Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep learning based Diagnosis of diseases using Image Classification

Any disease whether it is curable or not, it must be diagnosed properly with some time in hand to take the appropriate actions in time. As it is popularly said that early detection of any disease is half cured. For detection of the diseases like Pneumonia, Covid-19, Brain Tumor Radiography, Computed Tomography are a technique popularly used nowadays. The motivation towards the study of the topic was that due to our country’s population density, the vulnerability of getting infected and not being treated nicely was exposed during the outbreak of Covid-19. The ratio of doctors in India is nearly 1:1456 which means 1 doctor has approximately 1456 patients to deal with. That also results in a lot of time being wasted on diagnosis, scheduling appointments, collection of reports, etc. which could prove to be critical for a patient. To reduce all the time being wasted, with the help of machine learning we intend to learn if we can predict if the patient is infected with a certain disease or not. How do we do that is by using deep learning models and analyze on the basis of a few X-Ray scan of the Chest to detect Pneumonia.
Toward Collaborative Reinforcement Learning Agents that Communicate Through Text-Based Natural Language

Communication between agents in collaborative multi-agent settings is in general implicit or a direct data stream. This paper considers text-based natural language as a novel form of communication between multiple agents trained with reinforcement learning. This could be considered first steps toward a truly autonomous communication without the need to define a limited set of instructions, and natural collaboration between humans and robots. Inspired by the game of Blind Leads, we propose an environment where one agent uses natural language instructions to guide another through a maze. We test the ability of reinforcement learning agents to effectively communicate through discrete word-level symbols and show that the agents are able to sufficiently communicate through natural language with a limited vocabulary. Although the communication is not always perfect English, the agents are still able to navigate the maze. We achieve a BLEU score of 0.85, which is an improvement of 0.61 over randomly generated sequences while maintaining a 100% maze completion rate. This is a 3.5 times the performance of the random baseline using our reference set.
Manufacturability of Highly Densed Arrays of SC 20% Doped ALN Monolithic PMUT

The focus of this paper is to present a comparative study for highly dense arrays of Piezoelectric Micromachined Ultrasonic Transducers (PMUT), manufactured using a Aluminium Nitride (AlN) and emerging Aluminium Scandium Nitride (Al20%ScN) thin films. Monolithic PMUT manufacturing method is used to fabricate the devices. A 100 x 100um array of PMUT, each unit cell has 40μm cavity and the pitch is 57μm, is designed as a test vehicle to monitor the process performance of these two films.
Gender and Age detection using Deep Learning

Mankind has advanced technology to the extent that the 21st century is the crack of the dawn for unimaginable achievements. The aforementioned technology can be used for our benefit in identifying one’s age and even their gender just based on their glimpse from a camera, image and even a video. This research paper will methodically chalk out the whole procedure, multiple methodologies and algorithms that can be used, which one is the most accurate and how it all comes together. It will also stress on its importance and how it can be implemented to benefit our day to day life. The paramount objective of the paper is to build a gender and age detector that can approximately guess the gender and age of the face of an individual in a picture using Deep Learning on the audience dataset. Moreover, to get the most effective predictions and result by overcoming the problem of accuracy and time. Moreover, the map for the ways this technology can be used to our benefit and look at the huge spectrum where it can be implemented: ranging from security services, CCTV surveillance and policing to dating applications, matrimonial sites.
Output-Synchronization of Discrete-Time Multiagent Systems: A Cooperative Event-Triggered Dissipative Approach

This paper investigates output synchronization of heterogenous time-delay discrete-time multiagent systems via output couplings. Firstly, sufficient conditions are provided to guarantee dissipativity for an individual agent modeled by the descriptor dynamics. Fully distributed consensus protocol based on the outputs of the neighboring agents is proposed to show that the individual dissipativity leads to output synchronization of the overall multiagent system. The proposed synchronization scheme exhibits a strong robustness facing norm-bounded uncertainties. Moreover, the counterparts of the result of the cooperation based on dissipativity for the multiagent system with event triggering mechanism is also investigated. The proposed triggering mechanism and the consensus protocol solve the synchronization for all admissible uncertainties and time varying delay. Finally, a numerical heterogeneous model is carried out to illustrate the effectiveness of the proposed strategies.
Sentimental Analysis of Twitter Comments on Covid-19

The rise of social media offers users to share their state of mind and opinions in all aspects of their life. Vast amount of textual content is available and needs techniques to use of information meaningfully by separating and examining them. Sentimental Analysis mines the opinion of human being for a viewpoint. In this Pandemic condition of Corona, whole world is sharing their opinions on the social media. This paper shows a analysis of sentiments to get the opinion of people if they are positivity during this situation or not. The paper is using the technique of polarity to know the opinion is positive, negative, or nonpartisan [1]. Three main keywords "COVID", "Corona virus" and "COVID-19" are used to check the polarity.
Optimized Incremental Cooperative Communication for Underwater Acoustic Sensor Networks

In this article, we present incremental cooperative communication (ICC) for underwater acoustic sensor networks (UANs). This method uses adjacent active sensor nodes during the retransmission of DPs. Subsequently, a mathematical model to calculate the energy efficiency (EE) for ICC in UANs by examining the influences of distance-dependent usable bandwidth, ambient noises, and acoustic spreading is presented. Numerical and simulation results show that ICC significantly improves EE compared to the existing techniques over considerable distances between transceiving nodes. We later frame an optimization problem to optimize transmission power and packet size to improve EE over transmission distance further. The results confirm that the optimized can further increase the EE of UANs.
Deep Multimodal Fusion for Subject-Independent Stress Detection

This paper explores the influence of convolutional layer in deep multimodal fusion (intermediate fusion) for the detection of subject-independent stress using physiological signals including Electrocardiogram (ECG) and Electrodermal Activity (EDA). We compare performance of deep multimodal fusion models based on Convolutional Neural Network (CNN) by combining modals at different levels of the network. On two benchmark datasets ASCERTAIN and CLAS, the proposed approach is validated and the results shown to be higher for intermediate fusion on convolutional layer.
Optical Butting of Plane Array Sensors for PA-LiDAR

In order to expand the field of view and improve the resolution, a method of optical butting was proposed for two plane array sensors using beam splitter prism in this paper. The principle of optical butting, the optical design, calibration and focusing technology, and alignment method were presented and analyzed. In addition, the gray image stitch and point cloud data registration were also realized. To prove the concept of the optical butting method, we built a prototype system for experiment. The splicing of two <inline-formula> <tex-math notation="LaTeX">$320\times240$ </tex-math></inline-formula> sensors of plane array laser imaging detection and ranging (PA-LiDAR) was implemented. After optical butting, the total effective pixel number was <inline-formula> <tex-math notation="LaTeX">$320\times480$ </tex-math></inline-formula>, the total effective area was <inline-formula> <tex-math notation="LaTeX">$6.4\times4.8$ </tex-math></inline-formula>mm, and the field of view was <inline-formula> <tex-math notation="LaTeX">$22\,\,^{\circ }\times 15\,\,^{\circ }$ </tex-math></inline-formula>. Compared with the conventional laser radar system, the optical butting system can improve the field of view and resolution by a factor about <inline-formula> <tex-math notation="LaTeX">$2\times 1$ </tex-math></inline-formula>. This method had the advantages of simple constitution, easy accomplishment, small space and high assembly precision which guaranteed the operating requirement of the PA-LIDAR sensor.
BeeGround - An Open-Source Simulation Platform for Large-Scale Swarm Robotics Applications

This paper presents an open-source simulation platform developed for implementation of both homogeneous and heterogeneous robotic swarm scenarios. BeeGround is a fully modular simulation software that allows for a variety of experimental setups with different robotic platforms and population sizes. Users are able to define environmental conditions, e.g. size, various properties like temperature and humidity, and obstacles arrangements. The swarm controller, the individual's behaviour, is defined with a separate programming script. In this paper, we simulated honeybees aggregation mechanism as a case study to investigate the feasibility of the developed simulation platform. The results demonstrated that the developed platform is a reliable simulation software for implementing multi-agent and swarm robotics scenarios with very large population sizes, e.g. 1000 robots.
Centralized Route Control for Expanding Coverage by Wireless LAN with Many Vehicle APs

In this paper, we propose a centralized routing method for vehicle access points (APs). Our proposed method improves the communication quality of pedestrians by controlling the routes of many running vehicles and is used as a mobile AP for providing a high-speed wireless local area network (LAN) to pedestrians. Within the allowed computational time, the control system collectively controls the vehicle AP routes to improve the throughput of pedestrians. The proposed centralized control method consists of two types of control targets: intersection and vehicle, and three types of calculation methods: exhaustive search, ε-exhaustive search, and greedy method. We evaluated how much the proposed method improves the average throughput of pedestrians on a grid road model. We also evaluated the computational time. Compared with the conventional method, the average throughput of pedestrians increases up to 142.9%.
A Wireless Cardiovascular Pressure Sensor Based on an Iontronic Film with High Sensitivity

This work presents an ultra-sensitive iontronic wireless pressure sensor. The device consists of an inductor coil and a parallel-plate capacitor for pressure sensing. A microstructured iontronic film, which was embedded on the capacitor as dielectric layer, was the key element for enhancing the device performance. Assisted by the ultrahigh capacitance of electron double layers forming at the iontronic film/electrode interface, the sensitivity of the proposed device can be remarkably increased. Externally applied pressure on the device changes the capacitance, and then induces a shift in resonant frequency, which can be wirelessly retrieved by using the phase-dip technique. The measured pressure sensitivity of the proposed device was 0.014 mmHg-1, which is about 50 times larger than that of the device without iontronic film.
Self-Organised Swarm Flocking with Deep Reinforcement Learning

Optimising a set of parameters for swarm flocking is a tedious task as it requires hand-tuning of the parameters. In this paper, we developed a self-organised flocking mechanism with a swarm of homogeneous robots. The proposed mechanism used deep reinforcement learning to teach the swarm to perform the flocking in a continuous state and action space. Collective motion was represented by a self-organising dynamic model that is based on linear spring-like forces between self-propelled particles in an active crystal. We tuned the inverse rotational and translational damping coefficients of the dynamic model for swarm populations of $N\in \{25,\ 100\}$ E {25, 100} robots. We study the application of reinforcement learning in a centralised multi-agent approach, where we have a global state space matrix that is accessible by actor and critic networks. Furthermore, we showed that our method could train the system to flock regardless of the sparsity of the swarm population, which is a significant result.
Jodani: A spell checking and suggesting tool for Gujarati language

The spell checker is used in the pre-processing phase of Natural Language Processing systems for applications like Opinion Mining, Text Summarization, Machine Translation, Chatbot etc. A traditional spell checker tool compares the inputted string with an available dictionary of correct words. But as it works on string matching concepts, it is not able to handle inflected words. So, there is a need for a spell checker that overcomes this limitation. In this paper, the Gujarati spell checker tool 4Jodani, is proposed which works on root words, uses string similarity measures to identify wrongly spelled words, and attempts to auto-correct the word or suggest syntactically relevant words.
Numerical Simulation of Tunable Terahertz Graphene-Based Sensor for Breast Tumor Detection

This article presents a modeling and analysis of a tunable graphene-based sensor for breast tumor detection operating in the terahertz frequency range using the wave concept iterative process (WCIP) method. Through the novel implementation approach of the biological tissues in the WCIP method, we can integrate the normal and tumor of human breast tissues into this algorithm. At the beginning, the details of the algorithms of human breast tissue and graphene modeling in the WCIP method are presented. In order to verify the results obtained by the WCIP algorithm, we presented a comparative study with the CST simulator. Then, using the WCIP method, the numerical simulation results demonstrate the capability of the proposed sensor to detect the normal breast tissue with good sensitivity of 7.11 (THz/RIU) and breast tumor with a high sensitivity of 8.21 THz/RIU and large figure of merits of 17.51 and 20.23 RIU−1, respectively. Finally, the simplicity, efficiency and tunability are the benefits of the proposed sensor, which makes it suitable for breast tumor detection in the THz band.
Detection and Classification of Power Quality Disturbances Using Variational Mode Decomposition and Convolutional Neural Networks

Power quality gains more and more attentions because disturbances in power quality may damage equipment security, power availability and system reliability in power system. Detection and classification of the power quality disturbances is the first step before taking measures to lessen their harmful effects. Common methods to classify power quality disturbances includes signal processing methods, machine learning methods and deep learning methods. Signal processing methods are good at feature extraction, while machine learning methods and deep learning methods are expert in multi-classification tasks. Via combing their respective advantages, this paper proposes a combined method based on variational mode decomposition and convolutional neural networks, which needs a small quantity of samples but achieves high classification precision. The proposed method is proved to be a qualified and competitive scheme for the detection and classification of power quality disturbances.
Joint Task Offloading and Resource Allocation for NOMA-Enabled Multi-Access Mobile Edge Computing

Multi-access mobile edge computing (MEC) allows each user to offload partial task bits to multiple MEC servers via multi-access radio transmission, enabling the collaboration of edge computing. By leveraging the advantage of nonorthogonal multiple access (NOMA) in improving the transmission efficiency, it is expected to effectively reduce the energy consumption of users in multi-access MEC with the aid of NOMA. However, considering the co-channel interference of NOMA and computation collaboration among MEC servers, the joint task offloading and resource allocation is a challenging problem. In this article, with the objective to minimize the weighted sum energy of users, we first propose optimal task offloading and resource allocation algorithms for the special single-user (OTORA-SU) and general multi-user (OTORA-MU) cases, by exploiting the convex and layered structure of the formulated problem. Interestingly, it is found that the single user always preferentially offloads task bits to the MEC servers with better channel gains, regardless of the computation capacity of each MEC server. Then, a low-complexity algorithm (LTORA-MU) is proposed for the general multi-user case, which converges fast and achieves near-optimal performances. Considering the channel estimation error, the impacts of imperfect channel state information (CSI) and successive interference cancellation (SIC) are also investigated. Simulation results demonstrate that for both perfect and imperfect CSI and SIC, 1) the proposed OTORA-SU and LTORA-MU outperform the local computing, full offloading, FDMA-based offloading and non-collaborative MEC schemes; 2) as the number of MEC servers grows, the ratio of offloading task bits increases while the energy consumption is decreased.
Energy Harvesting Optimization of Uplink-NOMA System for IoT Networks Based on Channel Capacity Analysis Using the Water Cycle Algorithm

This article presents energy harvesting (EH) in 5G networks for uplink-NOMA systems for Rician and Rayleigh fading channels via optimizing the radio frequency (RF) energy charging period and the data transmission rate for each user. The model aims to maximize the EH system’s efficiency depending on the number of users, time-slot, resource blocks, path loss, and base station power (BS). In this work, a simple, efficient, and multi-objective the Water Cycle Algorithm (WCA) is presented for calculating transmission rate and EH duration. Furthermore, simulation, performance analysis, and complexity results of WCA are shown by comparing its performance to those of the standard well-known Exhaustive Search Algorithm (ESA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). According to the optimization results within 6,800,000 possibilities, the WCA provides 11 percent more power allocation for the energy harvesting and more smooth time-sharing per user than the literature.
Wafer-Scale Encapsulated Saw Temperature and Pressure Sensors for Harsh Environments

Passive SAW sensors do not require circuitry or power and are ideal for harsh environments or rotating machinery. AlN-based thin film SAW resonators for temperature and pressure sensors have been developed in this work using AlGe wafer-scale encapsulation for device protection, pressure reference, high stability, and small size. Temperature and pressure co-testing of both sensors up to 160°C and 60 bar revealed ~1% full-scale accuracy.
Surface Recognition via Force-Sensory Walking-Pattern Classification for Biped Robot

Real-time surface recognition has become a critical factor for ensuring safe walking of intelligent biped robots in a complex human living environment. This work aims at enabling wide cost-efficient implementation of sensing solutions for surface recognition via walking-pattern classification by restricting the necessary hardware to a cost-economic microprocessor and a single type of force sensors. For experimental analysis, we explored the walking-pattern classification performance using a framework which combines a support vector machine (SVM) and four time-domain feature descriptors, i.e., mean of amplitude (MA), integral of absolute value (IAV), variance (VAR), and root mean square (RMS). During the online pattern classification, the dynamical force-sensory-data stream was extracted using a real-time overlapped-window-based method. Multiple binary SVM classifiers were applied for solving the multi-class classification problem, due to the reasonably high accuracy and the relatively small complexity for hardware implementation, allowing simultaneous strength exploitation of above four individual feature descriptors with a one-versus-one (OVO) strategy. The experimental results, obtained with 250 samples/surface, verified 93.8% mean average precision, 93.7% average accuracy and recall rates of 98.8%, 91.6%, 82.0%, 98.0%, 98.0% for smooth wood, rough foam, smooth foam, thick carpet, and thin carpet, respectively. Only the dynamical force-sensing data were employed for a 10-fold cross validation, which enabled the high processing speed of 0.73 ms/stride. The developed cost-efficient and accurate surface-recognition system can be useful for ensuring safe in-door locomotion for the biped robot and can help the robot to better understand the human living environment by increasing its sensing diversity.
A Low Supply Voltage All-Digital Phase-Locked Loop With a Bootstrapped and Forward Interpolation Digitally Controlled Oscillator

An all-digital phase-locked loop (ADPLL) with a multiphase digitally controlled oscillator (DCO) incorporating the bootstrapped and interpolated schemes is proposed in this paper. The bootstrapped ring oscillator can boost the output voltage to a higher level than the supply voltage. Thus, the oscillator can be operated in low-supply-voltage applications. MOS varactor is used in the bootstrapped capacitor to reduce the area cost. Circuit analysis and simulated verification were performed for an optimized design. The interpolated DCO has multiphase outputs and a high operating frequency. The test chip was implemented in a 90-nm CMOS process, and the core area was <inline-formula> <tex-math notation="LaTeX">$60\times 117\,\,\mu \text{m}^{2}$ </tex-math></inline-formula>. The power consumptions at 1160 MHz and 20 MHz were <inline-formula> <tex-math notation="LaTeX">$912.6~\mu \text{W}$ </tex-math></inline-formula> (at 0.6 V) and <inline-formula> <tex-math notation="LaTeX">$2.94~\mu \text{W}$ </tex-math></inline-formula> (at 0.2 V), respectively. In the worst-case jitter performance, the root mean square (RMS) jitters were less than 0.42%.
Deep-Unfolded Sparse CDMA: Multiuser Detector and Sparse Signature Design

Sparse code division multiple access (SCDMA) is a promising non-orthogonal multiple access technique for future wireless communications. In SCDMA, transmitted symbols from multiple users are coded by their own sparse signature sequences, and a base station attempts to detect those symbols using the signature sequences. In this paper, we present a new deep-unfolded multiuser detector called a complex sparse trainable projected gradient (C-STPG) detector for SCDMA systems. Deep unfolding is a deep learning method that tunes trainable parameters in iterative algorithms using supervised data and deep learning techniques. The proposed detector provides a much superior detection performance over that of the LMMSE detector. Other advantages of the proposed detector include a low computational complexity in execution and a low training cost owing to the small number of trainable parameters. In addition, we propose a novel joint learning strategy called gradual sparsification for designing sparse signature sequences based on deep unfolding. This method is computationally efficient in optimizing a set of sparse signature sequences. Numerical results show that the gradual sparsification successfully yields sparse signature sequences with a smaller symbol error rate than those of randomly designed sparse signature sequences.