Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Belief Network for the Enhancement of Ultrasound Images with Pelvic Lesions

Abstract It is well known that ultrasound images are cost-efficient and exhibit hassle-free usage. However, very few works have focused on exploiting the ultrasound modality for lesion diagnosis. Moreover, there is no reliable contribution reported in the literature for diagnosing pelvic lesions from the pelvic portion of humans, especially females. While few contributions are found for diagnosis of lesions in the pelvic region, no effort has been made on enhancing the images. Inspired from the neural network (NN), our methodology adopts deep belief NN for enhancing the ultrasound image with pelvic lesions. The higher-order statistical characteristics of image textures, such as entropy and autocorrelation, are considered to enhance the image from its noisy environment. The alignment problem is considered using skewness. The proposed method is compared with the existing NN method to demonstrate its enhancement performance.
Improved Adaptive Filter-Based Control of a Magnetostrictive Vibration Isolator

In this paper, an improved adaptive filtered-x normalized least-mean-square (FxNLMS) algorithm is proposed to achieve active microvibration isolation. A developed magnetostrictive device is used as an isolator to mitigate the microvibration. Whereas, the magnetostrictive material characterizes hysteresis. Although the constitutive and Bouc- Wen model were used to compensate the hysteresis, they are non-analytical with certain approximation. Otherwise, the Preisach model, a popular operator-based model, was also used to compensate the hysteresis. However, due to the double integrals in this model, the hysteresis identification and inversion process is complicated. Instead, this paper applies a modified Prandtl- Ishlinskii (PI) model to estimate and invert hysteresis, which is more simple but effective. The inverse hysteresis is then applied to compensate the magnetostrictive- induced hysteresis with conventional FxNLMS algorithm. Experimental results demonstrate that the improved FxNLMS algorithm enhance the performance of the microvibration isolation effectively.
A Model-Compensation ADRC Strategy of Wind Energy Conversion System with Direct-Driven PMSG

This paper proposed two strategies based on the active disturbance synchronous controller (ADRC) designed for the WECS based on the direct-driven PMSG, which maximize the power output by tacking the optimal tip-speed ratio in real time. Since the direct-driven PMSG is a nonlinear system with a large amount of uncertainties and rapid dynamic variations, it is difficult to execute the model-based control designs. Considering these characteristics of the system, a basic ADRC strategy is proposed, which has a good performance without a detailed mathematical model of the system. Furthermore, considering the large changes in system dynamics and disturbances may substantially degrade the performance of WECS, a model-compensation ADRC strategy is presented, which incorporate the available model information into an extended state observer (ESO) to compensate the lumped disturbance efficiently. Simulation results show that, compared with the traditional ADRC strategy, the proposed ADRC has a stronger robustness against uncertain dynamics and external disturbances.
Distributed Secondary Voltage Control of Microgrids with Nonuniform Time-Varying Delays

In this paper, we propose a distributed secondary voltage control for microgrids. First, the communication network of the distributed generators (DGs) in a microgrid is modeled by a fixed directed graph with nonuniform time-varying delays. Next, the nonlinear dynamics of the DGs is transformed to a second-order linear system through an input-output linearization technique. A distributed controller is proposed for each generator to ensure its voltage magnitude to converge to a reference voltage. Moreover, the stability of the closed-loop system is analyzed with a Lyapunov-Razumikhin function method. Finally, some simulation results are given to verify our results.
GraphBolt: Streaming Graph Approximations on Big Data

Graphs are found in a plethora of domains, including online social networks, the World Wide Web and the study of epidemics, to name a few. With the advent of greater volumes of information and the need for continuously updated results under temporal constraints, it is necessary to explore novel approaches that further enable performance improvements. In the scope of stream processing over graphs, we research the trade-offs between result accuracy and the speedup of approximate computation techniques. We believe this to be a natural path towards these performance improvements. Herein we present GraphBolt, through which we conducted our research. It is an innovative model for approximate graph processing, implemented in Apache Flink. We analyze our model and evaluate it with the case study of the PageRank algorithm, perhaps the most famous measure of vertex centrality used to rank websites in search engine results. In light of our model, we discuss the challenges driven by relations between result accuracy and potential performance gains. Our experiments show that GraphBolt can reduce computational time by over 50% while achieving result quality above 95% when compared to results of the traditional version of PageRank without any summarization or approximation techniques.
Pseudoinverse Learning Algorithom for Fast Sparse Autoencoder Training

Sparse autoencoder is one approach to automatically learn features from unlabeled data and received significant attention during the development of deep neural networks. However, the learning algorithm of sparse autoencoder suffers from slow learning speed because of gradient descent based algorithms have many drawbacks. In this paper, a fast learning algorithm for sparse autoenceder is proposed which based on pseudoinverse learning algorithm (PIL). The proposed method calculates encoder weight matrix by truncating the pseudoinverse matrix of input data. The pseudoinverse truncation matrix is used as the weights of encoder, and then the input data is mapped to the hidden layer space through the biased ReLU activation function. The decoder weights are also can computed by the PIL. Unlike the gradient descent based algorithm, the proposed method does not require a time-consuming iterative optimization process and select many user-dependent parameters such as learning rate or momentum constant too. The experimental results indicate the superiority of proposed method which is very efficient and also can learned the sparsity of samples.
On the Security of Rotation Operation Based Ultra-Lightweight Authentication Protocols for RFID Systems

Passive Radio Frequency IDentification (RFID) tags are generally highly constrained and cannot support conventional encryption systems to meet the required security. Hence, designers of security protocols may try to achieve the desired security only using limited ultra-lightweight operations. In this paper, we show that the security of such protocols is not provided by using rotation functions. In the following, for an example, we investigate the security of an RFID authentication protocol that has been recently developed using rotation function named ULRAS, which stands for an Ultra-Lightweight RFID Authentication Scheme and show its security weaknesses. More precisely, we show that the ULRAS protocol is vulnerable against de-synchronization attack. The given attack has the success probability of almost ‘1’, with the complexity of only one session of the protocol. In addition, we show that the given attack can be used as a traceability attack against the protocol if the parameters’ lengths are an integer power of 2, e.g., 128. Moreover, we propose a new authentication protocol named UEAP, which stands for an Ultra-lightweight Encryption based Authentication Protocol, and then informally and formally, using Scyther tool, prove that the UEAP protocol is secure against all known active and passive attacks.
Context Aware Telco Churn Prediction Powered By Temporal Feature Engineering

It is still challenging in telecommunication (Telco) industry to precisely predict those who will churn from the current Telco operator to another one in next month. State of art approaches suffer from the issue that the predicted churners might be those silent customers with trivial profits to Telco operators. Thus, it is quite necessary to focus on the churn prediction problem with respect to active customers, instead of silent ones. Thus, we propose 3 kinds of new features in order to capture the context-aware customer behaviour in terms of temporal features. Such features include long-range Feature, trend feature and regression-based feature for Telco churn prediction. After that, we employ an ensemble process on the base learners: Random Forst (RF), XGB and GBDT + SVM. Experimental results confirm that the prediction performance has been improved by using these new features. From millions of active customers, this system can provide a list of prepaid customers who are most likely to churn in the next month, having 0.69 precision for the top 25000 predicted churners in the list. At the same time, achieve 44.69% improvement when given $5 \times 10 ^{4}$.
Low-Cost S-Band FMCW Radar-Based Short-Range Displacement Sensor

The following paper presents a detailed design of a frequency-modulated continuous wave radar system operating in the S-band, which has a very low cost and can be used for millimetric displacement measurements at close ranges. The low cost has been achieved by excluding any monolithic microwave integrated circuits thus all of the system blocks were designed using discrete components. The market price of all semiconductor components used in this project: radio frequency (RF) transistors, diodes, operational amplifiers, and voltage regulators, is about 9 USD when buying them in minimum quantity. The discrete nature of the system makes it an excellent tutorial on FMCW radar design and RF circuit techniques.
Arabic words Recognition using CNN and TNN on a Smartphone

Arabic script recognition has been a challenging due to the variability of writing styles, to the nature of Arabic scripts, to the complexities of processing steps and to the varieties of recognition methods. This paper uses a Convolutional Neural Network (CNN) for character recognition and Transparent Neural Network (TNN) for words reading. Because Arabic character segmentation is a very complicated step, we recognize only the first, the last character of all connected components of the recognized word and the isolated ones. A combination between the CNN and the TNN will complete the recognition of the whole word. CNN is a multi-layer feed-forward neural network that extracts features and properties from the input data. TNN is a special NN that recognize words from already activated characters and part of words. These methods are already used on computer recognition system. The proposed work is to integrate these methods and adapt them to the android operating system to apply them on smartphone. The evaluation is done on a database of Signboards Images of printed town names and the recognition rate is 98%.
Joint SAR Image Time Series and PSInSAR Data Analytics: An LDA Based Approach

Due to the constant increase in Earth Observation (EO) data collections, the monitoring of land cover is facilitated by the temporal diversity of the satellite images datasets. Due to the capacity of Synthetic Aperture Radar (SAR) sensors to operate independently of sunlight and weather conditions, SAR image time series offer the possibility to form a dataset with almost regular temporal sampling. This paper aims at mining the SAR image time series for an analysis of target’s behavior from the perspective of both temporal evolution and coherence. The authors present a two-level analytical approach envisaging the assessment of global (related to perceivable structures on the ground) and local (related to changes occurred within a perceivable structure on the ground) evolution inside the scene. The Latent Dirichlet Allocation (LDA) model is implemented to identify the categories of evolution present in the analyzed scene, while the statistical and coherent proprieties of the dataset’s images are exploited in order to identify the structures with stable electromagnetic response, the so-called Persistent Scatterers (PS). A comparative study of the two algorithms’ classification results is conducted on ERS and Sentinel-1 data. At global scale, the results fit human perception, as most of the points which can be exploited for Persistent Scatterers Interferometry (PS-InSAR) are classified within the same class, referring to stable structures. At local scale, the LDA classification demands for an extended number of classes (defined through a perplexity-based analysis), enabling further differentiation inside the evolutional character of those stable structures. The comparison against the map of detected PS reveals which classes present higher temporal correlation, determining a stable evolutionary character, opening new perspectives for validation of both PS detection and SITS analysis algorithms.
Little-Big Deep Neural Networks for Embedded Video Surveillance

Embedded systems are under continuous development of innovative technological trends, such as adoption of smart devices which are becoming capable of running complex video analytics tasks locally. Lately, deep neural networks have been successfully applied in the field of computer vision achieving state-of-the-art results. These techniques are not yet suitable for resource limited deployments due to high memory footprint and computational cost, factors that affect the inference time. To tackle these constraints, we propose a person re-identification architecture based on the DarkNET Deep Learning Neural Network architecture for person detection and segmentation, which is combined with SIFT algorithm for feature extraction and SVM for the classification task. The algorithm is implemented on a low processing embedded hardware system, namely Raspberry PI. The experiments were conducted on the proposed SPOTTER dataset. The results are conclusive to continue further investigation of applying specialized algorithms for real-time applications which can run on resource limited embedded systems.
An image processing method for green apple lesion detection in natural environment based on GA-BPNN and SVM

The technologies of information processing, especially the image processing, are important technical means to realize intelligent management and control of modern orchard. In order to realize the intelligentization of orchard production, the orchard image database, especially the lesion image database needs to be set up. Because of the uneven illumination and the complex background under the natural light condition, traditional image segmentation methods cannot solve the adaptive threshold issue during the lesion image processing of green apples. This paper proposes an image processing method based on a BP neural network updated by genetic algorithm (GA-BPNN) and support vector machine (SVM) to realize lesion image processing of green apples in orchard. With this method, apple images can be processed in batch and the lesion image database can be consummated automatically. Furthermore, the recognition of the diseased apple is realized. The experimental results show that the proposed method can obtain green apple segmentation and lesion detection with good efficiency and robustness in complex natural orchard environment.
High-Gain Observer-Based Distributed Tracking Control of Heterogeneous Nonlinear Multi-Agent Systems

We address the problem of leader-follower tracking control of nonlinear multi-agent systems. The considered heterogeneous models of the agents and the leader are allowed to be uncertain and can accept unknown bounded disturbances. In addition, the leader's position is only known to its immediate neighbors. Given this setting, a high-gain observer-based output feedback control system is designed that achieves ultimate boundedness of closed-loop system trajectories. Moreover, the ultimate bound can be made arbitrarily small. The output feedback control system can also recover the trajectories of the state feedback control performance when the observer gain be made sufficiently large. The design procedure is demonstrated by a simulation example for a number of interconnected Van der Pol oscillators.
Intrusion Detection using NBHoeffding Rule based Decision Tree for Wireless Sensor Networks

The objective of this paper is to build a practical intrusion detection system for wireless sensor networks which analyze the characteristics of traffic patterns and identify the intrusive activities in the network. It is to show that the choice of efficient and fast decision tree paradigm for intrusion detection with optimal features enhance the detection capability as well as saves energy, computation and memory of sensor networks. In addition, various rule based decision tree classifiers like Alternating Decision Tree, Decision Stump, J48, Logical Model Tree, Naive Bayes Tree and Fast Decision Tree learner have been compared with a family of Hoeffding rule based decision tree which shows better and fast detection capability. The evaluation of the enhanced feature space and the decision tree paradigm, on three different public dataset containing normal and anomalous data have been performed for various Hoeffding as well as other decision tree algorithms. With this approach it is proved that Hoeffding tree are best suited for online detection and handling of streaming sensor data with the efficient usage of memory in a resource constraint environment like sensor networks
Developing Bilingual Arabic-English Ontologies of Al-Quran

The main aim of developing a Quranic ontology is to facilitate the retrieval of knowledge from Al-Quran. Additionally, Quranic ontologies will enrich the raw Arabic and English Quran text with Islamic semantic tags. However, current Quran ontologies have different: scopes, formats, and entity names for the same concepts. Additionally, a single Quranic ontology does not cover most of the knowledge in Al-Quran. Therefore, these ontologies need to be increased, normalised, aligned and combined with other Quran resources such as Quran chapter and verse names, Quran word meanings, and other Quranic datasets. This paper reviews current Quran ontologies and datasets. Then, it presents several stages for developing Arabic-English Quran ontologies from different datasets related to Al Quran.
Optimal Experiment Design for Magnetic Resonance Fingerprinting: Cramér-Rao Bound Meets Spin Dynamics

Magnetic resonance (MR) fingerprinting is a new quantitative imaging paradigm, which simultaneously acquires multiple MR tissue parameter maps in a single experiment. In this paper, we present an estimation-theoretic framework to perform experiment design for MR fingerprinting. Specifically, we describe a discrete-time dynamic system to model spin dynamics, and derive an estimation-theoretic bound, i.e., the Cramér-Rao bound, to characterize the signal-to-noise ratio (SNR) efficiency of an MR fingerprinting experiment. We then formulate an optimal experiment design problem, which determines a sequence of acquisition parameters to encode MR tissue parameters with the maximal SNR efficiency, while respecting the physical constraints and other constraints from the image decoding/reconstruction process. We evaluate the performance of the proposed approach with numerical simulations, phantom experiments, and in vivo experiments. We demonstrate that the optimized experiments substantially reduce data acquisition time and/or improve parameter estimation. For example, the optimized experiments achieve about a factor of two improvement in the accuracy of ${T}_{2}$ maps, while keeping similar or slightly better accuracy of ${T}_{1}$ maps. Finally, as a remarkable observation, we find that the sequence of optimized acquisition parameters appears to be highly structured rather than randomly/pseudo-randomly varying as is prescribed in the conventional MR fingerprinting experiments.
Emerging Treatment Approaches for Parkinson’s Disease

Parkinson’s disease (PD) is the second most common neurodegenerative disease, manifesting as a characteristic movement disorder with a number of additional non-motor features. The pathological hallmark of PD is the presence of intra-neuronal aggregates of α-synuclein (Lewy bodies). The movement disorder of PD occurs largely due to loss of dopaminergic neurons of the substantia nigra, resulting in striatal dopamine depletion. There are currently no proven disease modifying treatments for PD, with management options consisting mainly of dopaminergic drugs, and in a limited number of patients, deep brain stimulation. Long-term use of established dopaminergic therapies for PD results in significant adverse effects, and there is therefore a requirement to develop better means of restoring striatal dopamine, as well as treatments that are able to slow progression of the disease. A number of exciting treatments have yielded promising results in pre-clinical and early clinical trials, and it now seems likely that the landscape for the management of PD will change dramatically in the short to medium term future. Here, we discuss the promising regenerative cell-based and gene therapies, designed to treat the dopaminergic aspects of PD whilst limiting adverse effects, as well as novel approaches to reducing α-synuclein pathology.
Reconstruction of Complex Time-varying Weighted Networks Based on LASSO

Network reconstruction is significant in a variety of domains including physics, biology, engineering and social science. Although a lot of effort has been put into static network reconstructions, time-varying networks are rarely studied. Existing studies of time-varying networks usually assume nodes' dynamics to be linear or consider nodes' nonlinear dynamics as prior knowledge. In this paper, identification of time-varying weighted networks is discussed under the condition where network topology, link weights and nodes' nonlinear dynamics are all unknown or unavailable. In fact, networks in real world are naturally sparse which enables the transformation from network reconstruction problem into sparse signal recovering, so that $l_{1}$ regularization can be effectively applicable. Based on the assumption that most natural networks can be mathematically expressed by only a few important analytical functions, we construct a function set consisting of both linear and nonlinear candidate functions. Then LASSO is applied to select the possible function assemblies from the proposed set and optimize the coefficients of the corresponding function terms in order to best describe time-varying structures as well as nodes' nonlinear dynamics. To prove the feasibility and effectiveness of this reconstruction method, both two complex models are testified in networks of different sizes. And this method shows potential ability applying to a wide range of dynamical time-varying networks.