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Virtually lossless self-compression of 10-mJ 3.9-um sub-100 fs pulses in bulk YAG resulting in 9-mJ 33-fs pulses is reported. Generated peak power exceeds 250 GW which is suitable for filamentation in ambient air

Automated visual inspection of fabric image using deep learning approach for defect detection

As a popular topic in automation, fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. The main challenge for automatically detecting fabric damage, in most cases, is the complex structure of the textile. This article presents a two-stage approach, combining novel and traditional algorithms to enhance image enhancement and defect detection. The first stage is a new combined local and global transform domain-based image enhancement algorithm using block-based alpha-rooting. In the second stage, we construct a neural network based on the modern architecture to detect fabric damage accurately. This solution allows localizing defects with higher accuracy than traditional methods of machine learning and modern methods of deep learning. All experiments were carried out using a public database with examples of damage to the TILDA fabric dataset

250-GW Sub-Three-Cycle Multi-Millijoule Mid-IR Pulses Self-Compressed in a YAG plate

Virtually lossless self-compression of 10-mJ 3.9-um sub-100 fs pulses in bulk YAG resulting in 9-mJ 33-fs pulses is reported. Generated peak power exceeds 250 GW which is suitable for filamentation in ambient air

Thermoelectric properties of low-cost transparent single wall carbon nanotube thin films obtained by vacuum filtration

Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.The dispersions of semiconducting (sc-) and metallic (m-) SWCNTs with purity more than 98 and 86%, correspondingly, were obtained by using the aqueous two-phase extraction method. The unseparated (un-) SWCNTs contained ~3/4 of semiconducting and ~1/4 of metallic nanotubes. Thin films based on unseparated, semiconducting and metallic SWCNTs were prepared by vacuum filtration method. An Atomic Force Microscopy (AFM) and a Transmission Electronic Microscopy (TEM) were used to investigate the thin film microstructure. The thin SWCNT film transmittance was measured in the wavelength range of 300–1500 nm. Thermoelectric properties were carried out in the temperature range up to 200 °C. The largest Seebeck coefficient was observed for thin films based on semiconducting SWCNTs. The maximum value was 98 μV/K under the temperature of 170 °C. The lowest resistivity was 7.5·10−4·Ohm·cm at room temperature for thin un-SWCNT films. The power factor for m-SWCNT and un-SWCNT films was 47 and 213 μWm−1 K−2, correspondingly, at room temperature and 74 and 54 μWm−1 K−2 at 200 °C, respectively. For a thin sc-SWCNT film the maximum power factor was 2.8 μWm−1 K−2 at 160 °C. The un-SWCNT film thermal conductivity coefficient was 5.63 and 3.64Wm−1 K−1 and a thermoelectric figure of merit was 0.011 and 0.016 at temperatures of 23 and 50 °C, respectively