Development of a Three Dimensional Neural Sensing Device by a Stacking Method

This study reports a new stacking method for assembling a 3-D microprobe array. To date, 3-D array structures have usually been assembled with vertical spacers, snap fasteners and a supporting platform. Such methods have achieved 3-D structures but suffer from complex assembly steps, vertical interconnection for 3-D signal transmission, low structure strength and large implantable opening. By applying the proposed stacking method, the previous techniques could be replaced by 2-D wire bonding. In this way, supporting platforms with slots and vertical spacers were no longer needed. Furthermore, ASIC chips can be substituted for the spacers in the stacked arrays to achieve system integration, design flexibility and volume usage efficiency. To avoid overflow of the adhesive fluid during assembly, an anti-overflow design which made use of capillary action force was applied in the stacking method as well. Moreover, presented stacking procedure consumes only 35 minutes in average for a 4 × 4 3-D microprobe array without requiring other specially made assembly tools. To summarize, the advantages of the proposed stacking method for 3-D array assembly include simplified assembly process, high structure strength, smaller opening area and integration ability with active circuits. This stacking assembly technique allows an alternative method to create 3-D structures from planar components

Correction: Chang, C.W., et al. Development of a Three Dimensional Neural Sensing Device by a Stacking Method. Sensors 2010, 10, 4238–4252

The authors would like to correct the affiliations and acknowledgement of this paper [1] as follows: [...

Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs

Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach

Since edge device failures (i.e., anomalies) seriously affect the production of industrial products in Industrial IoT (IIoT), accurately and timely detecting anomalies is becoming increasingly important. Furthermore, data collected by the edge device may contain the user's private data, which is challenging the current detection approaches as user privacy is calling for the public concern in recent years. With this focus, this paper proposes a new communication-efficient on-device federated learning (FL)-based deep anomaly detection framework for sensing time-series data in IIoT. Specifically, we first introduce a FL framework to enable decentralized edge devices to collaboratively train an anomaly detection model, which can improve its generalization ability. Second, we propose an Attention Mechanism-based Convolutional Neural Network-Long Short Term Memory (AMCNN-LSTM) model to accurately detect anomalies. The AMCNN-LSTM model uses attention mechanism-based CNN units to capture important fine-grained features, thereby preventing memory loss and gradient dispersion problems. Furthermore, this model retains the advantages of LSTM unit in predicting time series data. Third, to adapt the proposed framework to the timeliness of industrial anomaly detection, we propose a gradient compression mechanism based on Top-\textit{k} selection to improve communication efficiency. Extensive experiment studies on four real-world datasets demonstrate that the proposed framework can accurately and timely detect anomalies and also reduce the communication overhead by 50\% compared to the federated learning framework that does not use a gradient compression scheme.Comment: IEEE Internet of Things Journa

NASA SBIR abstracts of 1990 phase 1 projects

The research objectives of the 280 projects placed under contract in the National Aeronautics and Space Administration (NASA) 1990 Small Business Innovation Research (SBIR) Phase 1 program are described. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses in response to NASA's 1990 SBIR Phase 1 Program Solicitation. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 280, in order of its appearance in the body of the report. The document also includes Appendixes to provide additional information about the SBIR program and permit cross-reference in the 1990 Phase 1 projects by company name, location by state, principal investigator, NASA field center responsible for management of each project, and NASA contract number

Control of a hand prosthesis using mixed electromyography and pressure sensing

During the last years, new technologies approaches have helped to develop realistic robotic hands for prosthetic use. Even so, the strategies to control them (input signals, prediction algorithms) are still limiting a complete match between the robotic hand and the real hand movements and behaviors. On this thesis, two different input signals (FMG and sEMG) were evaluated. From this analysis characteristic properties from each kind of signal were obtained, related with wrist and hand movements. In this way two different learning methods were implemented for the first time on robotic hand research. The goal of these two methods was to combine both kind of input signals, supported by the feature analysis previously done, in order to improve the movements prediction performance. The methods’ performance were compared with the separate input signals methods, so the improvement could be measured. Both mixing methods presented better results than the single input signal ones. These results along with other considerations defined, could lead to a robotic hand performance improvement from different perspective

Graphene for biomedical applications:a review

Since its discovery in 2004, graphene has enticed engineers and researchers from various fields to explore its possibilities to be incepted into various devices and applications. Graphene is deemed a ‘super’ material by researchers due to its extraordinary strength, extremely high surface-to-mass ratio and superconducting properties. Nonetheless, graphene has yet to find plausible footing as an electronics material. In biomedical field, graphene has proved useful in tissue engineering, drug delivery, cancer teraphy, as a component in power unit for biomedical implants and devices and as a vital component in biosensors. Graphene is used as scaffolding for tissue regeneration in stem cell tissue engineering, as active electrodes in supercapacitor for powering wearable and implantable biomedical devices and as detectors in biosensors. In tissue engineering, the extreme strength of monolayer graphene enables it to hold stem cell tissues as scaffold during in-vitro cell regeneration process. In MEMS supercapacitor, graphene’s extremely high surface-to-mass ratio enables it to be used as electrodes in order to increase the power unit’s energy and power densities. A small yet having high energy and power densities cell is needed to power often space constrainted biomedical devices. In FET biosensors, graphene acts as detector electrodes, owing to its superconductivity property. Graphene detector electrodes is capable of detecting target molecules at a concentration level as low as 1 pM, making it the most sensitive biosensor available today. Graphene continues to envisage unique and exciting applications for biomedical field, prompting continuous research which results and implementation could benefit the general public in decades to come