Miracle-dz/UniFAR-FTUs huggingface dataset:
Depth of Field Extension in Laser Speckle Contrast Imaging

A new technique that can increase the depth of field (DOF) for in vivo tissue imaging is presented. Extended DOF for the imaging of strongly scattering media is realized through combining laser speckle contrast imaging (LSCI) with light field (LF) microscopy. To the best of our knowledge, it is for the first time to combine these two imaging techniques into one system to provide enhanced functionality. The entire imaging process requires only one single detection with no mechanical adjustment involved. The feasibility and capability of the proposed technique is experimentally demonstrated in volumetric flow speed measurement.
A Multi VDD Wide Voltage Range Up Level Shifter for Smart SoC Applications

This paper presents a wide voltage range up level shifter which is capable to convert or shift low levels of input voltage into high output voltage with less delay and power dissipation. power consumption has become a major problem in modern integrated circuits and system on chip (SoC). To reduce static power as well as dynamic power consumption scale down the supply voltages and operate the circuit near threshold voltages and also using multiple supply voltages for critical and noncritical blocks. The proposed design in a 90nm technology that shifts the input voltage of 0.3 V into 1.2 V of the output voltage. For a VDDL of 0.3 V and VDDH of 1.2 V, it exhibits delay of 3.65 ns, the average power consumption of 1.43 uW and energy per one transition is 1.43 pJ at 1 MHz input pulse signal with equal rise time and fall time.
Using Reinforcement Learning to Attenuate for Stochasticity in Robot Navigation Controllers

Braitenberg vehicles are bio-inspired controllers for sensor-based local navigation of wheeled robots that have been used in multiple real world robotic implementations. The common approach to implement such non-linear control mechanisms is through neural networks connecting sensing to motor action, yet tuning the weights to obtain appropriate closed-loop navigation behaviours can be very challenging. Standard approaches used hand tuned spiking or recurrent neural networks, or learnt the weights of feedforward networks using evolutionary approaches. Recently, Reinforcement Learning has been used to learn neural controllers for simulated Braitenberg vehicle 3a – a bio-inspired model of target seeking for wheeled robots – under the assumption of noiseless sensors. Real sensors, however, are subject to different levels of noise, and multiple works have shown that Braitenberg vehicles work even on outdoor robots, demonstrating that these control mechanisms work in harsh and dynamic environments. This paper shows that a robust neural controller for Braitenberg vehicle 3a can be learnt using policy gradient reinforcement learning in scenarios where sensor noise plays a non negligible role. The learnt controller is robust and tries to attenuate the effects of noise in the closed- loop navigation behaviour of the simulated stochastic vehicle. We compare the neural controller learnt using Reinforcement Learning with a simple hand tuned controller and show how the neural control mechanism outperforms a naïve controller. Results are illustrated through computer simulations of the closed-loop stochastic system.
Automating Collective Robotic System Design

This paper presents a study on methods for body-brain (behavior-morphology) co-evolution in a collective evolutionary robotics system. We investigate a neuro-evolution developmental encoding method designed for the co-evolution of robot behavior-morphology couplings. This behavior-morphology evolution method is evaluated across increasingly complex (difficult) collective behavior task environments. This is in comparison to controller evolution within pre-engineered robot morphologies (sensory configurations). Task-complexity is equated with the degree of cooperation required in collective robotics tasks. Results indicate that the developmental method produces significantly more effective behavior-morphology couplings, compared to those evolved with direct encoding methods and controllers evolved within fixed morphologies. These results suggest that such developmental encoding methods could serve as a general evolutionary simulation design tool for automating collective robotic designs. An end goal is for such collective robotic system designs to be rapidly prototyped and deployed in the physical task environments for which they were evolved.
Uncovering informative genes from colon cancer gene expression data via multi-step clustering based on generalized SOMs with splitting-merging structures

The objective and contribution of this work is to apply our data clustering method using generalized SOMs (self-organizing maps) with splitting-merging evolving structures to a multi-step clustering of colon cancer gene expression data coming from microarray experiments. Such a clustering aims at uncovering a small set of informative genes (that is, the disease relevant genes) from their overall huge number. Our approach operates in an unsupervised way, that is, (i) it uses unlabelled data and (ii) it does not need the cluster number to be predefined. Such a property is very important in the clustering of genes coming from the microarray data because we do not know in advance the number of gene clusters. In the case of the clustering of samples from the microarray data, our method provides the results which outperform the results presented in the literature. The alternative techniques usually require the number of clusters to be determined in advance. The aforementioned experiments are preceded by an outline of our clustering technique and an illustration of its operation with the use of a benchmark data set.
Efficiency Roadmap for Evolutionary Upgrades of PERC Solar Cells by TOPCon: Impact of Parasitic Absorption

Passivating contacts created via a thin interfacial oxide and a highly doped polysilicon layer, e.g., the TOPCon technology, are on the verge of being implemented in solar cell mass production. Investment decisions rely on R&D to identify the most promising implementation option, meaning a trustworthy roadmap based on predicted performance gains. This article shows how to thoroughly quantify the performance potential via numerical simulation, focusing on an evolutionary upgrade of a busbarless p-type bifacial passivated emitter rear cell (PERC) technology. We specifically highlight the need to consider not only the electrical gains of passivating contacts, but also the associated optical losses due to parasitic absorption in the polysilicon layers for front and rear illumination. The influence of free-carrier absorption in polysilicon on the solar cell optics is characterized on experimental test structures in order to verify our optical simulation model. Introducing TOPCon fully at the rear and also locally aligned to the front fingers can boost the PERC efficiency by approximately 1%abs. The final device is strongly limited by losses in the p-type c-Si bulk and phosphorus-doped front emitter. Consequently, the presented evolutionary TOPCon upgrades may well be of increased relevance for future improved p-PERC cells, as an alternative to the current focus on n-type TOPCon cells with boron emitter.
Performance Evaluation of State-of-the-Art Filtering Criteria Applied to SIFT Features

Feature matching is an important and crucial task in computer vision. Unfortunately, the features extracted from images are usually redundant or irrelevant, leading to many ambiguities and false positives during matching. This work provides an exhaustive study of state-of-the-art feature filtering strategies investigating different criteria, such as the contrast level, inner primary ratio, entropy, saliency and median value, in the purpose of discarding irrelevant features. The evaluation on the well-known Oxford-5k dataset demonstrates the relevance of these criteria to reduce the amount of data involved in the matching step and the number of false positives, thus leading to faster and more accurate matching with almost no loss in performance. Our results are also compared to convolutional neural networks (CNNs), providing extensive experiments that corroborate recently published work.
Red-FLC: an Adaptive Fuzzy Logic Controller with Reduced Learning Parameters

In this paper, an adaptive Takagi-Sugeno (TS)-fuzzy controller is developed for nonlinear dynamical systems, where a new structure of the controller with reduced learning parameters is proposed. The proposed controller is named as a reduced learning parameter based fuzzy logic controller (Red-FLC). Being a model-free controller, the classical TS-fuzzy one performs well in slow-process control-based complex applications. However, the controller’s structure is associated with several antecedent and consequent parameters, which need to be adapted during control operation. Adaptation of a high number of parameters is computationally expensive, especially in controlling a system where a fast response is expected. From this research gap, in our developed adaptive fuzzy controller, the tuning parameters have reduced significantly since it has no antecedent parameters. The closed-loop stability of the controller has been proved using a new adaptation law. To evaluate the proposed controller’s performance, it has been utilized to stabilize an inverted pendulum’s simulated plant on a cart by considering an impulse disturbance. The performance of Red-FLC has been compared with a classical TS-fuzzy controller and a Proportional Integral Derivative (PID) controller, where better tracking of the cart’s position and better disturbance rejection is observed from the proposed TS-fuzzy controller.
Depthwise Separable Convolutional Neural Network for Skin Lesion Classification

Melanoma is one of the deadliest skin cancers. Early diagnosis plays an essential role in effective treatment planning and reducing the mortality rate of skin cancer. In this study, we propose a compact deep learning-based classification model with a separable convolutional neural network for melanoma detection. The proposed architecture is aimed to minimize the need for task-specific data pre-processing methods such as noise reduction, artifact removal, and low contrast adjustment to support a better generalization ability for unseen / test data. We validated the performance of the proposed method on the ISIC 2018 dataset. Our results show that the proposed architecture achieves comparable accuracy to the widely-used architectures presented in the literature while being more compact and simpler. The proposed methodology achieves 87.24% accuracy, 95.94% sensitivity and 98.47% specificity.
University of Birmingham Learning Transferable Variation Operators in a Continuous Genetic Algorithm

—The notion of experience has often been neglected within the domain of evolutionary computation while in machine learning a large variety of methods has emerged in the recent years under the umbrella of transfer learning. Notably, realizing experience-based methods suffers from a variety of conceptual key problems. The ﬁrst one being in regards to what constitutes problem-similarity from an algorithm perspective and the second one being what constitutes the transferable experience by itself. Ideally, one would envision that a learning optimization algorithm could be expected to act similarly to a human-problem solver who tackles novel tasks initially without any preconceptions. Experience only comes into play until sufﬁcient similarity to known problems is established. Our paper therefore has two aims. First, to outline existing related ﬁelds and methodologies and highlight their insufﬁciencies. Second, to make the case for experience-based optimization by a demonstration using a novel and statistics-based approach with a real-coded genetic algorithm as a case study. In this paper we do not claim to construct universal problem solvers, but instead propose that from an algorithm-speciﬁc-view, problem characteristics can be learned and harnessed to improve future performance of similarly-structured optimization tasks.
Voice Activity Detection Using Novel Teager Energy Based Band Spectral Entropy

There are many features proposed in the literature for voice activity detection (VAD). Shen et al. [20] first used a spectral entropy-based feature to detect regions of speech spurts under noisy conditions. However, VAD employing this feature was unreliable when the noise level greatly exceeds the speech level. To improve the performance of spectral entropy based VAD under low signal-to-noise ratios (SNRs), spectrum of a signal over a frame is divided into sub bands and spectral entropy is computed over these bands. Later, these spectral entropies are weighted and summed to obtain the entropy. Based on the amount of noise in each band, weights were found empirically. This approach was named as banded spectral entropy (BSE) [21]. In [24], deviation threshold computed from approximate ramp line and the sorted spectral coefficients of the band are adopted to decide useful/useless bands. In this paper, we propose a novel Teager Energy Band Spectral Entropy (TE_BSE) feature for VAD. Here, we carryout enhancement of spectral peaks employing Teager energy of each frequency transformed speech frame. This is followed with dividing of spectrum into sub bands and entropy computation over each band. The summing of entropy from each useful band is done to get TE _ BSE feature. We identify useful/useless bands following [24]. Later, we present the performance of our proposed VAD in terms of probability of detection $(\pmb{P}_{\pmb{D}})$, probability of false alarm $(\pmb{P}_{\pmb{FA}})$ and probability of error under different noises and SNRs. Finally, from the VAD results on real-world sample, proposed VAD outperforms statistical based VAD by Sohn et. al. [8] with improved $\pmb{P}_{\pmb{D}}$ not at the cost of increase in $\pmb{P}_{\pmb{FA}}$.
On The Stability of Kernelized Control Functionals On Partial And Biased Stochastic Inputs

We investigate some theoretical properties of kernelized control functionals (CFs), a recent technique for variance reduction, regarding its stability when applied to subsets of input distributions or biased generating distributions. This technique can be viewed as a highly efficient control variate obtained by carefully choosing a function of the input variates, where the function lies in a reproducing kernel Hilbert space with known mean thus ensuring unbiasedness. In large-scale simulation analysis, one often faces many input distributions for which some are amenable to CFs and some may not due to technical difficulties. We show that CFs retain good theoretical properties and lead to variance reduction in these situations. We also show that, even if the input variates are biasedly generated, CFs can correct for the bias but with a price on estimation efficiency. We compare these properties with importance sampling, in particular a version using a similar kernelized approach.
A Network Intrusion Detection Approach Using Variant of Convolution Neural Network

Intrusion detection system(IDS) is a preventing measure against different harmful cyber-attacks. It has a key role in securing our information and communication technology system. As the internet evolved, the dynamic growth of network traffic data generated which become a challenge for traditional machine learning algorithms. Performance of a classifier solely depends on feature selection and classifier. Traditional machine learning algorithms can't perform well with huge network traffic dataset. So ML algorithms are not optimistic. In this paper, we implemented Convolution neural network(CNN-1D) for NIDS(Network Intrusion detection system) on NSL-KDD benchmark dataset. CNN automatically fetch network traffic features from the dataset. in this paper, we trained our model 500 epoch. We also compared performance of different ML(machine learning) algorithms & DL(deep learning) approaches with our CNN-1D approach with metrics of AC (accuracy), DR (detection rate) and FAR (False alarm rate).
Mobile Robot Self-LocalizationUsing Visual Odometry Based on Ceiling Vision

Self-localization is an important topic for mobile robot. Although some ceiling-based visual odometry have been proposed, the localization process is still cumbersome. In this paper, we propose a novel monocular visual odometry based on ceiling vision that is fast and easy to implement. By pointing the camera to the ceiling, visual odometry reduces the computation and eliminates interference from dynamic environments. We apply Oriented FAST and Rotated BRIEF (ORB) to match the features between two frames of the ceiling view and use sliding window filter to remove incorrect matches, and then get the orientation and position of the robot through coordinate transformation. Compared to traditional visual odometry, our algorithm performs better in speed, and only needs one correct match to get the displacement and rotation angle of robot.
Deep Learning for Tree Crown Detection In Tropical Forest

The task of detecting trees in tropical forest has been a challenging task for decades as it is still not ready for operational purpose. Remote sensing and LiDAR technology has been the dominant research field in adhering the problems of tree crown detection. However, the advancement of deep learning technology could bring new results. This study proposed a deep learning approach using object detection algorithm to detect tropical trees based on tree crowns. The method used state-of-the-art object detection model and K-Means clustering for management of anchor ratio to improve the prediction of the model. Hand-annotations labelled used on the aerial imagery for supplementing the training data. The model produced more than 70% mean Average Precision (mAP) using a small training dataset. The result could open doors to further improve the model for detecting the trees especially in complex canopy forest like tropical forest.
Synchronization of Distributed Photovoltaic Generation with an Active network using Phase Locked Loop technique

The above paper gives the detail design of Solar PV system along with its synchronization with grid connected systems. In this paper detailed design of system along with the design of boost converter, design of inverter system for 180 degree conduction mode along with its SPWM technique and grid synchronization using Phase locked loop technique has been discussed. The PV panel's DC output is amplified by using boost converter up to desired value. which is further converter into A.C. supply by using 3-phase inverter which is working under 180 degree conduction mode which can be controlled by Sinewave pulse width modulation techniques. The reference signal for the system is derived by the grid using phase locked loop so the output of the inverter is in synchronism with grid with respect to phase as well as frequency.
Differences in the Clinical Characteristics of Early- and Late-Onset Asthma in Elderly Patients

Differences between early-onset and late-onset asthma in elderly subjects have not been comprehensively described in China. We conducted a cross-sectional study to determine the phenotypic differences between early-onset asthma (EOA) and late-onset asthma (LOA) in elderly patients. We collected clinical and physiological data from 176 elderly patients with asthma. Participants were divided into two groups: EOA group and LOA group. Demographics, comorbidities, inflammatory parameters, lung function, severity, asthma control, and medication use among EOA and LOA elderly patients were compared. Elderly subjects with EOA had more atopic disease, a stronger positive family history of asthma, higher IgE, and exhaled nitric oxide levels as compared to those with LOA. In contrast, elderly subjects with LOA had lower lung function and more marked fixed airflow obstruction (FAO). Elderly subjects with LOA had a higher incidence of chronic obstructive pulmonary disease (COPD). No differences were observed in age, gender, BMI, history of smoking, severity, and asthma control between the two groups. Both similarities and differences exist between elderly subjects with EOA and those with LOA in China. Further work is required to determine the pathophysiological, clinical, and therapeutic implications for different asthma phenotypes in elderly subjects.
Biomimetic Effect of Nano-Hydroxyapatite in Demineralized Enamel before Orthodontic Bonding of Brackets and Attachments: Visual, Adhesion Strength, and Hardness in In Vitro Tests

Dietary habits with high consumption of acidic food can induce in orthodontic patients an increased risk of demineralization lesions around orthodontic brackets and bands. The purpose of the present laboratory study is to assess the in vitro visual efficacy of a biomimetic nano-hydroxyapatite remineralizing solution in a hypomineralized enamel surface and its effect on adhesion of fixed orthodontic appliances and on enamel microhardness. Intact teeth were demineralized, and subsequently the areas of demineralization were visually recorded using a 0–100 scale. Subsequently, a remineralizing solution (Biorepair® Repair Shock Treatment) was applied for ten minutes once a day/for one week per month for a total remineralizing treatment of 3 months. Visual effects were recorded. Moreover, bond strength was recorded and adhesive remnant index scores were measured for both orthodontic brackets and composite attachments both before demineralization and after demineralization and application of remineralizing solution. Also, Vickers microhardness was measured. All data were submitted to statistical analysis. The application of remineralizing solution induced a significant in vitro reduction of demineralized areas after the first week of application. No significant differences between untreated enamel surfaces and remineralized surfaces were detected after 2 months of remineralizing treatment. Bond strength values were significantly reduced for both brackets and attachments after remineralizing treatment. However, attachments showed higher adhesion values than brackets in both conditions tested. Remineralized enamel showed significantly higher microhardness values than demineralized enamel and lower values than intact enamel.