Enhance Contrast and Balance Color of Retinal Image

This paper proposes a simple and effective retinal fundus image simulation modeling to enhance contrast and adjust the color balance for symmetric information in biomedicine. The aim of the study is for reliable diagnosis of AMD (age-related macular degeneration) screening. The method consists of a few simple steps. Firstly, local image contrast is refined with the CLAHE (Contrast Limited Adaptive Histogram Equalization) technique by operating CIE L*a*b* color space. Then, the contrast-enhanced image is stretched and rescaled by a histogram scaling equation to adjust the overall brightness offsets of the image and standardize it to Hubbard’s retinal image brightness range. The proposed method was assessed with retinal images from the DiaretDB0 and STARE datasets. The findings in the experimentation section indicate that the proposed method results in delightful color naturalness along with a standard color of retinal lesions.

High-Linearity Direct Conversion Receiver with the Transconductance Equalization Technique and DCOC Method

To improve the linearity of direct conversion receivers (DCRs), two high-linearity methods for high second-order intercept points (IP2s) and high third-order intercept points (IP3s) are proposed. To improve IP3s, a transconductance equalization technique for a complementary input operational amplifier (OPAMP) is proposed in an active-RC low-pass filter (LPF), while a digital-analog hybrid DC offset calibration (DCOC) method is proposed to improve IP2s. For one thing, the proposed transconductance equalization technique employs a pair of resistors to guarantee high voltage gain for an OPAMP with two-stage Miller topology under a high-input voltage swing to improve linearity with little deterioration of the noise performance. For another, during the DCOC method, the low-noise amplifier is turned off and replaced by an equivalent resistance of the output impedance of the low-noise amplifier to ensure the accuracy and effectiveness of the DCOC method. Fabricated in 40-nm CMOS technology, the receiver with proposed methods can realize a noise figure of 2.6–3.5 dB in the full frequency band, with an OIP3 of 28 dBm, an IM2 more than 70 dBc, and a remaining DC of −53.2 dBm under the total voltage gain of 60 dB.

Sub-array equalization technique for the parametric array loudspeaker to reduce nonlinear distortion

The parametric array loudspeaker (PAL) is a directional loudspeaker which uses the nonlinear acoustic effect, namely the parametric array, to produce an audio beam from narrow ultrasonic beams. The PAL can efficiently deliver audible information, without generating noise to the surroundings. One significant drawback of the PAL is the nonlinear distortion. Therefore, many sophisticated methods have been proposed to preprocess the input signal of the PAL. However, those methods usually request a flat frequency response of the ultrasonic transducer array (UTA). In the past, equalization has been tried out for the whole UTA, but the performance was sometimes not satisfactory due to the inconsistent productions of ultrasonic transducers. This paper proposes to group the ultrasonic transducers by their impedances. Several sub-arrays are thereafter formed and equalized individually. The comparison results demonstrate that the propose sub-array equalization technique can suppress the nonlinear distortion of the PAL more effectively than the previous method.

Semi-Supervised Extreme Learning Machine Channel Estimator and Equalizer for Vehicle to Vehicle Communications

Wireless vehicular communications are a promising technology. Most applications related to vehicular communications aim to improve road safety and have special requirements concerning latency and reliability. The traditional channel estimation techniques used in the IEEE 802.11 standard do not properly perform over vehicular channels. This is because vehicular communications are subject to non-stationary, time-varying, frequency-selective wireless channels. Therefore, the main goal of this work is the introduction of a new channel estimation and equalization technique based on a Semi-supervised Extreme Learning Machine (SS-ELM) in order to address the harsh characteristics of the vehicular channel and improve the performance of the communication link. The performance of the proposed technique is compared with traditional estimators, as well as state-of-the-art machine-learning-based algorithms over an urban scenario setup in terms of bit error rate. The proposed SS-ELM scheme outperformed the extreme learning machine and the fully complex extreme learning machine algorithms for the evaluated scenarios. Compared to traditional techniques, the proposed SS-ELM scheme has a very similar performance. It is also observed that, although the SS-ELM scheme requires the largest operation time among the evaluated techniques, its execution time is still far away from the latency requirements specified by the standard for safety applications.

Enhanced LION Optimization with Efficient Path Routing Equilization Technique Against DOS Attack in WSN

In WSN, DOS (Denial of service) attack makes shortcoming system. The packets travel over and over in the sensor network. By that all the assets like data transmission, memory, and vitality are squandered by this attack. However, the attacker ought to optimize its attacker plan for request to boost the impact on the system performance because of the deficiency of vitality at the aggressor side. Denial of service (DoS) attack on the Internet has become a squeezing issue. By staying away from these sorts of attacks network performance can be improved. Therefore, Security is a fundamental requirement for these networks. Hence, to overcome the issues faced by the cross layer in the presence of DOS attack in WSN. For reliable data transmission, effective routing is required. This research work mainly focuses on the performance evaluation using optimization methods. To establish the efficient path in the Cross layer against DOS attack, this paper has proposed Enhanced lion optimization with efficient path routing equalization technique (LOEPRE). If there is any failure node occurs in the network then the node is recognized and communication of data packets again transmitted in another node. Retransmission of data causes overload in the network. The proposed model focuses on these issues and overcome these issues by improving the path efficiently with robust security. It consists of three phases: In initial phase include the route discovery in the network. In second phase, enhanced lion optimization technique is used for establish a route to transfer data with high security level. Finally, efficient path routing equalization technique is used for minimize the overload in the network, it provides the equalize path length in the network and is highly efficient. Hence, the proposed LOEPRE technique is used to achieve energy efficiency in wireless network for prolong network lifetime, minimum packet latency, minimize consumption of energy. Moreover, the simulation outcome of the proposed LOEPRE method highly robust while comparing to the existing methods EFCRS, SSPRA ELOER, EFLOR and TSTP. It achieves better performance than existing algorithms in comparing metrics connectivity ratio, end to end delay, overhead, throughput and packet delivery ratio.

Improving radiographic image contrast using multi layers of histogram equalization technique

<span id="docs-internal-guid-43432eef-7fff-9949-6deb-865191ff0740"><span>Usually, X-ray image has distortion in many parts because it is focusing on bones rather than other, However, when dentist needs to make decision analysis, he does that by using X-ray and many opinions can be judged by looking closely on it like (inflammation, infection, tooth nerve, root of the tooth…). This paper proposes on new suggested technique by applying multilayers of histogram equalization (HE) and contrast limited adaptive histogram equalization (CLAHE) in order to make high contrast of X-ray, this technique provides very satisfied results and smooth intensity which leads to high clear X-ray image, by using Python3 and OpenCV.</span></span>

Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation

Both light emitting diode (LED) characteristics for illumination and communication simultaneously have made visible light communication-orthogonal frequency division multiplexing (VLC-OFDM) a strong competitive to radio frequency (RF). In this juncture, to improve signal to noise ratio (SNR) and coverage contour, the wavelet-OFDM is suggested for indoor VLC systems. In this paper, a wavelet VLC-OFDM is proposed for imaging multiple-input multiple-output (MIMO) systems. The proposed wavelet-OFDM is exploited for a hybrid space-frequency domain pre-equalization technique instead of the traditional fast Fourier transform (FFT)-OFDM technique. The Meyer filter is selected and employed in the proposed technique. A comparable achievement is elaborated for several numbers of channels to achieve the enhanced performance in terms of bit rate and coverage contour. In addition, a useful comparison is executed between our wavelet VLC-OFDM and the traditional FFT-OFDM for a hybrid space-frequency domain pre-equalization technique. The simulation results emphasize the superiority point of wavelet VLC-OFDM MIMO system by improving the coverage contour by ~20% over the traditional OFDM at a 10−3 bit error rate (BER) target. Hence, the proposed technique can be potentially executed in indoor VLC-MIMO systems.