Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Indoor UAV-based Tracking Systems: Classification, Status, and Challenges

The recent development of Unmanned aerial vehicle (UAV) technology and the UAVs' unique features offered the impetus for new UAV-based applications and services. The UAV's low maintenance cost, small size, outstanding maneuvering capability, and the possibility to be equipped with various technologies(e.g., sensors, cameras, and artificial intelligence (AI) systems) made the UAV-based systems suitable for performing autonomoustasks in indoor and hard-to-reach environments. One of the most promising applications of UAVs in indoor environments is autonomous monitoring and tracking. In this paper, we highlight the unique characteristics of UAV-based tracking systems, discussthe various system classifications based on the target behavior and the number of used UAVs, present some of the work that hasbeen done to design UAV-based indoor monitoring and tracking systems, and discuss the critical design challenges specific to indoor target tracking systems. Finally, we highlight some of the most promising technologies and solutions to develop effective UAV-based indoor monitoring and tracking systems.
A Comparative Study on Performance Validation of Ring Oscillator for UWB & Low Power

With the advancement of internet of things voltage controlled oscillator (VCO's) are most pivotal basic building blocks, where they are predominantly used as a high frequency clock generating subsystems, notably in cutting edge wireless systems used for sub 5G band or millimetre range of frequency that either encompasses the implementation of the VCO with high oscillation frequency and low close-in phase noise, in PLL and in frequency synthesizer. This paper compares the performance parameters of CMOS Ring oscillators architecture and current starved Voltage-controlled oscillators on the basis of (Power consumption, Phase-Noise, tunning range and supply voltage). Here state of art design of different ring oscillator are utilized to get comparative analysis for utilization in ultra wide band
Tensor-Based Multi-Modal Multi-Target Regression for Alzheimer’s Disease Prediction

The assessment of Alzheimer’s Disease (AD) progression via the analysis of physical changes within the brain has attracted great interest from the fields of healthcare, computational medicine, and machine learning alike. Recent studies have demonstrated that using both multi-modal data and multiple AD assessment scores in a predictive model can better reflect pathological characteristics and enhance prediction performance. However, using such high-dimensional structure information to model inter-correlation between multiple targets remains a challenging task. In this paper, we propose a Tensor-based Multi-modal Multi-Target Regression (TMMTR) method for AD detection and prediction, which enables simultaneously modeling multilinear structure information as well as intrinsic inter-target correlations in a general learning framework. We also investigate the tensor-structured sparsity that supports the interpretability of our prediction. Experiments conducted on the ADNI dataset validate the superior performance of our method when compared to other state-of-the-art methods.
Investigation of the Extrinsic Base Effect on High Frequency Performance of SOI Lateral Bipolar Transistors ( LBT )

The extrinsic base in LBTs influence high frequency performance by decreasing the effective collector crosssection, increasing collector current density and increasing the effective transit time through the base – collector depletion region. The effect is common to both, laterally contacted base and base contact on top structures, which are compared in this work. Influence of the technological parameters, such as extrinsic base width, emitter height and link-base length, on fT and fmax are examined and optimization and scaling scheme for these structures are proposed. In contrast to previous tendencies in LBT technology, this work shows that link base region should not be scaled down as much as possible, since fT is sacrificed to obtain higher fmax,. It is shown that the increase of 43% in fT can be achieved with sacrificing fmax for only 23%, only by changing device geometry with the same doping profile of the intrinsic transistor region.
EEG-based Seizure Detection Using Generative Model and Deep Learning

It is essential to automatically distinguish epileptic seizure forms of electroencephalography (EEG) signals in assisting in clinical diagnosis and monitoring of epilepsy. We found there is a special phenomenon that limits the ability to accurately identify epileptic forms. Some non-epileptic EEG signals show similar features to that of epileptic ones. We call such epileptic-like abnormalities boundary-type signals. Considering the existence of such type of signals, in this work a novel workflow employing deep learning is put forward for an automated seizure detection and epilepsy diagnosis. To distinguish the boundary type prior to the classification, a generative model, deep convolutional generative adversarial network, is employed, which is trained to find a representative time frequency diagram for the non-epileptic data, so as to distinguish more implicit boundary-type signals. Next, a structure-modified VGG16, a deep convolutional neural network is trained to detect normal and seizure classes. Tested on the Bonn University database, the proposed method achieved good performance.
Classification of scenery using multinomial logistic regression in a sugarcane crop

Autonomous navigation is an important skill for robots that perform tasks in the agricultural field. The main sensors used for this application are GPS, camera, and LiDAR. Navigation methodologies for such robots either focus on global localization and path planning based on GPS information or local path planning employing a perception system, using cameras and LiDAR sensors to extract crop characteristics to suggest control actions. Since mobile platforms are limited in both energetic and computational power, this situation highlights the need for a high-level system that chooses which navigation algorithm should be employed depending on the environment. Based on this information, this work presents a system for classifying agricultural scenarios based on LiDAR data. In this work, we separate four conditions concerning the position of the crop around the robot: ‘betweenRowCrop’, ‘leftRowCrop’, ‘rightRowCrop’, and ‘noRowCrop’. We used two Hokuyo UTM30-LX LiDARs on an agricultural robot to collect extensive data on a sugarcane crop. With the data from the LiDAR, several statistical measures are obtained and, for each variable, outlier samples are removed using the IQR rule. After that, these variables are used as predictors in a Multinomial Logistic Regression to classify agricultural scenes. The final model presented an accuracy of approximately 99%. This indicates that this model could be a promising solution for classifying the agricultural scenery the robot encounters and then passing this information to the robot's navigation system to choose the appropriate navigation method.
ARTS: An adaptive regularization training schedule for activation sparsity exploration

Brain-inspired event-based processors have attracted considerable attention for edge deployment because of their ability to efficiently process Convolutional Neural Networks (CNNs) by exploiting sparsity. On such processors, one critical feature is that the speed and energy consumption of CNN inference are approximately proportional to the number of non-zero values in the activation maps. Thus, to achieve top performance, an efficient training algorithm is required to largely suppress the activations in CNNs. We propose a novel training method, called Adaptive-Regularization Training Schedule (ARTS), which dramatically decreases the non-zero activations in a model by adaptively altering the regularization coefficient through training. We evaluate our method across an extensive range of computer vision applications, including image classification, object recognition, depth estimation, and semantic segmentation. The results show that our technique can achieve 1.41 × to 6.00 × more activation suppression on top of ReLU activation across various networks and applications, and outperforms the state-of-the-art methods in terms of training time, activation suppression gains, and accuracy. A case study for a commercially-available event-based processor, Neuronflow, shows that the activation suppression achieved by ARTS effectively reduces CNN inference latency by up to 8.4 × and energy consumption by up to 14.1 ×.
Role of Empathy to Identify Unmet Needs in Indian Villages through Service Learning Program - A Case of the Unnat Bharat Abhiyan Program

Service learning in India is witnessing a large-scale adoption in higher education through the Unnat Bharat Abhiyan (UBA) program, a national-level service-learning program launched by India's Ministry of Education to promote community engagement and foster social responsibility among undergraduate students. The mission of UBA is to enable higher education institutions to partner with communities who live in rural India and engage in service-learning activities. The program advocates to build long-term engagement between academic institutions and nearby communities so that they could collaborate toward sustainable reciprocal partnerships. One of the goals of UBA is to establish linkages between academia and the community so that the knowledge created in the institutions could be targeted to solve complex societal problems. Higher education institutions that are part of this service-learning initiative were encouraged to do field studies through household surveys and informal interaction with the community members with a goal to identify unmet needs in the community. However, it was observed through prior research that the members of the community are only comfortable sharing their problems after faculty and students gain their trust. The paper presents a case study of a rural immersion experience that was introduced as part of the Unnat Bharat Abhiyan program at a private engineering institution in India. The 2-day rural immersion experience was designed to facilitate discussions among faculty and students with the community's various stakeholders, build trustworthy relationships, and empathize with their problems. The study employed a qualitative approach to investigate how students who were part of the rural immersion exercise described their experiences in the villages. Seven focus group discussions were conducted with 28 students who shared their highlights from visiting the villages and the process taken by them to identify unmet needs in the community. Thematic analysis was conducted to analyze the data and the results revealed the role of empathy in critically identifying challenges faced by the community members. The paper at the end presents the list of unmet needs identified by the students because of the rural immersion experience.
ML-ACE: Machine Learning Admission Control at the Edge

ML inference has become an increasingly important workload for low-power, near-data edge computing platforms. There is a large existing body of work on how to optimize a trained model for inference on a resource-constrained device, however much of the work does not consider optimizations in how the model will be used by clients in the system. In this space, inference servers emerged to provide a client-server paradigm for inference, offering portable, practical client libraries for users of ML systems. These servers handle batching of requests, runtime optimizations, and placement of multiple replicas of models on CPU/GPU to maximize inference efficiency. Unlike the data center, much infrastructure at the edge lacks the ease in ability to recruit new machines to scale out these servers to meet increasing request demand. Because of this, efficient scheduling of models on these edge platforms is critical. This work presents ML-ACE, a system to systematically schedule ML inference on resource-constrained edge computing platforms. ML-ACE extends the existing client-server paradigm for inference serving by providing admission control, preventing user inference requests from over-saturating system resources.
Full azimuthal coverage in planar lens antennas

This paper describes the design of a generalized Luneburg lens antenna that can scan $360^{\mathrm{o}}$ in azimuth. The full azimuth scanning is achieved by deploying two identically stacked lenses and 3 dB90o hybrid couplers. The antenna operates in the n260 band, from 37-40GHz. Simulation results of the integrated antenna system are presented illustrating that the antenna can scan $360^{\mathrm{o}}$ in the azimuth plane.
A performance simulation analysis of the OPM algorithm in the spatial spectrum estimation

This paper introduces an orthogonal propagation method (OPM) with low complexity in spatial spectral estimation, and gives the theoretical derivation process of the algorithm. The algorithm reduces the computational complexity of the algorithm by using linear operations instead of eigendecomposition of the covariance matrix for far-field narrowband signals in uniformly equidistant line arrays. In this paper, the algorithm is simulated under different parameter scenarios, and the performance of the spatial spectrum estimation of the method is analyzed under different experimental parameters, which provides a reference for the selection of experimental parameters in future array orientation processing, and also has a guiding significance for the signal orientation problems of actual arrays.
Finite-Amplitude Baroclinic Waves with Small Dissipation

Abstract A study of finite-amplitude baroclinic instability for a two-layer system with small but non-zero dissipation is presented. The presence of dissipation, however slight, allows the existence of steady finite-amplitude wave solutions. For sufficiently small friction, however, the steady wave may be unstable if a certain criterion, presented in this paper, is satisfied. Calculations indicate that in such cases a continuous, slow, periodic amplitude pulsation exists which is independent of the initial conditions.
Soter: Deep Learning Enhanced In-Network Attack Detection Based on Programmable Switches

Though several deep learning (DL) detectors have been proposed for the network attack detection and achieved high accuracy, they are computationally expensive and struggle to satisfy the real-time detection for high-speed networks. Recently, programmable switches exhibit a remarkable throughput efficiency on production networks, indicating a possible deployment of the timely detector. Therefore, we present Soter, a DL enhanced in-network framework for the accurate real-time detection. Soter consists of two phases. One is filtering packets by a rule-based decision tree running on the Tofino ASIC. The other is executing a well-designed lightweight neural network for the thorough inspection of the suspicious packets on the CPU. Experiments on the commodity switch demonstrate that Soter behaves stably in ten network scenarios of different traffic rates and fulfills per-flow detection in 0.03s. Moreover, Soter naturally adapts to the distributed deployment among multiple switches, guaranteeing a higher total throughput for large data centers and cloud networks.
A Network-based Distributed Data Storage System for Data Security in a Hostile Network

Security in hostile and dynamic mobile networks (e.g., a robot network) under constraints (e.g., limited Internet connection) is still a problem to securely store the sensitive data while maintaining the data availability, integrity, and confidentiality. The compromise of a physical device (e.g., robot) could result in data loss stored in the device and data leakage to adversaries. In this paper, we address this problem by proposing a Network-based Distributed Data Storage (NDDS) system using a threshold-based secret sharing and blockchain technology. A polynomial-based encoding method is leveraged to split a data file into multiple fragments with desirable redundancy. Each fragment is the encoded secret share as randomly generated symbols without disclosing original data information. These fragments are distributed into a set of nodes in the network such that the compromise of a device will not result in data loss and the original data can be fully recovered from the survival devices. In support of data operations, a directory server is proposed to be implemented in a lightweight blockchain to maintain metadata that describe the fragment associations. Our theoretical and experimental results demonstrate NDDS security and performance under different strategies, depending on the data security and performance requirements in the network.
Reler: Relearning Controversial Regions to Accurately Segment Nasopharyngeal Carcinoma

Accurate nasopharyngeal carcinoma (NPC) segmentation is significant in preventing local recurrence and improving patients’ survival rates. However, existing deep learning-based methods often yield unsatisfactory segmentation results, especially in fine-grained detail. Because NPC is a tiny and infiltrative tumor with a huge background, traditional deep neural networks tend to be dominated by salient information, thus missing the fine-grained details of NPC. To achieve accurate NPC segmentation, a relearning controversial regions method (Reler) is proposed. It consists of three modules, including the controversial features generator (CFG), controversial features finding module (CFF), and controversial regions arbitration module (CRA). First, CFG constructs global and local feature extractors to generate two types of different features. Then, CFF finds the controversial features and corresponding regions by comparing the global and local features’ estimates of the segmentation results of the same input regions. Next, the CRA focuses on controversial features, relearns new features, and produces new segmentation results through a proposed Transformer-based self-attention network. Finally, the uncontroversial segmentation results from CFF and CRA are combined as the final segmentation results. Extensive experiments are conducted on a large NPC dataset containing 6342 images from 596 patients. The experimental results show that the proposed method Reler is effective and superior to the nine state-of-the-art methods.
Alignment of Visual Senses and Acoustical Senses based on Emotion Recognitions

Recently, due to the rapid advances on multimedia technology, multimedia data such as pictures and music has heavily invaded our daily life. By the mobile camera, users capture the interested scenes as the preferred pictures, and then browse these pictures in either computers or mobile devices. This incurs an interesting issue that, how to provide the users with a better browsing/listening atmosphere and experience by music. Actually, it can be viewed as the problem for aligning photos with music and also as an exciting research topic for cross-media retrieval. However, very few studies focus their attention on this topic. To aim at this issue, in this paper, we propose a novel cross-media alignment method that bridges visual pictures to harmonic music by visual emotions and acoustical emotions. In this method, the pictures and music are projected onto the emotion space first. Next, the emotion features are fuzzed to be near the human senses. Finally, the effective alignment method for associating pictures with music is performed. The results of subjective and objective evaluations on the real datasets reveal that, our proposed method can successfully provide the users with a rich experience on audiovisual presentations. Also, the proposed alignment algorithm is shown to be effective in terms of Normalized Discounted Cumulative Gain.
Crossbar Switch Arbitration with Traffic Control for NoC

NoC is a network of computing cores (IP cores) for processing information and routers for transferring data between IP cores. NoC has a lot of applications: it is used in communications, transport, in the Internet of Things ecosystems and the Internet of Drones. Routers are used to efficiently exchange messages between IP cores. One of the important functions of a router is arbitration. In this paper, we review the main arbitrage methods: Fixed Priority Arbitrage (FPA), Matrix Arbitrage, Round Robin Arbitrage (RRA), and some of its modifications for crossbar switching systems. We propose a conceptual framework for traffic control of NoC routers using arbitration techniques. Fairness as an important property of an arbitrator is considered. Different approaches have been proposed and some have been justified. The comparison of the simulated data shows that the results are in good agreement with both available experimental data and proposed models in the literature.
Extraction of Active Compounds from Armoracia rusticana Using Maceration and Ultrasound Assisted Extraction

Current research in the food field is primarily focused on obtaining biologically active compounds to replace chemical food additive from food products. In this study, the active compounds from horseradish (Armoracia rusticana) were extracted by maceration and ultrasonic assisted extraction using two solvents, ethanol and methanol. For this extraction the compounds phenolic, antioxidant activity and vitamin C concentration were determined. The results showed that maceration is a slow method resulting in a low yield of the total polyphenols, unlike ultrasound assisted extraction. The results for antioxidant activity show that in the case of maceration, the solvent does not influence the extraction process and the maceration efficiency is between 35% and 50%. In the case of ultrasonic assisted extraction both the solvent and the ultrasound time influence the oxidative activity of the extracts. Extracts obtained by maceration and ultrasonication contain concentration between 10.7 and 24.4 mg vitamin C / 100 g. It can be stated that the ultrasound assisted extraction method could successfully replace the conventional extraction obtaining the highest content of polyphenolic compounds over a period of 30 minutes.
Learning-based Observer Evaluated on the Kinematic Bicycle Model

The knowledge of the states of a vehicle is a necessity to perform proper planning and control. These quantities are usually accessible through measurements. Control theory brings extremely useful methods - observers - to deal with quantities that cannot be directly measured or with noisy measurements. Classical observers are mathematically derived from models. In spite of their success, such as the Kalman filter, they show their limits when systems display high non-linearities, modeling errors, high uncertainties or difficult interactions with the environment (e.g. road contact). In this work, we present a method to build a learning-based observer able to outperform classical observing methods. We compare several neural network architectures and define the data generation procedure used to train them. The method is evaluated on a kinematic bicycle model which allows to easily generate data for training and testing. This model is also used in an Extended Kalman Filter (EKF) for comparison of the learning-based observer with a state of the art model-based observer. The results prove the interest of our approach and pave the way for future improvements of the technique.
Circuitry of Micro-Power JFET and CMOS Input Differential Stages for Op-Amps on Silicon and Wide-Band Semiconductors

The study presents an analysis of universal JFET and CMOS differential stages (DS) circuits. Such cascades provide a smaller range of transistor currents (10-100 $\mu$A) compared to classical cascades. The described DSs are implemented on combined process technologies, and therefore can be designed on JFET, BJT or CMOS chips. In addition, gallium arsenide, diamond gallium nitride, thin-film TFT and silicon carbide transistors. A computer simulation of the static modes of the developed DSs and their current dependences is presented. The results of GaAs DS modeling and its frequency dependence are presented.