Descreening of Color Halftone Images in the Frequency Domain

Scanning a halftone image introduces halftone artifacts, known as Moiré patterns, which significantly degrade the image quality. Printers that use amplitude modulation (AM) screening for halftone printing position dots in a periodic pattern. Therefore, frequencies relating halftoning are easily identifiable in the frequency domain. This paper proposes a method for descreening scanned color halftone images using a custom band reject filter designed to isolate and remove only the frequencies related to halftoning while leaving image edges sharp without image segmentation or edge detection. To enable hardware acceleration, the image is processed in small overlapped windows. The windows are filtered individually in the frequency domain, then pieced back together in a method that does not show blocking artifacts

Recaptured Raw Screen Image and Video Demoir\'eing via Channel and Spatial Modulations

Capturing screen contents by smartphone cameras has become a common way for information sharing. However, these images and videos are often degraded by moir\'e patterns, which are caused by frequency aliasing between the camera filter array and digital display grids. We observe that the moir\'e patterns in raw domain is simpler than those in sRGB domain, and the moir\'e patterns in raw color channels have different properties. Therefore, we propose an image and video demoir\'eing network tailored for raw inputs. We introduce a color-separated feature branch, and it is fused with the traditional feature-mixed branch via channel and spatial modulations. Specifically, the channel modulation utilizes modulated color-separated features to enhance the color-mixed features. The spatial modulation utilizes the feature with large receptive field to modulate the feature with small receptive field. In addition, we build the first well-aligned raw video demoir\'eing (RawVDemoir\'e) dataset and propose an efficient temporal alignment method by inserting alternating patterns. Experiments demonstrate that our method achieves state-of-the-art performance for both image and video demori\'eing. We have released the code and dataset in https://github.com/tju-chengyijia/VD_raw

Field-effect based chemical and biological sensing : theory and implementation

Electrochemical sensors share many properties of an ideal (bio)chemical sensor. They can be easily miniaturized with high parallel sensing capabilities,with rugged structure and at low cost. The response obtained from thetarget analyte is directly in electrical form allowing convenient data post-processing and simple interfacing to standard electrical components. With field-effect transistor (FET) based sensors, the transducing principle relies on direct detection of interfacial charge allowing detection of various ions and charged macromolecules. This thesis investigates FET based sensors for biological and chemical sensing. First, an ion-sensitive floating gate FET (ISFGFET) structure is studied and modeled. The proposed model reveals novel abilities of the structure not found in conventional ion-sensitive FETs (ISFETs). With IS-FGFET, we can simultaneously optimize the transistor operating point and modulate the charging of the surface and the ionic screening layer via the field effect. This control is predicted to allow reduced electric double layer screening as well as the possibility to enhance charged molecule attachment to the sensing surface. The model can predict sensor characteristic curves in pH sensing in absolute terms and allows any potential to be computed in the sensor including the electrical part and the electrolyte solution. Furthermore, a compact ISFGFET variant is merged into electric circuit simulator, which allows it to be simulated as a standard electrical component with electrical simulations tools of high computational efficiency, and allows simple modifications such as addition of parasitic elements, temperature effects, or even temporal drifts. Next, another transistor based configuration, the extended-gate ISFET is studied. The simplicity of the proposed configuration allows a universal potentiometric approach where a wide variety of chemical and biological sensors can be constructed. The design philosophy for this sensing structure is to use the shelf electric components and standard electric manufacturing processes. Such an extended-gate structure is beneficial since the dry electronics can be completely separated from the wet sensing environment. The extended-gate allows simple functionalization towards chemical and biological sensing. A proof-of-concept of this structure was verified through organo modified gold platforms with ion-selective membranes. A comparison with standard open-circuit potentiometry reveals that the sensing elements in a disposable sensing platform arrays provide comparable performance to traditional electrodes. Finally, a universal battery operated hand-held electrical readout device is designed for multiplexed detection of the disposable sensors with wireless smartphone data plotting, control, and storage. Organic polymers play an important role in the interfacial properties of sensors studied in this thesis. The polymer coating is attractive in chemical sensing because of its redox sensitivity, bio-immobilization capability, ion-to-electron transducing capability, and applicability, for example via a simple low-cost drop-casting. This structure simplifies the design of the sensor substantially and the coating increases the amount of possible target applications.Siirretty Doriast

Computational Methods in Biophysics and Medicinal Chemistry: Applications and Challenges

In this thesis I described the theory and application of several computational methods in solving medicinal chemistry and biophysical tasks. I pointed out to the valuable information which could be achieved by means of computer simulations and to the possibility to predict the outcome of traditional experiments. Nowadays, computer represents an invaluable tool for chemists. In particular, the main topics of my research consisted in the development of an automated docking protocol for the voltage-gated hERG potassium channel blockers, and the investigation of the catalytic mechanism of the human peptidyl-prolyl cis-trans isomerase Pin1

Theoretical and computational modeling of rna-ligand interactions

Ribonucleic acid (RNA) is a polymeric nucleic acid that plays a variety of critical roles in gene expression and regulation at the level of transcription and translation. Recently, there has been an enormous interest in the development of therapeutic strategies that target RNA molecules. Instead of modifying the product of gene expression, i.e., proteins, RNAtargeted therapeutics aims to modulate the relevant key RNA elements in the disease-related cellular pathways. Such approaches have two significant advantages. First, diseases with related proteins that are difficult or unable to be drugged become druggable by targeting the corresponding messenger RNAs (mRNAs) that encode the amino acid sequences. Second, besides coding mRNAs, the vast majority of the human genome sequences are transcribed to noncoding RNAs (ncRNAs), which serve as enzymatic, structural, and regulatory elements in cellular pathways of most human diseases. Targeting noncoding RNAs would open up remarkable new opportunities for disease treatment. The first step in modeling the RNA-drug interaction is to understand the 3D structure of the given RNA target. With current theoretical models, accurate prediction of 3D structures for large RNAs from sequence remains computationally infeasible. One of the major challenges comes from the flexibility in the RNA molecule, especially in loop/junction regions, and the resulting rugged energy landscape. However, structure probing techniques, such as the “selective 20-hydroxyl acylation analyzed by primer extension” (SHAPE) experiment, enable the quantitative detection of the relative flexibility and hence structure information of RNA structural elements. Therefore, one may incorporate the SHAPE data into RNA 3D structure prediction. In the first project, we investigate the feasibility of using a machine-learning-based approach to predict the SHAPE reactivity from the 3D RNA structure and compare the machine-learning result to that of a physics-based model. In the second project, in order to provide a user-friendly tool for RNA biologists, we developed a fully automated web interface, “SHAPE predictoR” (SHAPER) for predicting SHAPE profile from any given 3D RNA structure. In a cellular environment, various factors, such as metal ions and small molecules, interact with an RNA molecule to modulate RNA cellular activity. RNA is a highly charged polymer with each backbone phosphate group carrying one unit of negative (electronic) charge. In order to fold into a compact functional tertiary structure, it requires metal ions to reduce Coulombic repulsive electrostatic forces by neutralizing the backbone charges. In particular, Mg2+ ion is essential for the folding and stability of RNA tertiary structures. In the third project, we introduce a machine-learning-based model, the “Magnesium convolutional neural network” (MgNet) model, to predict Mg2+ binding site for a given 3D RNA structure, and show the use of the model in investigating the important coordinating RNA atoms and identifying novel Mg2+ binding motifs. Besides Mg2+ ions, small molecules, such as drug molecules, can also bind to an RNA to modulate its activities. Motivated by the tremendous potential of RNA-targeted drug discovery, in the fourth project, we develop a novel approach to predicting RNA-small molecule binding. Specifically, we develop a statistical potential-based scoring/ranking method (SPRank) to identify the native binding mode of the small molecule from a pool of decoys and estimate the binding affinity for the given RNA-small molecule complex. The results tested on a widely used data set suggest that SPRank can achieve (moderately) better performance than the current state-of-art models