Минимизация искажений сигнала при локально-оптимальном способе цифрового преобразования

BackgroundCardiac fibrosis disrupts the normal myocardial structure and has a direct impact on heart function and survival. Despite already available digital methods, the pathologist’s visual score is still widely considered as ground truth and used as a primary method in histomorphometric evaluations. The aim of this study was to compare the accuracy of digital image analysis tools and the pathologist’s visual scoring for evaluating fibrosis in human myocardial biopsies, based on reference data obtained by point counting performed on the same images.MethodsEndomyocardial biopsy material from 38 patients diagnosed with inflammatory dilated cardiomyopathy was used. The extent of total cardiac fibrosis was assessed by image analysis on Masson’s trichrome-stained tissue specimens using automated Colocalization and Genie software, by Stereology grid count and manually by Pathologist’s visual score.ResultsA total of 116 slides were analyzed. The mean results obtained by the Colocalization software (13.72 ± 12.24%) were closest to the reference value of stereology (RVS), while the Genie software and Pathologist score gave a slight underestimation. RVS values correlated strongly with values obtained using the Colocalization and Genie (r > 0.9, p < 0.001) software as well as the pathologist visual score. Differences in fibrosis quantification by Colocalization and RVS were statistically insignificant. However, significant bias was found in the results obtained by using Genie versus RVS and pathologist score versus RVS with mean difference values of: -1.61% and 2.24%. Bland-Altman plots showed a bidirectional bias dependent on the magnitude of the measurement: Colocalization software overestimated the area fraction of fibrosis in the lower end, and underestimated in the higher end of the RVS values. Meanwhile, Genie software as well as the pathologist score showed more uniform results throughout the values, with a slight underestimation in the mid-range for both.ConclusionBoth applied digital image analysis methods revealed almost perfect correlation with the criterion standard obtained by stereology grid count and, in terms of accuracy, outperformed the pathologist’s visual score. Genie algorithm proved to be the method of choice with the only drawback of a slight underestimation bias, which is considered acceptable for both clinical and research evaluations.Virtual slidesThe virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/9857909611227193

This paper presents an efficient digital down conversion method for multiple wideband signals. This technique can be used to down convert odd and even subband signals to base band with only one polyphase filter. We can change filter coefficients to receive odd subband signals and then double odd subband bandwidth to receive even band signals. On the other hand, we can keep filter coefficients unchanged and double even subband bandwidth to receive odd band signals.

Synthetic aperture interferometric radiometers require phase coherent detection to allow the visibility function to be measured and subsequently converted to brightness temperature images. Products of signals between channels produce the complex "visibility" terms of the spatial spectrum of the brightness temperature scene. Today's technology offers the choice of either analog or digital detection methods for relatively low microwave frequencies. Digital detection may offer advantages, particularly in certain instrument configurations. This paper reviews the results of an evaluation of a particular digital method and a variety of FIR filter designs. A programmable, real time processor was developed to study various Hilbert Transform algorithms for digitally forming the in-phase (I) and quadrature (Q) components of the received signals. An overview of the test set-up and test results using several FIR filters is presented, allowing a trade between numerical simplicity and the gain and phase balance between I and Q.

The digital transition poses relevant challenges and opportunities for older adults in aging European societies. To unleash the potential of the digital transition in old age and avoid the risk of exclusion, digital education for older adults seems to be a valuable solution. One of the most suitable approaches to digital education for older adults appears to be the peer-to-peer approach. However, not much literature is available on this topic. Within the ACTIVE-IT project, we aimed to design, implement, and evaluate a digital peer education course for older adults, focusing specifically on the use of smartphones and daily utility apps, such as mailing, e-Gov, and e-commerce. The purpose of this contribution is to document the protocol adopted to evaluate the course. The course involved 32 participants aged 65 or older, who, between March 2024 and June 2024, divided into three groups, attended a 10-lesson weekly course taught by a peer. We aim to measure the effect of the course on participants' digital skills and their perceived wellbeing. To do so, we will adopt a mixed methods approach, employing: digital methods by collecting and analyzing data on participants' smartphone use (i.e., log data on smartphone use before/during/after the intervention); a quasi-experiment, collecting information on course participants' wellbeing before/after the course attendance using a questionnaire survey; ethnographic observation conducted during the course, observing interactions between subjects during the course. The study has been approved by the Ethic Committee of the University of Milano Bicocca (prot.nr. 167541/2024).

A new method for digital model conversion of a continuous-time uncertain system and a new digital redesign method for robust control of a sampled-data uncertain system are presented. The concept of the principle of equivalent areas together with interval arithmetic is utilised for finding the discrete-time uncertain model and the digital robust control law from the continuous-time uncertain state equation and the analogue robust control law, respectively. Using the newly digitally redesigned controllers, the resulting dynamic states of the digitally controlled sampled-data uncertain systems are able to closely match those of the original analogously controlled continuous-time uncertain systems for a relatively longer sampling period.

The study analyzes transformational processes in education in the context of digitalization. It is noted that traditional distance learning tools, such as Moodle, Zoom, Google Meet and other platforms, continue to operate successfully. The authors of the article emphasize that in modern conditions the Internet is becoming a means of encouraging the formation of information culture among the subjects of the educational process, in particular, the skills of mastering digital technologies in professional activities. For example, using Zoom conference, BigBlueButton (Virtual Classroom Software), Google Meet, etc. for organizing the educational process and transmitting educational content in real time. It is noted that within the framework of e-learning it is necessary to develop such digital methods and skills as “scaffolding (long read)”, etc. In addition, it is emphasized that questioning of students through Internet constructors is becoming a leading skill. The article also notes that the most relevant aspect of the system of advanced training for specialists is the development of flexibility of professional abilities (“Soft skills”), which form a set of non-professional skills and ensure high productivity and effectiveness of professional activity. The issues of motivating and ensuring the success of students, as well as the optimal choice of technologies, methods and forms of teaching by means of electronic communications are studied. It is claimed that the digital competence of a teacher is based on their cognitive, social, and emotional components and takes into account their life in the digital environment. It has been found that professional growth of specialists occurs through advanced training, in particular, in the conditions of postgraduate education.

A digital peak voltage (DPV) predictive control for switching dc-dc converters is proposed and studied in this paper. With the predictive control technique, the duty ratio in each switching cycle is usually calculated and predicted based on the status in the previous switching cycles. Therefore, at least a one-switching-cycle time delay usually exists in predictivPe control, which significantly degrades the control performance of digital controlled switching dc-dc converters. To eliminate this time delay and to improve the control performance, an improved DPV (IDPV) predictive control technique is then proposed and studied. The control laws of DPV and IDPV with different pulsewidth modulations are derived, and their stabilities are analyzed. A simple digital slope compensation method is proposed to eliminate the subharmonic oscillation of DPV and IDPV control laws. The steady-state and transient performances of DPV and IDPV controlled buck converters are investigated by simulation and experiment. The comparison studies among DPV, IDPV, and digital peak current (DPC) control show that the transient performances of IDPV and DPV are much better than that of DPC and that IDPV is better than DPV.

A new wideband digital down conversion (WB-DDC) method based on intersection of frequency spectrum is proposed. In this method, the input wideband signal is split into several subbands by analysis filter bank and each of these subbands with explicit meanings can be processed independently. The outputs of useful subbands are combined by synthesis filter bank to get the complex baseband signal. That means subband operation becomes possible and great flexible comparing with the traditional structure. Moreover, the new method is computationally more efficient than the traditional method. The methods of designing filter bank and the perfect reconstruction condition are analyzed. The influence of the prototype filter to reconstruction is indicated by simulation. The simulation results show that the presented method is of good performance.

In this paper, we consider the perspective image for subspace of hemisphere space, which is observed from fish eye lens, by digital image conversion. The partial domain of an original image in fish eye image is selected, and this is converted to a perspective image based on a projection rule of fish eye lens so that it may become the same as a standard image which is obtained by central projection of common lens. However, since this conversion enlarges the original image spatially and is based on discrete data, crevice is generated in the converted image (sparse problem). To solve this problem, interpolation based on an intensity of the image is performed for the crevice in the converted image. This paper provides the experimental results of perspective image conversion using the proposed conversion and interpolation methods for both gray-scale and color images, and the adequacy and improving point of the proposed techniques are discussed.

Optimal tax depreciation with uncertain future cash-flows

Dither Signal of Feedback Channel Quantization in Adaptive Digital Predistorter

Computed tomography is now widely used in cardiac surgery as a method of non-destructive study of internal structure of objects, including specific tasks, such as mathematical modeling of physiological processes, surgical interventions in augmented reality, 3D printing, and radiomics. One of the key steps in creating a 3D model from computed tomography data is segmentation the process of selecting objects in the image. Currently, there are several approaches to automating the segmentation process, including image processing methods, texture analysis and machine learning algorithms (in particular, clustering). Image processing methods are the simplest of the presented approaches and are found in various applications for segmentation of tomographic data. This paper reviews the advantages and disadvantages of various image processing methods (threshold, region growing, contour detection, and morphological watersheds) as tools for automated cardiac segmentation from computed tomography data. It was revealed that computed tomography images have characteristic features affecting the segmentation process (presence of noise, partial volume effect, etc.). The choice of the segmentation method is based on the brightness characteristics of the area of interest and also requires knowledge of the subject area, so it should be performed by a specialist with competence in anatomy and digital image processing. As independent methods of automated segmentation, the listed methods are applicable only in relatively simple cases (selection of homogeneous or high-contrast areas), otherwise, a combination of these methods, the use of machine learning algorithms or manual correction of the results is required.

Precise time difference (time interval) measurement has been widely applied in communication, detection, and metrology. Among the various methods of time difference measurement, the analog zero-crossing detection method and the full digital processing methods are commonly used in ultrasonic flow measurement application. Conventional full digital processing methods, which take advantage of total signals’ waveforms, can have high accuracy in high signal to noise ratio (SNR) when employing a high enough sampling frequency and extremely high performance digital signal processors; but their accuracies are severely limited by the hardware costs and can be easily affected by the noise and the distortion of signals. As a viable method, the analog zero-crossing detection method can achieve high resolution, whose accuracy, however, is easily influenced by the detection threshold and baseline shifts of the received signals, as well as the noises. In this paper, the digital zero-crossing detection algorithm is proposed for ultrasonic flow measurement to achieve high-precision and high-robustness time difference estimation at relatively low hardware costs, i.e., a relatively low sampling rate (e.g., 10 Msamples/s) and a relatively low computational load. The digital zero-crossing detection algorithm is performed with the signal reconstruction and the least-square fitting technologies. The simulation and experiment results show that compared with conventional methods, the proposed method presents higher accuracy and higher robustness, especially in the case of low SNR.

A 5-bit time to digital converter using time to voltage conversion and integrating techniques for agricultural products analysis by Raman spectroscopy

In this paper a new digital background calibration method for successive approximation register analog to digital converters is presented. For developing, a perturbation signal is added and also digital offset is injected. One of the main advantages of this work is that it is completely digitally and eliminates the nonlinear errors between analog capacitor and array capacitors due to converter’s capacitors mismatch error by correcting the relative weights. Performing of this digital dithering method does not require extra capacitors or double independent converters and it will eliminate mismatches caused by these added elements. Also, No extra calibration overhead for complicated mathematical calculation is needed. It unlike split calibration, does not need two independent converters for production of two specified paths and it just have one capacitor array which makes it possible with simple architecture. Furthermore, to improve DNL and INL and correct the missing code error, sub radix-2 is used in the converter structure. Proposed calibration method is implemented by a 10 bit, 1.87-radix SAR converter. Simulation results with MATLAB software show great improvement in static and dynamic characteristics in applied analog to digital converter after calibration. So, it can be used in calibration of successive approximation register analog to digital converters.