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Edge analytics support industrial Internet of Things by pushing some data processing capacity to the edge of the network instead of sending the streaming data captured by the sensor nodes directly to the cloud. It is advantageous to endow machine learners for data reduction with suitable security primitives for privacy protection in edge computing devices to conserve area and power consumption. In this paper, we propose a novel physical unclonable function (PUF) based on current mirror array (CMA) circuits that reuses the circuit implementation of a machine learner–the extreme learning machine (ELM), which is a randomized neural network. Seven different challenge activation and response readout schemes are proposed to realize different weak and strong PUF functions from within the same CMA array. ELM endowed with such reconfigurable challenge-response mechanism is more robust and adaptable to different authentication protocols and security functions. Measurement results on $0.35\mu m$ test chips demonstrate that the proposed strong PUF outperforms other state-of-the-art designs with smaller area/bit of $9\times 10^{-36} \mu m^{2}$ and lower native bit error rate (BER) of 0.16% with an added overhead of less than 2.5% power and 2.9% area over the native ELM implementation.

In this paper, we present a survey of recent works in developing neuromorphic or neuro-inspired hardware systems. In particular, we focus on those systems which can either learn from data in an unsupervised or online supervised manner. We present algorithms and architectures developed specially to support on-chip learning. Emphasis is placed on hardware friendly modifications of standard algorithms, such as backpropagation, as well as novel algorithms, such as structural plasticity, developed specially for low-resolution synapses. We cover works related to both spike-based and more traditional non-spike-based algorithms. This is followed by developments in novel devices, such as floating-gate MOS, memristors, and spintronic devices. CMOS circuit innovations for on-chip learning and CMOS interface circuits for post-CMOS devices, such as memristors, are presented. Common architectures, such as crossbar or island style arrays, are discussed, along with their relative merits and demerits. Finally, we present some possible applications of neuromorphic hardware, such as brain-machine interfaces, robotics, etc., and identify future research trends in the field. Published version

Laterites derived from different parent lithologies in the same tropical area and under the same climatic condition are likely to have different engineering properties. In this study, two nearby abandoned laterite quarries at Haripur and Tengara in Dhenkanal district (in the state of Odisha, India) were investigated. The first objective of this study was to evaluate the strength and physical properties of the indurated laterites with reference to the characterization of the selected profiles. The other drive of this work was to demonstrate how different parent lithologies of these two lateritic profiles that share same geographical conditions can be related to the variation of engineering properties of the indurated laterites. In order to address these two objectives, the lateritic profiles were characterized which was followed by the determination of strength and physical properties of the indurated laterites; petrographic study; and XRD analysis. Although the variation patterns of engineering properties (i.e. uniaxial compressive, point load and Brazilian tensile strengths; effective porosity; dry density; vug volumetric percentage; and slake durability indices) of the indurated laterites with profile depths at Haripur and Tengara show prominent differences, such variations depict a good compliance with the characterized horizons of the respective profiles. Based on the analysis of the engineering properties in relation to characterized profiles, it was shown that for construction purposes, the indurated laterites of both areas can be classified based on the point load strength. From the geological investigation, it was ascertained that the difference in engineering properties between the indurated laterites of Haripur and Tengara is very much dependent on the mineralogy such as sesquioxides, clay content and other minerals altered from the parent lithologies.

Strategies implemented worldwide to contain COVID-19 outbreaks varied in severity across different countries, and established a new normal for work and school life (i.e., from home) for many people, reducing opportunities for physical activity. Positive relationships of physical activity with both mental and physical health are well recognised, and therefore the aim was to ascertain how New Zealand’s lockdown restrictions impacted physical activity, mental health and wellbeing. Participants (n = 4007, mean ± SD: age 46.5 ± 14.7 years, 72% female, 80.7% New Zealand European) completed (10–26 April 2020) an online amalgamated survey (Qualtrics): International Physical Activity Questionnaire: Short Form, Depression, Anxiety and Stress Scale-9, World Health Organisation-Five Well-Being Index, Stages of Change Scale. Positive dose–response relationships between physical activity levels and wellbeing scores were demonstrated for estimates that were unadjusted (moderate activity OR 3.79, CI 2.88–4.92, high activity OR 8.04, CI 6.07–10.7) and adjusted (confounding variables: age, gender, socioeconomic status, time sitting and co-morbidities) (moderate activity 1.57, CI 1.11–2.52, high activity 2.85, CI 1.97–4.14). The study results support previous research demonstrating beneficial effects of regular physical activity on mental health and wellbeing. Governments may use these results to promote meeting physical activity guidelines in order to protect mental health and wellbeing during the ongoing COVID-19 restrictions and future pandemics.

The 11beta-hydroxysteroid dehydrogenase type 1 (11β-HSD1) enzyme generates cortisol from cortisone for local glucocorticoid action in hepatocytes and adipocytes. Functional polymorphisms within 11beta-hydroxysteroid dehydrogenase type 1 (HSD11B1) gene have shown an association with various factors including insulin resistance, diabetes, and obesity. In this study, we have assessed a candidate gene association study in order to examine the association of HSD11B1 gene polymorphisms with polycystic ovary syndrome (PCOS). A total of 200 women were investigated in this case-control study. DNA samples from PCOS women (n = 100) and the age-matched control women (n = 100) were analyzed. Genotyping of HSD11B1 rs12086634 and rs846910 variants was performed using tetra-primer amplification refractory mutation system (TETRA-ARMS) PCR. Odds ratio and 95% confidence interval were calculated to determine the association of HSD11B1 gene polymorphisms with polycystic ovary syndrome. The association analysis indicate that HSD11B1 rs12086634 showed positive association with polycystic ovary syndrome (OR = 1.95; 95%CI = 1.11–3.44, p = 0.0195) and women with HSD11B1 rs12086634 TG genotype had significantly higher body mass index (28.05 kg/mt2, p = 0.001), higher waist circumference (89.68 cms, p = 0.019), higher triglycerides (120.37 mg/dl, p = 0.049), and lower HDL levels (45.56 mg/dl, p = 0.004) compared to the women with HSD11B1 TT genotype. HSD11B1 rs846910 did not show any association with PCOS (OR = 0.57; 95%CI = 0.311–1.051, p = 0.072). Our results suggest that HSD11B1 rs12086634 polymorphism is associated with PCOS and HSD11B1 rs846910 gene polymorphism is not associated with PCOS in South Indian women.

Activation-induced cytidine deminase (AID) is crucial for controlling the immunoglobulin (Ig) diversification processes of somatic hypermutation (SHM) and class switch recombination (CSR). AID initiates these processes by deamination of cytosine, ultimately resulting in mutations or double strand DNA breaks needed for SHM and CSR. Levels of AID control mutation rates, and off-target non-Ig gene mutations can contribute to lymphomagenesis. Therefore, factors that control AID levels in the nucleus can regulate SHM and CSR, and may contribute to disease. We previously showed that transcription factor YY1 can regulate the level of AID in the nucleus and Ig CSR. Therefore, we hypothesized that conditional knock-out of YY1 would lead to reduction in AID localization at the Ig locus, and reduced AID-mediated mutations. Using mice that overexpress AID (IgAID yy1(f/f)) or that express normal AID levels (yy1(f/f)), we found that conditional knock-out of YY1 results in reduced AID nuclear levels, reduced localization of AID to the S switch region, and reduced AID-mediated mutations. We find that the mechanism of YY1 control of AID nuclear accumulation is likely due to YY1-AID physical interaction which blocks AID ubiquitination.

The creation of useful categories from data is an important cognitive ability, and from the extensive research on categorization, it is now known that the brain has distinct systems for category learning. In this paper, we present the first spiking neural network (SNN) model of human category learning. Here categories are combinations of features - such categories are observed in the prefrontal cortex (PFC). The system follows an architecture commonly used to model the cortex - features are arranged in a topological 2D grid with short range excitatory connectivity and long range inhibitory connectivity - however, here, this architecture is used differently from other models to model higher level cognition. Earlier we presented an artificial neural network (ANN) model of category learning; however, here, a simpler model was adequate, as desired functionality emerges from the SNN dynamics. We identified the objectives that had to be fulfilled for the model to achieve the desired functionality, and performed a design space exploration (DSE) to identify the parameter range in which each of the objectives was fulfilled, and the parameter range for which the system exhibits good performance. Finally, we compared triphasic STDP (a variant of spike time dependant plasticity (STDP)) with standard STDP and observed that triphasic STDP exhibited quicker convergence.

Shallow feed-forward networks are incapable of addressing complex tasks such as natural language processing that require learning of temporal signals. To address these requirements, we need deep neuromorphic architectures with recurrent connections such as deep recurrent neural networks. However, the training of such networks demand very high precision of weights, excellent conductance linearity and low write-noise- not satisfied by current memristive implementations. Inspired from optogenetics, here we report a neuromorphic computing platform comprised of photo-excitable neuristors capable of in-memory computations across 980 addressable states with a high signal-to-noise ratio of 77. The large linear dynamic range, low write noise and selective excitability allows high fidelity opto-electronic transfer of weights with a two-shot write scheme, while electrical in-memory inference provides energy efficiency. This method enables implementing a memristive deep recurrent neural network with twelve trainable layers with more than a million parameters to recognize spoken commands with >90% accuracy., Accomplishing complex cognitive tasks such as speech recognition calls for artificial intelligence hardware with high computing precision. John et al. propose deep recurrent neural networks based on optoelectronic transition metal dichalcogenide memristors with high weight precision for in-memory computing.

We present a novel readout circuit for a ferro-magnetic Hall cross-bar based random number generator. The random orientation of magnetic domains are result of anomalous Hall-effect. These ferromagnetic Hall cross-bar structures can be integrated with the read out circuit to form a plug and play random number generator. The system can resolve up to 15– $20~\mu \text{V}$ Hall-voltages from Hall probe. Application of current densities around $10^{12} {A/m}^{2}$ through the Ferromagnetic Hall cross-bar produces random Hall-voltage on the output terminals. To amplify the weak Hall-voltages (10– $100~\mu \text{V}$ ) in the presence of DC offsets, a modulation scheme is used to up-convert the signal and a band-pass amplifier is used to amplify the modulated signal. The band-pass amplifier circuit, motivated by neural recording amplifier is designed in 65nm CMOS and consumes $126~\mu \text{W}$ of power from a 1.2 V supply. Further, we present a successive approximation algorithm and its embedded implementation to set the desired threshold for digitizing the amplified Hall-voltage in presence of signal drift. Experimental results show that the resulting system can tolerate drifts in voltage up to $440~\mu \text{V}$ .

In this chapter, we provide a smorgasbord of emergent technologies on the watch. We begin with an introduction to telehomecare; continue our discussions on the topic of the use of robots for medical and healthcare applications, artificial intelligence and its usage in health and medical care, and virtual reality and its applications in healthcare and medicine; and conclude with blockchain technology. We discuss these technologies with a view to their present and future potential.

In this chapter, we discuss the role of collaborative tools for distance-based health-care delivery. By collaborative tools, we mean the tools that allow providers—working alone or in groups to work together with the patient. Such collaboration can be in real-time mode or asynchronous or store and forward mode. These collaborative efforts can engage multiple stakeholders both from the provider's side and also possibly from the patients’ side, that is, there may be relatives tuning in and multiple patients with similar problems. Some of them may be together and some separated by not only distance but also time. Collaborative telehealth hence can engage another mode of telehealth that may for now be termed hybrid, that is, some are meeting online or talking on the phone, while others respond later. Collaborative tools can serve to cover not only physical distance, that is, different geographical locations, but also care processes separated over time. Such collaborative tools include email exchange of notes between patients and providers, maintenance of wikis and patient-provider edited health records (OpenNote system), webpages, social media or social networking sites such as Twitter or Facebook, besides dedicated medical sites. These sites and systems can be used to boost communication and collaboration between patients and providers, and it is possible to arrange distance delivery of care using these media.

This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracorticalBrain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of input channels. These sparse connections ensure that with a high chance, few of the base classifiers should be less affected by the variations in some of the recording channels.We have tested the generality of this technique on different base classifiers - linear discriminant analysis (LDA), support vector machine (SVM), extreme learning machine (ELM) and multilayer perceptron (MLP). Results show decoding accuracy improvements of up to ≈ 21%,13%,19%,10% in non-human primate (NHP) A and 7%,9%,7%,9% in NHP B across test days while using the sparse ensemble approach over a single classifier model for LDA, SVM, ELM and MLP algorithms respectively.The technique also holds ground when the most informative electrode on the test day is dropped. Accordingly, improvements of up to ≈ 24%,11%,22%,9% in NHP A and 14%,19%,7%,28% in NHP B are obtained for LDA, SVM, ELM and MLP respectively.

In this paper, we present EBBIOT-a novel paradigm for object tracking using stationary neuromorphic vision sensors in low-power sensor nodes for the Internet of Video Things (IoVT). Different from fully event based tracking or fully frame based approaches, we propose a mixed approach where we create event-based binary images (EBBI) that can use memory efficient noise filtering algorithms. We exploit the motion triggering aspect of neuromorphic sensors to generate region proposals based on event density counts with >1000X less memory and computes compared to frame based approaches. We also propose a simple overlap based tracker (OT) with prediction based handling of occlusion. Our overall approach requires 7X less memory and 3X less computations than conventional noise filtering and event based mean shift (EBMS) tracking. Finally, we show that our approach results in significantly higher precision and recall compared to EBMS approach as well as Kalman Filter tracker when evaluated over 1.1 hours of traffic recordings at two different locations., 6 pages, 5 figures

Fully-implantable wireless intracortical Brain Machine Interfaces (iBMI) is one of the most promising next frontiers in the nascent field of neurotechnology. However, scaling the number of channels in such systems by another 10× is difficult due to power and bandwidth requirements of the wireless transmitter. One promising solution for that is to include more processing, up to the decoder, in the implant so that transmission data-rate is reduced drastically. Earlier work on neuromorphic decoder chips only showed classification of discrete states. We present results for continuous state decoding using a low-power neuromorphic decoder chip termed S pike-input E xtreme L earning Ma chine (SELMA) that implements a nonlinear decoder without memory and its memory-based version with time-delayed bins, SELMA-bins. We have compared SELMA, SELMA-bins against state-of-the-art S teady- S tate K alman F ilter (SSKF), a linear decoder with memory, across two different datasets involving a total of 4 non-human primates (NHPs). Results show at least a 10% (20%) or more increase in the fraction of variance accounted for (FVAF) by SELMA (SELMA-bins) over SSKF across the datasets. Estimated energy consumption comparison shows SELMA (SELMA-bins) consuming $\approx$ 9 $nJ/update$ (23 $nJ/update$ ) against SSKF's $\approx$ 7.4 $nJ/update$ for an iBMI with a 10 degree of freedom control. Thus, SELMA yields better performance against SSKF while consuming energy in the same range as SSKF whereas SELMA-bins performs the best with moderately increased energy consumption, albeit far less than energy required for raw data transmission. This paves the way for reducing transmission data rates in future scaled iBMI systems.

Energy-efficient machine-learning and physical unclonable function (PUF) becomes popular in Internet-of-Things (IoT) applications for saliency detection and privacy protection at sensor node. A machine-learning and PUF engine for IoT applications is presented in this work with a current mirror cross-bar (CMCB) array being a shared core, reducing silicon area. A novel dimension expansion technique is proposed to increase weight matrix dimension beyond the physically implemented array with small hardware and energy overhead. A signed multiply-and-accumulation is realized in CMCB with differential current path and 2-phase conversion. The proposed engine achieves an error rate of 6.34% on MNIST digit recognition task with an energy efficiency of 2.86 TOPS/W. The PUF achieves a native BER of 2.3% across corners and extremely low area/CRP of 4.17×10–59 µm2/CRP.

This live demo aims to show the performance of a two-layer neural network applied to predictive maintenance. The first layer encodes features based on prior knowledge, while the second layer is trained online to detect anomalies. The system is implemented on an FPGA, acquiring real-time data from sensors attached to a motor. Faults can be triggered artificially in real-time to demonstrate anomaly detection. NRF (Natl Research Foundation, S’pore) Accepted version

Summary Objectives : This paper provides a discussion about the potential scope of applicability of Artificial Intelligence methods within the telehealth domain. These methods are focussed on clinical needs and provide some insight to current directions, based on reports of recent advances. Methods : Examples of telehealth innovations involving Artificial Intelligence to support or supplement remote health care delivery were identified from recent literature by the authors, on the basis of expert knowledge. Observations from the examples were synthesized to yield an overview of contemporary directions for the perceived role of Artificial Intelligence in telehealth. Results : Two major focus areas for related contemporary directions were established. These were first, quality improvement for existing clinical practice and service delivery, and second, the development and support of new models of care. Case studies from each focus area have been chosen for illustration purposes. Conclusion : Examples of the role of Artificial Intelligence in delivery of health care remotely include use of tele-assessment, tele-diagnosis, tele-interactions, and tele-monitoring. Further developments of underlying algorithms and validation of methods will be required for wider adoption. Certain key social and ethical considerations also need consideration more generally in the health system, as Artificial-Intelligence-enabled-telehealth becomes more commonplace.

This paper presents for the first time a real-time closed loop neuromorphic decoder chip-driven intra-cortical brain machine interface (iBMI) in a non-human primate (NHP) based experimental setup. Decoded results show trial success rates and mean times to target comparable to those obtained by hand-controlled joystick. Neural control trial success rates of approximately 96% of those obtained by hand-controlled joystick have been demonstrated. Also, neural control has shown mean target reach speeds of approximately 85% of those obtained by hand-controlled joystick . These results pave the way for fast and accurate, fully implantable neuromorphic neural decoders in iBMIs., accepted at Neural Engineering Conference (NER), 2019

This paper presents a three layer spiking neural network based region proposal network operating on data generated by neuromorphic vision sensors. The proposed architecture consists of refractory, convolution and clustering layers designed with bio-realistic leaky integrate and fire (LIF) neurons and synapses. The proposed algorithm is tested on traffic scene recordings from a DAVIS sensor setup. The performance of the region proposal network has been compared with event based mean shift algorithm and is found to be far superior (~50% better) in recall for similar precision (~85%). Computational and memory complexity of the proposed method are also shown to be similar to that of event based mean shift, Accepted in IEEE ISCAS, 2019

With the current research impetus on neuromorphic computing hardware, realizing efficient drift and diffusive memristors are considered critical milestones for the implementation of readout layers, selectors, and frameworks in deep learning and reservoir computing networks. Current demonstrations are predominantly limited to oxide insulators with a soft breakdown behavior. While organic ionotronic electrochemical materials offer an attractive alternative, their implementations thus far have been limited to features exploiting ionic drift a.k.a. drift memristor technology. Development of diffusive memristors with organic electrochemical materials is still at an early stage, and modulation of their switching dynamics remains unexplored. Here, halide perovskite (HP) memristive barristors (diodes with variable Schottky barriers) portraying tunable diffusive dynamics and ionic drift are proposed and experimentally demonstrated. An ion permissive poly(3,4‐ethylenedioxythiophene):polystyrene sulfonate interface that promotes diffusive kinetics and an ion source nickel oxide (NiOx) interface that supports drift kinetics are identified to design diffusive and drift memristors, respectively, with methylammonuim lead bromide (CH3NH3PbBr3) as the switching matrix. In line with the recent interest on developing artificial afferent nerves as information channels bridging sensors and artificial neural networks, these HP memristive barristors are fashioned as nociceptive and synaptic emulators for neuromorphic sensory signal computing. Ministry of Education (MOE) National Research Foundation (NRF) Accepted version NRF‐CRP14‐2014‐03 NRF2018‐ITC001‐001 MOE2016‐T2‐1‐100 MOE2018‐T2‐2‐083

Key Points Transcription factor YY1 regulates the IgH Eμ-3′RR long-distance DNA loop without the YY1 transcriptional activation domain. YY1 constructs that rescue the Eμ-3′RR DNA loop also restore CSR strongly arguing for the necessity of this long-distance DNA loop for CSR.

In Industry 4.0, predictive maintenance (PdM) is one of the most important applications pertaining to the Internet of Things (IoT). Machine learning is used to predict the possible failure of a machine before the actual event occurs. However, main challenges in PdM are: (a) lack of enough data from failing machines, and (b) paucity of power and bandwidth to transmit sensor data to cloud throughout the lifetime of the machine. Alternatively, edge computing approaches reduce data transmission and consume low energy. In this paper, we propose Anomaly Detection based Power Saving (ADEPOS) scheme using approximate computing through the lifetime of the machine. In the beginning of the machine's life, low accuracy computations are used when machine is healthy. However, on detection of anomalies as time progresses, system is switched to higher accuracy modes. We show using the NASA bearing dataset that using ADEPOS, we need 8.8X less neurons on average and based on post-layout results, the resultant energy savings are 6.4--6.65X.

The use of universal body mass index (BMI) cutoffs do not take into account variation in the association between BMI and health risk across diverse ethnic groups. We used the New Zealand Health Survey data collected between 2002/2003 and 2014/2015 to calculate the predictive marginal means of hypertension, cardiovascular diseases, and type 2 diabetes mellitus (T2DM) after adjusting for demographic variables and health-related behaviors. Compared with European group, we found that Pacific had a lower prevalence of hypertension at a BMI of ≥35 kg/m2, and Māori had a higher prevalence of hypertension, T2DM, and cardiovascular diseases at higher BMI intervals. Whereas Asian had a higher T2DM prevalence compared with Māori, Pacific, and European at some BMI intervals. Using universal BMI cutoffs on the ethnically diverse population to compare obesity rates does not address the risk of chronic diseases associated with high BMI and may stigmatize certain ethnic groups.

The advent of electronic sensors and other developments has helped the textile technologists to develop smart/intelligent textiles. In the present study, a smart textile garment has been developed which can sense the heartbeat, body temperature and geographical position of the wearer. The software, developed under this project, sends information to the caregivers and relatives whenever these values cross the threshold limits, which has been fixed based on practical data.

Supplemental material, Revision_Supplementary_Figure_-_The_Prevalence_of_BMI_Associated_Chronic_Diseases_in_Diverse_Ethnic_Groups_in_New_Zealand for The Prevalence of Body Mass Index–Associated Chronic Diseases in Diverse Ethnic Groups in New Zealand by Andrew Adiguna Halim, Arindam Basu and Ray Kirk in Asia Pacific Journal of Public Health

International audience; Inexact design has proved to be an efficient way of obtaining power savings with a marginal penalty on performance in applications which do not need high degree of output accuracy. Such applications are often found in domains which deal with human sensorial systems. The 'not so perfect' state of human senses like sight, hearing which are important for video and audio related appliances can compensate for the error introduced due to inexact design. An efficient analysis and modelling of these compensation capabilities of human senses can help the designers to build optimum inexact architectures meeting the redefined requirements with low power. In this work, we choose hearing aid as our test application, which is based on human sense of hearing. We estimate a metric Intelligibility, for a set of audio samples, which is obtained from multiple surveys on human subjects to model the sensorial processing. Our methodology uses a novel way of introducing inexactness in an optimum manner. This includes a fine grained analysis of the error that is being introduced in the FIR filters of the DSP present in hearing aid. The resulting inexact FIR filter bank is 1.92× or 2.56× more efficient in terms of power consumed while producing 10% or 20% less intelligible speech respectively when compared with a hearing-aid using exact filters.

The present research reports a facile synthesis of bare phase pure CaAl-LDH nanoparticle (sample A) and intercalation of anti-cancer drug etoposide (ETO) into the same via simple anion exchange technique to obtain ETO loaded CaAl-LDH (sample B). The basal spacing (d002) of sample A was increased from 8.5764 A to 17.18 A upon intercalation of ETO. Further ETO loading was found to be 27.72% as estimated by UV–Vis spectrophotometer. The release profile of ETO from sample B in phosphate buffer saline (PBS) at pH 7.4 follows quasi-Fickian diffusion phenomenon in Korsmeyer–Peppas kinetics model. In vitro cell viability was undertaken using A549 (human lung adenocarcinoma) cell line in a dose dependent manner to determine the synergistic anti-cancer potential of sample B. It was observed that at an equivalent dose, the cancer cell viability was substantially reduced, in case of sample B compared to sample A and bare etoposide drug, respectively, which was further corroborated by cell proliferation/migration assay. The cellular uptake using A549 cell line showed an increasing trend with increasing time period (11.67% and 19.30% at 24 h and 72 h) respectively, confirmed by flow cytometry, exhibiting a substantial reduction of CAMKIIα protein for sample A and sample B to 110.52 pg/ml and 95.14 pg/ml respectively. Results also showed significant down regulation of SOD gene activity. All the above results exhibited the synergistic anti-cancer potential of sample B for possible application in the management of non small cell lung carcinoma (NSCLC).

This paper presents an experiment based comparison of absolute threshold (AT) and non-linear energy operator (NEO) spike detection algorithms in Intra-cortical Brain Machine Interfaces (iBMIs). Results show an average increase in decoding performance of approx. 5% in monkey A across 28 sessions recorded over 6 days and approx. 2% in monkey B across 35 sessions recorded over 8 days when using NEO over AT. To the best of our knowledge, this is the first ever reported comparison of spike detection algorithms in an iBMI experimental framework involving two monkeys. Based on the improvements observed in an experimental setting backed by previously reported improvements in simulation studies, we advocate switching from state of the art spike detection technique - AT to NEO., accepted at NER (Neural Engineering Conference) - 2019

For many studies, particularly in the biomedical disciplines such as Epidemiology and biological sciences, a full replication is impossible. Therefore, if research is reproducible such that the data and code are disseminated widely and openly and made available for everyone to review and re-analyse, this may facilitate discovery of new knowledge and verification of claims. There are several barriers to the implementation of reproducible research: one of the identified barriers is the challenge of changing people's mindset of publishing and dissemination of results. We argue that using simple plain text tools and easy dissemination of research, codes, and data may help. In this article, I will discuss the the role and importance of using simple plain text based writing and data analysis tools can benefit dissemination of reproducible research by lowering the barrier to dissemination of robust research and findings. This article is both a discussion paper and a showcase.

In this article we describe concept maps, mind maps, and explain the differences between concept and mind maps. We also describe and their relative contributions to the process of assimilation and development of knowledge. We show with an example of motivation of students from a text to construct concept maps and mind maps and show how mind maps can take off from concept maps and reconstitute existing knowledge visually.

The concept of ‘readiness to practice’ of nursing students is not well understood even though being ‘ready for practice’ is the focus of undergraduate programs. The aim of this study was to critically explore senior student's views about their readiness to practice as a registered nurse. This study was undertaken in a school of nursing in the South Island of New Zealand. The participants included all senior nursing students assigned to their final clinical practicum from five cohorts across two and half years. The response rate for the survey across five cohorts was 46% (n = 245). The majority of students were New Zealand European females aged 30 years or younger. Students felt they were confident with their professional responsibilities and considered that they had prepared well for the profession. They agreed that they were confident with their communication skills. Areas of concern were; caring for a dying patient and caring for more than four patients at one time. This study suggests that senior nursing students feel they are work ready.

Spintronic devices promise an excellent opportunity for implementing ultra-low power neuromorphic platforms due to the inherent correspondence between their physical characteristics and the required neuronal, synaptic functionalities. Neuromorphic circuits using domain wall motion-based threshold neurons have been demonstrated in previous studies. However, threshold neurons are unable to realize linearly inseparable functions. Our work addresses this challenge by proposing a new domain wall motion-based neural activation unit with XOR-like activation function. We also develop a new learning algorithm for neurons with this activation unit. Offline training is performed on real-world datasets from the UCI machine learning repository. Neuromorphic circuits corresponding to these datasets are also simulated. The results suggest femto-Joule range energy consumption of a neuron with the proposed activation unit and $ 1.08\times -1.82\times $ lower misclassification rate (MCR) of the proposed algorithm in comparison to the traditional perceptron learning algorithm.