FPGA-based implementation of classification techniques: A survey

Recently, a number of classification techniques have been introduced. However, processing large dataset in a reasonable time has become a major challenge. This made classification task more complex and expensive in calculation. Thus, the need for solutions to overcome these constraints such as field programmable gate arrays (FPGAs). In this paper, we give an overview of the various classification techniques. Then, we present the existing FPGA based implementation of these classification methods. After that, we investigate the confronted challenges and the optimizations strategies. Finally, we highlight the hardware accelerator architectures and tools for hardware design suggested to improve the FPGA implementation of classification methods.     

Residual attention-based tracking-by-detection network with attention-driven data augmentation

Tracking-by-detection (TBD) is a significant framework for visual object tracking. However, current trackers are usually updated online based on random sampling with a probability distribution. The performance of the learning-based TBD trackers is limited by the lack of discriminative features, especially when the background is full of semantic distractors. We propose an attention-driven data augmentation method, in which a residual attention mechanism is integrated into the TBD tracking network as supplementary references to identify discriminative image features. A mask generating network is used to simulate changes in target appearances to obtain positive samples, where attention information and image features are combined to identify discriminative features. In addition, we propose a method for mining hard negative samples, which searches for semantic distractors with the response of the attention module. The experiments on the OTB2015, UAV123, and LaSOT benchmarks show that this method achieves competitive performance in terms of accuracy and robustness.     

Synthesis and slurry spray coating of barium strontium alumino silicate on SiC substrate

Barium strontium alumino silicate (BSAS); (Ba_0.6Sr_0.4Al₂Si₂O₈) was synthesized through solid state reaction between BaCO₃, SrCO₃, Al₂O₃ and SiO₂ subjected to wet milling in isopropanol for about 24 h. The sequence of the solid state reaction was studied by subjecting to DG/DTG from room temperature to 1550 °C. The crystallographic phase evolution was confirmed by X-ray diffraction of the powders calcined in the range 1000 to 1300 °C for 2 h. The monoclinic celsian phase obtained at 1300 °C, pelletized through uniaxial pressing was sinterable to 67 to 78% density in the temperature range of 1300 to 1500 °C. The density improved to 75 to 94% after ball milling for 76 h, while ZrO₂ addition further improved the density by 2%. The celcian phase of BSAS was dispersed in isopropyl alcohol, milled for about 24 h and spray coated on to plain SiC and mullite precoated SiC substrates. Sintering of coated samples and characterization for weight gain/loss, microstructure, scratch test prove that mullite + BSAS coating is more effective than single layer coating of BSAS on SiC substrates.     

Vulnerable objects detection for autonomous driving: A review

Object detection performed by Autonomous Vehicles (AV)s is a crucial operation that comes ahead of various autonomous driving tasks, such as object tracking, trajectories estimation, and collision avoidance. Dynamic road elements (pedestrians, cyclists, vehicles) impose a greater challenge due to their continuously changing location and behaviour. This paper presents a comprehensive review of the state-of-the-art object detection technologies focusing on both the sensory systems and algorithms used. It begins with a brief introduction on the autonomous driving operations and challenges. Then, different sensory systems employed on existing AVs are elaborated while illustrating their advantages, limitations and applications. Also, sensory systems employed by different research are reviewed. Moreover, due to the significant role Deep Neural Networks (DNN)s are playing in object detection tasks, different DNN-based networks are also highlighted. Afterwards, previous research on dynamic objects detection performed by AVs are reviewed in tabular forms. Finally, a conclusion summarizes the outcomes of the review and suggests future work towards the development of vehicles with higher automation levels.     

Transmission line detection in aerial images: An instance segmentation approach based on multitask neural networks

Camera-based transmission line detection (TLD) is a fundamental and crucial task for automatically patrolling powerlines by aircraft. Motivated by instance segmentation, a TLD algorithm is proposed in this paper with a novel deep neural network, i.e., CableNet. The network structure is designed based on fully convolutional networks (FCNs) with two major improvements, considering the specific appearance characteristics of transmission lines. First, overlaying dilated convolutional layers and spatial convolutional layers are configured to better represent continuous long and thin cable shapes. Second, two branches of outputs are arranged to generate multidimensional feature maps for instance segmentation. Thus, cable pixels can be detected and assigned cable IDs simultaneously. Multiple experiments are conducted on aerial images, and the results show that the proposed algorithm obtains reliable detection performance and is superior to traditional TLD methods. Meanwhile, segmented pixels can be accurately identified as cable instances, contributing to line fitting for further applications.     

Metal chlorides-promoted ammonia absorption of deep eutectic solvent

Ammonia is considered as a promising hydrogen or energy carrier. Ammonia absorption or adsorption is an important aspect for both ammonia removal, storage and separation applications. To these ends, a wide range of solid and liquid sorbents have been investigated. Among these, the deep eutectic solvent (DES) is emerging as a promising class of ammonia absorbers. Herein, we report a novel type of DES, i.e., metal-containing DESs for ammonia absorption. Specifically, the NH₃ absorption capacity is enhanced by ca. 18.1–36.9% when a small amount of metal chlorides, such as MgCl₂, MnCl₂ etc., are added into a DES composed of resorcinol (Res) and ethylene glycol (EG). To our knowledge, the MgCl₂/Res/EG (0.1:1:2) DES outperforms most of the reported DESs. The excellent NH₃ absorption performances of metal–containing DESs have been attributed to the synergy of Lewis acid–base and hydrogen bonding interactions. Additionally, good reversibility and high NH₃/CO₂ selectivity are achieved over the MgCl₂/Res/EG (0.1:1:2) DES, which enables it to be a potential NH₃ absorber for further investigations.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

High performance H2 sensor based on rGO-wrapped SnO2–Pd porous hollow spheres

Hydrogen (H₂) sensors based on metal oxide semiconductors (MOS) are promising for many applications such as a rocket propellant, industrial gas and the safety of storage. However, poor selectivity at low analyte concentrations, and independent response on high humidity limit the practical applications. Herein, we designed rGO-wrapped SnO₂–Pd porous hollow spheres composite (SnO₂–Pd@rGO) for high performance H₂ sensor. The porous hollow structure was from the carbon sphere template. The rGO wrapping was via self-assembly of GO on SnO₂-based spheres with subsequent thermal reduction in H₂ ambient. This sensor exhibited excellently selective H₂ sensing performances at 390 °C, linear response over a broad concentration range (0.1–1000 ppm) with recovery time of only 3 s, a high response of ～8 to 0.1 ppm H₂ in a minute, and acceptable stability under high humidity conditions (e. g. 80%). The calculated detection limit of 16.5 ppb opened up the possibility of trace H₂ monitoring. Furthermore, this sensor demonstrated certain response to H₂ at the minimum concentration of 50 ppm at 130 °C. These performances mainly benefited from the special hollow porous structure with abundant heterojunctions, the catalysis of the doped-PdO_x, the relative hydrophobic surface from rGO, and the deoxygenation after H₂ reduction.     

Retinal microaneurysms detection using adversarial pre-training with unlabeled multimodal images

The detection of retinal microaneurysms is crucial for the early detection of important diseases such as diabetic retinopathy. However, the detection of these lesions in retinography, the most widely available retinal imaging modality, remains a very challenging task. This is mainly due to the tiny size and low contrast of the microaneurysms in the images. Consequently, the automated detection of microaneurysms usually relies on extensive ad-hoc processing. In this regard, although microaneurysms can be more easily detected using fluorescein angiography, this alternative imaging modality is invasive and not adequate for regular preventive screening.In this work, we propose a novel deep learning methodology that takes advantage of unlabeled multimodal image pairs for improving the detection of microaneurysms in retinography. In particular, we propose a novel adversarial multimodal pre-training consisting in the prediction of fluorescein angiography from retinography using generative adversarial networks. This pre-training allows learning about the retina and the microaneurysms without any manually annotated data. Additionally, we also propose to approach the microaneurysms detection as a heatmap regression, which allows an efficient detection and precise localization of multiple microaneurysms. To validate and analyze the proposed methodology, we perform an exhaustive experimentation on different public datasets. Additionally, we provide relevant comparisons against different state-of-the-art approaches. The results show a satisfactory performance of the proposal, achieving an Average Precision of 64.90%, 31.36%, and 33.55% in the E-Ophtha, ROC, and DDR public datasets. Overall, the proposed approach outperforms existing deep learning alternatives while providing a more straightforward detection method that can be effectively applied to raw unprocessed retinal images.     

高深溜井井筒堵塞的爆破处理实践

溜井是采用平硐-溜井方式开拓矿山的运输咽喉,溜井正常与否对矿山生产影响极大。本文通过黑沟铁矿高深溜井井筒堵塞处理实例,对其堵塞爆破处理方法及经验做了系统分析。重点介绍的爆破冲击波破拱疏通高深溜井井筒高位堵塞的爆破方法,富有新意,可供国内外同类型矿山参考借鉴。