Electron-Spin Regulation Driving Heterointerface Electron Distribution and Phase Transition toward Ultrafast and Durable Sodium Storage.

Phase engineering is an effective strategy for modulating the electronic structure and electron transfer mobility of cobalt selenide (CoSe2) with remarkable sodium storage. Nevertheless, it remains challenging to improve fast-charging and cycling performance. Herein, a heterointerface coupling induces phase transformation from cubic CoSe2 to orthorhombic CoSe2 accompanied by the formation of MoSe2 to construct a CoSe2/MoSe2 heterostructure decorated with N-doped carbon layer on a 3D graphene foam (CoSe2/MoSe2@NC/GF). The incorporated Mo cations in the bridged o-CoSe2/MoSe2 not only act an electron donor to regulate charge-spin configurations with more active electronic states but also trigger the upshift of d/p band centers and a decreased ∆d-p band center gap, which greatly enhances ion adsorption capability and lowers the ion diffusion barrier. As expected, the CoSe2/MoSe2@NC/GF anode demonstrates a high-rate capability of 447 mAh g-1 at 2 A g-1 and an excellent cyclability of 298 mAh g-1 at 1 A g-1 over 1000 cycles. The work deepens the understanding of the elaborate construction of heterostructured electrodes for high-performance SIBs.

An Edge Computing System with AMD Xilinx FPGA AI Customer Platform for Advanced Driver Assistance System.

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle's onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle's onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems.

Encapsulation of Sn Sub-Nanoclusters in Multichannel Carbon Matrix for High-Performance Potassium-Ion Batteries.

Sub-nanoclusters with ultra-small particle sizes are particularly significant to create advanced energy storage materials. Herein, Sn sub-nanoclusters encapsulated in nitrogen-doped multichannel carbon matrix (denoted as Sn-SCs@MCNF) are designed by a facile and controllable route as flexible anode for high-performance potassium ion batteries (PIBs). The uniformly dispersed Sn sub-nanoclusters in multichannel carbon matrix can be precisely identified, which ensure us to clarify the size influence on the electrochemical performance. The sub-nanoscale effect of Sn-SCs@MCNF restrains electrode pulverization and enhances the K+ diffusion kinetics, leading to the superior cycling stability and rate performance. As freestanding anode in PIBs, Sn-SCs@MCNF manifests superior K+ storage properties, such as exceptional cycling stability ( around 331 mAh g-1 after 150 cycles at 100 mA g-1) and rate capability. Especially, the Sn-SCs@MCNF||KFe[Fe(CN)6] full cell demonstrates impressive reversible capacity of around 167 mAh g-1 at 0.4 A g-1 even after 200 cycles. Theoretical calculations clarify that the ultrafine Sn sub-nanoclusters are beneficial for electron transfer and contribute to the lower energy barriers of the intermediates, thereby resulting in promising electrochemical performance. Comprehensive investigation for the intrinsic K+ storage process of Sn-SCs@MCNF is revealed by in situ analysis. This work provides vital guidance to design sub-nanoscale functional materials for high-performance energy-storage devices.

Dynamic Electronic and Ionic Transport Actuated by Cobalt-Doped MoSe2 /rGO for Superior Potassium-Ion Batteries.

Molybdenum selenium (MoSe2 ) has tremendous potential in potassium-ion batteries (PIBs) due to its large interlayer distance, favorable bandgap, and high theoretical specific capacity. However, the poor conductivity and large K+ insertion/extraction in MoSe2 inevitably leads to sluggish reaction kinetics and poor structural stability. Herein, Coinduced engineering is employed to illuminate high-conductivity electron pathway and mobile ion diffusion of MoSe2 nanosheets anchored on reduced graphene oxide substrate (Co-MoSe2 /rGO). Benefiting from the activated electronic conductivity and ion diffusion kinetics, and an expanded interlayer spacing resulting from Co doping, combined with the interface coupling with highly conductive reduced graphene oxide (rGO) substrate through Mo-C bonding, the Co-MoSe2 /rGO anode demonstrates remarkable reversible capacity, superior rate capability, and stable long-term cyclability for potassium storage, as well as superior energy density and high power density for potassium-ion capacitors. Systematic performance measurement, dynamic analysis, in-situ/ex-situ measurements, and density functional theory (DFT) calculations elucidate the performance-enhancing mechanism of Co-MoSe2 /rGO in view of the electronic and ionic transport kinetics. This work offers deep atomic insights into the fundamental factors of electrodes for potassium-ion batteries/capacitors with superior electrochemical performance.

VLSI Design Based on Block Truncation Coding for Real-Time Color Image Compression for IoT.

It has always been a major issue for a hospital to acquire real-time information about a patient in emergency situations. Because of this, this research presents a novel high-compression-ratio and real-time-process image compression very-large-scale integration (VLSI) design for image sensors in the Internet of Things (IoT). The design consists of a YEF transform, color sampling, block truncation coding (BTC), threshold optimization, sub-sampling, prediction, quantization, and Golomb-Rice coding. By using machine learning, different BTC parameters are trained to achieve the optimal solution given the parameters. Two optimal reconstruction values and bitmaps for each 4 × 4 block are achieved. An image is divided into 4 × 4 blocks by BTC for numerical conversion and removing inter-pixel redundancy. The sub-sampling, prediction, and quantization steps are performed to reduce redundant information. Finally, the value with a high probability will be coded using Golomb-Rice coding. The proposed algorithm has a higher compression ratio than traditional BTC-based image compression algorithms. Moreover, this research also proposes a real-time image compression chip design based on low-complexity and pipelined architecture by using TSMC 0.18 µm CMOS technology. The operating frequency of the chip can achieve 100 MHz. The core area and the number of logic gates are 598,880 µm2 and 56.3 K, respectively. In addition, this design achieves 50 frames per second, which is suitable for real-time CMOS image sensor compression.

Fast Control for Backlight Power-Saving Algorithm Using Motion Vectors from the Decoded Video Stream.

Backlight power-saving algorithms can reduce the power consumption of the display by adjusting the frame pixels with optimal clipping points under some tradeoff criteria. However, the computation for the selected clipping points can be complex. In this paper, a novel algorithm is created to reduce the computation time of the state-of-the-art backlight power-saving algorithms. If the current frame is similar to the previous frame, it is unnecessary to execute the backlight power-saving algorithm for the optimal clipping points, and the derived clipping point from the previous frame can be used for the current frame automatically. In this paper, the motion vector information was used as the measurement of the similarity between adjacent frames, where the generation of the motion vector information requires no extra complexity since it is generated to reconstruct the decoded frame pixels before the display. The experiments showed that the proposed work can reduce the running time of the state-of-the-art methods by 25.21% to 64.22%, while the performances are maintained; the differences with the state-of-the-art methods in PSNR are only 0.02~1.91 dB, and those in power are only -0.001~0.008 W.

Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph.

Apical lesions, the general term for chronic infectious diseases, are very common dental diseases in modern life, and are caused by various factors. The current prevailing endodontic treatment makes use of X-ray photography taken from patients where the lesion area is marked manually, which is therefore time consuming. Additionally, for some images the significant details might not be recognizable due to the different shooting angles or doses. To make the diagnosis process shorter and efficient, repetitive tasks should be performed automatically to allow the dentists to focus more on the technical and medical diagnosis, such as treatment, tooth cleaning, or medical communication. To realize the automatic diagnosis, this article proposes and establishes a lesion area analysis model based on convolutional neural networks (CNN). For establishing a standardized database for clinical application, the Institutional Review Board (IRB) with application number 202002030B0 has been approved with the database established by dentists who provided the practical clinical data. In this study, the image data is preprocessed by a Gaussian high-pass filter. Then, an iterative thresholding is applied to slice the X-ray image into several individual tooth sample images. The collection of individual tooth images that comprises the image database are used as input into the CNN migration learning model for training. Seventy percent (70%) of the image database is used for training and validating the model while the remaining 30% is used for testing and estimating the accuracy of the model. The practical diagnosis accuracy of the proposed CNN model is 92.5%. The proposed model successfully facilitated the automatic diagnosis of the apical lesion.

Caries and Restoration Detection Using Bitewing Film Based on Transfer Learning with CNNs.

Caries is a dental disease caused by bacterial infection. If the cause of the caries is detected early, the treatment will be relatively easy, which in turn prevents caries from spreading. The current common procedure of dentists is to first perform radiographic examination on the patient and mark the lesions manually. However, the work of judging lesions and markings requires professional experience and is very time-consuming and repetitive. Taking advantage of the rapid development of artificial intelligence imaging research and technical methods will help dentists make accurate markings and improve medical treatments. It can also shorten the judgment time of professionals. In addition to the use of Gaussian high-pass filter and Otsu's threshold image enhancement technology, this research solves the problem that the original cutting technology cannot extract certain single teeth, and it proposes a caries and lesions area analysis model based on convolutional neural networks (CNN), which can identify caries and restorations from the bitewing images. Moreover, it provides dentists with more accurate objective judgment data to achieve the purpose of automatic diagnosis and treatment planning as a technology for assisting precision medicine. A standardized database established following a defined set of steps is also proposed in this study. There are three main steps to generate the image of a single tooth from a bitewing image, which can increase the accuracy of the analysis model. The steps include (1) preprocessing of the dental image to obtain a high-quality binarization, (2) a dental image cropping procedure to obtain individually separated tooth samples, and (3) a dental image masking step which masks the fine broken teeth from the sample and enhances the quality of the training. Among the current four common neural networks, namely, AlexNet, GoogleNet, Vgg19, and ResNet50, experimental results show that the proposed AlexNet model in this study for restoration and caries judgments has an accuracy as high as 95.56% and 90.30%, respectively. These are promising results that lead to the possibility of developing an automatic judgment method of bitewing film.

Sulfidation-reoxidation enhances heavy metal immobilization by vivianite.

Iron minerals in nature are pivotal hosts for heavy metals, significantly influencing their geochemical cycling and eventual fate. It is generally accepted that, vivianite, a prevalent iron phosphate mineral in aquatic and terrestrial environments, exhibits a limited capacity for adsorbing cationic heavy metals. However, our study unveils a remarkable phenomenon that the synergistic interaction between sulfide (S2-) and vivianite triggers an unexpected sulfidation-reoxidation process, enhancing the immobilization of heavy metals such as cadmium (Cd), copper (Cu), and zinc (Zn). For instance, the combination of vivianite and S2- boosted the removal of Cd2+ from the aqueous phase under anaerobic conditions, and ensured the retention of Cd stabilized in the solid phase when shifted to aerobic conditions. It is intriguing to note that no discrete FeS formation was detected in the sulfidation phase, and the primary crystal structure of vivianite largely retained its integrity throughout the whole process. Detailed molecular-level investigations indicate that sulfidation predominantly targets the Fe(II) sites at the corners of the PO4 tetrahedron in vivianite. With the transition to aerobic conditions, the exothermic oxidation of CdS and the S sites in vivianite initiates, rendering it thermodynamically favorable for Cd to form multidentate coordination structures, predominantly through the Cd-O-P and Cd-O-Fe bonds. This mechanism elucidates how Cd is incorporated into the vivianite structure, highlighting a novel pathway for heavy metal immobilization via the sulfidation-reoxidation dynamics in iron phosphate minerals.

Heterostructured Bimetallic MOF-on-MOF Architectures for Efficient Oxygen Evolution Reaction.

Electron modulation presents a captivating approach to fabricate efficient electrocatalysts for the oxygen evolution reaction (OER), yet it remains a challenging undertaking. In this study, an effective strategy is proposed to regulate the electronic structure of metal-organic frameworks (MOFs) by the construction of MOF-on-MOF heterogeneous architectures. As a representative heterogeneous architectures, MOF-74 on MOF-274 hybrids are in situ prepared on 3D metal substrates (NiFe alloy foam (NFF)) via a two-step self-assembly method, resulting in MOF-(74 + 274)@NFF. Through a combination of spectroscopic and theory calculation, the successful modulation of the electronic property of MOF-(74 + 274)@NFF is unveiled. This modulation arises from the phase conjugation of the two MOFs and the synergistic effect of the multimetallic centers (Ni and Fe). Consequently, MOF-(74 + 274)@NFF exhibits excellent OER activity, displaying ultralow overpotentials of 198 and 223 mV at a current density of 10 mA cm-2 in the 1.0 and 0.1 M KOH solutions, respectively. This work paves the way for manipulating the electronic structure of electrocatalysts to enhance their catalytic activity.