Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices

Smart homes assist users by providing convenient services from activity classification with the help of machine learning (ML) technology. However, most of the conventional high-performance ML algorithms require relatively high power consumption and memory usage due to their complex structure. Moreover, previous studies on lightweight ML/DL models for human activity classification still require relatively high resources for extremely resource-limited embedded systems; thus, they are inapplicable for smart homes’ embedded system environments. Therefore, in this study, we propose a low-power, memory-efficient, high-speed ML algorithm for smart home activity data classification suitable for an extremely resource-constrained environment. We propose a method for comprehending smart home activity data as image data, hence using the MNIST dataset as a substitute for real-world activity data. The proposed ML algorithm consists of three parts: data preprocessing, training, and classification. In data preprocessing, training data of the same label are grouped into further detailed clusters. The training process generates hyperplanes by accumulating and thresholding from each cluster of preprocessed data. Finally, the classification process classifies input data by calculating the similarity between the input data and each hyperplane using the bitwise-operation-based error function. We verified our algorithm on ‘Raspberry Pi 3’ and ‘STM32 Discovery board’ embedded systems by loading trained hyperplanes and performing classification on 1000 training data. Compared to a linear support vector machine implemented from Tensorflow Lite, the proposed algorithm improved memory usage to 15.41%, power consumption to 41.7%, performance up to 50.4%, and power per accuracy to 39.2%. Moreover, compared to a convolutional neural network model, the proposed model improved memory usage to 15.41%, power consumption to 61.17%, performance to 57.6%, and power per accuracy to 55.4%

Wafer-Scale Two-Dimensional Molybdenum Diselenide Phototransistor Array via Liquid-Precursor-Assisted Chemical Vapor Deposition

Monolayer transition metal dichalcogenides (TMDs) have received considerable interest as a candidate material for ultrathin photodetectors, but their practical applications are hindered by difficulties in synthesizing high-quality films over centimeter-scale areas. Although chemical vapor deposition (CVD) based on liquid-phase precursors has shown promise for large-area synthesis of TMDs, the resulting films typically exhibit much lower optoelectronic performance due to the lack of wafer-scale uniformity with monolayer thickness and inferior electrical properties induced by defects. In this article, the authors present a wafer-scale, gate-tunable photodetector array from high-quality monolayer molybdenum diselenide (MoSe2) films synthesized with the liquid-phase CVD process assisted by a growth promoter. Continuous monolayer MoSe2 can form by introducing potassium iodide as a growth promoter for selenizing the metal-precursor film. Side-by-side comparison between MoSe2 formed with and without the potassium iodide reveal that the promoter-assisted growth strategy significantly improves the crystallinity, which results in enhanced optoelectronic properties. The resulting photodetector array is highly photosensitive over the visible wavelengths with photoresponsivities higher than those of the previously reported devices based on CVD-synthesized monolayer TMDs and even comparable to the devices fabricated from mechanical exfoliation approach

Hierarchical Nanoscale Structuring of Solution-Processed 2D van der Waals Networks for Wafer-Scale, Stretchable Electronics

Two-dimensional (2D) semiconductors are promising for next-generation electronics that are lightweight, flexible, and stretchable. Achieving stretchability with suppressed crack formation, however, is still difficult without introducing lithographically etched micropatterns, which significantly reduces active device areas. Herein, we report a solution-based hierarchical structuring to create stretchable semiconducting films that are continuous over wafer-scale areas via self-assembly of two-dimensional nanosheets. Electrochemically exfoliated MoS2 nanosheets with large lateral sizes (∼1 μm) are first assembled into a uniform film on a prestrained thermoplastic substrate, followed by strain relief of the substrate to create nanoscale wrinkles. Subsequent strain-relief cycles with the presence of soluble polymer films produce hierarchical wrinkles with multigenerational structures. Stretchable MoS2 films are then realized by curing an elastomer directly on the wrinkled surface and dissolving the thermoplastic. Three-generation hierarchical MoS2 wrinkles are resistant to cracking up to nearly 100% substrate stretching and achieve drastically enhanced photoresponsivity compared to the flat counterpart over the visible and NIR regimes, while the flat MoS2 film is beneficial in creating strain sensors because of its strain-dependent electrical response

Improved Efficiency of Inverted Organic Light-Emitting Diodes Using Tin Dioxide Nanoparticles as an Electron Injection Layer

We demonstrated highly efficient inverted bottom-emission organic light-emitting diodes (IBOLEDs) using tin dioxide (SnO₂) nanoparticles (NPs) as an electron injection layer at the interface between the indium tin oxide (ITO) cathode and the organic electron transport layer. The SnO₂ NP layer can facilitate the electron injection since the conduction band energy level of SnO₂ NPs (−3.6 eV) is located between the work function of ITO (4.8 eV) and the lowest unoccupied molecular orbital (LUMO) energy level of typical electron transporting molecules (−2.5 to −3.5 eV). As a result, the IBOLEDs with the SnO₂ NPs exhibited a decrease of the driving voltage by 7 V at 1000 cd/m² compared to the device without SnO₂ NPs. They also showed a significantly enhanced luminous current efficiency of 51.1 cd/A (corresponds to the external quantum efficiency of 15.6%) at the same brightness, which is about two times higher values than that of the device without SnO₂ NPs. We also measured the angular dependence of irradiance and electroluminescence (EL) spectra in the devices with SnO₂ NPs and found that they had a nearly Lambertian emission profile and few shift in EL spectrum through the entire viewing angles, which are considered as remarkable and essential results for the application of OLEDs to display devices

Modular Fabrication of Hybrid Bulk Heterojunction Solar Cells Based on Breakwater-like CdSe Tetrapod Nanocrystal Network Infused with P3HT

We demonstrate the modular fabrication of nanocrystal/polymer hybrid bulk heterojunction solar cells based on breakwater-like CdSe tetrapod (TP) nanocrystal networks infused with poly­(3-hexylthiophene) (P3HT). This fabrication method consists of sequential steps for forming the hybrid active layers: the assembly of a breakwater-like CdSe TP network followed by nanocrystal surface modification and the infusion of semiconducting polymers. Such a modular approach enables the independent control of the nanoscopic morphology and surface chemistry of the nanocrystals, which are generally known to exhibit complex correlations, in a reproducible manner. Using these devices, the influence of the passivation ligands on solar cell characteristics could be clarified from temperature-dependent solar cell experiments. We found that a 2-fold increase in the short-circuit current with 1-hexylamine ligands, compared with the value based on pyridine ligands, originates from the reduced depth of trap states, minimizing the trap-assisted bimolecular recombination process. Overall, the work presented herein provides a versatile approach to fabricating nanocrystal/polymer hybrid solar cells and systematically analyzing the complex nature of these devices

Efficacy Evaluation of SDF-1α-Based Polypeptides in an Acute Myocardial Infarction Model Using Structure-Based Drug Design

Stromal cell-derived factor-1 alpha (SDF-1α, CXCL12) mediates the migration of circulating cells to desired sites for tissue development, homeostasis, and regeneration and can be used to promote cardiac regeneration by recruiting stem cells. However, the use of SDF-1α in the injured heart necessitates not only higher binding affinity to its receptor, CXCR4+, but also better robustness against enzymatic degradation than other SDF-1 isoforms. Here, we conduct a screening of SDF-1α analog peptides that were designed by structure-based drug design (SBDD), a type of computer-aided drug design (CADD). We have developed in vitro and in vivo methods that enable us to estimate the effect of peptides on the migration of human mesenchymal stem cells (hMSCs) and cardiac regeneration in acute myocardial infarction (AMI)-induced animals, respectively. We demonstrate that one type of SDF-1α analog peptide, SDP-4, among the four analog peptides preselected by SBDD, is more potent than native SDF-1α for cardiac regeneration in myocardial infarction. It is interesting to note that the migratory effects of SDP-4 determined by a wound healing assay, a Transwell assay, and a 2D migration assay are comparable to those of SDF-1α. These results suggest that in vivo, as well as in vitro, screening of peptides developed by SBDD is a quintessential process to the development of a novel therapeutic compound for cardiac regeneration. Our finding also has an implication that the SDP-4 peptide is an excellent candidate for use in the regeneration of an AMI heart