Robotic Image Acquisition

Tongue prints have recently emerged as a promising biometric trait in forensic odontology. Their unique surface morphology, protection from external factors, and long-term stability position them as potential identifiers in forensic investigations. Despite these advantages, research on tongue prints remains limited, with inconsistencies in methodology and classification. This systematic review aimed to evaluate the forensic reliability of tongue prints as a biometric identifier and to compare their performance with established oral markers, particularly palatal rugae. A comprehensive search of four databases covering the period 2015–2025 identified 1,886 records, of which six studies met the inclusion criteria. Together, these studies analyzed 826 participants using visual inspection, digital imaging, or lingual impression techniques. All studies consistently demonstrated that tongue prints exhibit individual uniqueness and morphological stability over time. Findings also indicated sex- and age-related variations in tongue morphology, while correlations with blood group were inconsistent. The comparison between tongue prints and palatal rugae suggested comparable discriminatory potential and forensic relevance. Nevertheless, most studies were geographically restricted to India and displayed methodological heterogeneity, including differences in image acquisition and classification systems, which limit the generalizability of the evidence. Despite these limitations, the reviewed literature highlights the potential of tongue prints to serve as reliable biometric markers when supported by standardized protocols and digital analytic methods. Tongue prints appear to be a stable, non-invasive, and reproducible biometric modality that could complement established forensic identifiers. Their integration with other oral features such as palatal rugae and dental records may enhance personal identification accuracy, particularly in cases involving decomposed or unrecognizable remains. To establish global applicability, future research should emphasize standardized classification methods, examiner calibration, and inclusion of multi-ethnic populations, while leveraging emerging technologies such as digital imaging and artificial intelligence for improved precision in forensic identification.

VME image acquisition and processing using standard TV CCD cameras

Image retrieval and classication using shifted Legendre invariant moments and Radial Basis Functions Neural Networks

In the age of ever-increasing "-omics" studies, the accurate and statistically robust determination of microbial cell numbers within often-complex samples remains a key task in microbial ecology. Microscopic quantification is still the only method to enumerate specific subgroups of microbial clades within complex communities by, for example, fluorescence in situ hybridization (FISH). In this study, we improved an existing automatic image acquisition and cell enumeration system and adapted it for usage at high seas on board an oceanographic research ship. The system was evaluated by testing settings such as minimal pixel area and image exposure times ashore under stable laboratory conditions before being brought on board and tested under various wind and wave conditions. The system was robust enough to produce high-quality images even with ship heaves of up to 3 m and pitch and roll angles of up to 6.3°. On board the research ship, on average, 25% of the images acquired from plankton samples on filter membranes could be used for cell enumeration. Automated enumeration was highly correlated with manual counts (r(2) > 0.9). Even the smallest of microbial cells in the open ocean, members of the alphaproteobacterial SAR11 clade, could be confidently detected and enumerated. The automated image acquisition and cell enumeration system developed here enables an accurate and reproducible determination of microbial cell counts in planktonic samples and allows insight into the abundance and distribution of specific microorganisms already on board within a few hours.IMPORTANCE In this research article, we report on a new system and software pipeline, which allows for an easy and quick image acquisition and the subsequent enumeration of cells in the acquired images. We put this pipeline through vigorous testing and compared it to manual microscopy counts of microbial cells on membrane filters. Furthermore, we tested this system at sea on board a marine research vessel and counted bacteria on board within a few hours after the retrieval of water samples. The imaging and counting system described here has been successfully applied to a number of laboratory-based studies and allowed the quantification of thousands of samples and FISH preparations (see, e.g., H. Teeling, B. M. Fuchs, D. Becher, C. Klockow, A. Gardebrecht, C. M. Bennke, M. Kassabgy, S. Huang, A. J. Mann, J. Waldmann, M. Weber, A. Klindworth, A. Otto, J. Lange, J. Bernhardt, C. Reinsch, M. Hecker, J. Peplies, F. D. Bockelmann, U. Callies, G. Gerdts, A. Wichels, K. H. Wiltshire, F. O. Glöckner, T. Schweder, and R. Amann, Science 336:608-611, 2012, http://dx.doi.org/10.1126/science.1218344). We adjusted the standard image acquisition software to withstand ship movements. This system will allow for more targeted sampling of the microbial community, leading to a better understanding of the role of microorganisms in the global oceans.

In this paper, we develop a new web-based system for unsupervised classification of satellite images available from the Google Maps engine. The system has been developed using the Google Maps API and incorporates functionalities such as unsupervised classification of image portions selected by the user (at the desired zoom level). For this purpose, we use a processing chain made up of the well-known ISODATA and k-means algorithms, followed by spatial post-processing based on majority voting. The system is currently hosted on a high performance server which performs the execution of classification algorithms and returns the obtained classification results in a very efficient way. The previous functionalities are necessary to use efficient techniques for the classification of images and the incorporation of content-based image retrieval (CBIR). Several experimental validation types of the classification results with the proposed system are performed by comparing the classification accuracy of the proposed chain by means of techniques available in the well-known Environment for Visualizing Images (ENVI) software package. The server has access to a cluster of commodity graphics processing units (GPUs), hence in future work we plan to perform the processing in parallel by taking advantage of the cluster.

3-Phosphoinositide-dependent protein kinase-1 (PDK1) phosphorylates the T-loop of several AGC (cAMP-dependent, cGMP-dependent, protein kinase C) family protein kinases, resulting in their activation. Previous structural studies have revealed that the alpha C-helix, located in the small lobe of the kinase domain of PDK1, is a key regulatory element, as it links a substrate interacting site termed the hydrophobic motif (HM) pocket with the phosphorylated Ser-241 in the T-loop. In this study we have demonstrated by mutational analysis that interactions between the phosphorylated Ser-241 and the alpha C-helix are not required for PDK1 activity or substrate binding through the HM-pocket but are necessary for PDK1 to be activated or stabilized by a peptide that binds to this site. The structure of an inactive T-loop mutant of PDK1, in which Ser-241 is changed to Ala, was also determined. This structure, together with surface plasmon resonance binding studies, demonstrates that the PDK1(S241A)-inactive mutant possesses an intact HM-pocket as well as an ordered alpha C-helix. These findings reveal that the integrity of the alpha C-helix and HM-pocket in PDK1 is not regulated by T-loop phosphorylation.

In this paper, a multi-input computer system for image acquisition and processing is presented. This system is equipped with a 2-wavelength thermal channel and any 4 other ones, which can be used to capture visual, X-ray, ultrasound images, etc. Novel image processing tools are described, mainly for geometrical and 3D transformations.

Protein crystallization under micro gravity has been already tried many times in the United States and other countries, and it is reported that about 20% of proteins were better crystallized under microgravity than on earth. This verified that microgravity is sometimes effective in protein crystallization. However, if these procedures continued to be carried out without clarifying which processes are effective, improved development of protein crystallization cannot be expected. The most effective way to study the process is to carry out protein crystallization experiments, each elementary stage of which is clearly observed. To this end, the dissolution rate of a single crystal of hen egg-white lysozyme has been measured both under microgravity and on earth in order to study the mechanism of protein crystallization. In May 1997, we had an opportunity to have an experiment on protein crystallization with use of STS-84 space shuttle missions (the time duration of which was 210 hours). The apparatus for protein crystallization by vapor diffusion techniques was available and we have tried to use it for measurement of crystal dissolution rate under microgravity. The barrels of two syringes (20 ml x 2) were filled with unsaturated protein solutions (hen egg-white lysozyme (HEWL): 0.0wt%, 0.1wt%, 0.2wt%, 0.3wt%; NaCl 3wt% aqueous solution) and a crystal (HEWL: tetragonal form; 1 mm +/- 0.2 mm in diameter) was put on the bottom of the one syringe. Dissolution of the crystal was started by extruding the unsaturated solutions onto the syringe tip with the crystal. The crystal dissoluted in 40 ml droplets that are extruded from syringes. The temperature was kept at 20 degrees C. Just before the Space Shuttle begins returning to the earth, the protein solution was withdrawn back into the syringes by the astronaut, and the melting experiment was finished. In the one syringe the incompletely melted crystal was withdrawn as well. The solution concentration in the other syringe was measured. In the flight experiment, only the initial and final solution concentrations can be measured. In the control experiment on earth the solution concentrations in the course of dissolution were also measured. 20 undersaturated solutions (40 ml in volume) were prepared and a crystal (1 mm +/- 0.2 mm in diameter) was put in each solution. The solution concentration was measured at constant time intervals. When the crystal starts to dissolute [correction of dissolue], molecules in the crystal surface leave the crystal, the concentration around the crystal becomes high and there occurs a radial concentration gradient centered on the nucleus. Molecules diffuse along the gradient and go farther from the crystal. On earth, the concentration gradient might be disturbed by convection and the high concentration around the crystal might be disturbed more quickly than under microgravity. We might therefore expect that dissolution rate on the ground should be larger than under micro-gravity. However, our experimental results show that the expectation was not correct. What might the reason be? One possible reason is that there might be Marangoni convection effects in the apparatus for protein crystallization by vapor diffusion techniques used for the melting experiment. This effect has also been observed by Chayen et al.

The digital image acquisition and processing technology already mature also in domains and so on visual communication obtains day by day[1]. However, the image acquisition and processing technology had many kinds, for example white balance processing and image denoising methods. The traditional image acquisition and processing systems are implemented using software by the PC or DSP chip[2], and sometimes software in series can not meet the requirement of high speed image processing. this article has given FPGA in the image acquisition and processing simple application, applied FPGA in the image acquisition and processing to be possible to enhance the image processing the speed, reduced takes the logical unit. The system designed by above-mentioned way achieves a satisfying effect by experimental verification.

With the scale of digital electronic systems, data transfering in different modules become one the key technology. One common method is to use the handshake signal.This method is not only difficult to implement, but also needs to consider the speed of the both sides’ clock. In response, t the article puts forward a design of FIFO integrated into FPGA, which has been applied to the image acquisition and storage system. The interior resources of FPGA have been fully used and the system design has been simplified yet.

A high-speed multi-channel multi time delay and integration charge couple device(TDICCD) image acquisition and processing system is introduced in this paper. FPGA is the core logic control of the system. The main hardware component of the system and the implementation method of FPGA are described and the diagrams of main modules are presented. Software workflow and a variety of image processing methods are also given. Test results show that the design achieves data transfer speed of 2.4Gbps and realizes real-time processing of image for high-speed multi-channel TDICCD camera so that meets the system requirement.

An in-situ image acquisition and analysis system was developed to characterize the dynamics of particle size distribution (PSD) during flocculation. The system consisted of a flocculator coupled with an external flow-through cell for observation and photography, a microscopic charge-coupled device video recorder with backlighting, and an image analyzer. This non-intrusive side-stream setup was used to record the evolution of the PSD to determine the flocculation dynamics of two types of particle systems: Clean kaolin, kaolin coated with natural organic matter (NOM). The results were discussed and the error induced during measurement was evaluated.

The advancement of automated medical diagnosis in biomedical engineering has become an important area of research. Image classification is one of the diagnostic approaches that do not require segmentation which can draw quicker inferences. The proposed non-invasive diagnostic support system in this study is considered as an image classification system where the given brain image is classified as normal or abnormal. The ability of deep learning allows a single model for feature extraction as well as classification whereas the rational models require separate models. One of the best models for image localization and classification is the Visual Geometric Group (VGG) model. In this study, an efficient modified VGG architecture for brain image classification is developed using transfer learning. The pooling layer is modified to enhance the classification capability of VGG architecture. Results show that the modified VGG architecture outperforms the conventional VGG architecture with a 5% improvement in classification accuracy using 16 layers on MRI images of the REpository of Molecular BRAin Neoplasia DaTa (REMBRANDT) database.

Exploitation of the spectral capabilities of modern hyperspectral image demands efficient preprocessing and analyses methods. Analysts' choice of classifier and dimensionality reduction (DR) method and the harmony between them determine the accuracy of image classification. Multiple classifier system (MCS) has the potential to combine the relative advantages of several classifiers into a single image classification exercise for the hyperspectral image classification. In this paper, we propose an algorithmic extension of the MCS, named as dynamic classifier system (DCS), which exploits the context-based image and information class characteristics represented by multiple DR methods for hyperspectral image classification for land cover mapping. The proposed DCS algorithm pairs up optimal combinations of classifiers and DR methods specific to the hyperspectral image and performs image classifications based only on the identified combinations. Further, the impact of various trainable and nontrainable combination functions on the performance of the proposed DCS has been assessed. Image classifications were carried out on five multi-site airborne hyperspectral images using the proposed DCS and were compared with the MCS and SVM based supervised image classifications with and without DR. The results indicate the potential of the proposed DCS algorithm to increase the classification accuracy considerably over that of MCS or SVM supervised image classifications.

We are developing a testbed for automated identification of specimens, and acquisition of high resolution images for 3D macromolecular EM. Several software package which have been developed specifically for acquisition of tomographic data sets have illustrated the benefits of automation. These software packages require the user to identify a specimen of interest and then automatically acquire a single tilt series from the identified specimen. The testbed we are developing will provide a generalized system for specimen identification, image acquisition and image quality assessment. The goals of the testbed are to provide a system that will acquire EM images unattended by an operator. The testbed integrates a computer controlled TEM with a distributed real-time image processing environment. Computerized control and image acquisition from the TEM uses the emScope software package (1). This is coupled to existing image processing software packages and new software tools developed specifically for this project.