This paper presents a step down, switched mode power converter for use in multi-standard envelope tracking radio frequency power amplifiers (RFPA). The converter is based on a programmable order sigma delta modulator that can be configured to operate with either 1st, 2nd, 3rdor 4thorder loop filters, eliminating the need for a bulky passive output filter. Output ripple, sideband noise and spectral emission requirements of different wireless standards can be met by configuring the modulator's filter order and converter's sampling frequency. The proposed converter is entirely digital and is implemented in 14nm bulk CMOS process for post layout verification. For an input voltage of 3.3V, the converter's output can be regulated to any voltage level from 0.5V to 2.5V, at a nominal switching frequency of 150MHz. It achieves a maximum efficiency of 94% at 1.5 W output power.

In this paper, an improved architecture for RF power amplifier envelope tracking supply modulator is presented. It consists of a single switched mode supply regulator and one linear regulator. The switched mode supply regulator has two outputs, one of which is used in conjunction with the linear regulator to provide a wideband, high efficiency power supply to the RF amplifier, whereas the second output provides a band limited high efficiency supply to the linear regulator. The design offers improved power efficiency, reduced system complexity and area savings since the dual output switched mode regulator requires one inductor and a simple control loop. The design was implemented in 14nm CMOS process and validated with simulations. The supply modulator achieves a peak efficiency of 74% with a 6 dB PAPR 20MHz LTE signal at 29dBm output power.

This paper presents a switching DC-DC Buck converter with enhanced light-load efficiency for use in noise-sensitive applications. Low noise, spur free operation is achieved by using a sigma-delta-modulator (ΣΔ) based controller, while light load efficiency is realized through the introduction of fine step frequency scaling (FSFS) which continuously adjusts the switching frequency of the converter with load conditions. Regulation efficiency is further improved by adoption of mode hopping (continuous conduction mode (CCM)/discontinuous conduction mode (DCM)) and utilization of a fully digital implementation. Furthermore, the presented converter maintains low output voltage ripple across its entire load range by reconfiguring the ΣΔ modulator's quantization step and introducing dither to the loop filter. The proposed modulator was implemented in 14nm bulk CMOS process and validated with post layout simulations. It attains a peak efficiency of 95% at heavy load conditions and 79% at light loads with a maximum voltage ripple of 15mV at light loads.

We analyze the use of caching of video frames at network routers for reducing average retransmission delay. We formulate an expression for the average retransmission delay using video caching routers. In turn, we use this expression to formulate a mathematical program to minimize the average retransmission delay. We find a dynamic programming solution to the resultant non-linear convex binary integer program. We compare the results obtained from the dynamic programming solution with those obtained via experimental exhaustive enumeration. The experimental results validate our models and dynamic programming solution. Finally, we use numerical analysis to quantify the retransmission delay difference between the best and worst caching router placements. Our findings show that the optimal placement of caching routers can significantly reduce cost by minimizing the number of caching routers required to meet a desired retransmission delay performance. Furthermore, our dynamic programming solution shows that such optimal placement can be efficiently determined from network parameters, without an exhaustive search.

In this paper, a selective perceptual-based (SELP) framework is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual human visual system model is proposed to compute local contrast sensitivity thresholds. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. Processing only a set of perceptually significant pixels reduces significantly the computational complexity of SR algorithms without losing the achievable visual quality. The proposed SELP framework is integrated into a maximum-a posteriori-based SR algorithm as well as a fast two-stage fusion-restoration SR estimator. Simulation results show a significant reduction on average in computational complexity with comparable signal-to-noise ratio gains and visual quality.

This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness being able to predict with high accuracy the relative amount of blurriness in images with different content.

In this paper, a selective perceptual-based (SELP) scheme is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual Human Visual System (HVS) model is proposed to compute the contrast sensitivity threshold for a given background intensity. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. This is accomplished by estimating the contrast sensitivity threshold locally over a block. Next, the absolute difference between each pixel and its neighbors is computed and compared to the threshold upon which a decision is made to include the pixel in the SR estimator for the next iteration or not. The perceptual model is integrated into a MAP-based SR algorithm as well as a fast ML estimator. Simulation results show up to 47% reduction on average in computational complexity with comparable SNR gains and visual quality.

A no-reference perceptual sharpness quality metric, inspired by visual attention information, is presented for a better simulation of the Human Visual System (HVS) response to blur distortions. Saliency information about a scene is used to accentuate blur distortions around edges present in conspicuous areas and attenuate those distortions present in the rest of the image. Simulation results are presented to illustrate the performance of the proposed metric.

This work presents a perceptual-based no-reference objective image sharpness/blurriness metric by integrating the concept of just noticeable blur (JNB) into a probability summation model. Unlike existing objective no-reference image sharpness/blurriness metrics, the proposed metric is able to predict the relative amount of blurriness in images with different content. Results are provided to illustrate the performance of the proposed perceptual-based sharpness metric. These results show that the proposed sharpness metric correlates well with the perceived sharpness.

This paper presents an efficient lossless DNA compression algorithm, DNA-residual, that significantly decreases the average bit-rate required to losslessly code correlated DNA sequences. The algorithm can be divided into two parts: modeling and coding. The modeling part consists of mapping the DNA bases into a binary representation and, then, a forward linear prediction filter is used to predict the current input from the previous ones. The prediction error is then transformed into a binary error sequence that is coded using an adaptive binary arithmetic coder. Compared to state-of-the-art compressors using benchmark DNA sequences, the proposed algorithm reveals a significantly higher compression ratio whenever correlation between bases is high

This work is motivated by the fact that existing no-reference sharpness metrics fail in predicting the correct amount of blurriness in images with different contexts. This paper presents a no-reference objective sharpness metric that can be applied to images with different contexts. The metric combines a human visual system (HVS)-based sharpness perception model as well as a local features extractor resulting in a content-invariant metric. The proposed HVS-based sharpness perception model is derived from performed subjective tests. Simulation results and comparison with existing no-reference sharpness metrics show that the proposed HVS-based sharpness metric correlates well with the perceived sharpness and is able to correctly predict the relative amount of blurriness in images with different contexts.

In this paper, a CAPTCHA is presented based on the masking characteristics of the human visual system (HVS). Knowing that noise can be masked by high activity regions and showing that edges can be masked by noise for a human observer while still being detected by machines, the suggested CAPTCHA is composed of English alphabets that are picked randomly and written with a combination of texture and edges with added noise such as to deceive the machine by randomly changing the visibility of characters for humans. The proposed CAPTCHA is highly legible and robust to brute-force attacks and sophisticated object character recognition (OCR) segmentation algorithms

This paper focuses on no-reference image sharpness/blurriness metrics due to their importance in image, video, and biomedical applications. Simulation results show that existing no-reference objective image sharpness metrics fail to predict correctly the sharpness of images in the presence of noise. A noise-immune wavelet-based sharpness metric is proposed based on the Lipschitz regularity for differentiating between edges and noise singularities. Comparison results reveal the superiority of the proposed method when dealing with a moderate noisy environment.

The Al-Alaoui algorithm is a weighted mean-square error (MSE) approach to pattern recognition. It employs cloning of the erroneously classified samples to increase the population of their corresponding classes. The algorithm was originally developed for linear classifiers. In this paper, the algorithm is extended to multilayer neural networks which may be used as nonlinear classifiers. It is also shown that the application of the Al-Alaoui algorithm to multilayer neural networks speeds up the convergence of the back-propagation algorithm.