Miracle-dz/UniFAR-FTUs huggingface dataset:
A Diversified Denoising System for Biomedical Images

In this paper, we propose an image denoising system which reduce the speckle noise in medical images, while preserving the image features. The proposed denoising methods are based on wavelets functions. We have chosen denoising methods based on the Dual Tree Complex Wavelet Transform (DTCWT) and three variants of the bishrink filter. More, we propose an original denoising method that takes into account the textural and structural properties of the analyzed images.
Towards the insulin resistance and obesity diagnosis: A dimensional analysis approach

Diabetes mellitus and cardiovascular diseases are ailments related to insulin resistance (IR) and obesity. The existing methods for IR and obesity diagnosis are the HOMA-IR and body mass index (BMI), respectively. Those methods have limitations in the diagnosis. The aim of this research is to propose dimensionless indexes that can diagnose subjects with IR and obesity using anthropometrics and biochemical variables (waist circumference, height, weight, systolic blood pressure, diastolic blood pressure and triglycerides). Three dimensionless indexes, designed from the π Vaschy-Buckingham theorem, were assessed using receiver operating characteristic (ROC) curves and a database of 829 subjects. The index π1, constructed with the variables: waist circumference, triglycerides height and weight; obtained a better performance as classifier of IR and obesity, presenting an area under the ROC curve of 0.78, sensitivity of 0.82 and specificity of 0.68 for IR diagnosis, and an area under the ROC curve of 0.68, sensitivity of 0.66 and specificity of 0.65 for obesity diagnosis. The π1 dimensionless index designed in this study is a simple method that allows diagnosing two pathologies from variables that can be collected in a routine medical examination.
A Hybrid Method for Text Line Extraction in Handwritten Document Images

Text line segmentation in handwritten document image, as one of the preliminarily steps for document image recognition, is a challenging problem. In this paper, a hybrid method for text line extraction in handwritten document images is presented. Initially, a connected component (CC) labelling method following by a CC filtering is employed to extract a set of CCs from the input document image. A new distance measure is introduced to compute normal distances between the extracted CCs. By traversing the normal distance matrix from both the right and left directions, half-chains of CCs are constructed. The CCs half-chains are merged to obtain CCs full-chains. From the extracted full-chains separator lines are obtained. A gradient metric is proposed to detect and remove touching text lines. Using remaining separator lines the adaptive projection profile of the image is computed. Based on the projection profile, coarse text line extraction is performed. Finally, a fine text lines extraction is performed by applying a postprocessing step. To evaluate the method, two benchmarks named ICDAR2013 handwriting segmentation contest, and Kannada datasets composed of handwritten document images in English, Greek, Bengali, and Kannada languages were considered for experimentation. Experimental results indicate a promising performance was obtained compared to some of the state-of-the-art methods.
Quantitative Vascular Blood Flow Imaging: A Comparison of Vector Velocity Estimation Schemes

The recently developed flowline based ultrasound simulation tool FLUST was used to simulate multiple realizations of echo data from the systolic flow field of a carotid artery bifurcation. The data was used to quantify the bias and standard deviation of three different vector velocity estimators in every point of the image, using different acquisition setups and a range of SNR values. The results showed that in high SNR conditions, both cross-correlation based (speckle tracking) and autocorrelation based velocity estimation techniques performed well in terms of bias, but that speckle tracking yielded a higher standard deviation. In poor SNR conditions, a consistent negative bias was found when using speckle tracking, especially for the vx component. The negative bias increased with decreasing SNR. The autocorrelation based velocity estimators showed a more consistent performance over a large range of SNR values. A setup where axial resolution (pulse length) and frame rate was traded for increased SNR and lateral resolution (coherent compounding based on two transmit angles) was found to increase the performance of the cross-correlation based technique. When using this balanced setup in high SNR conditions, the cross-correlation based technique yielded a slightly lower median bias in the vx component than the autocorrelation based technique. Increasing the pulse length further did not improve the performance of the autocorrelation based estimator.
Writer Identification for Historical Manuscripts: Analysis and Optimisation of a Classifier as an Easy-to-Use Tool for Scholars from the Humanities

Several methods have been proposed for the task of writer identification for historical manuscripts. Most of these methods have been evaluated on private historical datasets only, while few have been evaluated on the recently published historical dataset provided by the Historical-WI competition at ICDAR-2017. Nevertheless, there is no thorough analysis in the literature available w.r. t. the degradation typically found in the digitized manuscripts. Furthermore, the currently proposed methods are beyond the reach of the scholars from the humanities; either because of the impracticality of the method itself, or because of the lack of an easy-to-use implementation. In this paper, we analyse a state-of-the-art method against common degradation types in historical manuscripts using images from the virtual manuscript library of Switzerland. Furthermore, we show that, by optimising a key parameter, we can enhance the performance of the method and significantly outperform the winner method of the Historical-WI competition. Finally, we demonstrate the practicality of our implementation yielding intuitively comprehensible results for direct use of scholars from the humanities.
Layered Space-Time Index Coding

Multicasting <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> independent messages via multiple-input multiple-output channels to multiple users where each user already has a subset of messages as side information is studied. A general framework of constructing layered space-time index coding (LSTIC) from a large class of space-time block codes (STBC), including perfect STBC, is proposed. We analyze the proposed LSTIC and show that it provides minimum determinant gains that are exponential with the amount of information contained in the side information for <italic>any possible side information</italic>. When constructed over a perfect STBC, the proposed LSTIC is itself a perfect STBC and hence many desired properties are preserved. To illustrate, we construct LSTIC over the following well-known STBCs: Golden code; <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$6\times 6$ </tex-math></inline-formula> perfect STBCs; and Alamouti code. Simulation results show that the obtained side information gain can be well predicted by our analysis.
Analyzing the Effects of PA Variations on the Performance of Phased Array Digital Predistortion

This paper shows how digital predistortion of a phased array can benefit from the parametric variations over parallel power amplifiers (PAs). Different antenna configurations are simulated by varying the PA input drive levels by the Monte-Carlo method. The error vector magnitude (EVM) at the steering angle and total radiated adjacent channel power ratio (TRACPR) are used as performance metrics. The simulation results indicate that array predistortion can benefit from the variations between the PAs to improve the EVM significantly. However, at the same time, the TRACPR performance is reduced. This gives a new trade-off to balance between in-band and out-off-band distortion in the fifth generation beamforming systems.
Security in Fog Computing: A Novel Technique to Tackle an Impersonation Attack

Fog computing is an encouraging technology in the coming generation to pipeline the breach between cloud data centers and Internet of Things (IoT) devices. Fog computing is not a counterfeit for cloud computing but a persuasive counterpart. It also accredits by utilizing the edge of the network while still rendering the possibility to interact with the cloud. Nevertheless, the features of fog computing are encountering novel security challenges. The security of end users and/or fog nodes brings a major dilemma in the implementation of real life scenario. Although there are several works investigated in the security challenges, physical layer security (PLS) in fog computing is not investigated in the above. The distinctive and evolving IoT applications necessitate new security regulations, models, and evaluations disseminated at the network edge. Notwithstanding, the achievement of the current cryptographic solutions in the customary way, many aspects, i.e., system imperfections, hacking skills, and augmented attack, has upheld the inexorableness of the detection techniques. Hence, we investigate PLS that exploits the properties of channel between end user and fog node to detect the impersonation attack in fog computing network. Moreover, it is also challenging to achieve the accurate channel constraints between end user and fog node. Therefore, we propose Q-learning algorithm to attain the optimum value of test threshold in the impersonation attack. The performance of the propose scheme validates and guarantees to detect the impersonation attack accurately in fog computing networks.
Towards Slicing for Transport Networks: The Case of Flex-Ethernet in 5G

Current solutions for network virtualization in the transport network relies on Virtual Local Area Network (VLAN) and Virtual Private Network (VPN) technologies, which facilitate logically isolated connectivity, providing data traffic seperation. However, co-existing VLAN/VPNs on top of a common infrastructure may compete for the same physical resources and introduce performance degradation, especially in case of congestion. This paper introduces Flex-Ethernet (Flex-E) and investigates its use as candidate interface solution for facilitating “hard” slicing in transport networks. This study sheds light on how Flex-E together with newly developed techniques like Segment Routing (SR), can enable a dynamic path allocation with strict performance guarantees. The standardization gaps and open challenges are also elaborated.
Improving semantic segmentation in urban scenes with a cartographic information

This paper presents three different approaches to inject a location information in semantic segmentation Convolutional Neural Networks (CNN) applied to urban scenes. The assumption that a location information would improve semantic segmentation performance emerges from the idea that some elements of urban scenes are located in a predictable manner. This assumption is confronted to realistic data on the CARLA autonomous driving simulator, which is used to create our own synthetic dataset with images, depth maps and bird-eye-view cartographic images. Simulators circumvent the difficulties due to the scarcity of publicly available synchronous labeled images and location information. We encode the location information as a cartographic image to process it as a camera image. We assess the relevance of injecting the cartographic information in three different manners: as a Conditional Random Field potential, as an additional task and as an additional encoder input of a CNN. The three methods are evaluated and compared with a state of the art CNN with regards to the pixel-wise accuracy, mean intersection over union and intersection over union of some important classes. The multi-encoder approach improves the intersection over union of the pedestrians, vehicles and traffic signs classes by respectively 4%, 1.6% and 9%.
Mimicking Leaky-Integrate-Fire Spiking Neuron Using Automotion of Domain Walls for Energy-Efficient Brain-Inspired Computing

Although an average human brain might not be able to compete with modern day computers in performing arithmetic operations, when it comes to recognition and classification tasks, biological systems are clear winners in terms of performance and energy efficiency. Building blocks of all such biological systems are neurons and synapses. In order to exploit the benefits of such systems, novel devices are being explored to mimic the behavior of neurons and synapses. We propose a leaky-integrate-fire (LIF) neuron using the physics of automotion in magnetic domain walls (DWs). Due to the shape anisotropy in a high-aspect ratio magnet, DW has a tendency to move automatically, without any external driving force. This property can be exploited to mimic the realistic dynamics of spiking neurons, without any extra energy penalty. We analyze the dynamics of a DW under automotion and show that it can be approximated to mimic the LIF neuronal dynamics. We propose a compact, energy-efficient magnetic neuron, which can directly be cascaded to memristive crossbar array of synapses, thereby evading additional interfacing circuitry. Furthermore, we develop a device-to-system-level behavioral model to underscore the applicability of the proposal in a typical handwritten-digit recognition application.
Estimating the Change in Cellular Size Variance During Cell Death Using the Polydisperse Structure Factor Model

Quantitativ e UltraSound (QUS)techniques for determining the tissue microstructure are promising tools to detect and quantify cell death, and thus monitor the tumor response to therapy. Previous in vitro experimental studies suggested that the changes in the backscatter coefficient (BSC)during cell death were linked to the increase of the cellular size variance. The aim of this work was to estimate the change in cellular size variance from the polydisperse structure factor model (SFM)by using experimental ultrasonic measurement before and after therapy. The polydisperse SFM was recently demonstrated to be efficient for explaining the measured BSCs on cell pellet biophan-toms. Cell pellet biophantoms consist in centrifugated densely packed cells and serve as simplified in vitro versions of real tumors. Ultrasonic backscatter measurements were performed at frequencies ranging from 10 to 42 MHz on colon adenocarcinoma cell pellet biophantoms treated with staurosporine, a drug which induces mainly cell apoptosis. Blind estimates of QUS parameters were performed by fitting one measured BSC with a standard ultrasonic scattering model, namely the fluid sphere model (FSM). A novel approach was also proposed to estimate QUS parameters from the polydisperse SFM by using two measured BSCs before and after therapy. Finally, the relationship between the actual cellular structures and QUS parameters was investigated.
Queue Performance of Energy Harvesting Cognitive Radio Sensor Networks With Cooperative Spectrum Sharing

In this paper, we study the queue performance for energy-harvesting cognitive radio sensor networks (EHCRSNs) where the cooperation between the primary user and the sensor is applied. In EHCRSNs, energy harvesting provides a continual energy source for the power-limited sensor, while cognitive radio enables the sensor access the licensed spectrum, which can mitigate the spectrum-scarcity problem of the wireless sensor network. First, with the help of queue theory, we study the queue performance of the EHCRSN and the primary user when the sensor does not assist the service of the primary user. Through analyzing the dynamic energy harvesting and consuming process, we obtain the effect of energy harvesting on the queue performance of the EHCRSN. Then, we further analyze the queue performance when the cooperation between the sensor and the primary user is adopted. Through comparing the result, we observe the effect of energy harvesting on both the performance of the sensor and the primary user and find out the condition under which the cooperation can improve the system performance. The numerical results in different situations further demonstrate the relation between the queue performance and the energy harvesting rate and verify the analysis and the conclusion that the cooperation can improve queue performance under certain conditions. These results are very meaningful for the quality-of-service guarantee in EHCRSNs.
Image Manipulation Detection and Localization Based on the Dual-Domain Convolutional Neural Networks

In multimedia forensics, many efforts have been made to detect whether an image is pristine or manipulated with high enough accuracies based on specially designed features and classifiers in the past decade. However, the important task for localizing the tampering regions in a fake image still faces more challenges compared with the manipulation detection and relatively a few algorithms attempt to tackle it. With this in mind, a technique that utilizes the dual-domain-based convolutional neural networks (D-CNNs) taking different kinds of input into consideration is proposed in this paper. In the proposed framework, two sub-networks, named the spatial-domain CNN model (Sub-SCNN) and the frequency-domain-based CNN model (Sub-FCNN), are designed and trained, respectively. With the well-trained parameters, a transfer policy is applied to the training process of the D-CNN. While CNNs are capable of learning classification features directly from data, in their standard form they tend to learn features related to the image’s content. To overcome this issue in image forensics tasks, a new image pre-processing layer is proposed to jointly suppress image’s content and adaptively learn manipulation detection and localization features. After investigating the properties of datasets, two post-processing operations are finally proposed and compared to obtain the final results of the pixel-wise manipulation region localization. The D-CNNs is trained and validated using 75 percent of images in the CASIA v2.0 and tested using the remaining images in the CASIA v2.0, all images in Columbia Uncompressed and Carvalho datasets. The extensive experiments show that the proposed post-processing operations optimize the final tamper probability map, and our framework with the combination of Sub-SCNN and Sub-FCNN significantly outperforms the state-of-art techniques with the best F1 scores on the datasets.
Recognition of Falls and Daily Living Activities Using Machine Learning

A robust fall detection system is essential to support the independent living of elderlies. In this context, we develop a machine learning framework for fall detection and daily living activity recognition. Using acceleration data from public databases, we test the performance of two algorithms to classify seven different activities including falls and activities of daily living. We extract new features from the acceleration signal and demonstrate their effect on improving the accuracy and the precision of the classifier. Our analysis reveals that the quadratic support vector machine classifier achieves an overall accuracy of 93.2% and outperforms the artificial neural network algorithm.
Statistics Computation for Low Complexity Spectrum Sensing Based on Multiple Antennas

This paper presents a statistics computation method for computationally efficient spectrum sensing in cognitive radio. The presented technique computes the statistics for signal detection after combining all signals. As the computation of the statistics for all received antennas is not required, the presented technique enables the reduction of the computational complexity. Some results show that the spectrum sensing performances of the presented technique are almost the same as that of conventional techniques despite the complexity of the presented technique being almost half that of the conventional techniques.
An Improved Network-Coded Multiple Access for Power-Balanced Non-Orthogonal Multiple Access

A recently proposed network-coded multiple access (NCMA), consisting of XOR-based channel decoding and multi-user complete decoding (MUD-CD), can significantly improve throughput over conventional successive interference cancellation (SIC) decoding under near power-balanced channels. In this paper, we propose an improved NCMA (I-NCMA) for non-orthogonal multiple access (NOMA) in the 5G wireless communications, consisting of multiple users and a single base station (BS). At the transmitter side, the proposed scheme enables all users to simultaneously transmit messages to the BS in the same time and frequency. At the receiver side, we propose a joint-user channel decoding (JU-CD) to jointly decode all user messages by a single decoder. Then, the extrinsic messages from JU-CD can be jointly utilized by MUD-CD for decoding all user messages. Also, other than conventional NCMA that mainly deals with two users, I-NCMA adapts to the NOMA networks with an arbitrary number of users. Simulation results indicate that the proposed I-NCMA achieves a better decoding performance than both SIC and conventional NCMA over near power-balanced channels in the low-to-medium SNR regime, while retaining a performance close to conventional NCMA in the high SNR regime.
A General Approach for Deriving the Sliding Mode State-Space Equation in Second Order CCM Operated DC-DC Converters

A general method for deriving the sliding mode state-space equation for second order continuous conduction mode (CCM) DC-DC converters starting from the classical state space description is proposed. Opposed to most of the results in the literature that refer mainly to the classical buck, boost and buck-boost converters, the new method can be applied to any DC-DC converter. To prove this, a first order sliding mode controller for an unconventional step-down second order coupled inductor (SDSOCI) converter is designed and validated.
Development of a graphic interface for the three-dimensional semiautomatic glioblastoma segmentation based on magnetic resonance images

Glioblastoma is the most common and aggressive glioma in adults. Its complexity demands the development of methods able to maximize the capture of personalized information to let the design of patient-specific therapies, which can be achieved through radiomics studies. In this process, initial segmentation of the image is fundamental. In glioblastoma, the resulting segmented region of interest (ROI) must include the active tumor, its inner necrosis and the peripheral edema, a zone estimated to be infiltrated by tumor cells. In a first step, images corresponding to the different modalities of the MRI were registered to achieve spatial coincidence and the same three-dimensional resolution. In a second step the whole brain were segmented based on T1 images, to eliminate not-nervous tissues. Then the complete ROI region were determined through on a combination of FLAIR and T2 modalities and, finally, inner ROI components were defined working with the contrasted T1 modality. During these processes, K-means clustering, Chan-Vese active contours, adaptive thresholds, dilatation, erosion and replenishment algorithms were developed and grouped in the Matlab graphic interface RMIanalizer to interact with the user and visualize results. This interface can upload any format of medical image, segmentate semiautomatic and three-dimensionally the ROI components, and determine the estimated volume of each one. Preliminary results were compared with the "ground truth" cases submitted by the web database used, obtaining a Dice similarity coefficient of 0.886 +/− 0.0386 for the complete ROI region, over a total of 10 glioblastoma cases.