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Summary An analog background calibration approach is presented for the full calibration of pipeline analog‐to‐digital converters (ADCs). A well‐trained neural network acts close to the ideal 1.5‐bit stage, and its residue is compared with the real 1.5‐bit stage including gain error and amplifier nonlinearities. The detected error is used to compensate for the imperfect residue using four calibration polynomial coefficients. The corrected residue enters the second stage of the pipeline ADC and follows a normal path to achieve high resolution. The introduced structure is verified in a 12‐bit pipelined ADC composed of 11 stages; the first 10 stages have a 1.5‐bit structure, while the last stage is a 2‐bit flash. The sampling frequency is 100 MHz, and 10% non‐ideal factors (5% for each of the nonlinear and gain errors and 10% for the aggregated error) are considered for the first stage, while the input is 19.5 MHz sinusoidal waveform. A random noise is applied to the input to limit the effective number of bits (ENOB) to almost 11.8. The evaluation parameters of the ADC are extracted, signal‐to‐noise and distortion ratio increases from 39.14 to 72.91 dB, spurious free dynamic range improves from 40.94 to 79.69 dB, and the ENOB enhances from 6.2 to 11.82. The presented mechanism shows an acceptable accuracy in the high‐speed and high‐resolution ADCs. [ABSTRACT FROM AUTHOR]

An adaptive neuro‐fuzzy inference system is presented which is optimized by a genetic algorithm to classify normal and abnormal brain tumours. The classifier is fast and simple, named genetic algorithm‐adaptive neuro‐fuzzy inference system, and the determined learning rules minimize its error and improve its accuracy. The presented system follows five steps including preprocessing, morphological operation, feature extraction, feature selection, and classification. Morphological operators segment the abnormal regions and calculate the tumour area. The statistical features and the grey‐level co‐occurrence matrix are employed for feature extraction. Magnetic resonance images are considered and 12 statistical features are extracted, then the genetic algorithm‐based selection technique helps to select features and reduce the extracted features and improves the accuracy and decision time. So, the high dimensionality and the computational complexity of the adaptive neuro‐fuzzy inference system are reduced, and the classifier decides more efficiently. The input data are the figshare brain tumour dataset with 670 abnormal and 670 normal magnetic resonance images, and the classifier requires 10.788 s for classification. The efficient performance of the genetic algorithm‐adaptive neuro‐fuzzy inference system is confirmed by the accuracy of 99.85%, sensitivity of 99.7%, specificity of 100%, precision of 100%, and mean square error of 0.0027. [ABSTRACT FROM AUTHOR]

Summary A ripple carry adder (RCA) structure is presented which can concurrently correct a gain error in low‐resolution pipeline analog‐to‐digital converters (ADCs). The RCA is composed of a new swing‐boosted full adder (FA) based on the transmission gate (TG) technique. So, it is low‐power, high‐speed, and full‐swing with high driving capability. In 90 nm technology, the results of power, delay, and power‐delay‐product (PDP) of the FA are 1.5296 μW, 2.1141 ns, and 3.2337 fJ, respectively. The RCA with an area of 408.09 μm2 is evaluated by a 3‐bit pipeline ADC with 1.5‐bit per stage. When the erroneous codes are passed through the RCA, the maximum and minimum integral nonlinearity (INL) are improved by 19.83% and 90.98%, while the maximum and minimum of the differential nonlinearity (DNL) are improved by 50%, and 42.06%, respectively. [ABSTRACT FROM AUTHOR]

Summary: Sophisticated systems take advantage of approximate circuits for energy management. A gate‐level structure is introduced to make a novel imprecise full adder (FA) cell by the gate diffusion input (GDI) strategy. The carbon nanotube field‐effect transistor (CNTFET) technology lowers the FA power, and the swing issue is resolved by the dynamic threshold (DT) technique. The FA specifications are 10 transistors, two internal nodes, 0.191‐μm2 area, three errors, and an error rate (ER) of 37.5%. Some inputs are directly passed to the final stage of the FA to reduce the delay and dynamic power. The FA is used in three 4:2 compressors with 12, 16, and 20 transistors, respectively. The compressors are based on the modified stacking concept to attain low power, small area, and high speed. The compressors are used up in the partial product reduction tree (PPRT) of a new 8‐bit inexact multiplier. Regarding the multiplier, the power–delay product (PDP) is decreased by 25.21%, and the normalized mean error distance (NMED) shows high accuracy. An imprecise discrete cosine transform (DCT) is implemented by the multiplier for image compression, and compared with the literature, the peak signal‐to‐noise ratio (PSNR) and structural similarity index metric (SSIM) are amended by 8.26% and 7.29%, respectively. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is a high‐quality medical image that is used to detect brain tumours in a complex and time‐consuming manner. In this study, a back propagation neural network (BPNN) along with the Levenberg–Marquardt algorithm (LMA) is proposed to classify MRIs and diagnose brain tumours in a simple and fast process. The BPNN has 10 neurons in the hidden layer, and the default function of the feedforward feeds is mean squared error (MSE). The LMA is optimized as a multivariable adaptive approach and considerably decreases the MSE of the BPNN, so the errors of the tumour classification are diminished. The proposed method follows four steps including preprocessing, skull removal, feature extraction, and classification. The input MRIs are converted to greyscale, resized, and thresholding is performed in the preprocessing step and followed by skull removal. Morphological operations of closing, opening, and dilation are used to segment abnormal areas in the MRIs, and the opening operator recognizes the tumour more accurately. Using statistical analysis and a grey‐level co‐occurrence matrix (GLCM) 12 features are extracted from the MRIs and used as the inputs of the BPNN. To evaluate the proposed method, 670 normal and 670 abnormal brain MRIs are used as input data, and the classification is performed in 0.494 s. The accuracy, sensitivity, specificity, precision, dice, recall, and MSE are 98.7%, 97.61%, 99.7%, 97.61%, 98.6%, 97.61%, and 0.005, respectively. The approach is accurate and fast for medical images classification. [ABSTRACT FROM AUTHOR]

A new low‐power and high‐speed multiplier is presented based on the voltage over scaling (VOS) technique and new 5:3 and 7:3 counter cells. The VOS reduces power consumption in digital circuits, but different voltage levels of the VOS increase the delay in different stages of a multiplier. Hence, the proposed counters are implemented by the gate‐diffusion input technique to solve the speed limitation of the VOS‐based circuits. The proposed GDI‐based 5:3 and 7:3 counters save power and reduce the area by 2x and 2.5x, respectively. To prevent the threshold voltage (Vth) drop in the suggested GDI‐based circuits, carbon nanotube field‐effect transistor (CNTFET) technology is used. In the counters, the chirality vector and tubes of the CNTFETs are properly adjusted to attain full‐swing outputs with high driving capability. Also, their validation against heat distribution under different time intervals, as a major issue in the CNTFET technology is investigated, and their very low sensitivity is confirmed. The low complexity, high stability and efficient performance of the presented counter cells introduce the proposed VOS‐CNTFET‐GDI‐based multiplier as an alternative to the previous designs. [ABSTRACT FROM AUTHOR]

A new 1‐bit full adder (FA) cell illustrating low‐power, high‐speed, and a small area is presented by a combination of transmission gate (TG), pass transistor logic (PTL), and float techniques. Using the proposed cell, a 4:2 compressor is implemented and its performance is investigated under diverse circumstances of voltage, temperature, and driving. The process and corners are evaluated through the process‐voltage‐temperature (PVT) variations and the Monte Carlo method (MCM), respectively. The accuracy and reliability of the proposed 4:2 compressor are confirmed carefully. Utilising the proposed FA and the compressor, an efficient 8‐bit subtractor is implemented for bioimage processing, in particular for difference detection of images. A new mechanism is presented to improve the detection performance of digital signal processors (DSPs) by the addition and subtraction of two images for their difference. The quality of the resulted image confirms the efficiency of the proposed circuits and the method. The high performance of the circuits makes them a promising candidate for the next generation of integrated circuits (ICs) applicable to medical image processing. [ABSTRACT FROM AUTHOR]

Summary: The performance of the programmable integrated circuits (ICs) like digital signal processors (DSPs), central processing units (CPUs), and microcontrollers is highly dependent on the magnitude comparators. This article presents a new reliable and efficient 1‐bit comparator based on carbon nanotube field‐effect transistors (CNTFETs) technology. The gate diffusion input (GDI) technique is used to reduce the number of transistors. Also, single swing restoration (SR) transistors and transmission gate (TG) techniques are combined to attain full‐swing outputs with high driving capability. The low internal capacitances and the fewer number of internal nodes of the proposed comparator make it low‐power both in single‐bit and multibit (2‐bit, 4‐bit, 8‐bit, and 16‐bit) implementations. Variations analyses of the circuit and technology parameters are performed that show the cell efficiency. From the layout results, the 1‐bit and 16‐bit comparators occupy 0.1944 μm2 and 37.485 μm2 as the total die area, respectively. The proposed circuit is implemented in image processing in which the best performance compared with state‐of‐the‐art designs regarding hardware complexity, circuitry performance, and image quality assessments are confirmed, by at least 50% improvements of different critical parameters. The resulted figure of merits nominates the cell for future ICs. [ABSTRACT FROM AUTHOR]

Brain tumour detection is still a challenging problem both in medical and image processing. In this study, two scenarios are applied to diagnose human brain tumours, classical image processing (CIP) and a new algorithm called binary image with variable fuzzy level (BIVFL). Magnetic resonance imaging (MRI) process from the BraTs 2019 database. Edge detection and prognosis area cropping are parts of the high‐computational CIP algorithm. For the CIP, the best filter is the combination of the horizontal and vertical modes of the Prewitt filter. In the BIVFL, by changing the fuzzy level for binarisation images two clusters are filled with tumour and no tumour areas, and the tumour area is extracted in various accuracies. Sensitivity, specificity, precision, and accuracy of the BIVFL are varied according to the fuzzy level, and the best value of sensitivity is 0.9, but the value of three other parameters is 1 for an interval of fuzzy levels. The BIVFL is a simple and fast tumour area extraction algorithm, and also it is easy to implement with digital signal processors. Histogram and power signal‐to‐noise ratio of the BIVFL is remarkable. [ABSTRACT FROM AUTHOR]

Here, a new 4:2 approximate compressor is presented by the gate diffusion input (GDI) technique. Although GDI cells suffer from threshold voltage drop, the dynamic threshold approach and carbon nanotube field‐effect transistors are merged to overcome the mentioned problem. The proposed cell has full‐swing outputs, while its error and power delay product are at low rates. Therefore, low voltage multipliers that are used in image processing can benefit from the proposed compressor. [ABSTRACT FROM AUTHOR]