Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

Role of Cannabinoid CB1 Receptor in Object Recognition Memory Impairment in Chronically Rapid Eye Movement Sleep-deprived Rats

Objective We aimed to investigate whether antagonism of the cannabinoid CB1 receptor(CB1R)could affect novel object recognition(NOR)memory in chronically rapid eye movement sleep-deprived(RSD)rats.Methods The animals were examined for recognition memory following a 7-day chronic partial RSD paradigm using the multiple platform technique.The CB1R antagonist rimonabant(1 or 3 mg/kg,i.p.)was administered either at one hour prior to the sample phase for acquisition,or immediately after the sample phase for consolidation,or at one hour before the test phase for retrieval of NOR memory.For the reconsolidation task,rimonabant was administered immediately after the second sample phase.Results The RSD episode impaired acquisition,consolidation,and retrieval,but it did not affect the reconsolidation of NOR memory.Rimonabant administration did not affect acquisition,consolidation,and reconsolidation;however,it attenuated impairment of the retrieval of NOR memory induced by chronic RSD.Conclusions These findings,along with our previous report,would seem to suggest that RSD may affect different phases of recognition memory based on its duration.Importantly,it seems that the CB1R may,at least in part,be involved in the adverse effects of chronic RSD on the retrieval,but not in the acquisition,consolidation,and reconsolidation,of NOR memory.

Adaptive key SURF feature extraction and application in unmanned vehicle dynamic object recognition

A new method based on adaptive Hessian matrix threshold of finding key SRUF （ speeded up robust features） features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First, the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then, the standard local invariant feature extraction algorithm SRUF is analyzed, the Hessian Metrix is especially discussed, and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last, different dynamic object recognition experi- ments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for un- manned vehicle systems.

Redundant discrete wavelet transforms based moving object recognition and tracking

A method for moving object recognition and tracking in the intelligent traffic monitoring system is presented. For the shortcomings and deficiencies of the frame-subtraction method, a redundant discrete wavelet transform （RDWT） based moving object recognition algorithm is put forward, which directly detects moving objects in the redundant discrete wavelet transform domain. An improved adaptive mean-shift algorithm is used to track the moving object in the follow up frames. Experimental results show that the algorithm can effectively extract the moving object, even though the object is similar to the background, and the results are better than the traditional frame-subtraction method. The object tracking is accurate without the impact of changes in the size of the object. Therefore the algorithm has a certain practical value and prospect.

Adaptive Threshold Estimation of Open Set Voiceprint Recognition Based on OTSU and Deep Learning

Aiming at the problem of open set voiceprint recognition, this paper proposes an adaptive threshold algorithm based on OTSU and deep learning. The bottleneck technology of open set voiceprint recognition lies in the calculation of similarity values and thresholds of speakers inside and outside the set. This paper combines deep learning and machine learning methods, and uses a Deep Belief Network stacked with three layers of Restricted Boltzmann Machines to extract deep voice features from basic acoustic features. And by training the Gaussian Mixture Model, this paper calculates the similarity value of the feature, and further determines the threshold of the similarity value of the feature through OTSU. After experimental testing, the algorithm in this paper has a false rejection rate of 3.00% for specific speakers, a false acceptance rate of 0.35% for internal speakers, and a false acceptance rate of 0 for external speakers. This improves the accuracy of traditional methods in open set voiceprint recognition. This proves that the method is feasible and good recognition effect.

Exploring Local Regularities for 3D Object Recognition

In order to find better simplicity measurements for 3D object recognition, a new set of local regularities is developed and tested in a stepwise 3D reconstruction method, including localized minimizing standard deviation of angles（L-MSDA）, localized minimizing standard deviation of segment magnitudes（L-MSDSM）, localized minimum standard deviation of areas of child faces （L-MSDAF）, localized minimum sum of segment magnitudes of common edges （L-MSSM）, and localized minimum sum of areas of child face （L-MSAF）. Based on their effectiveness measurements in terms of form and size distortions, it is found that when two local regularities： L-MSDA and L-MSDSM are combined together, they can produce better performance. In addition, the best weightings for them to work together are identified as 10% for L-MSDSM and 90% for L-MSDA. The test results show that the combined usage of L-MSDA and L-MSDSM with identified weightings has a potential to be applied in other optimization based 3D recognition methods to improve their efficacy and robustness.

Threshold Filtering Semi-Supervised Learning Method for SAR Target Recognition

The semi-supervised deep learning technology driven by a small part of labeled data and a large amount of unlabeled data has achieved excellent performance in the field of image processing.However,the existing semisupervised learning techniques are all carried out under the assumption that the labeled data and the unlabeled data are in the same distribution,and its performance is mainly due to the two being in the same distribution state.When there is out-of-class data in unlabeled data,its performance will be affected.In practical applications,it is difficult to ensure that unlabeled data does not contain out-of-category data,especially in the field of Synthetic Aperture Radar(SAR)image recognition.In order to solve the problem that the unlabeled data contains out-of-class data which affects the performance of the model,this paper proposes a semi-supervised learning method of threshold filtering.In the training process,through the two selections of data by the model,unlabeled data outside the category is filtered out to optimize the performance of the model.Experiments were conducted on the Moving and Stationary Target Acquisition and Recognition(MSTAR)dataset,and compared with existing several state-of-the-art semi-supervised classification approaches,the superiority of our method was confirmed,especially when the unlabeled data contained a large amount of out-of-category data.

Melatonin Enhances Object Recognition Memory through Melatonin MT1 and MT2 Receptor-Mediated and Non-Receptor-Mediated Mechanisms in Male Mice

Melatonin (MEL) has been reported to have acute enhancing effects on some aspects of cognition. Recently, we revealed that N1-acetyl-5-methoxyquinuramine (AMK), a brain metabolite of MEL, is much more potent than MEL in converting short-term memory (STM) to long-term memory (LTM) with a single administration immediately after the acquisition trial of the novel object recognition (NOR) task. These data suggest that the memory-enhancing effects of MEL may be mediated by mechanisms independent of the activation of MEL MT1 and MT2 receptors. In the present study, we examined the contribution of MT1 and MT2 receptor-mediated and non-receptor-mediated mechanisms to the acute memory-enhancing effects of MEL using NOR task. Mice were administered with either MEL, AMK, or a highly selective MT1/MT2 receptor agonist ramelteon (RAM) immediately after the acquisition trial and the effects of varying doses of these drugs on both STM and LTM performance were compared. We found that both AMK and RAM were more potent than MEL in both facilitating STM and promoting LTM formation. We also found that pretreatment with luzindole, a MT1/MT2 receptor antagonist, markedly suppressed only the effects of RAM. These results suggest that acutely administered MEL enhances NOR memory through both MT1 and MT2 receptor-mediated and non-receptor-mediated mechanisms.

Underwater Object Recognition Based on Deep Encoding-Decoding Network

Ocean underwater exploration is a part of oceanography that investigates the physical and biological conditions for scientific and commercial purposes. And video technology plays an important role and is extensively applied for underwater environment observation. Different from the conventional methods, video technology explores the underwater ecosystem continuously and non-invasively. However, due to the scattering and attenuation of light transport in the water, complex noise distribution and lowlight condition cause challenges for underwater video applications including object detection and recognition. In this paper, we propose a new deep encoding-decoding convolutional architecture for underwater object recognition. It uses the deep encoding-decoding network for extracting the discriminative features from the noisy low-light underwater images. To create the deconvolutional layers for classification, we apply the deconvolution kernel with a matched feature map, instead of full connection, to solve the problem of dimension disaster and low accuracy. Moreover, we introduce data augmentation and transfer learning technologies to solve the problem of data starvation. For experiments, we investigated the public datasets with our proposed method and the state-of-the-art methods. The results show that our work achieves significant accuracy. This work provides new underwater technologies applied for ocean exploration.

Optimizing Deep Learning Parameters Using Genetic Algorithm for Object Recognition and Robot Grasping

The performance of deep learning(DL)networks has been increased by elaborating the network structures. However, the DL netowrks have many parameters, which have a lot of influence on the performance of the network. We propose a genetic algorithm(GA) based deep belief neural network(DBNN) method for robot object recognition and grasping purpose. This method optimizes the parameters of the DBNN method, such as the number of hidden units, the number of epochs, and the learning rates, which would reduce the error rate and the network training time of object recognition. After recognizing objects, the robot performs the pick-andplace operations. We build a database of six objects for experimental purpose. Experimental results demonstrate that our method outperforms on the optimized robot object recognition and grasping tasks.

Circular object recognition based on shape parameters

To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented. The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique. Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects. The algorithm is described in detail, and comparison experiments with the randomized Hough transformation （RHT） are also provided. The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming. In addition, the method performs well when some circular objects are little deformed and partly misshapen.

A Wavelet Approach for Partial Occluded Object Recognition

A complete 2-D object recognition algorithm applicable for both standalone and partial occluded object is presented. The main contributions in our work are: we developed a scale and partial occlusion invariant boundary partition algorithm and a multiresolution feature extraction algorithm using wavelet. We also implemented a hierarchical matching strategy for feature matching to reduce computational load,but increase matching accuracy. Experiment result shows proposed recognition algorithm is robust to similarity transform and partial occlusion.

Novel object recognition is not affected by age despite age-related brain changes

Age-related memory impairments show a progressive decline across lifespan. Studies have demonstrated equivocal results in biological and behavioral outcomes of aging. Thus, in the present study we examined the novel object recognition task at a delay period that has been shown to be impaired in aged rats of two different strains. Moreover, we used a strain of rats, Fisher 344XBrown Norway, which have published age-related biological changes in the brain. Young (10 month old) and aged (28 month old) rats were tested on a standard novel object recognition task with a 50-minute delay period. The data showed that young and aged rats in the strain we used performed equally well on the novel object recognition task and that both young and old rats demonstrated a righthanded side preference for the novel object. Our data suggested that novel object recognition is not impaired in aged rats although both young and old rats have a demonstrated side preference. Thus, it may be that genetic differences across strains contribute to the equivocal results in behavior, and genetic variance likely influences the course of cognitive aging.

3D Object Recognition by Classification Using Neural Networks

In this Paper, a classification method based on neural networks is presented for recognition of 3D objects. Indeed, the objective of this paper is to classify an object query against objects in a database, which leads to recognition of the former. 3D objects of this database are transformations of other objects by one element of the overall transformation. The set of transformations considered in this work is the general affine group.

DM-L Based Feature Extraction and Classifier Ensemble for Object Recognition

Deep Learning is a powerful technique that is widely applied to Image Recognition and Natural Language Processing tasks amongst many other tasks. In this work, we propose an efficient technique to utilize pre-trained Convolutional Neural Network (CNN) architectures to extract powerful features from images for object recognition purposes. We have built on the existing concept of extending the learning from pre-trained CNNs to new databases through activations by proposing to consider multiple deep layers. We have exploited the progressive learning that happens at the various intermediate layers of the CNNs to construct Deep Multi-Layer (DM-L) based Feature Extraction vectors to achieve excellent object recognition performance. Two popular pre-trained CNN architecture models i.e. the VGG_16 and VGG_19 have been used in this work to extract the feature sets from 3 deep fully connected multiple layers namely “fc6”, “fc7” and “fc8” from inside the models for object recognition purposes. Using the Principal Component Analysis (PCA) technique, the Dimensionality of the DM-L feature vectors has been reduced to form powerful feature vectors that have been fed to an external Classifier Ensemble for classification instead of the Softmax based classification layers of the two original pre-trained CNN models. The proposed DM-L technique has been applied to the Benchmark Caltech-101 object recognition database. Conventional wisdom may suggest that feature extractions based on the deepest layer i.e. “fc8” compared to “fc6” will result in the best recognition performance but our results have proved it otherwise for the two considered models. Our experiments have revealed that for the two models under consideration, the “fc6” based feature vectors have achieved the best recognition performance. State-of-the-Art recognition performances of 91.17% and 91.35% have been achieved by utilizing the “fc6” based feature vectors for the VGG_16 and VGG_19 models respectively. The recognition performance has been achieved by considering 30 sample images per class whereas the proposed system is capable of achieving improved performance by considering all sample images per class. Our research shows that for feature extraction based on CNNs, multiple layers should be considered and then the best layer can be selected that maximizes the recognition performance.

3-81 A Plugin for 3D-confocal Object Recognition

The research of ionizing radiation induced foci is an important method of DNA damage repair. Although the visualization technology of foci has been mature, the traditional foci recognition analysis technology has a lot of defects due to the spatial overlap of foci.

Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

A new progressive open-set recognition method with adaptive probability threshold

In the traditional pattern classification method,it usually assumes that the object to be classified must lie in one of given(known)classes of the training data set.However,the training data set may not contain the class of some objects in practice,and this is considered as an Open-Set Recognition(OSR)problem.In this paper,we propose a new progressive open-set recognition method with adaptive probability threshold.Both the labeled training data and the test data(objects to be classified)are put into a common data set,and the k-Nearest Neighbors(k-NNs)of each object are sought in this common set.Then,we can determine the probability of object lying in the given classes.If the majority of k-NNs of the object are from labeled training data,this object quite likely belongs to one of the given classes,and the density of the object and its neighbors is taken into account here.However,when most of k-NNs are from the unlabeled test data set,the class of object is considered very uncertain because the class of test data is unknown,and this object cannot be classified in this step.Once the objects belonging to known classes with high probability are all found,we re-calculate the probability of the other uncertain objects belonging to known classes based on the labeled training data and the objects marked with the estimated probability.Such iteration will stop when the probabilities of all the objects belonging to known classes are not changed.Then,a modified Otsu’s method is employed to adaptively seek the probability threshold for the final classification.If the probability of object belonging to known classes is smaller than this threshold,it will be assigned to the ignorant(unknown)class that is not included in training data set.The other objects will be committed to a specific class.The effectiveness of the proposed method has been validated using some experiments.

YOLOv8 for Fire and Smoke Recognition Algorithm Integrated with the Convolutional Block Attention Module

The complexity of fire and smoke in terms of shape, texture, and color presents significant challenges for accurate fire and smoke detection. To address this, a YOLOv8-based detection algorithm integrated with the Convolutional Block Attention Module (CBAM) has been developed. This algorithm initially employs the latest YOLOv8 for object recognition. Subsequently, the integration of CBAM enhances its feature extraction capabilities. Finally, the WIoU function is used to optimize the network’s bounding box loss, facilitating rapid convergence. Experimental validation using a smoke and fire dataset demonstrated that the proposed algorithm achieved a 2.3% increase in smoke and fire detection accuracy, surpassing other state-of-the-art methods.