oral feeding ability in premature infants.

Sleep posture, which affects the quality of sleep and could lead to medical conditions, such as pressure ulcers, is a key metric for sleep analysis in Internet of Medical Things (IoMT). In this article, a real-time and low-cost smart mat system for sleep posture recognition based on frequency channel selection is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. In addition, to enable real-time recognition with a relatively low-cost STM32 processor system, a lightweight algorithm that includes frequency channel selection, model pretraining, and real-time classification is proposed. Through a series of short-term and overnight experiments with 21 subjects, the feasibility and reliability of the proposed system were evaluated. Experimental results show that the accuracy of the short-term experiment is up to 95.43% and of the overnight experiment is up to 86.80% for four posture categories (supine, prone, right, and left) classification. The model size is just 56 kB which is much smaller than other methods. The runtime of the complete algorithm is about 6 ms with a low-power STM32 embedded system, which shows the system’s ability to provide real-time posture recognition. As an edge device, the proposed system could lead to the development of fast, convenient, and low-cost sleep posture recognition products for IoMT.

Sleep posture has been proven to be a crucial index for sleep monitoring in the Internet of Medical Things (IoMT). In this paper, an edge-computing system based on a smart mat for sleep posture recognition in IoMT is proposed. The system can recognize postures unobtrusively with a dense flexible sensor array. To meet the requirements of embedded system in IoMT, a light-weight algorithm that includes pre-processing, EdgeNet pre-training, model quantization, model deployment is proposed. Finally, the complete algorithm is deployed in embedded systems (STM32) and edge computing for sleep posture monitoring is implement in IoMT. Through a series of short-term and overnight experiments with 21 subjects, results exhibit that the accuracy of the short-term experiment is up to 92.10

Millions of medical consultations are conducted each year in Burkina Faso using the Electronic Register of Consultations (REC). Based on the consultation data collected, we present a method to quantify the quality of individual and ensembles of consultations conducted by frontline healthcare workers (FHWs). We focus on anthropometric measurements and vital signs (age, weight, height, mid-upper arm circumference and temperature) of children aged between six months and five years old. We compare individual and ensemble of consultations to a multivariate probability distribution defined by an external population-specific, gold standard consultation dataset. By comparing the distributions of consultations to the reference probability distribution, we define a score to rate the quality of measurements and data entry of FHWs. The defined scores allow us to detect which measurements are most problematic. They also allow us to detect potential biases in the consultation and treatment of different patient groups. No systematic gender-bias was found among FHWs. Height measurements were the most challenging; consultations with the lowest scores were associated with underestimated heights in children. Among these consultations, height was found to be even more underestimated among boys than girls. Our findings enable us to support capacity building of frontline healthcare workers. Based on our work, we present how the REC can be enriched with real-time alert on specific errors, and individual FHW can be proposed targeted trainings. We also propose dynamic dashboards that can support district managers to navigate the entire population of FHWs, understand FHWs' main challenges and prioritise their interventions in primary healthcare centres.

This paper presents a novel TFET-CMOS co-integrated comparator-less, energy-efficient ADC architecture. The design utilizes the Negative Differential Resistance property of TFETs to generate thermometer code without using comparators. The design supports Dynamic Voltage Frequency Scaling. Binary-weighted TFET device sizing is used to generate thermometer code. TFETs used in this work are compatible with a 28nm FDSOI-CMOS process for fabrication. The most relevant performance numbers for 3- to 10-bit ADC architectures include speed of operation of 68 MHz with an ENOB evaluated greater than 2.38 for the 3-bit ADC; the FOM is in the range of 0.07 to 1.3 fJ/conversion for 3- to 10-bit designs with supply voltages from 0.4V to 1.2V, respectively. The proposed 5- and 6-bit designs show 46x [1] and 265x [2] improvement in FOM, respectively.

A refresh free and scalable ultimate DRAM (uDRAM) with 1T1C bitcell is introduced in this paper. The memory uses the Negative Differential Resistance (NDR) property of Tunnel Field Effect Transistors (TFET) and storage capacitor leakage to retain data statically. The static data retention eliminates the need for refresh. The uDRAM allows more than 5x scaling of a storage capacitor in comparison to Dual-Data-Rate (DDR) and embedded DRAMs. This concept is further extended to design a 2T1C ultimate SRAM (uSRAM) and 3T1C ultimate CAM (uCAM). Area of 0.0275 μm 2 , 0.07 μm 2 and 0.104 μm 2 are achieved for DRAM, SRAM and CAM bitcells, respectively, when implemented in TFET-compatible 28 nm FDSOI-CMOS process. The uDRAM achieves an estimated throughput gain up-to 9.94% in comparison with a CMOS DRAM, owing to refresh removal in the DDR configuration. The 2T1C SRAM with read and write cycle times of sub-2ns and sub-4ns are demonstrated. The results show ultra-low leakage of less than 1 fA/bit for the proposed designs.

Sleep posture, as a crucial index for sleep quality assessment and pressure ulcer prevention, has been widely studied for medical diagnoses and sleep disease treatment. In this paper, an unobtrusive smart mat system for sleep posture recognition is proposed, which is based on a dense flexible sensor array and printed electrodes and along with an algorithmic framework. With the dense flexible sensor array, the system offers a comfortable and high-resolution solution for long-term pressure sensing. Meanwhile, compared with other large-area and low-density mat systems, it reduces the area to minimize manufacturing cost and computational complexity, while also increases the density of the sensor to improve accuracy. To distinguish the sleep postures, the algorithmic framework that includes pre-processing, feature extraction, and posture classification is developed. Pilot studies in two scenarios including subject-dependent and subject-independent classification are performed with 7 persons for 4 different postures recognition. The experimental results show that the accuracy of the smart mat system can achieve over 78% using Support Vector Machines (SVMs) and k-Nearest Neighbor (kNN) for the subject-independent scenario. For the subject-dependent scenario, the accuracy can reach over 95%. It proves that the proposed method can recognize different sleep postures effectively.

Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations that model transmission through a community. Here we present the first data-driven malaria epidemic early warning system that can predict the 13-week case rate in a primary health facility in Burkina Faso. Using the extraordinarily high-fidelity data of infant consultations taken from the Integrated e-Diagnostic Approach (IeDA) system that has been rolled out throughout Burkina Faso, we train a combination of Gaussian Processes and Random Forest Regressors to estimate the weekly number of malaria cases over a 13 week period. We test our algorithm on historical epidemics and find that for our lowest threshold for an epidemic alert, our algorithm has 30% precision with > 99% recall at raising an alert. This rises to > 99% precision and 5% recall for the high alert threshold. Our two-tailed predictions have an average 1σ and 2σ precision of 5 cases and 30 cases respectively.

Sleep posture, as a crucial index for sleep quality assessment, has been widely studied in sleep analysis. In this paper, an unobtrusive smart mat system based on a dense flexible sensor array and printed electrodes along with an algorithmic framework for sleep posture recognition is proposed. With the dense flexible sensor array, the system offers a comfortable and high-resolution solution for long-term pressure sensing. Meanwhile, compared to other methods, it reduces production costs and computational complexity with a smaller area of the mat and improves portability with fewer sensors. To distinguish the sleep posture, the algorithmic framework that includes preprocessing and Deep Residual Networks (ResNet) is developed. With the ResNet, the proposed system can omit the complex hand-crafted feature extraction process and provide compelling performance. The feasibility and reliability of the proposed system were evaluated on seventeen subjects. Experimental results exhibit that the accuracy of the short-term test is up to 95.08% and the overnight sleep study is up to 86.35% for four categories (supine, prone, right, and left) classification, which outperform the most of state-of-the-art studies. With the promising results, the proposed system showed great potential in applications like sleep studies, prevention of pressure ulcers, etc.

Here we present a combined early warning system and malaria predictor that can predict the 13 week trajectory of malaria cases in an primary health facility in Burkina Faso. Using the extraordinarily high fidelity data taken from the Integrated e-Diagnostic Approach (IeDA) system that has been rolled out throughout Burkina Faso, we train a combination of Gaussian Processes and Random Forest Regressors to estimate the trajectory of malaria cases over a 13 week period. We calibrate and test our algorithm such that it can return robust 1 and 2σ one-tailed and two-tailed confidence bounds. Given our lowest threshold for an epidemic alert our algorithm has 30% precision with > 99% recall. This rises to > 99% precision and 5% recall for the high alert threshold. Our two-tailed predictions have an average 1σ and 2σ precision of 5 cases and 30 cases respectively.Funding Statement: This work was in part funded by Cloudera Foundation, the Marguerite Foundation and the Delta ITP institute, and technically supported by Cloudera Foundation and Tableau Foundation.Declaration of Interests: MISSING

Presents the President’s message for this issue of the publication.

ACADEMIC POSITIONS 2016-present Professor, Schools of Geographical Sciences & Urban Planning (SGSUP) and Earth & Space Exploration (SESE), Arizona State University (ASU), Tempe, AZ, USA 2019-present Associate Director, Graduate Research Programs, SGSUP, ASU 2020-present Director, Ron Greeley Planetary Geology Wind Tunnel, SESE, ASU 2015-2016 Professor, Department of Geography, University of Victoria (UVic), BC, Canada 2014-2015 Chair, Undergraduate Affairs Committee, Department of Geography, UVic 2009-2011 Chair, Graduate Affairs Committee, Department of Geography, UVic 2005-2015 Associate Professor, Department of Geography, UVic 2000-2005 Assistant Professor, Department of Geography, UVic

Child protection systems across the global South suffer from common problems, one of the most critical among which is low number and skills of relevant professionals to deliver services. Additionally, child protection professionals are often demotivated, uncoordinated and isolated, with limited access to continuous training and support. Peer learning and capacity building networks help address these issue, and often leverage the spread and scope of information and communications technologies. We present one such network, ChildHub, initially developed and deployed in South-East Europe, a region whose child protection systems present features similar to those in Africa and Asia. The success of this platform, evinced by a continuously growing community and confirmed by an evaluation after three years of operation, provides motivation and lessons for contextualization to sub-Saharan Africa and south Asia. Thanks to its inherent modularity, ChildHub will easily be adapted to the contexts and needs of the two regions, thus building on the interest generated in Asia and Africa for such networks. The paper also presents the approach that will be taken to implement the platform for Africa and Asia.

Information and Communications Technologies (ICT) are increasingly, and increasingly effectively, being used in development and humanitarian work. Whereas health and education lead this use, application to child protection remains sparse and ill-understood. This paper helps address these two gaps. On the one hand, it enhances understanding of the use of ICT in child protection, by presenting the global and south Asia-specific landscapes, focusing on notable initiatives, partnerships and tools; on the other, it hopes to guide future use by putting forth a concept for an ICT-strengthened, child-centred system for case identification and management. The concept can be implemented using existing solutions of proven utility and efficacy. Therefore, we show that ICTs have a very high potential for use in child protection and can build on an increasingly solid evidence base and important successes.