For the care of neonatal infants, abdominal auscultation is considered a safe, convenient, and inexpensive method to monitor bowel conditions. With the help of early automated detection of bowel dysfunction, neonatologists could create a diagnosis plan for early intervention. In this article, a novel technique is proposed for automated peristalsis sound detection from neonatal abdominal sound recordings and compared to various other machine learning approaches. It adopts an ensemble approach that utilises handcrafted as well as one and two dimensional deep features obtained from Mel Frequency Cepstral Coefficients (MFCCs). The results are then refined with the help of a hierarchical Hidden Semi-Markov Models (HSMM) strategy. We evaluate our method on abdominal sounds collected from 49 newborn infants admitted to our tertiary Neonatal Intensive Care Unit (NICU). The results of leave-one-patient-out cross validation show that our method provides an accuracy of 95.1% and an Area Under Curve (AUC) of 85.6%, outperforming both the baselines and the recent works significantly. These encouraging results show that our proposed Ensemble-based Deep Learning model is helpful for neonatologists to facilitate tele-health applications.

Abdominal auscultation is a convenient, safe and inexpensive method to assess bowel conditions, which is essential in neonatal care. It helps early detection of neonatal bowel dysfunctions and allows timely intervention. This paper presents a neonatal bowel sound detection method to assist the auscultation. Specifically, a Convolutional Neural Network (CNN) is proposed to classify peristalsis and non-peristalsis sounds. The classification is then optimized using a Laplace Hidden Semi-Markov Model (HSMM). The proposed method is validated on abdominal sounds from 49 newborn infants admitted to our tertiary Neonatal Intensive Care Unit (NICU). The results show that the method can effectively detect bowel sounds with accuracy and area under curve (AUC) score being 89.81% and 83.96% respectively, outperforming 13 baseline methods. Furthermore, the proposed Laplace HSMM refinement strategy is proven capable to enhance other bowel sound detection models. The outcomes of this work have the potential to facilitate future telehealth applications for neonatal care. The source code of our work can be found at: https://bitbucket.org/chirudeakin/neonatal-bowel-sound-classification/.

Technological advancements in high-throughput genomics enable the generation of complex and large data sets that can be used for classification, clustering, and bio-marker identification. Modern deep learning algorithms provide us with the opportunity of finding most significant features in such huge dataset to characterize diseases (e.g., cancer) and their sub-types. Thus, developing such deep learning method, which can successfully extract meaningful features from various breast cancer sub-types, is of current research interest. In this paper, we develop dual stage (unsupervised pre-training and supervised fine-tuning) neural network architecture termed AFExNet based on adversarial auto-encoder (AAE) to extract features from high dimensional genetic data. We evaluated the performance of our model through twelve different supervised classifiers to verify the usefulness of the new features using public RNA-Seq dataset of breast cancer. AFExNet provides consistent results in all performance metrics across twelve different classifiers which makes our model classifier independent. We also develop a method named 'TopGene' to find highly weighted genes from the latent space which could be useful for finding cancer bio-markers. Put together, AFExNet has great potential for biological data to accurately and effectively extract features. Our work is fully reproducible and source code can be downloaded from Github: https://github.com/NeuroSyd/breast-cancer-sub-types.

Artificial intelligence (AI) and health sensory data-fusion hold the potential to automate many laborious and time-consuming processes in hospitals or ambulatory settings, e.g. home monitoring and telehealth. One such unmet challenge is rapid and accurate epileptic seizure annotation. An accurate and automatic approach can provide an alternative way to label seizures in epilepsy or deliver a substitute for inaccurate patient self-reports. Multimodal sensory fusion is believed to provide an avenue to improve the performance of AI systems in seizure identification. We propose a state-of-the-art performing AI system that combines electroencephalogram (EEG) and electrocardiogram (ECG) for seizure identification, tested on clinical data with early evidence demonstrating generalization across hospitals. The model was trained and validated on the publicly available Temple University Hospital (TUH) dataset. To evaluate performance in a clinical setting, we conducted non-patient-specific pseudo-prospective inference tests on three out-of-distribution datasets, including EPILEPSIAE (30 patients) and the Royal Prince Alfred Hospital (RPAH) in Sydney, Australia (31 neurologists-shortlisted patients and 30 randomly selected). Our multimodal approach improves the area under the receiver operating characteristic curve (AUC-ROC) by an average margin of 6.71% and 14.42% for deep learning techniques using EEG-only and ECG-only, respectively. Our model's state-of-the-art performance and robustness to out-of-distribution datasets show the accuracy and efficiency necessary to improve epilepsy diagnoses. To the best of our knowledge, this is the first pseudo-prospective study of an AI system combining EEG and ECG modalities for automatic seizure annotation achieved with fusion of two deep learning networks.

The simplicity of deployment and perpetual operation of energy harvesting devices provides a compelling proposition for a new class of edge devices for the Internet of Things. In particular, Computational Radio Frequency Identification (CRFID) devices are an emerging class of battery free, computational, sensing enhanced devices that harvest all of their energy for operation. Despite wireless connectivity and powering, secure wireless firmware updates remains an open challenge for CRFID devices due to: intermittent powering, limited computational capabilities, and the absence of a supervisory operating system. We present,for the first time, asecurewireless code dissemination (SecuCode) mechanism for CRFIDs by entangling adevice intrinsic hardware security primitive—Static Random Access Memory Physical Unclonable Function (SRAM PUF)—to a firmware update protocol. The design of SecuCode: i) overcomes the resource-constrained and intermittently powered nature of the CRFID devices; ii) is fully compatible with existing communication protocols employed by CRFID devices—in particular, ISO-18000-6C protocol; and ii) is built upon a standard and industry compliant firmware compilation and update method realized by extending a recent framework for firmware updates provided by Texas Instruments. We build an end-to-end SecuCode implementation and conduct extensive experiments to demonstrate standards compliance, evaluate performance and security.

Biological neural networks, unlike artificial neural networks (ANNs), can process information from data that is inherently noisy, unstructured, sparse and dynamic. How is this possible? And how can we replicate this? A crucial clue comes from neuroscience: the brain is a complex physical system and the network topology of its neural circuitry is a determinant of its emergent collective properties. Indeed, as the brain's neural network is already entrained in its physical hardware, it clearly does not require an ANN software add-on to learn from natural data. Self-assembled nanowire networks with memristive junctions represent arguably the closest hardware architecture to real biological neural networks and are thus uniquely placed to demonstrate genuinely neuromorphic information processing. Here, we present preliminary results on polymer-coated silver nanowire networks. Their neuromorphic architecture (densely structured network topology, memristive switch junctions and efficient interconnect) gives rise to a rich repertoire of collective nonlinear dynamics manifested through adaptive current transport pathways. The potential for associative learning is demonstrated in a test protocol in which a nanowire network is stimulated by multiple electrodes mapped to different spatial patterns. The capacity to process information in the temporal domain is demonstrated via simulations of a reservoir computing implementation in which nanowire networks are shown to perform tasks such as time series prediction and handwritten digit recognition. Overall, their unique properties and neuromorphic information processing capabilities make nanowire networks promising candidates for emerging applications in cognitive devices in particular, at the edge.

Epilepsy affects nearly [Formula: see text] of the global population, of which two thirds can be treated by anti-epileptic drugs and a much lower percentage by surgery. Diagnostic procedures for epilepsy and monitoring are highly specialized and labour-intensive. The accuracy of the diagnosis is also complicated by overlapping medical symptoms, varying levels of experience and inter-observer variability among clinical professions. This paper proposes a novel hybrid bilinear deep learning network with an application in the clinical procedures of epilepsy classification diagnosis, where the use of surface electroencephalogram (sEEG) and audiovisual monitoring is standard practice. Hybrid bilinear models based on two types of feature extractors, namely Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), are trained using Short-Time Fourier Transform (STFT) of one-second sEEG. In the proposed hybrid models, CNNs extract spatio-temporal patterns, while RNNs focus on the characteristics of temporal dynamics in relatively longer intervals given the same input data. Second-order features, based on interactions between these spatio-temporal features are further explored by bilinear pooling and used for epilepsy classification. Our proposed methods obtain an F1-score of [Formula: see text] on the Temple University Hospital Seizure Corpus and [Formula: see text] on the EPILEPSIAE dataset, comparing favourably to existing benchmarks for sEEG-based seizure type classification. The open-source implementation of this study is available at https://github.com/NeuroSyd/Epileptic-Seizure-Classification.

Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices.

Emerging non-volatile reduction-oxidation (redox)-based resistive switching memories (ReRAMs) exhibit a unique set of characteristics that make them promising candidates for the next generation of low-cost, low-power, tiny, and secure physical unclonable functions (PUFs). Their underlying stochastic ionic conduction behavior, intrinsic nonlinear current-voltage characteristics, and their well-known nano-fabrication process variability might normally be considered disadvantageous ReRAM features. However, using a combination of a novel architecture and special peripheral circuitry, this paper exploits these non-idealities in a physical one-way function, nonlinear resistive PUF, potentially applicable to a variety of cyber-physical security applications. We experimentally verify the performance of valency change mechanism (VCM)-based ReRAM in nano-fabricated crossbar arrays across multiple dies and runs. In addition to supporting a massive pool of challenge-response pairs (CRPs), using a combination of experiment and simulation our proposed PUF exhibits a reliability of 98.67%, a uniqueness of 49.85%, a diffuseness of 49.86%, a uniformity of 47.28%, and a bit-aliasing of 47.48%.

Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection (ACS) engine as a pre-processing stage to the seizure detection procedure. The ACS engine consists of supervised classifiers which aim to find iEEG channels which contribute the most to a seizure. Seizure detection stage involves feature extraction and classification. Feature extraction is performed in both frequency and time domains where spectral power and correlation between channel pairs are calculated. Random Forest is used in classification of interictal, ictal and early ictal periods of iEEG signals. Seizure detection in this paper is retrospective and patient-specific. iEEG data is accessed via Kaggle, provided by International Epilepsy Electro-physiology Portal. The dataset includes a training set of 6.5 h of interictal data and 41 min in ictal data and a test set of 9.14 h. Compared to the state-of-the-art on the same dataset, we achieve 2 times faster in run-time seizure detection. The proposed model is able to detect a seizure onset at 89.40% sensitivity and 89.24% specificity with a mean detection delay of 2.63 s for the test set. The area under the ROC curve (AUC) is 96.94%, that is comparable to the current state-of-the-art with AUC of 96.29%.

Thermodynamic-driven filament formation in redox-based resistive memory and the impact of thermal fluctuation on switching probability of emerging magnetic memory are probabilistic phenomena in nature. Therefore, process of binary switching in these nonvolatile memories are considered stochastic that varies from switching-to-switching. Moreover, position-dependent, spatially correlated, and distance-dependent variation in these electron devices, like advanced CMOS processes, provide rich in-situ spatiotemporal stochastic characteristics. Based on a partial characterization of the switching variation, this preliminary work presents highly scalable neuromorphic hardware based on crossbar array of 1-bit resistive elements as distributed stochastic synapses. The network shows the ability to emulate selectivity of synaptic potentials in neurons of primary visual cortex to the orientation of a visual image. The proposed model could be configured to accept a wide range of emerging non-volatile memory technologies.

Integration density is a major drawback of today's associative memories required for intelligent data processing for such as pattern recognition or classification. Ultra dense complementary resistive switch-based architectures are thought to overcome this issue, offering a footprint of 4F2. Here we demonstrate the feasibility of an 8 by 16 associative capacitive network.

This paper presents a CMOS image sensor (CIS) VLSI architecture based on a single-inverter time-to-threshold pulsewidth modulation circuitry capable of operating as low as 330-mV supply voltage while retaining a signal-to-noise ratio of 24 dB; an important characteristic being demanded by very low voltage portable CIS-based equipment such as disposable medical cameras and on-chip autonomous wireless security vision systems. A 64 × 64 pixel array was fabricated using standard 130-nm CMOS process consuming only 5.9 nW/pixel with integration time of 2 ms at +0.5 V supply. The high fill factor of 58% facilitated a better SNR at a low supply voltage when compared with other CIS architectures. The pixel has a dynamic range of 54 dB with 7.8 frame per second.

In this paper, we present a flexible biphasic pulse generating and accurate charge balancing stimulator with a 1μW neural recording amplifier. To achieve accurate charge balance, the stimulator is implemented using a dynamic current copying technique. It can generate a biphasic current pulse with an anodic scaling feature. To reduce power consumption, the recording amplifier is implemented to operate with 1V, while the stimulator works with 3V supply. They are implemented using a 65nm CMOS process. Simulation results show charge balance of less than 2% error in current pulses. The recording amplifier has an input referred noise of 4.5μVrms and passband from 1.5Hz to 5KHz with the power consumption of 1μW.

Recent work in neuroscience is revealing how the blowfly rapidly detects orientation using neural circuits distributed directly behind its photo receptors. These circuits like all biological systems rely on timing, competition, feedback, and energy optimization. The recent realization of the passive memristor device, the so-called fourth fundamental passive element of circuit theory, assists with making low power biologically inspired parallel analog computation achievable. Building on these developments, we present a memristor-based neuromorphic competitive control (mNCC) circuit, which utilizes a single sensor and can control the output of N actuators delivering optimal scalable performance, and immunity from device variation and environmental noise.

This live demonstration presents a high fill factor 6 transistor per pixel CMOS image sensor (CIS) based on a single inverter that modulates light illumination to pulse width supporting ultra low supply voltage requirements. It has a compact readout circuitry for pulse-based signal processing without A/D converter at the output. A 64 × 64 pixel array was fabricated using 130 nm CMOS technology. The chip operated under a +VDDas low as 500 mV with power consumption of only 27 nW per pixel. The fill factor is 58%, which is significantly larger than those conventional CMOS imagers.

The nonvolatile memory property of a memristor enables the realization of new methods for a variety of computational engines ranging from innovative memristive-based neuromorphic circuitry through to advanced memory applications. The nanometer-scale feature of the device creates a new opportunity for realization of innovative circuits that in some cases are not possible or have inefficient realization in the present and established design domain. The nature of the boundary, the complexity of the ionic transport and tunneling mechanism, and the nanoscale feature of the memristor introduces challenges in modeling, characterization, and simulation of future circuits and systems. Here, a deeper insight is gained in understanding the device operation, leading to the development of practical models that can be implemented in current computer-aided design (CAD) tools.

Large-capacity content addressable memory (CAM) is a key element in a wide variety of applications. The inevitable complexities of scaling MOS transistors introduce a major challenge in the realization of such systems. Convergence of disparate technologies, which are compatible with CMOS processing, may allow extension of Moore's Law for a few more years. This paper provides a new approach towards the design and modeling of Memory resistor (Memristor)-based CAM (MCAM) using a combination of memristor MOS devices to form the core of a memory/compare logic cell that forms the building block of the CAM architecture. The non-volatile characteristic and the nanoscale geometry together with compatibility of the memristor with CMOS processing technology increases the packing density, provides for new approaches towards power management through disabling CAM blocks without loss of stored data, reduces power dissipation, and has scope for speed improvement as the technology matures.

High order compressors play a specific role in realizing high speed multipliers. By increasing the demand for fast multiplication process, high order compressors have attracted many researchers to this field. In this paper a new implementation for 7:2 compressors, based on the conventional architecture, is proposed. According to the results, the design presented achieves a remarkable improvement in terms of speed (especially in low voltages) and power consumption over the best counterpart. This accomplishment is the direct result of shortening the critical delay path in the proposed circuit design. As the simulation results demonstrate, the structure presented here has improved the power consumption from minimum 0.07% (at supply voltage = 3.5 volt) through maximum 11% (at 1.2 volt), and the speed of the circuit from minimum 19% (at 3.5 volt) through maximum 23% (at 1.2 volt). HSPICE is the circuit simulator used, and the technology being used for simulations is 0.25 mum technology.