Deep-Learning Based Estimation of Dielectrophoretic Force

The ability to accurately quantify dielectrophoretic (DEP) force is critical in the development of high-efficiency microfluidic systems. This is the first reported work that combines a textile electrode-based DEP sensing system with deep learning in order to estimate the DEP forces invoked on microparticles. We demonstrate how our deep learning model can process micrographs of pearl chains of polystyrene (PS) microbeads to estimate the DEP forces experienced. Numerous images obtained from our experiments at varying input voltages were preprocessed and used to train three deep convolutional neural networks, namely AlexNet, MobileNetV2, and VGG19. The performances of all the models was tested for their validation accuracies. Models were also tested with adversarial images to evaluate performance in terms of classification accuracy and resilience as a result of noise, image blur, and contrast changes. The results indicated that our method is robust under unfavorable real-world settings, demonstrating that it can be used for the direct estimation of dielectrophoretic force in point-of-care settings.

The dynamics of decision-making and action during active sampling

Embodied Cognition Theories (ECTs) of decision-making propose that the decision process pervades the execution of choice actions and manifests itself in these actions. Decision-making scenarios where actions not only express the choice but also help sample information can provide a valuable, ecologically relevant model for this framework. We present a study to address this paradigmatic situation in humans. Subjects categorized (2AFC task) a central object image, blurred to different extents, by moving a cursor toward the left or right of the display. Upward cursor movements reduced the image blur and could be used to sample information. Thus, actions for decision and actions for sampling were orthogonal to each other. We analyzed response trajectories to test whether information-sampling movements co-occurred with the ongoing decision process. Trajectories were bimodally distributed, with one kind being direct towards one response option (non-sampling), and the other kind containing an initial upward component before veering off towards an option (sampling). This implies that there was an initial decision at the early stage of a trial, whether to sample information or not. Importantly, in sampling trials trajectories were not purely upward, but rather had a significant horizontal deviation early on. This result suggests that movements to sample information exhibit an online interaction with the decision process, therefore supporting the prediction of the ECTs under ecologically relevant constrains.

Implementation Of Encryption And Hidden Data Using Tiny Encryption Algorithm And End Of File

Security and confidentiality of data is one important aspect of an information system. The information can be misused very large losses in high-profile cases such as vital information confidential corporate, customer data banks and etc. Information security solutions in one of them can be used with cryptography. Cryptographic algorithms used in this study is a tiny encryption algorithm. Cryptographic data security attacks can always wear can occur, with this in mind the authors added security techniques to perform data hiding with the media as a placeholder, this term is called steganography. Steganography is used in this study is the end of the file. These techniques make the process of data hiding which is located at the end of the image, so it does not affect the image quality of the reservoir. In this study, a system built using microsoft visual studio 2010 C#. This system can work well, but has a color image blur caused to the container caused by the inserted message, where the greater the size of the message was inserted then color the image blur that arises will be many more.

Salient Region Guided Blind Image Sharpness Assessment

Salient regions provide important cues for scene understanding to the human vision system. However, whether the detected salient regions are helpful in image blur estimation is unknown. In this study, a salient region guided blind image sharpness assessment (BISA) framework is proposed, and the effect of the detected salient regions on the BISA performance is investigated. Specifically, three salient region detection (SRD) methods and ten BISA models are jointly explored, during which the output saliency maps from SRD methods are re-organized as the input of BISA models. Consequently, the change in BISA metric values can be quantified and then directly related to the difference in BISA model inputs. Finally, experiments are conducted on three Gaussian blurring image databases, and the BISA prediction performance is evaluated. The comparison results indicate that salient region input can help achieve a close and sometimes superior performance to a BISA model over the whole image input. When using the center region input as the baseline, the detected salient regions from the saliency optimization from robust background detection (SORBD) method lead to consistently better score prediction, regardless of the BISA model. Based on the proposed hybrid framework, this study reveals that saliency detection benefits image blur estimation, while how to properly incorporate SRD methods and BISA models to improve the score prediction will be explored in our future work.

The dynamics of decision making and action during active sampling

Embodied Cognition Theories (ECTs) propose that the decision process continues to unfold during the execution of choice actions, and its outcome manifests itself in these actions. Scenarios where actions not only express choice but also help sample information can provide a valuable test of this framework. Remarkably almost no studies so far have addressed this scenario. Here, we present a study testing just this paradigmatic situation with humans. On each trial, subjects categorized a central object image, blurred to different extents (2AFC task) by moving a cursor toward the left or right of the display. Upward cursor movements, orthogonal with respect to choice options, reduced the image blur and could be freely used to actively sample information. Thus, actions for decision and actions for sampling were made orthogonal to each other. We analyzed response trajectories to test a central prediction of ECTs; whether information-sampling movements co-occurred with the ongoing decision process. Trajectory data revealed were bimodally distributed, with one kind being direct towards one response option (non-sampling trials), and the other kind containing an initial upward component before veering off towards an option (sampling trials). This implies that there was an initial decision at the early stage of a trial whether to sample information or not. Importantly, the trajectories in sampling trials were not purely upward, but rather had a significant horizontal deviation that was visible early on in the movement. This result suggests that movements to sample information exhibit an online interaction with the decision process. The finding that decision processes interact with actions to sample information supports the ECT under novel, ecologically relevant constrains.

RFID multi-tag dynamic detection measurement based on conditional generative adversarial nets and YOLOv3

The quality of multi-tag imaging greatly affects the effective detection of multi-tag. When multi-tag moves rapidly, the image may have serious dynamic blur, and tags can not be detected efficiently. In previous work, it is generally assumed that blur kernel and noise stationary to improve image quality. However, the dynamic deblurring of Radio Frequency Identification (RFID) multi-tag imaging is an ill-posed inverse problem. In this paper, firstly, blur-sharp multi-tag image pairs are made by superimposing and averaging the adjoin random frames. Then, we propose blind deblurring for dynamic RFID multi-tag imaging based on conditional generative adversarial nets (CGANs), which adds perceptual loss and content loss to generator to make image sharper. Finally, tags are detected by YOLOv3 in real time in end-to-end manner. Experimental results demonstrate that PSNR is at least 0.56dB higher and speed is at least 31.25 % faster than that of the current improved convolution neural networks (CNN). CGANs can remove image blur better, which has great superiority in the field of dynamic multi-tag imaging. In addition, YOLOv3 detects multi-tag quickly, thereby improving the detection accuracy.