Miracle-dz/UniFAR-FTUs huggingface dataset:
Object Detection and Classification of Hyperspectral Images Using K-NN

This Object detection and classification using Hyperspectral images is a critical aspect of remote sensing and computer vision. This technology involves identifying objects of interest within an image and classifying them based on their spectral signatures. Hyperspectral imaging provides a more detailed representation of objects compared to traditional color images, enabling more precise classification. The increased accuracy and reliability provided by this technology make it useful in a range of applications, such as environmental monitoring, military surveillance, and agriculture. However, object detection and classification in hyperspectral images can be challenging due to the large size of the data and the complexity of the algorithms involved. Nevertheless, ongoing research in this area continues to improve the performance of object detection and classification using hyperspectral images. In this paper, we are utilizing the K-Nearest Neighbor algorithm as part of the research work to determine the accuracy of our model.
Pharmacodynamics modeling based on in vitro 3D cell culture experiments

Optimized therapies for the individual patient provide a more cost-effective, more secure method of chemotherapy. Therapy optimization demands a reliable mathematical model of tumor growth, pharmacodynamics, and the effect of the chemotherapeutic agent used during the therapy. Examining these effects and processes in vitro cell culture experiments maintain a cheaper and more ethical method to create tumor growth and pharmacodynamics models and estimate their parameters. In this work, parameter identification was carried out by using cytotoxicity measurements of two- and three-dimensional (spheroid) tumor cell cultures. In vitro experiments were accomplished on mice breast cancer cell line. Four experiments were done: two experiments on two-dimensional cell cultures, with the initial number of 5000 and 10000 cells, and two experiments on three-dimensional spheroid cell cultures, with the initial number of 5000 and 10000 cells. The drug used was Doxorubicin at the same serial dilution in all cases. Parameter identification was performed by fitting to fluorescent intensity confluence data measured by an automatic time-lapse microscope for five days. We compared the results of two- and three-dimensional cell cultures and identified the model parameters in all four cases. The value of the sum square error showed that the fitting and identification performed better in the case of tumor spheroid cultures.
ML-based Anomalies Detection in Wireless Network Link Layer of the Internet of Things (IoT)

Internet of things is the most emergent technology, expanding interconnected device networks day by day to enhance the ease of device control and monitoring. IoTs are not covering the commercial utility but also providing emergency benefits to health care centres too. The extensive number of device connections over a network challenges a huge cost of operation management. The most recent research activity deals with anomaly detection in IoT networks over wide IoT networks. The automatic malfunctioning over the network is the most tedious task for researchers. The real-world IoT system analysis motivates this work to identify the anomalies in wireless networks. The link layer is selected to analyse, identifies and detect the wireless network anomalies. A comprehensive review on the performance threshold of machine learning-based algorithms is presented for automatic detection.
TI-16 DNS Labeled Dataset for Detecting Botnets

Botnets continue to evolve despite many efforts by law enforcement agencies and security researchers. As a result, there is an increase in the number of cybercrimes. This has led to a greater research focus on botnet detection. Among the reasons for growth in botnet and cybercrimes despite greater research focus are that significant number of the proposed techniques are not reproducible (unavailability of source code), do not contain a detailed description for effective comparison, and the absence of a real world labeled dataset for effective comparison. There is a grave problem of the unavailability of the labeled real-world dataset for bot infection detection. This paper aims to create a public labeled real-world Domain Name System (DNS) dataset for bot infection detection. The dataset contains real world DNS traffic of benign and malicious hosts. The dataset containing 24 features is labeled to list infected Domain Generation Algorithms (DGA) hosts along with the botnet family name and the DGA domains used for C&C communication. A total of 7644 hosts were found infected with nine different botnets namely modpack, virut, necurs, conficker, ud3, suppobox, nymain, tofsee and pitou. Finally, a machine learning classifier is developed to distinguish DGA bots from normal hosts using these features with an accuracy of 99.59%.
A Deep Learning Method for Classification in Brain-Computer Interface

Neural activity is the controlling signal used in enabling BCI to have direct communication with a computer. An array of EEG signals aid in the selection of the neural signal. The feature extractors and classifiers have a specific pattern of EEG control for a given BCI protocol, which is tailor-made and limited to that specific signal. Although a single protocol is applied in the deep neural networks used in EEG-based brain-computer interfaces, which are being used in the feature extraction and classification of speech recognition and computer vision, it is unclear how these architectures find themselves generalized in other area and prototypes. The deep learning approach used in transferring knowledge acquired from the source tasks to the target tasks is called transfer learning. Conventional machine learning algorithms have been surpassed by deep neural networks while solving problems concerning the real world. However, the best deep neural networks were identified by considering the knowledge of the problem domain. A significant amount of time and computational resources have to be spent to validate this approach. This work presents a deep learning neural network architecture based on Visual Geometry Group Network (VGGNet), Residual Network (ResNet), and inception network methods. Experimental results show that the proposed method achieves better performance than other methods.
Leveraging Robotics to Enhance Accessibility and Engagement in Mathematics Education for Vision-Impaired Students

There is potential to use robotics in education to revolutionize teaching and learning in mathematics. This is particularly relevant for vision-impaired students, who face different challenges when accessing mathematical content. Educational robotics can potentially enhance accessibility, motivation, and engagement in mathematics for students through enjoyable and novel interactions. Students commonly experience positive interactions with educational robots during learning activities, which influences their learning motivation. Recent studies show that students with disabilities face issues related to classroom participation, lack of collaborative learning, reduced social engagement, and potential for isolation. Digital-based learning technologies have transformed how vision-impaired students engage with and learn mathematics. Leveraging robotics in mathematics teaching and learning through personalised guidelines offers considerable benefits for vision-impaired students, including enhanced engagement, multimodal learning opportunities, and improved collaboration and communication skills, which enhances the opportunities for inclusive classroom experiences. This paper outlines the role of educational robotics in inclusive education. It examines the challenges and benefits of using educational robotics in mathematics for vision-impaired students. The importance of human-robot interaction (HRI) in steering the design and functionality of educational robots and their potential use within the classroom to facilitate learning is also highlighted.
Mutual Coupling Reduction in MIMO Antenna for 5G Application by Self-Decoupled Method

This paper shows a simple, compact and small 8×8 MIMO antenna. The ability of MIMO antennas is to broadcast signals in a various patterns and polarizations is extremely advantageous for contemporary communications systems. Excellent diversity gain (DG > 9.99dB), a low envelop correlation coefficient (ECC 0.002), and extremely low mutual coupling (S2l≥ 27) dB are all characteristics of the constructed MIMO antenna. S-boundaries, energy pattern characteristics, maximum gain, diversity gain, and envelop coefficient are used to evaluate the antenna performance. The mutual Coupling is the most basic fundamental parameter for the MIMO antenna, was likewise analyzed and it was observed that its value is under −27dB. self-decoupled MIMO antennas have a loop type ground antenna which reduces the requirement for extra decoupling structures or a postponed tuning method Antennas for ground radiation and loop antenna are magnetic-current and electric-current components, respectively. The suggested MIMO antennas feature a self-decoupled despite the antenna parts being near together, reduced profile and easy fabrication advantages the suggested, a revolutionary approach called MIMO antenna system is expected to offer excellent isolation and minimal mutual coupling, making it suitable for 5G applications.
A Noble Approach to 2D Ear Recognition System using Hybrid Transfer Learning

Convolutional Neural Networks (CNNs) have emerged as a popular choice of researchers for their robust feature extraction and information mining capability. In the last decades, CNNs have depicted impressive performance on various applications of computer vision tasks like object detection, image segmentation, and image classification. As a consequence, the ear-based recognition system has not gained many benefits from deep learning and CNN-based applications and is still lacking behind due to the availability of sufficient data and varying conditions of captured sample images. In this paper, transfer learning techniques have been applied to the well-known convolutional neural network model VGG16 integrated with the support vector machine(SVM) that acts as a hybrid algorithm for recognizing the person using their ear images. The proposed model is validated on an ear dataset containing a total of 2600 images with variability in terms of pose, rotation, and illumination changes. The proposed model is able to classify the ear images with the highest recognition accuracy of 98.72%. To show the effectiveness of the proposed model, comparative studies of the proposed model with other existing methods have been reported in the literature.
Near-field Microwave Radiometer for Measurement of the Depth Temperature of the Dielectric Environments

The paper describes the block diagram of the microwave radiometer, implemented according to a new method of designing systems for contact measurements of the parameters of layered dielectric mediums. The radiometer makes it possible to eliminate the influence of dynamically changing of reflection coefficient at the antenna-object boundary during contact measurements. A block diagram of the developed microwave radiometer with a detailed description of the operating principle is given. The mathematical apparatus of the developed system shows an increase in the accuracy of measurements of parameters of layered dielectric environment. The important part of the work is the developed mathematical model of errors of radiometric measurements, taking into account the interference of the own noise of the receiver due to non-ideal decoupling of the first and second arms of the microwave isolator. The given results of numerical experiments confirm, that the developed microwave radiometer has a improved stability at dynamically changing of reflection coefficient at the antenna-object boundary and reduces the influence of interference of the radiometric receiver own noises on the measurement results.
AdvDDoS: Zero-Query Adversarial Attacks Against Commercial Speech Recognition Systems

Automatic speech recognition (ASR) has been widely and commercially employed in health care, autonomous vehicles, and finance. Yet, recent studies have shown that universal adversarial perturbations (UAPs) pose a serious threat to white-box ASR systems when the adversary has access to the target model. Until now, the impacts of such a threat on commercial systems are still open since their models are not publicly available. To understand the security weakness in the practical black-box setting, this paper introduces the first zero-query UAP attacks, called AdvDDoS, with black-box access to ASR systems: we do not need to pay any query expense to estimate UAPs. Specifically, we craft targeted UAPs under a popular feature extractor and a local ASR model by reversing the robust target-category features, in which adversarial perturbations containing robust features are believed to have better transferability. Compared with vanilla UAPs, our UAPs incorporated with target-category features lead to better attacks against commercial ASR systems. We validate the efficacy of our AdvDDoS by launching attacks against a range of commercial ASR systems, i.e., three API services (Alibaba, Tencent, and Baidu), and three personal assistants (Apple Siri, iFlytek, and Google). Extensive experimental results demonstrate the superiority of AdvDDoS. For example, AdvDDoS achieves 83.26% word error rate (WER) and 53.25% success rates of attacks (SRoA) for the universal attack against Tencent ASR API, which outperforms the vanilla UAPs by up to 61.56% on WER and 11.6% on SRoA. The success of our attack sheds light on zero-query UAP attacks against commercial ASR systems.
A Runtime Switchable Multi-Phase Convolutional Neural Network for Resource-Constrained Systems

Convolutional Neural Networks (CNNs) are widely used in various systems, including text resource-constrained embedded systems or IoT devices. In such systems, it is typical to deploy compressed or pruned CNNs, instead of original ones, at the cost of reduced accuracy. Existing CNN pruning techniques have primarily focused on minimizing resource requirements. However, today’s embedded systems are increasingly dynamic in both resource demands and availability. Thus, the previous techniques that only consider given static cases are no longer efficient. In this paper, we propose a novel text multi-phase CNN that enables a text multi-objective exploration of a number of pruning candidates out of a single CNN. In the proposed technique, a CNN can operate in various versions depending on which subsets of weights are used and can be transformed to the one best matches to the given constraint adaptively and efficiently. For that, a CNN is first pruned to the sparsest form; then a set of parameters (sub-network) is additionally supplemented as the phase goes by. As a result, a number of network versions for all different phases can be represented by a single network and they form a pareto solution over the accuracy and resource usage trade-off. In this work, we target CPU-based CNN inference engines as most embedded systems do not have the luxury of specialized text co-processor support such as GPUs or HW accelerators. The proposed technique has been implemented in a publicly available CPU inference engine, Darknet, and its effectiveness has been validated with a popular CNN in terms of design space exploration capability and runtime switchability.
Design and Prototyping of a Ground-Aerial Robotic System

This paper presents an investigation into the feasibility of using a ground-aerial robotic system for data collection missions. It outlines the design of a system consisting of a tracked unmanned ground vehicle (UGV) and a multirotor unmanned aerial vehicle (UAV). The UGV is designed to enable the possibility of changing and charging batteries for the UAV, which is equipped with sensors for precise landing on the UGV platform. Rapid prototyping technologies were used to create a small experimental aircraft with a simple battery change airframe that can be tested indoors or outdoors. Parts of the chassis and drive elements were designed and manufactured for the UGV platform, and then the drive assembly and testing were carried out. The control systems of the UAV and UGV robots were evaluated through preliminary experiments. In future work, the integration of the control system and prototyping of the mechanism and electronics of the module for charging and changing batteries are planned in order to facilitate further advancements in the field of data collection missions.
An Extensive Study: Creation of A New Inverted Microscope Image Data Set and Improving Auto-Encoder Models for Higher Accuracy Segmentation of HaCaT Cell Culture Line

In cell culture investigations, it is assumed that the number of cells is counted automatically without the need for forward planning, additional tools, or consumables. The automatic cell counting process is greatly facilitated and made functional by the segmentation of cell culture lines. Many studies with high-accuracy results have been developed for cell or tissue segmentation in images acquired from microscopic systems, but this success cannot be achieved for segmentation of cell culture line (especially in images acquired from inverted microscope). There are still restrictions in segmentation of cell culture line studies in inverted microscope systems. In this context, a new inverted microscope image data set is created and higher accuracy auto-encoder models are developed for segmentation of HaCat cell culture line. The analyzes of the developed auto-encoder models are evaluated using four different performance evaluation procedures which are Accuracy, Sensitivity, Specificity and Jaccard. In terms of the performance evaluation procedures used in this study, the achievements of the auto-encoder models are clearly satisfactory for the automated segmentation of the HaCat cell culture line.
An Automated Method for Analyzing and Monitoring The Condition of Lithium-Ion Batteries in Microgrids

The versatility of an equivalent circuit model with huge lookup tables for each circuit element is often used for simulating lithium battery cells, allowing the model to closely match observed data. As grid resiliency takes center stage there has been an increase in the need for grid-scale energy storage. Utilities are interested in energy storage because of the many grid-based uses for the technology, butthe high price of big energy storage systems is typically prohibitive. By reusing batteries from electric vehicles, the price of a utility-scale energy storage system may be greatlyreduced. Although these systems often provide an inaccuracy of no more than 10% when taking into account all the factors, their precision has long been a topic of contention. Batteries are the lifeblood of every automation system, and they're increasingly being used in every industry where constant access to electricity is necessary. Regular checks are necessary to ensure that the battery supply can keep the load running smoothly and without interruption. The acid level, state of charge, voltage, current, and remaining charge capacity of the lead acid battery are all monitored and recorded by our suggested system. This system gives a brief explanation of how lithium-ion battery works and gives results. It possesses various terminologies such as State of health, state of charge, and voltage-current determination. This would provide a experimental performance of lithium-ion batteries. As it addresses problems with existing works, such as a lack of long-term operation, medium-scale capacity, alarms for a safe range of critical magnitudes, real-world operating circumstances, and compatibility management, the idea is original in the scientific literature. To demonstrate its viability and effectiveness, we provide both the surveillance system's design and execution, as wellas experimental data from Lithium-ion batteries.
Design of CMOS Readout Circuits for ISFET Aptasensor

We present the design and integration of ISFET aptasensor readout circuits on a single silicon substrate, integrated into a 1.2-μm 5V complementary metal oxide semiconductor process. The ISFET is based on a MOSFET with its gate connected to a metal pad brought to the surface of the chip through passivation opening. The ISFET design was determined by the ratio of the areas of the MOSFET and the sensitive surface of the floating gate. An aluminum oxide film was formed as the sensitive surface. Readout circuits based on differential measurements have been developed to implement a differentiation circuit into the chip using an instrumentation amplifier and due to processing by an external device using a repeater circuit and a transimpedance amplifier. For all circuits, an amplifier with a calculated gain of more than 1000 and a phase margin of 60.4 degrees at a frequency of 5.77 MHz and a gain margin of 7 dB at a frequency of 10.3 MHz with a load of 15 pF and 50 kOhm was used. DC measurements of the manufactured circuits were carried out and their operability was shown in direct contact with a metal probe and in contact with a buffer solution biased using platinum reference electrode. For the repeater and instrumentation amplifier circuits, the linearity of the characteristics is close to 1, and for the transimpedance amplifier circuit more than 0.98. The developed device provides a basis for the development of aptasensors for biomarkers detection.
Dual - Band MIMO Antenna Design for 5G Applications

In this paper, a MIMO antenna design is presented that operates at 28GHz and 38GHz frequency bands. These frequency bands are considered because the amount of attenuation due to rain is less when compared with other frequency bands. The antenna is designed by using Rogers RT 5880 substrate with dielectric constant 2.2 and copper is used as conducting material. Using CST Microwave studio software, the antenna is modelled and stimulated. The designed antenna exhibits a bandwidth of 2.12GHz and 1.58GHz, gain of 7.25dBi and 9.21dBi for 28 and 38GHz frequency bands respectively.
Automatic Fault Detection for Resistivity Systems in Logging-While-Drilling Tools

Today’s oil and gas industry frequently uses logging-while-drilling (LWD) tools to operate in extreme environmental conditions. These conditions include elevated temperature, vibration, and pressure, which can result in accelerated tool degradation rates and possible failures. Detecting such failures before conducting a new drilling job is of great significance to guarantee the efficiency and success of the job. Previously the fault detection task required field engineers to check relevant signal channels manually, which is inefficient, labor-intensive, and could even be inaccurate sometimes. Considering this challenge, the authors developed a data-driven model that enables automatic fault detection of the resistivity system, a critical subsystem of LWD tools. Specifically, this model is based on the support vector machine method using features extracted from a few signal channels selected by a domain expert. Experimental results on actual field data show that the model is effective.
A Smart Environmental Safty Monitoring System Using IoT

Now a days, air pollution becomes global issues and human life both natural and waves. Air pollution general cost by manmade and natural resources. Air pollution is a significant environmental issue that also has a negative impact on people's health. There have been numerous efforts made in the past to identify air pollution. In order to make a city smart and liveable, it is vital to regularly check the air quality index. Therefore, it is essential to create a real-time system for monitoring pollution and air quality. It is proposed build an Arduino platform which can provided to continually monitoring. To assure the quality of air. The effectiveness of the suggested system and also quantitatively measure and the results are promising.