Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Sedentary Activity Classification Model to Illustrate an Adaptable Freelance Workspace*

Freelancing and remote-work is fast gaining acceptance in the global community, and thus resulting into sedentary behaviours that poses threat to healthy living. Recent studies have shown that several health hazards are related to sedentary behaviors. Specifically, too much sitting is adversely associated with health outcomes, of which includes cardio-metabolic risk biomarkers, type 2 diabetes and premature mortality. In this paper a one dimensional(1D) convolutional neural network based sedentary activity classification model is proposed for classification of sit, stand and sit-stand transitions using sensor data fusion collected with a tri-axial Inertial Measurement Unit (IMU) sensor. Specifically, normalized data from the IMU axes within a specific time window were considered to classify sedentary behaviors (sit, stand and sit-stand transition). The precision of the classification of the three studied sedentary behaviors exceeded 96.8% using IMU data fusion. The applicability of the proposed classification model is extended to a simulated condition of a typical freelancer by modeling an adaptable automated workspace.
Proposed HOTS based Knowledge Management Model for Research Community under University

The knowledge management as a key aspects of academic institutions and especially academic research; the study focuses on the issues related to academic research community. This is a Qualitative Descriptive Research; The research study includes 372 research scholars response through survey method to recognize their challenges problems and their requirements. The research outputs of academic institutes/universities are the major factor for measuring their quality standards. Institutions are now trying to enhance their research impact by various methods. The Research Scholars are facing challenges to provide quality output, so they need a common platform for discussing their queries, current status of the research, expert guidance, and for research collaboration. This study proposed a conceptual HOTS based Knowledge Management model for Research Community to implement in universities as a authorized KM System.
Silk Fibroin Enabled Optical Fiber Methanol Vapor Sensor

An optical fiber long period grating coated with silk fibroin is proposed and demonstrated for monitoring methanol vapors. Results show an exponential increase in the wavelength shift of the grating’s attenuation band versus methanol vapor pressure, while reaching a value of 4 nm for 100 mbar of methanol. Results are reported on the repeatability of the response of the sensor and show that after multiple exposures to methanol vapors the overlayer retains its functionality 12 days after drop casting.
Resource Allocation in Multi-User Cognitive Radio Network With Stackelberg Game

Resource allocation with sensing-based interference price is considered for multi-users cognitive radio (CR) network, in which the primary base station (PBS) controls the secondary users’ (SUs) transmission by pricing the SUs’ interference power. SUs firstly initiates data transmission based on the sensing decision and then PBS sets the interference price according to each SU’s interference power. Stackelberg game is formulated to jointly obtain the maximum revenue for PBS and optimize the resource allocation to maximize the transmission gain for SUs. Two practical CR network models are investigated: the sensing based spectrum sharing(SBSS) and the opportunistic spectrum access(OSA). For each scenario, the resource allocation strategy is investigated under the two pricing schemes, namely uniform interference pricing and non-uniform interference pricing. Especially, the stackelberg equilibriums for the proposed games is characterized, and the distributed sensing based interference price bargaining algorithm is proposed according to different channel state information (CSI) for the non-uniform interference pricing case. Numerical examples are carried out to demonstrate the effectiveness of the proposed game algorithm under different pricing scheme.
Cuffless Blood Pressure Estimation Using Single Channel Photoplethysmography: A Two-Step Method

Traditional cuff-based blood pressure (BP) monitoring procedure causes inconvenience and discomfort to the users. To overcome these limitations, cuffless BP estimation based on pulse transit time (PTT) and single-channel photoplethysmography (PPG) has been proposed. However, existing studies based on PTT and PPG for BP estimation did not achieve AAMI/ISO standard criteria for BP measurement (mean difference within ±5mmHg and SD of difference within ±8mmHg) under each BP category (Hypotensive, Normotensive and Hypertensive). This study aims to validate an innovative two-step method for PPG-based cuffless BP estimation. A combined database was derived from two online databases (Queensland and MIMIC II) to cover a wide range of corresponding BPs. In total, there were 18010 raw PPG signal segments (5 seconds for each) with corresponding BPs, separated into two halves for training and testing of algorithms (independent datasets). Each PPG signal segment was pre-processed to extract 16 signal features. Later, three significant features have been selected using multicollinearity test. The traditional generic (trained with uncategorized BP) algorithm and two-step algorithm (specifically optimized for each BP category) were developed using machine learning. Generally, the two-step algorithm achieved the AAMI/ISO standard in estimating systolic BP (mean ±SD: 0.07±7.1 mmHg, p<0.001) and diastolic BP (−0.08±6.0 mmHg, p<0.001). Categorically, the two-step method also achieved standard accuracy in all BP categories except Hypotensive systolic BP whereas generic algorithm did not conform to standard accuracy in any BP category except Hypotensive diastolic BP and Normotensive categories. Compared to the traditional generic algorithm, the two-step algorithm specifically designed for three different BP category patients and achieved standard accuracy for cuffless BP estimation.
Large Scale Category-Structured Image Retrieval for Object Identification Through Supervised Learning of CNN and SURF-Based Matching

In the modern era of Internet, mobile and digital information technology, image retrieval for object identification, just as wine label retrieval from a wine bottle image, has become an important and urgent problem in artificial intelligence. In comparison with the general image retrieval, it is rather challenging because there are a huge number of object identification or brand images which are very similar and difficult to discriminate, and the number of different brand images in the given dataset changes greatly, that is, the samples are strongly unbalanced for these brands. In this paper, we propose a CNN-SURF Consecutive Filtering and Matching (CSCFM) framework for this kind of image retrieval, specifically focalizing on wine label retrieval. In particular, Convolutional Neural Network (CNN) is utilized to filter out the impossible main-brands (manufacturers) for narrowing down the range of retrieval and the Speeded Up Robust Features (SURF) matching is improved by adopting the RANdom SAmple Consensus (RANSAC) mechanism and the modified Term Frequency–Inverse Document Frequency (TF-IDF) distance for the accurate retrieval of the sub-brand (item attribute under the manufacture). The experiments are conducted on a dataset containing approximately 548k images of wine labels with 17, 328 main-brands and 260, 579 sub-brands. It is demonstrated by the experimental results that our proposed method can solve the wine label retrieval problem effectively and efficiently. Moreover, our proposed method is further evaluated on two pubic benchmarks of the object identification image retrieval tasks, Oxford Buildings Benchmark (Oxford5k) and the University of Kentucky of Indoor Things Benchmark (UKB), and achieves 88.3% mean average precision and 3.92 N-S score in Oxford5k and UKB, respectively.
Discriminating High Arousal and Low Arousal Emotional Speech Using Mahalanobis Distance Among Acoustic Features

Emotion classification from emotional speech continues to be a challenging research domain. Few research studies have attempted to discriminate amongst a set of emotions, and categorize for valence, activation and dominance. Discriminating between high-arousal and low-arousal emotions is itself challenging, but discriminating emotions within each subcategory is further challenging problem. In this study, a new approach is proposed to discriminate between high and low arousal emotions, and also amongst emotions within each subcategory. Mahalanobis distances amongst acoustic feature vectors of emotional speech w.r. t. normal speech are examined. The approach, involving speech production features, has been validated on three databases: German (Berlin EMO-DB), English (RAVDESS) and Telugu (IITKGP-SESC). A common set of five emotions Angry, Happy, Fear, Disgust and Sad are examined with reference to normal speech. The vocal-tract filter features Mel-frequency cepstral coefficients (MFCCs), and combined source-filter features signal energy, zero-crossing rate and duration are used. A 2D projection of Mahalanobis distance for one emotion, w.r. t. normal, onto another emotion is observed to discriminate amongst emotions within each high/low-arousal sub-category. The Angry and Happy emotions are discriminated in high-arousal emotions sub-category, whereas Fear, Disgust and Sad are discriminated in low-arousal emotions sub-category. This study should be helpful in further classifying emotions within each subcategory of high/low arousal emotions in emotional speech.
Multi-Object Detection in Urban Scenes Utilizing 3D Background Maps and Tracking

In this paper we propose a novel approach for upgrading real time 3D dynamic object detection methods operating on rotating multi-beam (RMB) Lidar measurements using 3D background city maps stored in new generation geographic information systems (GIS) and previously detected dynamic objects propagated by tracking. First, we apply a state-of-the-art object detection method and distinguish the predicted dynamic object candidates and the remaining static regions of the current Lidar measurement. Next we find an optimal transformation between the static part of the RMB Lidar measurements and the background city map using a multimodal point cloud registration algorithm operating in the Hough space. After the accurate alignment, we filter false-positively detected object candidates in the RMB Lidar data based on the map. To find additional objects missed by the object detector on the current measurement, we apply a Kalman-filter based object tracking. Hereby we first predict the current state of the previously detected and tracked objects. Next, we apply a Hungarian matcher based assignment between the tracked and the current objects and update the object list according to the result. For better accuracy, we keep all predictions through a couple of frames. We evaluated our method qualitatively and quantitatively in crowded urban scenes of Budapest, Hungary, and the results showed that with background map based filtering we can achieve a 26,52% improvement detecting vehicles and 9,38% for pedestrians in precision, while via tracking, a 12,84% improvement for vehicles and 14,34% for pedestrians in recall against the state-of-the-art object detection method relying purely on a single Lidar time frame.
Efficient PWM Waveform Generation Using Rotary Encoder on Spartan-6E Starter Kit

In this paper, an efficient pulse width modulation (PWM) waveform generator with variable frequency and duty cycle is designed and implemented. An ISIM simulator from Xilinx is used to verify the simulation, and the reference clock and system clock required to generate the PWM waveforms are based on a rotary encoder signal and an oscillator on Spartan-6E FPGA Starter Kit. A starter kit is used to implement the logic proposed in this paper. The logic configuration is designed so that the duty cycle of the PWM waveform can be changed from 0% to 100% and there are no problems in PWM waveform generation even if the rotary encoder pulse period is changed. The FPGA used to construct the application logic provides a reconfigurable architecture and is economically useful, while also providing the best flexibility to replace a microcontroller.
Periodic steady state solution of photovoltaic generation systems in time domain

The modeling and representation in state space of conventional single-phase distributed generation system (in particular PV systems) allow, in the time domain, their analysis under operating conditions in steady state and transient state. The research reported in this article is oriented in particular to the determination of the periodic steady state under conditions of harmonic distortion of single-phase distributed generation systems. The analysis is carried out in the time domain, considering the particular modeling of the components of the PV system, their associated controls and their representation in the frame of reference of the state space. Results are validated against the simulator PSCAD/EMTDC®, widely accepted by the power industry.
A Novel Distributed CDS Algorithm for Extending Lifetime of WSNs With Solar Energy Harvester Nodes for Smart Agriculture Applications

Recent improvements in computer and software technologies affect different areas of production systems. The productivity of an agricultural process can be accepted as one of the many domains affected by these improvements. The production volume is increased not only with new designs of agricultural machines, but also by utilizing communication technologies in the production process. Even the wireless sensor technologies have started to be used for gathering some environmental features in order to optimize production parameters as well as preserving the product in determined quality levels. However, nodes which play an important part of such systems are prone to battery depletions. To alleviate this battery problem, incorporating harvester nodes into the system has been recently considered. However, only including such nodes is not sufficient for improving the lifetime of the system. In this paper, a new distributed connected dominating set algorithm on WSNs with solar energy harvester nodes for precision agriculture applications is proposed. The novel distributed connected dominating set construction with solar energy harvesting in smart agriculture applications algorithm, namely CDSSEHA, is compared with the traditional flooding methods and with an energy efficient CDS algorithm. According to the results, the proposed algorithm increased the WSNs’ lifetime by up to approximately 6 times and 1.4 times compared to the traditional flooding methods and CDS based method, respectively. Furthermore, the CDS construction process constitutes only about 15% of the whole lifetime.
Performance Analysis of Hoeffding and Logisitic Algorithm for Churn Prediction in Telecom Sector

Churn automatically introduces where the term customer emanates. Churn is irresistible thus it is better to develop models to predict the churn. In this paper, weka tool is used to perform various operations on data set and first of all data is converted into nominal form. It's divided into classes and cross-validation is performed on churn class. Hoeffding tree algorithm and Logistic algorithm have been applied on the dataset. Classifier performance evaluator is used to classify the data. To observe the results text viewer is used and the graphical representation is cast-off to demonstrate the comparisons. Confusion matrix suggest that Logistic algorithm is a better algorithm to predict the customer churn over Hoeffding tree algorithm.
Navel Orange Recognition Method Based on Improved Otsu Algorithm

An improved Otsu threshold segmentation algorithm with filling a circle method based on the center of mass(FCCM) is proposed to solve the problems of missing regional feature information and unsatisfactory effect in navel orange recognition by traditional image processing method. We first choose the appropriate color model. By analyzing the data, YCbCr model has better contrast compared with other color models, and the histogram of its Cr component has obvious peaks and troughs, which is conducive to image segmentation. We then segmented the extracted Cr component images by Otsu threshold segmentation algorithm. And further processing is carried out in combination with morphology and hole filling algorithm to make the image coherent, so as to ensure that the feature information of navel orange is not easy to be missing. And next, the center of mass of the image is determined and the radius of the detection circle is calculated by stepping method. Finally, the noise points outside the radius of the detection circle are eliminated and the recognition results are displayed in the original image. The experimental results show that the proposed method is effective in the recognition of low pixel images with both different light conditions and obscured condition.
Development of Speech Emotion Recognition Algorithm using MFCC and Prosody

Recently, in the field of Human Computer Interaction (HCI), speech emotion recognition (SER) is a highly challenging work. Various models have been proposed for better performance. In this paper, we use GRU model, which achieves comparably high performance with less parameters. We used not only MFCC, delta, and acceleration, but also delta of acceleration. Additionally, we propose the novel input feature that captures their pair simultaneously. Furthermore, we applied the prosody, the low-level feature of speech, for every step in GRU cell with MFCC feature. Our model obtained 64.47% of weighted accuracy, using only audio input from both of improvised and scripted data in IEMOCAP.
We've Got the Power: Overcoming the Distance Enlargement Fraud with Wireless Power Transfer

Distance bounding protocols have been previously introduced to establish upper and lower boundson the distance between two entities. This article demonstrates how coupled transmission techniques, stemming from the domain of wireless power transfer, can be used for distance bounding. The proposed approach can detect and prevent distance enlargement frauds, which were deemed unsolvable for electromagnetic propagation-based distance bounding protocols. The effectiveness of the proposed approach is demonstrated and evaluated via numerical simulations. The results suggest that the proposed approach is capable of measuring the distance between two devices communicating via inductive coupling with an average accuracy of 8.14cm when using a hardware with 12-bit voltage measurement resolution in the absence of noise.
Pattern Recognition for Knowledge Transfer in Robotic Assembly Sequence Planning

The autonomous assembly of customized products is highly demanded in future manufacturing scenarios. This requires robotic systems being able to adapt to individual products without increasing overall production time. However, increasingly complex assemblies lead to a growing number of potential assembly sequences that have to be considered. To cope with this, we present an algorithm that is able to transfer previously identified assembly constraints to novel product variants. This reduces the search space, and thus planning times. The approach consist of three main steps. 1) Deduct semantic assembly constraints, from an analysis of feasible and unfeasible solutions. 2) Match key features of assemblies on a semantic level, by performing graph matching in the representation of the assemblies. 3) Use pattern recognition and classification based on machine learning techniques to transfer the knowledge of constraints for sub-assemblies into the complete assembly. We demonstrate our contributions on a two-armed robotic setup that assembles product variants out of aluminum profiles.
On Performance of SWIPT Enabled Two-Way Relay System with Non-Linear Power Amplifier

In this work, we examine a dual-hop amplify-and-forward two-way relay system over independent and non-identically distributed Nakagami-m fading channels with integer values of fading parameter. We consider a three-node half-duplex relaying system with two source nodes and a relay node. To address the needs of present day energy constraint battery enabled relay nodes, we employ simultaneous wireless information and power transfer (SWIPT) for energy harvesting (EH) from ambient resources through time-switching protocol. Further, since a low-cost and low-complexity relay node has a non-linear power amplifier, we consider the impact of the same in the analysis. Framework, for outage probability of SWIPT enabled two-way relay system with non-linear amplifier is presented. Additionally, throughput analysis is performed and affect of non-linearity, EH and relay power over the system is analyzed. Accuracy of the derived expressions and impact of non-linear power amplifier over the SWIPT system are verified through the Monte-Carlo simulations.
A Layer-Partitioning Approach for Faster Execution of Neural Network-Based Embedded Applications in Edge Networks

As embedded systems become more prominent in society, the technologies that run on them must be used efficiently. One such technology is the Neural Network (NN). NN’s, combined with the Internet of Things (IoT), can utilize the massive amounts of data produced to optimize, control, and automate embedded systems, giving them more functionality than ever before. However, the status quo of offloading all NN functionality onto external devices has many flaws. It forces the embedded system to entirely rely on networks that may have high latency or connection issues. Networks may also expose them to security risks. To reduce the reliance of IoT devices on networks, we examined several solutions, such as delegating some NN’s to run solely on the IoT device or splitting the NN and distributing the subnetworks into different devices. It was found that, for shallow NN’s, the IoT device itself could run the NN at a rate faster than offloading it to an external device, but the IoT device needed to offload its inputs once the NN’s started to increase in layers and complexity. When splitting the NN, it was found that the number of messages sent between devices could be reduced by up to 97% while only reducing the accuracy of the NN by 3%.
A 10 $\mu \mathrm{W}-74.6\mathrm{dB}$ THD Arterial Pulse Waveform Sensing System with Automatic Bridge-Offset Calibration and Super Class-AB Output Stage

This paper presents a power-efficient, highly-linear sensing system for arterial pulse waveform (APW) acquisition. The system consists of a front-end circuit connected to a packaged silicone-coated resistive-bridge pressure sensor for detecting APW through direct skin contact in a non-invasive manner. Compared to other pulse sensing approaches, the proposed front-end only requires an instrumentation amplifier (IA) and a low-pass filtering analog-to-digital converter (LPF-ADC), and consumes a total power of 10 $\mu \mathrm{W}$. An automatic calibration circuit is proposed to compensate for the resistive-bridge offset and can reduce such offset down to 1 mV. High linearity in the IA is provided through the use of a super class-AB output stage. A LPF-ADC architecture is employed to extract and digitize the APW in a 100 Hz bandwidth, saving 90 % of the passives area. The front-end provides a measured input-referred noise of 7.2 $\mu \mathbf{Vrms}$ with a gain of 32–40 dB, and a THD of -74.6 dB. Together with an on-chip low dropout regulator (LDO) and a buffer stage, the prototype fabricated in 0.18 $\mu \mathrm{m}$ CMOS technology has an active area of 0.52 mm2.