Uniform hexagonal single phase Ni1–x Fe x O (x = 0, 0.01, 0.05, and 0.1) nanoparticles synthesized by a standard hydrothermal method are characterized with an enhanced lattice expansion along with a decrease in the microstrain, crystal size, and Ni occupancy as a function of the Fe concentration. The observed anomalous temperature and field dependent magnetic properties as a function of the Fe content were explained using a core–shell type structure of Ni1–x Fe x O nanoparticle such that the effect of Fe-doping has led to a decrease of disordered surface spins and an increase of uncompensated-core spins. Perfect incorporation of Fe3+ ions at the octahedral site of NiO was observed from the low Fe concentration; however, at a higher Fe content, 4:1 defect clusters (four octahedral Ni2+ vacancies surrounding an Fe3+ tetrahedral interstitial) are formed in the core of the nanoparticles, resulting in the transition of spin-glassy to the cluster-glassy system. An enhanced thermal magnetic memory effect is noted from the cluster-glassy system possibly because of increased intraparticle interactions. The outcome of this study is important for the future development of diluted magnetic semiconductor spintronic devices and the understanding of their fundamental physics.

Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient’s condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians with more than 10 years of clinical experience may still misjudge. Therefore, this study uses convolutional neural networks to classify ECG image types to assist in anesthesia assessment. The research uses Internet of Things (IoT) technology to develop ECG signal measurement prototypes. At the same time, it classifies signal types through deep neural networks, divided into QRS widening, sinus rhythm, ST depression, and ST elevation. Three models, ResNet, AlexNet, and SqueezeNet, are developed with 50% of the training set and test set. Finally, the accuracy and kappa statistics of ResNet, AlexNet, and SqueezeNet in ECG waveform classification were (0.97, 0.96), (0.96, 0.95), and (0.75, 0.67), respectively. This research shows that it is feasible to measure ECG in real time through IoT and then distinguish four types through deep neural network models. In the future, more types of ECG images will be added, which can improve the real-time classification practicality of the deep model.

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Many neurological and musculoskeletal disorders are associated with problems related to postural movement. Noninvasive tracking devices are used to record, analyze, measure, and detect the postural control of the body, which may indicate health problems in real time. A total of 35 young adults without any health problems were recruited for this study to participate in a walking experiment. An iso-block postural identity method was used to quantitatively analyze posture control and walking behavior. The participants who exhibited straightforward walking and skewed walking were defined as the control and experimental groups, respectively. Fusion deep learning was applied to generate dynamic joint node plots by using OpenPose-based methods, and skewness was qualitatively analyzed using convolutional neural networks. The maximum specificity and sensitivity achieved using a combination of ResNet101 and the naïve Bayes classifier were 0.84 and 0.87, respectively. The proposed approach successfully combines cell phone camera recordings, cloud storage, and fusion deep learning for posture estimation and classification.