Real-Time Recognition and Localization Based on Improved YOLOv5s for Robot's Picking Clustered Fruits of Chilies.

Chili recognition is one of the critical technologies for robots to pick chilies. The robots need locate the fruit. Furthermore, chilies are always planted intensively and their fruits are always clustered. It is a challenge to recognize and locate the chilies that are blocked by branches and leaves, or other chilies. However, little is known about the recognition algorithms considering this situation. Failure to solve this problem will mean that the robot cannot accurately locate and collect chilies, which may even damage the picking robot's mechanical arm and end effector. Additionally, most of the existing ground target recognition algorithms are relatively complex, and there are many problems, such as numerous parameters and calculations. Many of the existing models have high requirements for hardware and poor portability. It is very difficult to perform these algorithms if the picking robots have limited computing and battery power. In view of these practical issues, we propose a target recognition-location scheme GNPD-YOLOv5s based on improved YOLOv5s in order to automatically identify the occluded and non-occluded chilies. Firstly, the lightweight optimization for Ghost module is introduced into our scheme. Secondly, pruning and distilling the model is designed to further reduce the number of parameters. Finally, the experimental data show that compared with the YOLOv5s model, the floating point operation number of the GNPD-YOLOv5s scheme is reduced by 40.9%, the model size is reduced by 46.6%, and the reasoning speed is accelerated from 29 ms/frame to 14 ms/frame. At the same time, the Mean Accuracy Precision (MAP) is reduced by 1.3%. Our model implements a lightweight network model and target recognition in the dense environment at a small cost. In our locating experiments, the maximum depth locating chili error is 1.84 mm, which meets the needs of a chili picking robot for chili recognition.

A Water Quality Prediction Method Based on the Deep LSTM Network Considering Correlation in Smart Mariculture.

An accurate prediction of cage-cultured water quality is a hot topic in smart mariculture. Since the mariculturing environment is always open to its surroundings, the changes in water quality parameters are normally nonlinear, dynamic, changeable, and complex. However, traditional forecasting methods have lots of problems, such as low accuracy, poor generalization, and high time complexity. In order to solve these shortcomings, a novel water quality prediction method based on the deep LSTM (long short-term memory) learning network is proposed to predict pH and water temperature. Firstly, linear interpolation, smoothing, and moving average filtering techniques are used to repair, correct, and de-noise water quality data, respectively. Secondly, Pearson's correlation coefficient is used to obtain the correlation priors between pH, water temperature, and other water quality parameters. Finally, a water quality prediction model based on LSTM is constructed using the preprocessed data and its correlation information. Experimental results show that, in the short-term prediction, the prediction accuracy of pH and water temperature can reach 98.56% and 98.97%, and the time cost of the predictions is 0.273 s and 0.257 s, respectively. In the long-term prediction, the prediction accuracy of pH and water temperature can reach 95.76% and 96.88%, respectively.

A Self-Adaptive Progressive Support Selection Scheme for Collaborative Wideband Spectrum Sensing.

The sampling rate of wideband spectrum sensing for sparse signals can be reduced by sub-Nyquist sampling with a Modulated Wideband Converter (MWC). In collaborative spectrum sensing, the fusion center recovers the spectral support from observation and measurement matrices reported by a network of CRs, to improve the precision of spectrum sensing. However, the MWC has a very high hardware complexity due to its parallel structure; it sets a fixed threshold for a decision without considering the impact of noise intensity, and needs a priori information of signal sparsity order for signal support recovery. To address these shortcomings, we propose a progressive support selection based self-adaptive distributed MWC sensing scheme (PSS-SaDMWC). In the proposed scheme, the parallel hardware sensing channels are scattered on secondary users (SUs), and the PSS-SaDMWC scheme takes sparsity order estimation, noise intensity, and transmission loss into account in the fusion center. More importantly, the proposed scheme uses a support selection strategy based on a progressive operation to reduce missed detection probability under low SNR levels. Numerical simulations demonstrate that, compared with the traditional support selection schemes, our proposed scheme can achieve a higher support recovery success rate, lower sampling rate, and stronger time-varying support recovery ability without increasing hardware complexity.

Anti-biofilm effect of novel thiazole acid analogs against Pseudomonas aeruginosa through IQS pathways.

IQS has been proven to be a new quorum sensing (QS) system against bacterial biofilm formation, which is activated in the common phosphate-limiting environment of infected tissues taking over the central las system. Up to now, numerous biofilm inhibitors which function by affecting traditional QS system have been reported. However, no compound has been reported to exert anti-biofilm activity through IQS system. Herein, various novel IQS derivatives were synthesized by the reaction of thiazole-4-carboxylic acid with different linear alcohols (R-OH) or amines (R-NH2). IQS derivatives with four carbon chain length of R group were found to present the best biofilm inhibition activity. Compound B-11 as the model molecule was observed to inhibit biofilm formation only under phosphate-limiting condition, and increase in B-11 concentration significantly reduced the expression of rhlA-gfp and pqsA-gfp, but lasB-gfp. Moreover, B-11 reduced production of virulence factors of rhamnolipid and pyocyanin under phosphate limitation. These observations indicated that the synthesized compounds possessed the anti-biofilm activity through IQS pathways rather than traditional QS pathways, which pave a path for future molecular design against bacterial biofilm formation.

Preterm delivery induced by LPS in syngeneically impregnated BALB/c and NOD/SCID mice.

Strategies of lipopolysaccharide (LPS) stimulation with or without previous toll-like receptor 4 (TLR4) blocking were pursued to investigate the mechanism of LPS-induced preterm delivery in syngeneically impregnated BALB/c and non-obese diabetic (NOD)/LtSz-scid/scid (NOD/SCID [severe combined immunodeficiency] for short) mice. The LPS-stimulated mice were killed at the beginning of preterm labor and pooled placentas were collected in each mouse. Cell surface expression of TLR4, CD80, and intracellular TNF-alpha in placenta CD45(+) cell population was determined by flow cytometry. It displayed that preterm delivery could be induced by LPS in BALB/c, while the NOD/SCID seemed to be resistant to LPS induction. TLR4 expression was not changed in either BALB/c or NOD/SCID mice upon LPS-stimulation, but the CD45(+)CD80(+) cell percentage was elevated in both groups. The CD45(+)TNF-alpha(+) cell percentage was increased merely in BALB/c after the stimulation, while no such trend was observed in NOD/SCID mice. In BALB/c, the effect of LPS on CD80 and TNF-alpha expression could be abrogated by previous TLR4 blocking, subsequently prevent LPS-induced preterm delivery. In another design, NK cell blocking was performed at earlier stage of gestation by injections of anti-asialo GM1 antiserum (ASGM1). It appeared that LPS-induced preterm delivery could be partially prevented by this blocking in BALB/c mice. Such data, together with the diversity of sensitivity to LPS induction observed in BALB/c and NOD/SCID mice, imply that LPS interacts with TLR4, triggers the mobilization of CD45(+)CD80(+) cells, results in elevated production of inflammatory cytokines, and finally results in preterm delivery. In addition, NK cells may be involved in the signaling cascade, and the lack of functional NK cells in the NOD/SCID may be why these mice appeared to be less sensitive to LPS-induced premature labor.