A Study on Distortion Estimation Based on Image Gradients.

Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types. The first contribution of our research was to design a noise data feature extractor that can effectively extract noise information from the image pair. The second contribution of our work leveraged other noise parameter estimation algorithms that can only predict one type of noise. Our proposed method, DE-G, can estimate additive noise, multiplicative noise, and impulsive noise from single-source images accurately. We also show the capability of the proposed method in estimating multiple corruptions.

Fast transient thermal analysis of non-Fourier heat conduction using Tikhonov well-conditioned asymptotic waveform evaluation.

Non-Fourier heat conduction model with dual phase lag wave-diffusion model was analyzed by using well-conditioned asymptotic wave evaluation (WCAWE) and finite element method (FEM). The non-Fourier heat conduction has been investigated where the maximum likelihood (ML) and Tikhonov regularization technique were used successfully to predict the accurate and stable temperature responses without the loss of initial nonlinear/high frequency response. To reduce the increased computational time by Tikhonov WCAWE using ML (TWCAWE-ML), another well-conditioned scheme, called mass effect (ME) T-WCAWE, is introduced. TWCAWE with ME (TWCAWE-ME) showed more stable and accurate temperature spectrum in comparison to asymptotic wave evaluation (AWE) and also partial Pade AWE without sacrificing the computational time. However, the TWCAWE-ML remains as the most stable and hence accurate model to analyze the fast transient thermal analysis of non-Fourier heat conduction model.

50 MHz-10 GHz low-power resistive feedback current-reuse mixer with inductive peaking for cognitive radio receiver.

A low-power wideband mixer is designed and implemented in 0.13 µm standard CMOS technology based on resistive feedback current-reuse (RFCR) configuration for the application of cognitive radio receiver. The proposed RFCR architecture incorporates an inductive peaking technique to compensate for gain roll-off at high frequency while enhancing the bandwidth. A complementary current-reuse technique is used between transconductance and IF stages to boost the conversion gain without additional power consumption by reusing the DC bias current of the LO stage. This downconversion double-balanced mixer exhibits a high and flat conversion gain (CG) of 14.9 ± 1.4 dB and a noise figure (NF) better than 12.8 dB. The maximum input 1-dB compression point (P1dB) and maximum input third-order intercept point (IIP3) are -13.6 dBm and -4.5 dBm, respectively, over the desired frequency ranging from 50 MHz to 10 GHz. The proposed circuit operates down to a supply headroom of 1 V with a low-power consumption of 3.5 mW.

Optimizing chemotherapy treatment outcomes using metaheuristic optimization algorithms: A case study.

BACKGROUND: This study explores the dynamics of a mathematical model, utilizing ordinary differential equations (ODE), to depict the interplay between cancer cells and effector cells under chemotherapy. The stability of the equilibrium points in the model is analysed using the Jacobian matrix and eigenvalues. Additionally, bifurcation analysis is conducted to determine the optimal values for the control parameters. OBJECTIVE: To evaluate the performance of the model and control strategies, benchmarking simulations are performed using the PlatEMO platform. METHODS: The Pure Multi-objective Optimal Control Problem (PMOCP) and the Hybrid Multi-objective Optimal Control Problem (HMOCP) are two different forms of optimal control problems that are solved using revolutionary metaheuristic optimisation algorithms. The utilization of the Hypervolume (HV) performance indicator allows for the comparison of various metaheuristic optimization algorithms in their efficacy for solving the PMOCP and HMOCP. RESULTS: Results indicate that the MOPSO algorithm excels in solving the HMOCP, with M-MOPSO outperforming for PMOCP in HV analysis. CONCLUSION: Despite not directly addressing immediate clinical concerns, these findings indicates that the stability shifts at critical thresholds may impact treatment efficacy.

Dual therapy of cancer using optimal control supported by swarm intelligence.

BACKGROUND: The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment. OBJECTIVE: To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm. The developed technique is aimed to reduce the number of cancerous cells while utilizing the minimum necessary chemotherapy medications and minimizing toxicity to protect patients' health. METHODS: Two hybrid techniques are proposed in this paper. Both techniques combined OCT with the evolutionary algorithm and multi-objective swarm algorithm which included MOEA/D, MOPSO, SPEA II and PESA II. This study evaluates the performance of two hybrid techniques in terms of reducing cancer cells and drug concentrations, as well as computational time consumption. RESULTS: In both techniques, MOEA/D emerges as the most effective algorithm due to its superior capability in minimizing tumour size and cancer drug concentration. CONCLUSION: This study highlights the importance of integrating OCT and evolutionary algorithms as a robust approach for optimizing cancer chemotherapy treatment.

Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning.

Social media platforms such as Twitter and Facebook have become popular channels for people to record and express their feelings, opinions, and feedback in the last decades. With proper extraction techniques such as sentiment analysis, this information is useful in many aspects, including product marketing, behavior analysis, and pandemic management. Sentiment analysis is a technique to analyze people's thoughts, feelings and emotions, and to categorize them into positive, negative, or neutral. There are many ways for someone to express their feelings and emotions. These sentiments are sometimes accompanied by sarcasm, especially when conveying intense emotion. Sarcasm is defined as a positive sentence with underlying negative intention. Most of the current research work treats them as two distinct tasks. To date, most sentiment and sarcasm classification approaches have been treated primarily and standalone as a text categorization problem. In recent years, research work using deep learning algorithms have significantly improved performance for these standalone classifiers. One of the major issues faced by these approaches is that they could not correctly classify sarcastic sentences as negative. With this in mind, we claim that knowing how to spot sarcasm will help sentiment classification and vice versa. Our work has shown that these two tasks are correlated. This paper proposes a multi-task learning-based framework utilizing a deep neural network to model this correlation to improve sentiment analysis's overall performance. The proposed method outperforms the existing methods by a margin of 3%, with an F1-score of 94%.

The combined effect of optimal control and swarm intelligence on optimization of cancer chemotherapy.

BACKGROUND AND OBJECTIVES: In cancer therapy optimization, an optimal amount of drug is determined to not only reduce the tumor size but also to maintain the level of chemo toxicity in the patient's body. The increase in the number of objectives and constraints further burdens the optimization problem. The objective of the present work is to solve a Constrained Multi- Objective Optimization Problem (CMOOP) of the Cancer-Chemotherapy. This optimization results in optimal drug schedule through the minimization of the tumor size and the drug concentration by ensuring the patient's health level during dosing within an acceptable level. METHODS: This paper presents two hybrid methodologies that combines optimal control theory with multi-objective swarm and evolutionary algorithms and compares the performance of these methodologies with multi-objective swarm intelligence algorithms such as MOEAD, MODE, MOPSO and M-MOPSO. The hybrid and conventional methodologies are compared by addressing CMOOP. RESULTS: The minimized tumor and drug concentration results obtained by the hybrid methodologies demonstrate that they are not only superior to pure swarm intelligence or evolutionary algorithm methodologies but also consumes far less computational time. Further, Second Order Sufficient Condition (SSC) is also used to verify and validate the optimality condition of the constrained multi-objective problem. CONCLUSION: The proposed methodologies reduce chemo-medicine administration while maintaining effective tumor killing. This will be helpful for oncologist to discover and find the optimum dose schedule of the chemotherapy that reduces the tumor cells while maintaining the patients' health at a safe level.

Noise characteristics of grass-trimming machine engines and their effect on operators.

Over the last few years, interaction of humans with noisy power-driven agricultural tools and its possible adverse after effects have been realized. Grass-trimmer engine is the primary source of noise and the use of motorized cutter, spinning at high speed, is the secondary source of noise to which operators are exposed. In the present study, investigation was carried out to determine the effect of two types of grass-trimming machine engines (SUM 328 SE and BG 328) noise on the operators in real working environment. It was found that BG-328 and SUM-328 SE produced high levels of noise, of the order of 100 and 105 dB(A), respectively, to which operators are exposed while working. It was also observed that situation aggravates when a number of operators simultaneously operate resulting in still higher levels of noise. Operators should be separated 15 meters from each other in order to avoid the combined level of noise exposure while working with these machines. It was found that SPL, of the grass-trimmer machine engines (BG-328 and SUM-328 SE), were higher than the limit of noise recommended by ISO, NIOSH, and OSHA for an 8-hour workday. Such a high level of noise exposure may cause physiological and psychological problems to the operators in long run.

FPGA implementation of real-time SENSE reconstruction using pre-scan and Emaps sensitivities.

Sensitivity Encoding (SENSE) is a widely used technique in Parallel Magnetic Resonance Imaging (MRI) to reduce scan time. Reconfigurable hardware based architecture for SENSE can potentially provide image reconstruction with much less computation time. Application specific hardware platform for SENSE may dramatically increase the power efficiency of the system and can decrease the execution time to obtain MR images. A new implementation of SENSE on Field Programmable Gate Array (FPGA) is presented in this study, which provides real-time SENSE reconstruction right on the receiver coil data acquisition system with no need to transfer the raw data to the MRI server, thereby minimizing the transmission noise and memory usage. The proposed SENSE architecture can reconstruct MR images using receiver coil sensitivity maps obtained using pre-scan and eigenvector (E-maps) methods. The results show that the proposed system consumes remarkably less computation time for SENSE reconstruction, i.e., 0.164ms @ 200MHz, while maintaining the quality of the reconstructed images with good mean SNR (29+ dB), less RMSE (<5×10-2) and comparable artefact power (<9×10-4) to conventional SENSE reconstruction. A comparison of the center line profiles of the reconstructed and reference images also indicates a good quality of the reconstructed images. Furthermore, the results indicate that the proposed architectural design can prove to be a significant tool for SENSE reconstruction in modern MRI scanners and its low power consumption feature can be remarkable for portable MRI scanners.

An Automated and Intelligent Medical Decision Support System for Brain MRI Scans Classification.

A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the human brain magnetic resonance image (MRI) as normal or abnormal, to cater down the human error during identifying the diseases in brain MRIs. In this study, fast discrete wavelet transform (DWT), principal component analysis (PCA), and least squares support vector machine (LS-SVM) are used as basic components. Firstly, fast DWT is employed to extract the salient features of brain MRI, followed by PCA, which reduces the dimensions of the features. These reduced feature vectors also shrink the memory storage consumption by 99.5%. At last, an advanced classification technique based on LS-SVM is applied to brain MR image classification using reduced features. For improving the efficiency, LS-SVM is used with non-linear radial basis function (RBF) kernel. The proposed algorithm intelligently determines the optimized values of the hyper-parameters of the RBF kernel and also applied k-fold stratified cross validation to enhance the generalization of the system. The method was tested by 340 patients' benchmark datasets of T1-weighted and T2-weighted scans. From the analysis of experimental results and performance comparisons, it is observed that the proposed medical decision support system outperformed all other modern classifiers and achieves 100% accuracy rate (specificity/sensitivity 100%/100%). Furthermore, in terms of computation time, the proposed technique is significantly faster than the recent well-known methods, and it improves the efficiency by 71%, 3%, and 4% on feature extraction stage, feature reduction stage, and classification stage, respectively. These results indicate that the proposed well-trained machine learning system has the potential to make accurate predictions about brain abnormalities from the individual subjects, therefore, it can be used as a significant tool in clinical practice.

Wideband LTE power amplifier with integrated novel analog pre-distorter linearizer for mobile wireless communications.

For the first time, a new circuit to extend the linear operation bandwidth of a LTE (Long Term Evolution) power amplifier, while delivering a high efficiency is implemented in less than 1 mm2 chip area. The 950 µm × 900 µm monolithic microwave integrated circuit (MMIC) power amplifier (PA) is fabricated in a 2 µm InGaP/GaAs process. An on-chip analog pre-distorter (APD) is designed to improve the linearity of the PA, up to 20 MHz channel bandwidth. Intended for 1.95 GHz Band 1 LTE application, the PA satisfies adjacent channel leakage ratio (ACLR) and error vector magnitude (EVM) specifications for a wide LTE channel bandwidth of 20 MHz at a linear output power of 28 dBm with corresponding power added efficiency (PAE) of 52.3%. With a respective input and output return loss of 30 dB and 14 dB, the PA's power gain is measured to be 32.5 dB while exhibiting an unconditional stability characteristic from DC up to 5 GHz. The proposed APD technique serves to be a good solution to improve linearity of a PA without sacrificing other critical performance metrics.