Miracle-dz/UniFAR-FTUs huggingface dataset:
An Entropy Evaluation Algorithm to Improve Transmission Efficiency of Compressed Data in Pervasive Healthcare Mobile Sensor Networks

Data transmission is the most critical operation for mobile sensors networks in term of energy waste. Particularly in pervasive healthcare sensors network it is paramount to preserve the quality of service also by means of energy saving policies. Communication and data transmission are among the most critical operation for such devises in term of energy waste. In this paper we present a novel approach to increase battery life-span by means of shorter transmission due to data compression. On the other hand, since this latter operation has a non-neglectable energy cost, we developed a compression efficiency estimator based on the evaluation of the absolute and relative entropy. Such algorithm provides us with a fast mean for the evaluation of data compressibility. Since mobile wireless sensor networks are prone to battery discharge-related problems, such an evaluation can be used to improve the electrical efficiency of data communication. In facts the developed technique, due to its independence from the string or file length, is extremely robust both for small and big data files, as well as to evaluate whether or not to compress data before transmission. Since the proposed solution provides a quantitative analysis of the source’s entropy and the related statistics, it has been implemented as a preprocessing step before transmission. A dynamic threshold defines whether or not to invoke a compression subroutine. Such a subroutine should be expected to greatly reduce the transmission length. On the other hand a data compression algorithm should be used only when the energy gain of the reduced transmission time is presumably greater than the energy used to run the compression software. In this paper we developed an automatic evaluation system in order to optimize the data transmission in mobile sensor networks, by compressing data only when this action is presumed to be energetically efficient. We tested the proposed algorithm by using the Canterbury Corpus as well as standard pictorial data as benchmark test. The implemented system has been proven to be time-inexpensive with respect to a compression algorithm. Finally the computational complexity of the proposed approach is virtually neglectable with respect to the compression and transmission routines themselves.
Structural Analysis of Low Power and Leakage Power Reduction of Different Types of SRAM Cell Topologies

With the improvement of innovation as time passes, the interest for low power, fast, high thickness memory for convenient gadgets is expanding relatively. One of the significant territory where spillage is successful memory to decrease the spillage control in memory. The procedure that can be actualized incorporates Gated vdd, Source Basing, Sleep Stack and MT-CMOS the innovation utilized for 6T memory cell is 90 nm utilizing Cadence Virtuoso. This paper shows, we plan distinctive sort of SRAM cells. This paper looks at the execution of five SRAM cell topologies, which incorporate the traditional 6T, 7T, 8T, 9T and the 10T SRAM cell usage. [4]. Specifically, the spillage flows, spillage power and read conduct of each SRAM cells are analyzed. Perception is fundamentally centered around parameters like Read and Write Delay, Average Power, Leakage Power. Proposed SRAM gives 40-60% decrease in Leakage Power Consumption considering power supply voltage of 1 V and 10-20% abatement in read and compose delay. SRAM Architecture with its peripherals is planned, broke down and actualized in standard gpdk 90nm innovation library utilizing Cadence device.
An accurate black lung detection using transfer learning based on deep neural networks

Coal Worker Pneumoconiosis (CWP), commonly called Black Lung (BL), is an incurable respiratory disease caused by long-term inhalation of respirable dust. Privacy restrictions and disease incidence placed limits on the available BL datasets, which introduces significant challenges for training deep learning (DL) models. Recently, transfer learning has been seen as an efficient DL method for automatic disease detection with small datasets. This paper investigates BL detection in chest X-rays using transfer DL knowledge from a CheXNet model on a small dataset. A training image set of real, segmented lung X-ray images, with and without BL, was used as a benchmark for detection accuracy. The training data set was then augmented using a Cycle-Consistent Adversarial Networks (CycleGAN) and Keras Image Data Generator, to generate training data with real, augmented and synthetic CWP radiographs to the CheXNet model (with and without pre-trained weights). The effects of different dropout nodes as a blocking factor was also investigated. The accuracy, sensitivity (recall or true positive rate), specificity (true negative rate) and error rate (ERR or incorrect prediction rate) using 3-fold cross-validation experiments was compared for each transfer learning experiment. The total execution time for binary classification of our model also measured. While no definitive conclusion could be reached regarding the effect of dropout rates, results indicated an improvement of classification accuracy from transfer learning.
Metasurface for Wireless Power Transfer to Multiple Receivers

In this paper we present a metasurface for wireless power transfer system. The metasurface supports a surface wave, which greatly enhances the power transfer distance. In this way, WPT efficiency of 80% to a single receiver is obtained at a distance 80 cm. In addition, we show that an efficient power transfer to multiple receivers is possible. An overall efficiency of 82% can be obtained at the distance of 45 cm.
Identifying Topological Prototypes using Deep Point Cloud Autoencoder Networks

Data mining of engineering designs generated by topology optimization methods is a challenging task. A topology optimization method maximizes one or more performance objectives by redistributing the material in a design space for a given set of boundary conditions and constraints. The performance objective could be stiffness and the constraint could be the allowed mass fraction in the design space. If the constraints are not too restrictive or unknown at the initial stages of the design process, multiple feasible designs are possible and are generally generated to inspire new design ideas. Since a designer cannot manually review all the designs, one needs to select a few representative and interesting topologies based on performance or geometric features. In this paper, we propose a method to group designs with similar geometric features which are extracted from a point cloud representation of the geometry using a deep autoencoder network. The point cloud representation is generated by sampling points on the surface of geometry. The extracted features - called latent code - can be used to cluster topologies and identify prototypes in each cluster. The proposed method could be used on designs generated by topology optimization and other design generation methods. To evaluate the method, we use it on complex truss-like topology datasets with prespecified design types which have been recognized by the proposed method with high precision and recall. Also, the prototypes of the different categories are identified.
Deadlock-Free Layered Routing for Infiniband Networks

End-to-end network latency has become an important issue for big data processing on large-scale and heterogeneous data centers. To realize them, a low-latency inter-switch network is required. In recent years, a novel network based on a random regular graph such as Jellyfish has been proposed as a solution to these problems. To realize this network on Infiniband, we explore layered shortest path routing that guarantees deadlock freedom. Infiniband can avoid deadlocks by using Virtual Lane (VL), which is a QoS guarantee mechanism. However, the output VL is indirectly determined by using Service Level (SL) held by each packet. In this research, to minimize the number of consumed SLs, we propose an algorithm based on a graph coloring problem to assign SLs to all source-destination pairs. The proposed method can reduce the number of consumed SLs by up to 38%compared to the conventional SL mapping method.
Energy-Efficient Switching ON/OFF Strategies Analysis for Dense Cellular Networks With Partial Conventional Base-Stations

Focusing on the energy efficiency (EE) improving problem in a densely deployed cellular network with partial conventional base stations (BSs) that cannot be frequently switched on/off, this paper jointly optimizes the switching on/off strategy and user association policy with the consideration of quality-of-service (QoS). By applying a modified switching cost model, we formulate the EE improving problem as an energy efficiency maximization problem (EEMP). To solve the EEMP efficiently, we first concentrate on the EEMP with a constant user distribution where switching cost is omitted temporarily, i.e., static- EEMP, and then the EEMP can be settled by using the results of the static-EEMP. After analyzing the hardness of solving the static-EEMP directly, several sub-optimal strategies are proposed for the static-EEMP to optimize the BSs working states and user association policy. With the results obtained from solving the static-EEMP, we propose a QoS First Maximum EE (QFMEE) scheme to solve the EEMP in two steps. In the first step, necessary BSs are switched on when the user distribution changes to ensure user QoS. In the second step, we judge the extra cost and the energy saving caused by the switching off operations to improve the network EE. Simulation results verify the effectiveness of our proposed strategies for static-EEMP and show that QFMEE can improve the network EE and reduce switching cost significantly when the switching cost occupies a considerable proportion in the total energy consumption.
Train-Simulator for Energy Consumption Evaluation in Mass Rapid Transit Applications

Proper management of railway systems is currently mandatory in order to reach energy saving objectives that positively impact on both the internal railway system and its supplying power system. In such a context, the railway system has to be analyzed in terms of both structure renewal and energy efficiency strategies, including measures ranging from the eco-driving techniques to the energy recovery. The choice of the more appropriate measures to be implemented depends on the type of system under study, because diverse responses can be obtained for different system as a function of the technological characteristics and the imposed operating assignments. Therefore, the availability of tools able to accurately describe the behavior of a system characterized by high level of complexity phenomena becomes a fundamental instrument to assess the impact of alternative measures. Railway are complex systems, which depend of logistics, mechanical and electrical elements. These aspects are all together integrated in order to meet the expected requirements of customers. Motivated by above, this paper proposes a novel train-simulator developed in Simulink-MatLab framework. The proposed simulator calculates train dynamics and energy absorption for an assigned observation time. It performs the resolution of the motion equation for a rolling stock moving on assigned track and operating conditions. One of the advantages of the proposed simulator is its ability to be easily update to different type of railway systems as well as eco-driving techniques. In this paper, the simulator was tested on an urban metro system operating in the North of Italy.
A Novel Support Vector Machine Based on Hybrid Bat Algorithm and Its Application to Identification of Low Velocity Impact Areas

The standard support vector machine (SVM) performs poorly on the identification problem of low velocity impact areas due to its lower accuracy rate. Improving SVM’s performance using the bat algorithm (BA) is feasible, but BA has the premature convergence problem. In this study, a hybrid bat algorithm with double mutation operations (DMBA), in which the Cauchy mutation operator and the extremal optimization mutation operator are integrated into BA, is proposed to enhance BA’s ability to jump out of the local optima. Then, a novel SVM based on this hybrid BA, which is called SVM_DMBA, is developed to address the identification problem. Compared with the standard SVM and twelve improved SVM methods which are combined with the standard algorithms, advanced algorithms, and bat variants, the significant performance of SVM_DMBA is validated using UCI datasets. Moreover, to identify low velocity impact areas, SVM_DMBA is applied to the low velocity impact localization system based on fiber Bragg grating (FBG) sensors. The statistical results indicate that SVM_DMBA is a significantly effective method for identifying the low velocity impact areas and generates higher identification accuracy than comparative methods. For 64 low velocity impact areas of $30\,\,\text {mm}\times 30\,\,\text {mm}$ on an aluminium plate, the average identification error obtained by SVM_DMBA is 1.615%.
2(1 – 1/ℓ)-Factor Steiner Tree Approximation in Õ(n^1/3) Rounds in the CONGESTED CLIQUE

We study the Steiner tree problem in the CONGESTED CLIQUE model of distributed computing. We present a deterministic distributed approximation algorithm that computes a Steiner tree in Õ(n^1/3) rounds and Õ(n^7/3) messages for a given undirected weighted graph of n nodes with the approximation factor 2(1 - 1/ℓ) of the optimal, where ℓ is the number of terminal leaf nodes in the optimal Steiner tree. Note here that the Õ(⋅) notation hides polylogarithmic factors in n. To the best of our knowledge, this is the first work to study the Steiner tree problem in the CONGESTED CLIQUE model.
Solution-Processed P3HT:PbS-Based NIR Photodetector With FET Configuration

A solution-processed near-infrared (NIR) photodetector based on a mixture of PbS colloidal quantum dots (CQDs) and Poly(3-hexylthiophene) (P3HT) was presented. In a reverse field-effect transistor (FET) device configuration Au(S,D)/P3HT:PbS/PMMA/Al(G), uniform-sized and well-dispersed PbS CQDs were employed as NIR absorbing materials in the active layer. Meanwhile, the poly(methyl methacrylate) (PMMA) dielectric layer could be seen as an encapsulation to enhance the device stability. Herein, high “on/off” current ratio (<inline-formula> <tex-math notation="LaTeX">$I_{on}/I_{off})$ </tex-math></inline-formula> of 10<sup>4</sup> was obtained in dark, and the maximum photosensitivity (<inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>) of 947 was gotten under 200 mW/cm<sup>2</sup> 980 nm illumination. When the irradiance reduced to 0.1 mW/cm<sup>2</sup>, the responsivity (<inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>) and detectivity (<inline-formula> <tex-math notation="LaTeX">$D^{\ast }$ </tex-math></inline-formula>) of the NIR photodetector reached 9.4 mA/W and <inline-formula> <tex-math notation="LaTeX">$2.5\times 10^{11}$ </tex-math></inline-formula> Jones, respectively. Therefore, the P3HT:PbS hybrid FET-based NIR photodetector had shown both relatively high electrical and detecting performance, which provided an experimental foundation and method for the next fabrication of medical infrared detectors and sensors.
Development of an Intelligent Cane for Visually Impaired Human Subjects

People with visual disabilities are often dependent on external assistance which is provided by either humans, trained dogs, or other special electronic devices for decision making but there are certain limitations to these aids. Hence, an intelligent white cane is developed for visually challenged people which makes use of HR-SO4 ultrasonic sensors to detect any obstacle that lies in the range of the the sensor and determine its distance. The ultrasonic sensor has range up to 450 meters so that any object lying within this range can be easily detected and the warning signal is provided using the buzzer which gives beeping signals in order to alert the user for prompt action. Also, an intelligent technique of object detection and classification using the web camera which captures the image and hence classifies it is being used. The classification obtained is in the form of text which is further converted to audio signal using text-to-speech conversion which is implemented in Python using Espeak open source library.
Metabolic syndrome and professional aptitude

The development of civilisation has resulted in a growing problem of metabolic diseases, including metabolic syndrome. Scientific studies show that this disease is an epidemic of the 21st century. Metabolic syndrome is a collection of mutually related metabolic factors, such as obesity, impaired glucose tolerance, lipid disorders, arterial hypertension, and pro-inflammatory and prothrombotic state, increasing the risk of the development of atherosclerosis and type 2 diabetes, and their cardiovascular complications. Considering the scale of the prevalence of metabolic syndrome, the causes of its development and diagnostic criteria are the main contemporary directions of scientific research. The study presents an analysis of the relevant literature with respect to terminology, diagnostic criteria, and pathogenesis of metabolic syndrome. The second section of the study discusses the degree of epidemiological risk due to metabolic syndrome and its individual components among the adult Polish population. The effect of this phenomenon on professional competence is also taken into consideration.
Design of an integrated DC-DC converter for energy harvesting applications

This paper presents a multisource DC-DC boost converter for energy harvesting from thermoelectric and photovoltaic transducers. It uses a voltage supervisor in order to have the ability to simultaneously draw energy from sources independently of their output voltages. Furthermore, a novel approach of common PWM ZCS circuit is presented. The output voltage is greater than 1.8 V with a load of $\mathbf{2}\ \mathbf{k\Omega}$ and the peak power efficiency is close to 77% with 100 mV input voltage and irradiance from 3-W/m2 to 40-W/m2. It has been implemented in AMS $\mathbf{0.35\ \mu}\mathbf{m}$ CMOS process with a 0.082 mm2 die area.
Inference of Properties from Requirements and Automation of Their Formal Verification

Over the past decades, various techniques for the application of formal program analysis of software for embedded systems have been proposed. However, the application of formal methods for software verification is still limited in practise. It is acknowledged that the task of formally stating requirements by specifying the formal properties is a major hindrance. The verification step itself has its shortcoming in its scalability and its limitation to predefined proof tactics in case of automated theorem proving (ATP). These constraints are reduced today by the interaction of the user with the theorem prover (TP) during the execution of the proof. However, this is difficult for non-experts. The objectives of the presented PhD project are the automated inference of declarative property specifications from example data specified by the engineer for a function under development and their automated verification on abstract model level and on code level. We propose the meta-model for Scenario Modeling Language (SML) that allows to specify example data. For the automated property generation we are motivated by Inductive Logic Programming (ILP) techniques for first-order logic in pure mathematics. We propose modifications to its algorithm that allow to process the information that is incorporated in the meta-model of SML. However, this technique is expected to produce too many uninteresting properties. To turn this weakness into strength, our approach proposes to tailor the algorithm towards selection of the right properties that facilitate the automation of the proof. Automated property generation and less user interaction with the prover will leverage formal verification as it will relieve the engineer in the specification as well as in proofing tasks.
Permutation Invariant Training Based Single-Channel Multi-Talker Speech Recognition with Music Background

This paper addresses the single-channel multi-talker speech recognition task with music background, where the speech recognition accuracy will deteriorate significantly. To improve the speech recognition accuracy with music background, we propose two approaches: 1) music-separation method to separate human speech from the music background, 2) permutation invariant training (PIT) for single-channel multi-talker, specifically two-talker, speech separation. Experimental results show that all the proposed methods can improve the speech recognition accuracy. Specifically, we use the music-separation method instead of the de-noising feature-mapping method to extract the human speech. Compared with the de-noising feature-mapping method, the music separation method achieves consistent improvement on the music-distorted speech separation and recognition tasks.
Dynamic Positioning Control for Accommodation Vessels With Input Time Delay

Offshore operations have been moving towards ultra-deep waters where rich resources are detected and mined in a challenging environment. Dynamic Positioning (DP) control remains a challenge due to time delay in the input control and unknown wave or wind disturbance. In this paper, robust control is proposed for the DP of an accommodation vessel in the presence of the input time delay. In order to overcome the unknown dynamics and external disturbances, a robust controller with predefined tracking constraints is developed to guarantee a safe operation. Time delay caused by the control input is also considered on the predictor-based method. The boundedness of all closed-loop signals is demonstrated via rigorous Lyapunov analysis. Simulation studies help demonstrate outstanding performance and feasibility of the proposed control structure.
Privacy First: Designing Responsible and Inclusive Social Robot Applications for in the Wild Studies

Deploying social robots applications in public spaces for conducting in the wild studies is a significant challenge but critical to the advancement of social robotics. Real world environments are complex, dynamic, and uncertain. Human-Robot interactions can be unstructured and unanticipated. In addition, when the robot is intended to be a shared public resource, management issues such as user access and user privacy arise, leading to design choices that can impact on users’ trust and the adoption of the designed system. In this paper we propose a user registration and login system for a social robot and report on people’s preferences when registering their personal details with the robot to access services. This study is the first iteration of a larger body of work investigating potential use cases for the Pepper social robot at a government managed centre for startups and innovation. We prototyped and deployed a system for user registration with the robot, which gives users control over registering and accessing services with either face recognition technology or a QR code. The QR code played a critical role in increasing the number of users adopting the technology. We discuss the need to develop social robot applications that responsibly adhere to privacy principles, are inclusive, and cater for a broad spectrum of people.
Single Active Element Realization of Fractional-Order $I^{\lambda}D^{\mu}$ DC Motor Controller

This paper introduces a novel design for implementing a fractional-order $I^{\lambda}D^{\mu}$ DC motor controller, where only one active element is required. This element is a second generation current conveyor, capable of working in a low-voltage environment. The main advantage, the proposed implementation offers compared to the corresponding schemes in the literature, is a simple structure of low circuit complexity and reduced number of transistor count. The performance of the controller is evaluated using the Cadence software and MOS transistor models provided by the Austria Mikro Systeme $\mathbf{0.35}\mu \mathbf{m}$ CMOS process.