Miracle-dz/UniFAR-FTUs huggingface dataset:
U-Net Based Segmentation of Skin Lesions Using Dermoscopic Images

In this article, a new approach has been proposed to segment the skin lesion using Dermoscopic image. The hairs in the images are removed by Dull Razor's method with two iterations. Then the color artefacts in images are removed using standard color constancy methods such as color equalization and grey world method. Also, these two color corrected versions are blended using multiscale image fusion technique. Then the two color corrected versions and their fused version are given as input to a U-Net which is developed with three encoders. Then the feature maps obtained from the encoders are fused and concatenated with the decoder part to get the segmented skin lesion. This approach outperforms well compared with the existing works.
LTE and WLAN fair co-existence in unlicensed bands

With the growing demand for wireless communication, there is an increasing need for efficient and reliable sharing of unlicensed frequency bands. Due to the difficulties involved in sharing the same frequency spectrum, the coexistence between LTE and WLAN over unlicensed bands has emerged as a significant area of research. This paper presents a study on the LTE co-existence with WLAN in unlicensed spectrum using NS-3. Our paper comes up with a hybrid network analyzer that implements Maximum throughput scheduling and exponential rule algorithm along with cat4 LAA LBT for achieving fair co-existence. The performance of these mechanisms is evaluated in terms of throughput, latency and fairness. The study also includes an investigation of the impact of various network parameters such as network topology, traffic load, and interference. The outcomes demonstrate that the suggested hybrid network analyzer can effectively manage the LTE, and WLAN co- existence in unlicensed bands, providing high throughput and fair resource allocation.
Design of a Hand Rehabilitation Exoskeleton Prototype for Patients Affected by Osteoarthritis

Hand osteoarthritis is a disease that allows the degeneration of the joints, this impairs the mobility of the hand. Motor rehabilitation allows patients with arthritic hand to resume the natural use of their hand. This research has a quantitative approach and is non-experimental, the V methodology was used for the development of the prototype. The construction of the prototype was divided into three systems: structural system, biomechanical system, and control system. Using 3D modeling and printing, it was possible to design the parts used in the final assembly of the prototype. With the use of simulations, the safety of the material used, and the resistance of the pieces were determined. The results of this investigation indicate that the present prototype can perform movements that allow the user to complete rehabilitation exercises.
Modified Bidirectional LLC Converter for Electric Vehicle Application

This paper proposes a modified bidirectional Half-Full Bridge LLC resonant Converter with a control strategy for EV charging applications. With the topology shown, every switch is capable of soft switching, which is operated by zero voltage switching (ZVS). The modified LLC uses fewer switches than a conventional LLC converter, which greatly reduces reverse power and turn-off losses while also allowing for significantly higher conversion efficiencies. The system achieved bidirectional power flow for a proposed isolated converter. The converter always operates with a discontinuous resonant current. Clamping diodes were added to the converter to minimize the initial inrush current. Additionally, the working principle and design aspects are presented. Closed loop PID control is used to control an EV battery's charging and discharging modes. To evaluate the effectiveness of the converter and controller, the 3.3kW system is simulated in MATLAB/Simulink with 96% efficiency, and the expected results are obtained.
Quantitative Analysis of Regression-Based Temperature Dynamics Models for Households with A/C Units Subject to Unknown Disturbances

This paper focuses on the identification of thermodynamic models for temperature prediction in households. The proposed temperature dynamics model falls under the class of Linear Time-Invariant (LTI) models, making it suitable for model predictive control synthesis. However, the presence of significant and variable thermal disturbances in households adds complexity to the identification process. The performance of various prediction error methods, such as ARX, ARARMAX, and BJ models, along with simplified models incorporating persistent disturbance excitation, is analyzed. The findings highlight the substantial impact of unknown disturbances on temperature predictions, emphasizing the crucial need for accurate prediction of these disturbances for effective household heating and cooling planning. The identification and evaluation of model performance measures are conducted using two months of experimental data collected from five households. This study contributes to understanding of the significance of addressing unknown disturbances and variability in thermodynamic model identification for temperature prediction.
Analysis Of Undergraduate Student Performance Patterns and Profiling Potential Dropouts

Students' performance is a success component in greater education institutions. Predicting and analyzing the overall performance of the scholar is imperative to aid educators in figuring out weaknesses and bettering academic scores. However, accomplishing correct predictions is challenging due to the large quantity of instructional data. The important purpose in the back of this is the lack of research on exploring one-of-a-kind prediction strategies and key attributes that have an impact on the student's academic performance. This study found that the computer classification device mastery algorithm was most commonly used to predict student learning success. Four algorithms that have been used more frequently to predict learning about student success are ANN, naive boys, regression of logistics, SVM and decision tree. Meanwhile, the records used in these research papers mainly ranked students' success in the master's in two or three classes that are pass/fail; or fail/pass/excellent. The results of this research will give recommendations and contributions to policy makers and the higher education institutions in Somalia.
A Peristaltic Soft, Wearable Robot for Compression Therapy and Massage

Soft robotics is attractive for wearable applications that require conformal interactions with the human body. Soft wearable robotic garments hold promise for supplying dynamic compression or massage therapies to improve lymphatic and blood circulation. In this letter, we present a wearable robot capable of supplying dynamic compression and massage therapy via peristaltic motion of finger-sized, soft, fluidic actuators. We show that this peristaltic wearable robot can supply dynamic compression pressures exceeding 22 kPa at frequencies of 14 Hz or more, meeting requirements for compression and massage therapy. A large variety of software-programmable compression wave patterns can be generated by varying frequency, amplitude, phase delay, and duration parameters. We finally demonstrate the utility of this peristaltic wearable robot for compression therapy, showing fluid transport in a laboratory model of the upper limb. We theoretically and empirically identify driving regimes that optimize fluid transport. These findings show the potential of such a wearable robot for the treatment of several health disorders associated with lymphatic and blood circulation, such as lymphedema and blood clots.
Alzheimer Classification Based on Inception V3 Convolutional Neural Network

Alzheimer's disease (AD)is an incurable disease that occurs in old age and early old age, and is characterized by neuron death and brain shrinkage. In this paper, an improved convolutional neural network based on Inception V3 is proposed for the recognition of Alzheimer's disease. We used the SMOTE technique to balance the data and adjusted the study rate using the ReduceLROnPlateau callback function. And at the same time, to improve the robustness of our model, we use the Batch Normalization method. Through 60 times of training, our model can quickly and accurately classify the input images. Finally, the accuracy of our model reached 94.40% on the training set, 90.28% on the test set and 90.48% on the validation set. Besides, the AUC of the model on test set can reach 0.9857, the precision rate is 90.06%, the recall rate is 90.87%, and the Fl-score rate is 90.23%.
Multipath TCP for Short Flows Supported by SDN in Heterogeneous Networks

Multipath TCP (MPTCP) offers better performance, throughput, and resilience in connection failure cases by dividing the flow into multiple subflows and submitting them over many paths. Generally, MPTCP has a perfect impact on long flows in terms of throughput once transferring data with big sizes. However, MPTCP may degrade the efficiency of short flows, which are sensitive to latency (e.g., web transfer applications). This work investigates the impact of MPTCP on short flows in homogeneous and heterogeneous networks and confirms that a bandwidth gap between paths is a critical factor influencing the performance of the short flows. Therefore, the work uses Software-Defined Networking (SDN) architecture to address the MPTCP problems and improves its performance for short flows. Our performance results show an improvement in MPTCP performance for short flows compared to the disjoint approach.
An Efficient Optimal Cluster Head Selection Algorithm in MANET using Elephant Herding optimization

Many mobile devices are linked together to form a decentralized wireless mesh network by a MANET, an ad hoc network. Due to their complexity and dynamic nature, MANETs are a form of network that experiences frequent topology changes. The cluster-based structure may be utilized to decrease the expense of topology. The formulation of the clustering problem is done for selecting the optimal cluster head (CH) in MANET based on Elephant herding optimization (EHO). The simulation results used to validate the proposed EHO approach show that the work creates stable clusters in which simulation results are compared with existing algorithms. In terms of network lifetime, clustering overhead, etc., the EHO performs better than previous techniques.
Study on a miniaturized microwave near-field probe based on Split Ring Resonator

One of the key research of microwave power application is to measure the dielectric constant of the medium. Near-field probe is a nondestructive testing method that can effectively measure the dielectric constant of a medium without damaging the sample. In order to reduce the size of microwave near-field probes, a miniaturized near-field probe with microstrip open split resonant ring (SRR) designed at 9GHz is investigated in this paper. Based on the finite element method analysis, HFSS software was used to simulate the model. The results show that the design has the advantages of small volume, high sensitivity, and feasibility and reliability in the measurement of dielectric constant.
A Real-Time Speaker Diarization System Based On Convolutional Neural Networks Architectures

Speaker diarization, which entails dividing a single audio recording into numerous voice recordings that each belong to a different speaker, is an essential step in working with audio data. Diarization systems do not recognize the speakers themselves; instead, they use unsupervised algorithms to segment audio recordings and organize utterances into speaker-specific categories. While voice-based biometric systems can recognize people, they can only be used for recordings with a single speaker. In this paper, an architecture for segment- or group-level single speaker diarization systems is proposed. The findings of the proposed algorithm's evaluation on the VoxCeleb and RAVDESS datasets, two speech databases, demonstrate that the group- level method outperforms the segment-level strategy in terms of recognition results. The difficulty of detecting numerous speakers chatting freely in an audio recording while accounting for emotional swings that may impact speech patterns is addressed in these systems, which show promise. According to the study, diarization can obtain accuracy levels of over 95.4 percent when applied to the RAVDESS and VoxCeleb datasets, which suggests that the suggested approach from speech can achieve levels of accuracy of over 96.4 percent.
A Fast Belief Propagation-Based Distributed Gauss– Newton Method for Power System State Estimation

State estimation is the foundation for a variety of online power system applications in energy management systems, and the stability of power systems is directly impacted by the speed with which current system states can be obtained through state estimation. This paper proposed a fast Gaussian-Newton state estimation method for power systems based on parallel belief propagation, which implements the Gaussian belief process via multi-core and multi-thread parallel computation to achieve efficient state estimation. Simulation findings on numerous IEEE-standard power systems show that the suggested technique outperforms the traditional algorithm.
Student Performance Prediction Using AI and ML: State of the Art

The digitalization of educational processes has enabled the generation of large datasets that can be used to improve processes in academic environments. One particular problem is the prediction of student performances based on historical data. Efficient student performance prediction can be used not only to prevent dropouts at an early stage, but it can also help perspective students to determine the fields in which they can have high academic performance and build successful student profile. Due to large and diverse data, this process has to be conducted with high degree of automations. Therefore, in this paper we have conducted an extensive survey on the impact of AI and ML techniques in student performance prediction, with primary aim to detect opportunities, good practices, but most importantly to identify gaps and remaining research challenges with the ultimate goal to define an effective framework for a student performance prediction system.
Food recognition using neural network on mobile device

This paper presents a solution to the problem of real-time food detection on mobile devices. The solution compares a YOLOv4 tiny model and a multilayer classification neural network ensemble as the convolutional neural network responsible for food detection, using MobilenetV3 for classification and several YOLOv4 tiny models capable of detecting less food in the overall problem. The model was trained with 40 different food categories using an RTX 3070 video card. Results showed that the multilayer network ensemble performed better than a single YOLOv4 tiny model. The TP value was 258 higher, the FP value was 163 lower and the FN value was 773 lower. The accuracy increased from 0,56 to 0,61, the recall increased from 0,52 to 0,70 and the F1-score also increased from 0,54 to 0,65. Finally, the finished models were implemented on an Android mobile device, and the application allows users to log meals and generate statistics from the saved data.
Early Detection of Brain Tumour in MRI Images using Open by Reconstruction and Convolution Neural Networks

Classification and detection of the brain tumour at early stages have always been a concern to reduce the mortality rate. Though the brain tumour detection is possible in Magnetic Resonance Imaging (MRI), the detailed detection of the tumour type has been a concern. This article proposed a comparatively efficient method to detect the dangerous malignant tumour and hence begin the treatment at an early stage. At first, MRI images are filtered by cascading mean, median and Weiner filter. Due to the high density and texture, skull tends to appear as a detected region, which is often mistaken as part of a tumour. The stripping of the skull is done to isolate the Region of Interest (ROI) of the brain from the background. Once an abnormality in the image is confirmed for a tumour, its' classification into Low-Grade Glioma (LGG) and High-Grade Glioma (HGG) are done using Open by Reconstruction followed by thresholding segmentation method & Convolution Neural Networks (CNNs). An accuracy of 92.3% is obtained by first CNN in classifying abnormal brain MRI with normal brain MRI. An accuracy of 98.4% is obtained by second CNN in distinguishing HGG with LGG.
Design and Evaluation of a Mixed Reality-based Human-Robot Interface for Teleoperation of Omnidirectional Aerial Vehicles

Omnidirectional aerial vehicles are an attractive solution for visual inspection tasks that require observations from different views. However, the decisional autonomy of modern robots is limited. Therefore, human input is often necessary to safely explore complex industrial environments. Existing teleoperation tools rely on on-board camera views or 3D renderings of the environment to improve situational awareness. Mixed-Reality (MR) offers an exciting alternative, allowing the user to perceive and control the robot’s motion in the physical world. Furthermore, since MR technology is not limited by the hardware constraints of standard teleoperation interfaces, like haptic devices or joysticks, it allows us to explore new reference generation and user feedback methodologies. In this work, we investigate the potential of MR in teleoperating 6DoF aerial robots by designing a holographic user interface (see Fig. 1) to control their translational velocity and orientation. A user study with 13 participants is performed to assess the proposed approach. The evaluation confirms the effectiveness and intuitiveness of our methodology, independent of prior user experience with aerial vehicles or MR. However, prior familiarity with MR improves task completion time. The results also highlight limitation to line-of-sight operation at distances where relevant details in the physical environment can still be visually distinguished.
Ontology-oriented Method for Road Accident Geoanalysis

Location intelligence is an important approach in the geoanalysis of the urban environment. In this paper, the main attention is paid to the development of ontology-oriented location intelligence method for road accident geoanalysis by means of ontology matching for merging information from heterogeneous sources. The task of accident analysis is to identify potentially dangerous sections of the road network, which can be used to build routes along minimally dangerous roads. The novelty of the method is the integration of heterogeneous sources of spatio-temporal information in a common geoanalysis system by converting them into an ontology and conducting a verification and analysis of the frequency of accidents on individual sections of roads to determine the most frequent type and cause of accidents. The method was tested on traffic accident data from open information source in St. Petersburg, Russia. With the method data about road accidents was verified from various sources and clusters were located with high concentration of accidents.
Dual Contrastive Learning for Semi-Supervised Fault Diagnosis Under Extremely Low Label Rate

The application of deep learning in the field of fault diagnosis has achieved excellent performance in these years. However, most fault diagnosis models rely on large amounts of labeled data. In engineering practice, labeling large amounts of data requires a lot of manpower and expert knowledge. Industrial datasets typically have low label rates. To address the challenge in fault diagnosis under extremely low label rate, this article proposes a new semi-supervised fault diagnosis method using a novel contrastive learning model named dual contrastive learning (DCL). The designed DCL model can extract more representative features through a structure with dual contrast. Specifically, the semi-supervised fault diagnosis method contains two stages: pretraining and fine-tuning. In the first stage, the DCL is pretrained by augmented time-frequency versions of the bearing vibration signals, which are obtained by a time-frequency transform with different window lengths. In the second stage, label diffusion module (LDM) is applied to extend the limited label information to the large unlabeled data so as to increase the label rate. In addition, the whole diagnosis module rather than only the last layer is finetuned so that each layer of the module can fit the current diagnosis task. The proposed method was experimented on a public dataset and two self-built datasets under varying working conditions. The comparison results show that in the extreme case of 1% label rate, the proposed method is superior to the existing methods in terms of diagnosis accuracy, convergence speed, and stability.
Micro-focus electron source based on high current density carbon nanotube emitters

A high current density micro-focus cathode based on carbon nanotube (CNT) field emission was developed. The CNT film was grown directly on the nickel alloy substrate with a diameter of $\mathbf{70}\ \boldsymbol{\mu} \mathbf{m}$ by chemical vapor deposition. The effect of the cathode-grid spacing on the field emission properties was investigated, and improved field emission performances were demonstrated for the 1.4 mm spacing with a lower threshold field of $\mathbf{1.6}\ \mathbf{V}/\boldsymbol{\mu} \mathbf{m}$ and higher emission current (up to 19 A/cm2) comparing with spacing of 0.4 mm, indicating that increasing the electrode spacing could enhance the field emission characteristics of the CNT cathode. The X-ray tube based on the CNT micro-focus electron source reached the imaging resolution of about $\mathbf{80}\ \boldsymbol{\mu} \mathbf{m}$.
Dual-Branch Dynamic Modulation Network for Hyperspectral and LiDAR Data Classification

Deep learning algorithms that can effectively extract features from different modalities have achieved significant performance in multimodal remote sensing (RS) data classification. However, we actually found that the feature representation of one modality is likely to affect other modalities through parameter back-propagation. Even if multimodal models are superior to their uni-modal counterparts, they are likely to be underutilized. To solve the above issue, a dual-branch dynamic modulation network is proposed for hyperspectral (HS) and light detection and ranging (LiDAR) data classification. Firstly, a novel dynamic multimodal gradient optimization (DMGO) strategy is proposed to control the gradient modulation of each feature extraction branch adaptively. Then, a multimodal bi-directional enhancement (MBE) module is developed to integrate features of different modalities, which aims to enhance the complementarity of HS and LiDAR data. Furthermore, a feature distribution consistency (FDC) loss function is designed to quantify similarities between integrated features and dominant features, which can improve the consistency of features across modalities. Experimental evaluations on Houston2013 and Trento datasets demonstrate that our proposed network exceeds several state-of-the-art multimodal classification methods in terms of fusion classification performance.
Study on high-gain reflectarray antenna for X-band based on phase gradient ultra-thin surface structure

In this paper, a 19*19 dual-loop microstrip reflectarray antenna with high gain is obtained by designing dual-loop microstrip metasurface structure units with phase gradient variation and arranging these units according to the phase compensation formula. The reflector array antenna works in X band with a center frequency of 10 GHz, and its gain can reach 19.8 dBi, which is much larger than that of the feed source. Considering the focusing effect of double-loop microstrip metasurface, the sidelobe level of the reflector array antenna can be as low as -1.6 dB, which plays a good role of high orientation. It provides an effective method to realize the antenna with high gain and high orientation, and has a wide application prospect in the field of long distance wireless communication.