Miracle-dz/UniFAR-FTUs huggingface dataset:
A Google Colab Based Online Platform for Rapid Estimation of Real Blur in Single-Image Blind Deblurring

Referenceless or Blind Image Deblurring (BID) is a challenging task in image restoration, because of the absence of previous information about the blurring process. In BID, the Point Spread Function (PSF) that causes blur is unknown and finding it is a challenging task to do. In contrast, reference-based image deblurring techniques are better in terms of achieving deblurred images, as in such cases we know the cause of blur and many filters and methods get the job done easily. In real life imaging, the presence of blur is caused due to many reasons including motion, defocus, atmospheric turbulence and noise in capturing device. Removing such blur to produce sharp images is very important in many cases as of today’s technological driven era demands for images of unspoiled quality. This paper presents a method to eliminate blur in the fastest way. The scheme is devised to use a Genetic Algorithm (GA) on Google Colab to find PSF of the blur kernel. This work tries to estimate the parameters of blurring using quality score minimizing. Once the PSF is estimated, the Wiener filter is used for deblurring in Google Colab. This scheme presents the implementation of the algorithm on Google Colab to use GPU for achieving parallelism and reduce implementation time. Validation of the work has been carried out on different images and the system achieved marked improvements over existing approaches. The work presented is simple and efficient which requires no former information about the original sharp image or deblurring process.
Study on Urban Spatial Function Mixture and Individual Activity Space From the Perspectives of Resident Activity

The research on the relationship between residents’ daily activities and urban spatial structure is of considerable significance to urban planning engineering and the organization of urban functions. However, little research considers the perspective of micro-spatial scale or resident perception. The increasing user-generated activity check-in data in social networks provides a database for this research. In this study, we first divided the urban space into nine functions that satisfy the residents’ activities, then used the small-scale grid to divide the city blocks and used information entropy to evaluate the mixed degree of land use functions. We then introduced the latent Dirichlet allocation (LDA) topic model to identify 15 mixed patterns of land use functions and each spatial unit’s topic distribution. Moreover, the JS divergence index was employed to measure spatial units’ similarity, fit the distance-activity intensity decay curve, and studied the influence of the individual spatial function distribution choice. We demonstrate that in urban space, residents’ daily activities mold the blending of urban area functions and shift single-function urban planning to mixed-use, consisting of single-function dominant and multi-function mixed. Besides, the functional complementarity between the activity units weakens the distance attenuation effect of the activity-space interaction intensity to some extent. The research on the interaction between active space and spatial activities expect to support the combination of urban land use types, the layout of facilities, and the guidance of residents’ activities.
Lower-limb amputees can reduce the energy cost of walking when assisted by an Active Pelvis Orthosis

Exoskeletons could compete with active prostheses as effective aids to reduce the increased metabolic demands faced by lower-limb amputees during locomotion. However, little evidence of their efficacy with amputees has been provided so far. In this paper, a portable hip exoskeleton has been tested with seven healthy subjects and two transfemoral amputees, with the final goal to verify whether a hip flexion-extension assistance could be effective in reducing the metabolic cost of walking. The metabolic power of the participants was estimated through indirect calorimetry during alternated repetitions of three treadmill-based walking conditions: without the exoskeleton (NoExo), with the exoskeleton in zero-torque mode (ExoTM) and with the exoskeleton providing hip flexion-extension assistance (ExoAM). The results showed that the exoskeleton reduced the net metabolic power of the two amputees in ExoAM with respect to NoExo, by 5.0% and 3.4%. With healthy subjects, a 5.5±3.1% average reduction in the metabolic power was observed during ExoAM compared to ExoTM (differences were not statistically significant), whereas ExoAM required 3.9±3.0% higher metabolic power than NoExo (differences were not statistically significant). These results provide initial evidence of the potential of exoskeletal technologies for assisting lower-limb amputees, thereby paving the way for further experimentations.
A Novel Biomimic Soft Snail Robot Aiming for Gastrointestinal (GI) Tract Inspection

Gastrointestinal (GI) tract related diseases are common and deadly. To avoid the diseases developing into an advanced stage, early inspection and intervention are crucial. However, existing inspection methods are either uncomfortable or lack of active steering. Therefore, it is necessary to investigate a locomotion method that is friendly to the GI tract. This paper proposed a novel soft snail robot targeted for active locomotion inside the GI tract. By studying the locomotion mechanism of gastropods, a snail robot propelled with longitudinal travelling wave was designed. The wave was generated by deflating and inflating a series of chambers. The snail robot could adhere to different surfaces, including the ex vivo porcine intestine, by the mucus underneath. Experiments were designed to study the performance of the proposed snail robot. Results show that, the soft snail robot could crawl over different substrates including hard substrate, soft substrate and biological tissue. The speed of the snail is affected by multiple factors, including the surface condition, wave frequency and the mucus concentration.
Enhancing Robot-Environment Physical Interaction via Optimal Impedance Profiles

Physical interaction of robots with their environment is a challenging problem because of the exchanged forces. Hybrid position/force control schemes often exhibit problems during the contact phase, whereas impedance control appears to be more simple and reliable, especially when impedance is shaped to be energetically passive. Even if recent technologies enable shaping the impedance of a robot, how best to plan impedance parameters for task execution remains an open question. In this paper we present an optimization-based approach to plan not only the robot motion but also its desired end-effector mechanical impedance. We show how our methodology is able to take into account the transition from free motion to a contact condition, typical of physical interaction tasks. Results are presented for planar and three-dimensional open-chain manipulator arms. The compositionality of mechanical impedance is exploited to deal with kinematic redundancy and multi-arm manipulation.
Homology and Heterology Effects in Drosophila: Cohesin and Condensin as Chromosome Choreographers

The organization of interphase nuclei has numerous consequences for gene expression and genome stability in eukaryotes. Here, we present studies of chromosome organization in Drosophila melanogaster over the course of the cell cycle. We report surprising observations suggesting that cohesin, a protein complex essential in mitosis for holding together the products of DNA replication known as sister chromatids, may not be required to keep sister chromatids in close proximity during interphase. These observations raise questions regarding the nature of cohesinindependent connections between chromatids, why the cell might have such connections in addition to those mediated by cohesin proteins, and how cohesin-independent mechanisms might contribute to other interchromosomal associations and nuclear organization throughout the cell cycle. A well-known feature of nuclear organization in Drosophila is the somatic pairing of maternal and paternal homologous chromosomes; we find that certain factors contributing to the pairing of homologs, specifically, condensin II and its regulators, also contribute to the organization of sister chromatids, findings which have interesting implications for the nature of pairing. Finally, we examine another type of inter-chromosomal interaction occurring in Drosophila nuclei, which is the nonhomologous clustering of centromeres, and identify candidate genes that regulate this organization. Overall, this work expands current knowledge on both homologous and heterologous inter-chromosomal interactions, and highlights the relationship between chromosome organization in interphase and in mitosis.
Group Sparsity Assisted Synchrosqueezing Approach for Phonocardiogram Signal Denoising

Computer aided diagnosis (CAD) framework gains huge importance in today's medical world for the automatic cardiac disease monitoring. The CAD framework requires preprocessing of acquired phonocardiogram (PCG) signals to extract informative features from it. Denoising is an important preprocessing step for cardiac disease diagnosis. This paper proposes a novel, group sparsity assisted synchrosqueezing approach for improved PCG signal denoising. In the proposed approach, the PCG signals are initially denoised by exploiting the group sparse (GS) property of the heart sound signals. In the second step, the denoised result is improved by utilizing the synchrosqueezing wavelet transform (SSWT) by eliminating the unwanted higher order frequency components using the extracted intrinsic mode type functions. The robustness of the proposed approach is evaluated by using several PCG signal databases. The superior performance compared to other existing models confirms that the proposed approach can be useful for improved PCG denoising tasks in automatic cardiac monitoring systems.
Machine Learning based Malware Detection in Cloud Environment using Clustering Approach

Enforcing security and resilience in a cloud platform is an essential but challenging problem due to the presence of a large number of heterogeneous applications running on shared resources. A security analysis system that can detect threats or malware must exist inside the cloud infrastructure. Much research has been done on machine learning-driven malware analysis, but it is limited in computational complexity and detection accuracy. To overcome these drawbacks, we proposed a new malware detection system based on the concept of clustering and trend micro locality sensitive hashing (TLSH). We used Cuckoo sandbox, which provides dynamic analysis reports of files by executing them in an isolated environment. We used a novel feature extraction algorithm to extract essential features from the malware reports obtained from the Cuckoo sandbox. Further, the most important features are selected using principal component analysis (PCA), random forest, and Chi-square feature selection methods. Subsequently, the experimental results are obtained for clustering and non-clustering approaches on three classifiers, including Decision Tree, Random Forest, and Logistic Regression. The model performance shows better classification accuracy and false positive rate (FPR) as compared to the state-of-the-art works and non-clustering approach at significantly lesser computation cost.
A New Cryptic Species of Melanochlamys (Gastropoda: Heterobranchia: Cephalaspidea) from the Northwestern Pacific

Abstract: A new species of Melanochlamys is described based on specimens collected in Russia and Korea. This new species is genetically and anatomically different from other North Pacific species. Phylogenetic analyses of two mitochondrial (COI, 16S) and a nuclear (H3) gene confirmed the results of previous analyses and recovered a clade including specimens of the new species. Although this clade is poorly supported, species delimitation analyses and anatomical differences confirmed it constitutes a distinct species. These results confirm the presence of two distinct species of Melanochlamys in Russia and Korea, bringing the total diversity of the genus in the North Pacific to five.
Bio-inspired Approach to Microwave Circuit Design

The article describes a bio-inspired approach to the structural and parametric design of microwave circuits based on a genetic algorithm (GA), which permits algorithmic environment in the field of genetic search for solving NP complete problems, in particular, structural-parametric synthesis of a microwave amplifier. The article is aimed at finding ways of structure-parametric synthesis of microwave modules based on a bio-inspired theory. Scientific novelty of the research lies in the development of a modified genetic algorithm for a bio-inspired automated structural and parametric synthesis of microwave modules. The problem statement in the paper is as follows: to optimize the synthesis of passive and active microwave circuits by using a modified GA. The fundamental distinction of a new approach from the known is the use of new modified genetic structures in bio-inspired automatic structural and parametric synthesis, moreover, a new method for calculating a microwave amplifier based on modified GA is presented in the work. Thus, the problem of creating methods, algorithms and software for automated structural synthesis of microwave modules is currently of special actuality. The solution of the problem will improve the quality characteristics of the designed devices; reduce the time and cost of design and lower the requirements for implementation qualifications.
Oviposition site preference of Barbitistes vicetinus (Orthoptera, Tettigoniidae) during outbreaks

Barbitistes vicetinus is an endemic bush‐cricket of north‐east Italy that causes heavy damage to woody vegetation and crops. Because the species was described in the late 1990s and outbreaks occurred only in the last decade, no data are currently available on pest biology and ecology. Female oviposition preference, in particular, is a key factor for understanding species habitat use, as well as for developing species monitoring and control programmes. We tested the influence of vegetation type (forest, vineyard and hedgerow) and soil cover (broadleaf litter and grass) on the oviposition preference of the species. A 3‐year study was conducted and 18 sites across the outbreak area were sampled with emergence traps. A higher nymph density was recorded in woody vegetation than in vineyards without a density gradient from the inner forest to the edge. Moreover, a significantly lower density was found under grass cover (approximately three individuals per trap) compared with broadleaf litter (approximately 39 individuals per trap). Although B. vicetinus is commonly found in vineyards, where it can cause severe damage, these habitats did not offer to the species suitable oviposition sites. These results provide useful insights with respect to the management of B. vicetinus outbreaks.
Real-Time Detection of Cyber Attacks on the IoT Devices

Cyber attacks are used to exploit vulnerabilities present in various connected critical systems and the Internet of Things(IoT) is no exception to this. The state of art presents many approaches to detect and mitigate such cyber attacks. Realtime deployment of such methods faces many challenges including a selection of development tools and platforms, limits on computing capabilities of IoT devices, high velocities of data pushed to cloud and heterogeneity of devices. This paper presents an experimental setup for the real-time deployment of security analytics for IoT devices using Google cloud platform. This paper also presents a summary of alternate tools available for real-time deployment of IoT security analytics including Amazon web services, Microsoft Azure and Open source tools.
A Survey Report on Identifying Different Machine Learning Algorithms in Detecting Domain Generation Algorithms within Enterprise Network

Anomaly detection is done to identify the abnormal patterns that deviate from the majority of data. It is also known as outlier detection. Detection of Domain Generation Algorithms is also a type of anomaly detection. In this paper, we talked about Domain Generation Algorithms (DGA). Malware uses Domain Generation Algorithms to communicate with Command and Control (CnC) servers managed by hackers. As DGAs are generated randomly, it is hard to detect them in real-time using signature-based software. DGA activates for a short time. So threat intelligent software sometimes fails to recognize if the URL is DGA or genuine. In this paper, research is done in machine learning algorithms such as random forest and deep learning techniques such as LSTM and neural networks which will help to detect patterns of DGA. Using the DGA technique malware starts communicating with this server. As this is a type of advanced persistent threat, this results in intellectual and financial losses of enterprises. The traditional techniques consist of blacklisting malware and URLs. Automatic detection of this DGAs is a crucial task. This imposes an overheard as DGA gets generated in large volumes.
Dual-Vector Model Predictive Current Control of Permanent Magnet Synchronous Motor Drives With the Segment Golden Search Method

Dual vector model predictive current control (DVMPCC) has been receiving plenty of concern due to its excellent static and dynamic performance. However, the application time of the selected voltage vector may be a wrong value because the calculation of duty cycle for each voltage vector is independent of the cost function. Here, to overcome the drawbacks of the current calculation methods, the paper introduces a novel approach to calculate the duty cycles for the two voltage vectors in one sampling time by combining the duty cycle calculation into the cost function. In this algorithm, the segment golden search method is used to find the optimal duty cycles for any pair of two voltage vectors. The cost function compares the available pairs of voltage vectors based on the obtained optimal duty cycles of each pair of voltage vectors to select the optimal pair of voltage vectors and duty cycles. In addition, to reduce the complexity of the algorithm, the redundant pairs of voltage vectors are eliminated and the available pairs of the voltage vectors is limit to five. Furthermore, a novel switching pattern is introduced to reduce the average switching frequency. When the inverter adopts the proposed MPCC, the optimal switching time and optimal voltage vector pair are both obtained at the same time, and the result duty cycle is always a reasonable number within the sampling interval. Comparative studies between the proposed method and a recently introduced duty cycle-based MPCC are carried out. The performance of a permanent magnet synchronous motor drive under the proposed MPCC confirms superiority in terms of average switching frequency, better quality currents, and electromagnetic torque ripple.
Low Power 32-bit Synchronous and Reconfigurable ALU Design using Chain Structure

Low power consumption is one of the leading design challenges while implementing any digital circuit. In platforms like wireless sensor networks where sensor nodes, located at various remote locations, are battery operated this challenge becomes more critical. Due to the lesser access to the deployment site, it is possible neither to change the battery nor to reconfigure the hardware to change the functionality of the deployed sensor node. Our approach of designing an ALU (Arithmetic and logic unit) with different arrangement and selection of elements than that conventionally used results in low power consumption (reduced by > 50%) without effecting the design complexity, total speed of operation and functionality. Additionally, the system is implemented on ZedBoard zynq evaluation and development kit so that the additional changes can be done on the hardware simply by modifying the HDL (Hardware description language) code.
Interactive 3D Graph SLAM for Map Correction

This letter presents an interactive graph SLAM framework with a 3D LIDAR. This framework allows the user to interactively correct a 3D environmental map generated by an automatic SLAM system. By optimizing a pose graph consisting of pose constraints created by the automatic SLAM and map correction constraints, which are created by the user through a graphical user interface, we obtain a large and globally consistent 3D environmental map. We propose semi-automatic loop closing and plane-based map correction techniques for creating map correction constraints. We also devise a pose constraint update approach to refine the pose constraints given by the automatic SLAM. The evaluation results demonstrate that the proposed system enables us to improve the consistency of mapping results and obtain a mapping accuracy that outperforms state-of-the-art automatic SLAM frameworks with minimal human effort.
Comparison of Intramuscular and Surface Electromyography Recordings Towards the Control of Wearable Robots for Incomplete Spinal Cord Injury Rehabilitation

Spinal Cord Injury (SCI) affects thousands of people worldwide every year. SCI patients have disrupted muscle recruitment and are more predisposed to other complications. To recover or enhance lower limbs functions, conventional rehabilitation programs are typically used. More recently, conventional programs have been combined with robot-assisted training. Electromyography (EMG) activity is generally used to record the electrical activity of the muscles, which in turn can be used to control robotic assistive devices as orthoses, prostheses and exoskeletons. In this sense, surface EMG can be used as input to myoelectric control but presents some limitations such as myoelectric crosstalk, as well as the influence of motion artefacts, and electromagnetic noise. EMG can also be recorded using intramuscular detection systems, which allows the detection of electric potentials closer to the muscle fibres and the recording of EMG activity from deeper muscles. This paper evaluates the quality of intramuscular EMG recordings compared to surface EMG signals, as a preliminary step to control EMG-driven exoskeletons. Seven healthy subjects performed submaximal knee and ankle flexion/extension movements with and without the use of a lower limb exoskeleton. Intramuscular recordings presented early muscle activation detecting times, which is a very important feature in real-time control, and good signal-to-noise ratio values, showing the potential of these biosignals as reliable input measures to control exoskeletons for rehabilitation purposes.
Virtual Farmer: Real Time Crop Prediction and Automatic Irrigation System

There are a plethora of problems associated with agriculture and farming which require considerable improvements but remain untouched by technology. Farming is considered as one of the strong pillars of any economy. Considering this, very few technologies exist to aid the farmers in selecting the right crops depending on the environmental factors. Moreover most irrigation systems all around the world require at least some form of human intervention. Considering this, the proposed smart farming solution aims to aid the farmers by means of technology so as to increase their yield by suggesting to them the crops that will be most profitable for them as well as automating the irrigation system for them. This in turn will be a major help to the agriculture community as a whole and also set free the farmers from certain rudimentary tasks.