Reactive matching is extended to wide bandwidths using the impedance property of LC-ladder filters. In this paper, we present a systematic method to design wideband low-noise amplifiers. An SiGe amplifier with on-chip matching network spanning 3-10 GHz delivers 21-dB peak gain, 2.5-dB noise figure, and -1-dBm input IP3 at 5 GHz, with a 10-mA bias current.

A 3-10-Ghz low-noise amplifier with wideband LC-ladder matching network

| All drivers have habits behind the wheel. Different drivers vary in how they hit the gas and brake pedals, how they turn the steering wheel, and how much following distance they keep to follow a vehicle safely and comfortably. In this paper, we model such driving behaviors as car-following and pedal operation patterns. The relationship between following distance and velocity mapped into a two-dimensional space is modeled for each driver with an optimal velocity model approximated by a nonlinear function or with a statistical method of a Gaussian mixture model (GMM). Pedal operation patterns are also modeled with GMMs that represent the distributions of raw pedal operation signals or spectral features extracted through spectral analysis of the raw pedal operation signals. The driver models are evaluated in driver identification experiments using driving signals collected in a driving simulator and in a real vehicle. Experimental results show that the driver model based on the spectral features of pedal operation signals efficiently models driver individual differences and achieves an identification rate of 76.8% for a field test with 276 drivers, resulting in a relative error reduction of 55% over driver models that use raw pedal operation signals without spectral analysis.

Driver modeling based on driving behavior and its evaluation in driver identification : Analysis of car-pedal use and car-following habits may allow the help offered by intelligent driver assistance systems to be customized for individual drivers

This paper presents a fast color-to-gray conversion algorithm which robustly reproduces the visual appearance of a color image in grayscale. The conversion preserves feature discriminability and reasonable color ordering, while respecting the original lightness of colors, by simple optimization of a nonlinear global mapping. Experimental results show that our method produces convincing results for a variety of color images. We further extend the method to temporally coherent color-to-gray video conversion.

Robust Color-to-Gray via Nonlinear Global Mapping

COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits. So, the design and implementation of COVID-19 testing kits remain an open research problem. Several findings attained using radio-imaging approaches recommend that the images comprise important data related to coronaviruses. The application of recently developed artificial intelligence (AI) techniques, integrated with radiological imaging, is helpful in the precise diagnosis and classification of the disease. In this view, the current research paper presents a novel fusion model hand-crafted with deep learning features called FM-HCF-DLF model for diagnosis and classification of COVID-19. The proposed FM-HCF-DLF model comprises three major processes, namely Gaussian filtering-based preprocessing, FM for feature extraction and classification. FM model incorporates the fusion of handcrafted features with the help of local binary patterns (LBP) and deep learning (DL) features and it also utilizes convolutional neural network (CNN)-based Inception v3 technique. To further improve the performance of Inception v3 model, the learning rate scheduler using Adam optimizer is applied. At last, multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model was experimentally validated using chest X-ray dataset. The experimental outcomes inferred that the proposed model yielded superior performance with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%.

/pdf/a-novel-hand-crafted-with-deep-learning-features-based-4girvljwh3.pdf

A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images

Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

/pdf/precision-irrigation-management-using-machine-learning-and-upkfj22m.pdf

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

In this paper, a wideband circularly polarized (CP) textile multiple-input-multiple-output (MIMO) antenna is presented for wearable applications. The designed MIMO antenna consists of two sickle-shaped resonating elements and a common ground plane. Each antenna element is excited by a microstrip line feed, and an inverted L-shaped strip is introduced in the ground plane to support circular polarization. The antenna covers impedance bandwidth ( $\text{S}_{11}\le -10$ dB) of 3.6–13 GHz and 3-dB axial ratio bandwidth (ARBW) of 5.2–7.1 GHz. The proposed textile MIMO antenna exhibits envelope correlation coefficient (ECC) $>9.96$ , total active reflective coefficient (TARC) $ dB, channel capacity loss (CCL) 18) between the resonating elements. Also, the proposed antenna is investigated for different human body situations, and its specific absorption rate (SAR) for human tissues specimen is studied. The overall size of the proposed CP textile MIMO antenna is $32.5 \times 42 \times 1$ mm3. The designed MIMO antenna could be useful for wearable applications due to its textile layers, reasonable on-body performance, and compact size.

/pdf/wideband-circularly-polarized-textile-mimo-antenna-for-yzis2v66ha.pdf

Wideband Circularly Polarized Textile MIMO Antenna for Wearable Applications

Received: 9 July 2019 Accepted: 16 September 2019 The voice signals of human are a type of acoustic signal that transfers the information about the message or word delivered in the form of speech. The speech of each person has its unique acoustic features. The statistical analysis of such features is critical to the speech recognition. Therefore, this paper aims to identify the speaker through statistical analysis of acoustic features of voice signals. First, the data collection method for speech samples was introduced, the voice signals were divided into three categories, namely, normal voice (NV), lower pitch (LP) and raised pitch (RP), and the effects of the LP and RP on speech were discussed. Then, a feature extraction method was coupled with several classifers to identify the LP and RP for speaker identification. Next, the MFCC, ΔMFCC and ΔΔMFC were adopted to extract the acoustic features. Finally, the proposed method was verified through a speaker identification experiment. The results show that our method can accurately capture the acoustic features of each speaker, and correctly differentiate between the NV, LP and RP. The research results are of great significance to speech recognition and speaker identificaiton.

/pdf/statistical-analysis-of-lower-and-raised-pitch-voice-signal-5bop9bv9gn.pdf

Statistical Analysis of Lower and Raised Pitch Voice Signal and Its Efficiency Calculation

An efficient VLSI architecture design for logarithmic multiplication by using the improved operand decomposition

Digital signal processing and image processing applications require the noise free and efficient arithmetic operations. To overcome the noise problem, an efficient expanded operand decomposition (ExOD) based logarithm multiplication is proposed and applied for convolution of Gaussian smoothing filter to minimize the noise. The ExOD approach improves the average error percentage (AEP), mean square error (MSE) and the error rate in comparison of reported operand decomposition (OD) and improved operand decomposition (IOD). The peak signal-to-noise ratio (PSNR), MSE and the AEP of the ExOD logarithm multiplication method are improved as compared to the reported logarithm multiplication to the convolution operation. The ExOD method for logarithmic multiplication gives AEP of 0.276, 0.294 and 0.309%, whereas Mitchell’s algorithm gives 2.702, 2.661 and 2.830%, OD-Mitchell’s multiplication gives 1.441, 1.449 and 2.170% and IOD-Mitchell’s multiplication gives 1.627, 1.678 and 2.064% for 4-, 8-, and 16-bit operations respectively. ExOD-Mitchell’s multiplication gives 1.867% MSE, 45.453 db PSNR and 0.293% AEP, whereas Mitchell’s algorithm gives 18.688% MSE, 35.449 db PSNR and 2.731% AEP, OD-Mitchell’s multiplication gives 9.877% MSE, 38.218 db PSNR and 1.686% AEP and IOD-Mitchell’s multiplication gives 10.866% MSE, 37.804 db PSNR and 1.789% AEP. The proposed ExOD based Mitchell algorithm multiplication has useful applications in image processing with efficient accuracy.

An error-efficient Gaussian filter for image processing by using the expanded operand decomposition logarithm multiplication

Agriculture is one of the broadest economic sectors in our country, many farmers and labor depend upon agriculture to develop and to increase the economy of our country. We use many wireless communication modules to communicate the status of the field. We use the smart irrigator system
 and the smart sensing system and many other sensors to use smart irrigation techniques efficient. The placement of different sensors in different positions of the fields to know the real status of the field. By this, we can automatically water the crop and operate different motors without
 any physical work. The basic components of this idea are to use the sensors and to get an efficient output in the agricultural sector.

Durgesh Nandan

Papers

Wideband Circularly Polarized Textile MIMO Antenna for Wearable Applications

Statistical Analysis of Lower and Raised Pitch Voice Signal and Its Efficiency Calculation

An efficient VLSI architecture design for logarithmic multiplication by using the improved operand decomposition

An error-efficient Gaussian filter for image processing by using the expanded operand decomposition logarithm multiplication

A Review on the Smart Irrigation System