Notes

The effect of religiously designed along with fairly well balanced schooling about purpose regarding living appendage monetary gift between Islamic Americans.
Diffusion-based molecular communication system (DBMC) is a system in which information-carrying molecules are sent from the transmitter and passively transported to the receiver in a fluid environment. Nanomachines, which are the main part of this system, have limited processing capacity. Besides, at the receiver, high inter-symbol interference (ISI) occurs due to free movement of molecules and the variance of the observation noise is signal dependent. Hence, it is important to design high-performance and low complexity receiver detection methods. In this paper, finite impulse response (FIR) Wiener filter is introduced for the first time, which has considerably less computational complexity compared to the minimum mean square error (MMSE) algorithm proposed in the literature. Moreover, extended Kalman filter is introduced for the first time to DBMC as a receiver detection method. Finally, Viterbi algorithm is modified and used as a benchmark for performance evaluation.MR guided focused ultrasound (MRgFUS) therapy has been a promising treatment modality for many neurological disorders. However, the lack of real-time image processing software platform sets barriers for relevant pre-clinical researches. This work intends to develop an integrated software for MRgFUS therapy. The software contains three functional modules a communication module, an image post-processing module, and a visualization module. The communication module provides a data interface with an open-source MR image reconstruction platform (Gadgetron) to receive the reconstructed MR images in real-time. The post-processing module contains the algorithms of image coordinate registration, focus localization by MR acoustic radiation force imaging (MR-ARFI), temperature and thermal dose calculations, motion correction, and temperature feedback control. The visualization module displays monitoring information and provides a user-machine interface. The software was tested to be compatible with systems from two different vendors and validated in multiple scenarios for MRgFUS. The software was tested in many ex vivo and in vivo experiments to validate its functions. The in vivo transcranial focus localization experiments were carried out for targeting the focused ultrasound in neuromodulation.In the rapid serial visual presentation (RSVP) classification task, the data from the target and non-target classes are incredibly imbalanced. These class imbalance problems (CIPs) can hinder the classifier from achieving better performance, especially in deep learning. This paper proposed a novel data augmentation method called balanced Wasserstein generative adversarial network with gradient penalty (BWGAN-GP) to generate RSVP minority class data. The model learned useful features from majority classes and used them to generate minority-class artificial EEG data. It combines generative adversarial network (GAN) with autoencoder initialization strategy enables this method to learn an accurate class-conditioning in the latent space to drive the generation process towards the minority class. We used RSVP datasets from nine subjects to evaluate the classification performance of our proposed generated model and compare them with those of other methods. The average AUC obtained with BWGAN-GP on EEGNet was 94.43%, an increase of 3.7% over the original data. We also used different amounts of original data to investigate the effect of the generated EEG data on the calibration phase. Only 60% of original data were needed to achieve acceptable classification performance. These results show that the BWGAN-GP could effectively alleviate CIPs in the RSVP task and obtain the best performance when the two classes of data are balanced. The findings suggest that data augmentation techniques could generate artificial EEG to reduce calibration time in other brain-computer interfaces (BCI) paradigms similar to RSVP.Intelligent video summarization algorithms allow to quickly convey the most relevant information in videos through the identification of the most essential and explanatory content while removing redundant video frames. In this paper, we introduce the 3DST-UNet-RL framework for video summarization. A 3D spatio-temporal U-Net is used to efficiently encode spatio-temporal information of the input videos for downstream reinforcement learning (RL). An RL agent learns from spatio-temporal latent scores and predicts actions for keeping or rejecting a video frame in a video summary. We investigate if real/inflated 3D spatio-temporal CNN features are better suited to learn representations from videos than commonly used 2D image features. Our framework can operate in both, a fully unsupervised mode and a supervised training mode. We analyse the impact of prescribed summary lengths and show experimental evidence for the effectiveness of 3DST-UNet-RL on two commonly used general video summarization benchmarks. We also applied our method on a medical video summarization task. The proposed video summarization method has the potential to save storage costs of ultrasound screening videos as well as to increase efficiency when browsing patient video data during retrospective analysis or audit without loosing essential information.Few-shot learning suffers from the scarcity of labeled training data. Regarding local descriptors of an image as representations for the image could greatly augment existing labeled training data. Existing local descriptor based few-shot learning methods have taken advantage of this fact but ignore that the semantics exhibited by local descriptors may not be relevant to the image semantic. In this paper, we deal with this issue from a new perspective of imposing semantic consistency of local descriptors of an image. Our proposed method consists of three modules. The first one is a local descriptor extractor module, which can extract a large number of local descriptors in a single forward pass. The second one is a local descriptor compensator module, which compensates the local descriptors with the image-level representation, in order to align the semantics between local descriptors and the image semantic. The third one is a local descriptor based contrastive loss function, which supervises the learning of the whole pipeline, with the aim of making the semantics carried by the local descriptors of an image relevant and consistent with the image semantic. Theoretical analysis demonstrates the generalization ability of our proposed method. Comprehensive experiments conducted on benchmark datasets indicate that our proposed method achieves the semantic consistency of local descriptors and the state-of-the-art performance.Multi-class object detection in remote sensing images plays an important role in many applications but remains a challenging task because of scale imbalance and arbitrary orientations of the objects with extreme aspect ratios. In this paper, the Asymmetric Feature Pyramid Network (AFPN), Dynamic Feature Alignment (DFA) module, and Area-IoU regression loss are proposed on the basis of a one-stage cascaded detection method for the detection of multi-class objects with arbitrary orientations in remote sensing images. The designed asymmetric convolutional block is embedded into the AFPN for handling objects with extreme aspect ratios and improving the space representation with ignorable increases in calculation. The DFA module is proposed to dynamically align mismatched features, which are caused by the deviation between predefined anchors and arbitrarily oriented predicted boxes. The refined Area-IoU regression loss, which reconciles two new regression loss functions, the area-guided regression loss and IoU-guided regression loss, is proposed to simultaneously solve the scale imbalance problem and angle sensitivity problem. Experiments on three publicly available datasets, DOTA, HRSC2016, and ICDAR2015, show the effectiveness of the proposed method.High-frequency convex array transducer, featuring both high spatial resolution and wide field of view, has been successfully developed for ophthalmic imaging. To further expand its application range to small animals' imaging, this work develops a high-frequency microconvex array transducer possessing smaller aperture size and wider scanning angle. This transducer featured 128 array elements arranged in a curvilinear 2-2 piezoelectric composite configuration, yielding a maximum view angle of 97.8°. The array was composed of two front matching layers, a nonconductive backing layer, and a customized flexible circuit that electrically connected array elements to coaxial cables. The center frequency and the -6-dB fractional bandwidth were about 18.14 MHz and 69.15%, respectively. The image of a tungsten wire phantom resulted in approximately 62.9- [Formula see text] axial resolution and 121.3- [Formula see text] lateral resolution. The image of the whole kidney of a rat as well as its internal arteries was acquired in vivo, demonstrating the imaging capability of the proposed high-frequency microconvex array transducers for small animals' imaging applications.Ultrasound medical imaging is an entrenched and powerful tool for medical diagnosis. Image quality in ultrasound is mainly dependent on performance of piezoelectric transducer elements, which is further related to the electromechanical performance of the constituent piezoelectric materials. With rising need for piezoelectric materials with better performance and low-cost, a highly textured ceramics has been successfully fabricated. The fabricated transducers achieved a central frequency of 15 MHz, a fractional bandwidth of 67% (at -6 dB), a high effective electromechanical coupling coefficient, keff, of 0.55, and low insertion loss (IL) of 21 dB. Ex vivo ultraonic imaging of a porcine eyeball was used to assess the tomography quality of the transducer. The results show that utilized textured ceramic has a great potential in developing ultrasonic devices for biomedical imaging purposes.Multilevel information storage methods have the potential for increasing storage density and improving information security through obfuscation. Taking inspiration from the color quick response codes, we have developed a method for encoding layers of information in an array of thin piezoelectric wafers. Information storage is accomplished by altering the size of the circular polarization domain of individual wafers to engineer the response of the electromechanical resonances. By using this approach, we can store one layer of information per electromechanical resonance. In this study, we experimentally demonstrate this approach on a 20-element piezoelectric wafer array with up to two layers of information storage using binary encoding. We first discuss the relevant theory behind the proposed approach and the method for designing the polarization profiles for these wafer arrays to enable multilevel information storage. The effect of the size of the polarization domain on the strength of the electromechanical resonances and optimal size to enhance/suppress these resonances is discussed. Last, we describe how the proposed approach could be used to encode four or more layers of frequency-specific information. The proposed technology finds application in embedded barcodes, product tags, tamper-evident seals, and other secure applications such as shipping sensitive materials/containers.
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