Miracle-dz/UniFAR-FTUs huggingface dataset:
Black-Box Modelling of a DC-DC Buck Converter Based on a Recurrent Neural Network

Artificial neural networks allow the identification of black-box models. This paper proposes a method aimed at replicating the static and dynamic behavior of a DC-DC power converter based on a recurrent nonlinear autoregressive exogenous neural network. The method proposed in this work applies an algorithm that trains a neural network based on the inputs and outputs (currents and voltages) of a Buck converter. The approach is validated by means of simulated data of a realistic nonsynchronous Buck converter model programmed in Simulink and by means of experimental results. The predictions made by the neural network are compared to the actual outputs of the system, to determine the accuracy of the method, thus validating the proposed approach. Both simulation and experimental results show the feasibility and accuracy of the proposed black-box approach.
A Novel Dual Band Branch Line Coupler and its Application to Design a Dual Band 4 × 4 Butler Matrix

A novel dual band branch line coupler (BLC) has been proposed, designed, implemented, and analyzed in this paper. A dual band 4 <inline-formula> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula> Butler matrix (BM) has also been developed by employing the proposed BLC. To realize the proposed BLC, PI and modified PI-shaped transmission lines (TLs) as dual band TLs, with the complete derivation of the design equations, have also been introduced. The proposed BLC is equivalent to a dual band ±90° BLC and a dual band ±45° phase shifter combination and capable of performing a dual band operation for 2.3 to 4.4 frequency ratios. In order to verify the proposed approach, a dual band BLC for 1GHz and 2.85GHz frequencies has been designed and implemented. For the proposed BLC, equal power division between the output ports has been achieved with maximum ±0.2dB amplitude imbalance and a phase difference of +45°/-135°±1° at frequency 1GHz and +135°/−45°±2° at frequency 2.85GHz between the output ports for port1/port4 excitation. By employing the proposed BLC, a dual band 4 <inline-formula> <tex-math notation="LaTeX">$\times 4$ </tex-math></inline-formula> BM has also been developed. Compared to the existing dual band BMs, the 4 X 4 BM offers a simpler design, compact size, and design with fewer number of components.
Geographic and individual variation in carotenoid coloration in golden-crowned kinglets (Regulus satrapa)

Phenotypic variation arises through natural selection for local adaptation, sexual selection for conspicuous visual communication, and genetic drift. Large-scale variation is often demonstrated via clinal gradients, and small-scale variation is commonly exhibited by quality-indicating traits. My goal was to investigate geographic and individual phenotypic variation in golden-crowned kinglets, with particular focus on carotenoid-based ornaments. Through a museum study, I found that kinglet body size and coloration weakly followed well-established ecogeographic rules. However, sexual dichromatism was reduced in colder climates, providing support for a poorly recognized environmentally-induced cline. In a separate study, I captured migrating kinglets to determine how carotenoid content mediates interand intrasexual crown colour variation. I found that crown coloration was associated with migration timing, and females displayed additional condition-dependence of this trait. Overall, small-scale variation in crown coloration is dependent on pigment differences and individual quality, while largescale variation is likely governed by differences in selection pressures.
Pervasive Health Monitoring of Special Child using IoT and Cloud Technologies

In this era of disruptive technologies, one novel paradigm is the integration of IoT and cloud, extending the scope of these individual technologies to deal with the real world issues in a more distributed and dynamic way. In this paper cloud and IoT technologies are integrated to find a healthcare solution for a special child in the age group of 4 to 16 years. Considering that a special child face problems in expressing his/her issues, we have proposed a model where a special child would use a wearable IoT device that will sense the blood pressure and heart rate. The data captured in the proposed cloud architecture will be transmitted to the parents and respective registered doctors based on the event triggers. Pervasive monitoring of health attributes is collecting huge data, so proposed decision algorithms would raise alert notifications and forward the conditional data for further action.
A Motion Mapping System for Humanoids that Provides Immersive Telepresence Experiences

Motion capture and mapping systems have been evolving to enhance the virtual immersion experience in a human-in-the-loop model. In this work, a motion mapping system composed by a 3D printed humanoid robot, built with low-cost materials, an IMU-based motion capture suit, a binaural microphone and a virtual reality headset is presented. The movements of the robot are limited by its reduced amount of degrees of freedom, particularly due to its shoulder configuration, which differs from the human's own biomechanics. Once the motion capture suit's information is extracted, a ROS-based architecture maps orientations from the user to obtain the generalized coordinates of the robot in order to imitate the operator's arm's motion. Additionally, the headset is used to project a stereo vision of the robot's surroundings and to map the operator's head motion. Furthermore, the microphones located on each ear provide the ability to capture 3D sound. This project intends to provide an interactive telepresence puppetry system to encourage the involvement of a targeted audience on engineering subjects. The system shows acceptable results with moderate time response.
Automated Asphalt Pavement Crack Detection and Classification using Deep Convolution Neural Network

Asphalt pavement defects on road surface contribute one of the most important factors for traffic accident. Research on asphalt pavement using image processing techniques have been carried but there are still have challenges to the presence of shadows, oil stains and water spot. Therefore, considering the abovementioned issues, this study proposed a fully automated pavement crack detection and classification using deep convolution neural network (DCNN). First, the image of pavement cracks with dimension of 1024x768 pixels, will segmented into patches (32x32 pixels) to prepare training dataset. Next, the trained DCNN with different numbers of layers and different size of filters are employed in network. Upon the evaluation of proposed method, with respect to accuracy and processing time, the result found that the size of filters and convolution layers has an influence on the network performance. The experimental results achieved a high performance with overall accuracies above 94.25%.
Electronic System for Remote Monitoring of Environmental Parameters on a Bee Yard

Although there are plenty of commercial systems dedicated to measuring environmental parameters, because of need for specific parameters of interest (temperature, relative humidity, atmospheric pressure, ultraviolet light level, infrared light level and visible light level, micro-sized particles concentrations, magnetic flux density, and wind speed) we proposed a multi-sensors and low-cost platform designed for remote monitoring of environmental conditions on a bee yard. The proposed system has specific features such as portability, battery-power supply, and unit for transmitting data to the remote server. Initial testing showed ability of the system to process all data on time and to perform reliable acquisition.
Trip Planning for Electric Vehicles Considering the Available Charging Infrastructure

The ongoing trend towards electro mobility is radically changing automotive industry. However, the limited battery electric range and the resulting restrictions on personal mobility present a major customer concern that must be tackled by car manufacturers. Especially on longer trips, the time required for charging stops is troublesome. This research presents an approach that combines data on road, traffic and available charging infrastructure for a desired route to plan a time optimal trip for an electric vehicle. For this purpose, charging decisions, charging times and intentional speed reduction on highways are the degrees of freedom for the optimization. The underlying vehicle and consumption model was validated in a previous study and used to formulate a mixed integer linear program which minimizes the estimated consumption. Within an elaborate simulative study, the optimization results are compared to a heuristic baseline strategy which indicates reasonable computation time and significant reduction of the resulting trip time on trips that exceed the range of the considered vehicle.
Survive and Thrive: A Stochastic Game for DDoS Attacks in Bitcoin Mining Pools

Mining pools have become dominant in today’s bitcoin mining network, where miners can pool their powers together for reduced variance of block mining and steadier stream of potential income. Along with the continuous evolvement of mining pools are the increasingly intense competitions among them. Recent empirical studies have shown that the distributed denial-of-service (DDoS) attack is one of the most common ways for competing mining pools to sabotage the rivals and earn illegitimate rewards. Existing efforts have been made on using static game models to analyze the interactions between mining pools, and derive the Nash Equilibrium and optimal attacking strategies in a one-time static context. To better understand the impact of such DDoS attacks, in this paper, we take a starkly different approach, and for the first time address the dynamics in mining pool attacks. Specifically, we start by formulating the interactive competition among mining pools as a general-sum stochastic game. Then we propose an efficient Nash learning algorithm to obtain the near optimal attacking strategy that maximizes the expected long-term utility. Our theoretical analysis and extensive experimental results both show that the proposed strategy outperforms the baseline myopic learning algorithm, which only aims at maximizing the revenue in the current time stage. These findings, together with our proposed stochastic game model and learning algorithm, are expected to provide more practical guidelines for mining pools to survive and thrive in the highly-competitive bitcoin ecosystem.
Tracking Browser Fingerprint using Rule Based Algorithm

Browsers collects information for better user experience by allowing JavaScript's and other extensions. Advertiser and other trackers take advantage on this useful information to tracked users across the web from remote devices on the purpose of individual unique identifications the so-called browser fingerprinting. Our work explores the diversity and stability of browser fingerprint by modifying the rule-based algorithm. Browser fingerprint rely only from the gathered data through browser, it is hard to tell that this piece of information still the same when upgrades and or downgrades are happening to any browsers and software's without user consent, which is stability and diversity are the most important usage of generating browser fingerprint. We implemented device fingerprint to identify consenting visitors in our website and evaluate individual devices attributes by calculating entropy of each selected attributes. In this research, it is noted that we emphasize only on data collected through a web browser by employing twenty (20) attributes to identify promising high value information to track how device information evolve and consistent in a period of time, likewise, we manually selected device information for evaluation where we apply the modified rules. Finally, this research is conducted and focused on the devices having the closest configuration and device information to test how devices differ from each other after several days of using on the basis of individual user configurations, this will prove in our study that every device is unique.
Detecting Liver Cancer from Online Search Logs

There are many incurable diseases around us like various types of cancers which are detected at the last stage. But they are curable if early detection is possible. Web search log with symptoms can be effective for such early detection. Online search queries can act as a useful evidence of user's health condition as they search with symptoms in search engine. In this paper, we have used machine learning techniques to identify those attributes from online search log that can be used for early detection of liver cancer. Liver cancer is diagnosed very late most of the time. Primary stage symptoms are obscure and vague in most of the cases. It becomes very difficult to get the treatment properly because of the metastasis of the cancer when diagnosed. We have proposed a method to organize online search log data to detect liver cancer early. If we have online search log data containing symptoms searched on search engine and the time of the searches of each symptom, they can be easily organized by our method of dataset creation and detect liver cancer. We have estimated the performance of Support Vector Machine (SVM) based on different kernel scale values. The best classification accuracy for $\gamma=0.03$ is 94.30%. We have also compared the performance of different machine learning techniques. Among them, Random Forest has the highest classification accuracy of 97.50%.
Selecting Optimal Submatrix for Syndrome-Trellis Codes (STCs)-Based Steganography With Segmentation

Syndrome-Trellis Codes (STC), as the de facto state-of-the-art steganography framework, has drawn huge attention recently. However, the original STC merely developed with the general form of cover and message, and almost all the subsequent improvements for STC focused on the cover selection and design of the distortion cost function. How to select the optimal submatrix by the features for the given cover and message is an important issue and requires to be solved urgently. In this work, we propose to divide both the cover and message into segments and embed each message segment into its optimally matched cover segment. We name the proposed method as Segment-STC steganography. Specifically, by investigating the features of the submatrix used in STC, we first select the optimal submatrix, which could effectively reduce the number of distorted elements during the embedding. Then, the given cover and message will be split into a series of segments. Each message segment is adaptively matched with an optimal submatrix. We conduct the experiments on both the benchmark BOSS dataset and our collected on-line songs dataset. The experimental results show that, compared with the original STC, our method could effectively reduce the distortion and improve the undetectability of the stego.
Automated Detection of Plant Diseases Using Image Processing and Faster R-CNN Algorithm

The economy of Bangladesh highly depends on the field of agriculture and the production of the crops each year. This is one of the reasons that plant disease identification has become the most crucial factor in cultivating crops. Wrong identification or late identification can cause excessive loss of the production as well as in the financial status of the farmers. Bangladesh being an agriculture-based country, needs to have scientific methods and proper knowledge of this problem. In this condition, providing the farmers some automatic disease detection techniques can reduce their workload and the fear of loss of their production. This paper presents a method that detects diseases from plant leaf images using Tensorflow which is an object detection API, and the model was trained using a faster RCNN method. The rate of accuracy is also calculated. After extensive training on different samples of datasets, our machine learning approach learns gradually and can be more effective in detecting plant diseases.
Conic Section Representation of Operational Point Trajectories for IM IFOC Drive Under Constraints

The paper analyses trajectories of Induction Motor (IM) Indirect Field Oriented Control (IFOC) drive under operational constraints by representing model equations as conic sections in dq plane. By this, operating point trajectory is created by calculating intersection points of drive limits (voltage, current and break down slip) or different control strategies, e.g. Maximum Torque Per Ampere (MTPA) strategy. The approach is illustrated by set of computer simulations and experiments on low voltage IM IFOC drive with MTPA strategy used in battery supplied electric vehicle.
Parallel Computer For Face Recognition Using Artificial Intelligence

We implemented a facial recognition application with AI. We used the VGGFace model for our neural net to identify faces. The application includes training and recognizing. The training part is to add new faces to our system, while the recognizing part is to determine the identity of a face. The application runs on multiple cores and able to scale with different numbers of cores. The implementation for parallelism uses tensorflow. For performance measurements, we used the Task Manager application found in Windows with special option known as ‘affinity to choose the number of cores to run the application. The results show that our system scales in performance with number of processors up to twelve.
Design of a 7-Level Single-Stage/Phase PUC Grid-Connected PV Inverter with FS-MPC Control

Multilevel, transformer-less inverters are becoming more and more a subject of interest in grid connected photovoltaic system due to their low cost and high efficiency features. However, most of the designed grid tied systems show more than one stage conversion process to inject the amount of power generated to the grid. Mainly, a DC-DC conversion stage is always requested between the Photovoltaic (PV) panel and the inverter. In this paper, a single-stage grid connected design based on seven level packed U-cell inverter controlled by a finite-set model predictive algorithm with a maximum power point tracking capability is presented as a competitive single-stage/phase grid-connected topology. The overall design is less complex compared to existing topologies where a maximum power point operation of the PV system is well achieved without the use of a boost converter. A predictive algorithm takes in action of the inverter control. Simulation results in MATLAB/SIMULINK are presented to validate the proposed design in both constant and variable irradiation conditions.
Development of a low-cost balance assessment system for use in an affordable robot gym in low and middle income countries

Stroke results in adult disability worldwide with 80% of the occurrences in low and middle income countries (LMIC). There is a need for affordable and accessible rehabilitation to support functional recovery of these stroke survivors. Hemiplegia affects most stroke survivors and frequently can result in asymmetric gait and impaired balance. Low cost force boards using the Wii balance board have been shown to be an acceptable replacement to expensive clinical force plates. Center of pressure, velocity and step length are key gait and balance metrics that are measurable using low cost force boards. This paper describe two attempts to design and develop a low-cost gait assessment and training station within a novel robot gym for stroke neurorehabilitation of the upper and lower limbs. The final design will consist of low-cost two board force platform is now a part of the Rehab C.A. R.E. S (Community-based Affordable Robotic Exercise System).
Improving Predication Efficiency through Compaction/Restoration of SIMD Instructions

Vector processors offer a wide range of unexplored opportunities to improve performance and energy efficiency. However, despite its potential, vector code generation and execution have significant challenges, the most relevant ones being control flow divergence. Most modern processors including SIMD extensions (such as AVX) rely on predication to support divergence control. In predicated codes, performance and energy consumption are usually insensitive to the number of true values in a predicated mask. This implies that the system efficiency becomes sub-optimal as vector length increases. In this paper we focus on SIMD extensions and propose a novel approach to improve execution efficiency in predicated SIMD instructions, the Compaction/Restoration (CR) technique. CR delays predicated SIMD instructions with inactive elements and compacts them with instances of the same instruction from different loop iterations to form an equivalent dense vector instruction, where, in the best case, all the elements are active. After executing such dense instructions, their results are restored to the original instructions. Our evaluation shows that CR improves performance by up to 25% and reduces dynamic energy consumption by up to 43% on real unmodified applications with predicated execution. Moreover, CR allows executing unmodified legacy code with short vector instructions (AVX-2) on newer architectures with wider vectors (AVX-512), achieving up to 56% performance benefits.
A triple mode wide locking range, low phase noise injection locked oscillator based phase shifter covering the Sub-6 GHz 5G bands

Multiple-input and multiple-output (MIMO) systems with antenna arrays are a key enabling technology for upcoming 5G networks. It is predicted that initially most 5G networks will consist of the Sub-6 GHz spectrum bands. In such a phased array system the phase shifter will be the key component which is used to steer the beam. In this work we propose using an injection locked oscillator based phase shifter (ILPS) with a locking range wide enough to cover the entire Sub-6 GHz 5G bands without discontinuity. The work is based on a triple resonant frequency transformer resonator using a CMOS oscillator. Excellent tradeoff between locking range and phase noise performance has been achieved. The proposed ILPS has a locking range of 3.0GHz, 2.5GHz and 4GHz corresponding to center frequencies of 2.7GHz, 3.3GHz and 4.2GHz. Input injection power is 0 dBm. It also provides a low phase noise variation of 1.8dB (-141.2 to -139.4 dBc/Hz at 1 MHz offset) in all sub-bands under locked condition. Tuning phase shift range of 1800 is achieved . The ILPS is implemented in a 90 nm RF CMOS process that consumes 25 mW of DC power under a 1.8V supply.
Impact Modeling of Single Ionizing Particles on the CMOS Triple Majority Gate

Results of modeling 65 nm CMOS the Triple Majority Gate on AND and OR logical elements for redundant systems are presented. The AND and OR elements contain inverters located before and after transistors of their NAND and NOR elements. This reduces the duration of the noise pulses when exposed to single ionizing particles. TCAD simulations use particle tracks passing along the normal to the chip crystal surface. With a linear energy transfer of 60 MeV-cm2/mg, the reduction in the pulse duration of noise at the output of CMOS AND (OR) gates is from 2 to 5 times.
Invariants of Space Line Element Structure Based on Projective Geometric Algebra

Based on the theory of Conformal Geometric Algebra, this paper presents a geometric constraint structure consisting of seven straight lines on three adjacent planes and its projective invariants, which can be obtained from a single frame image. Comparing with the use of multi-frame images to calculate invariants, our method is more convenient. First, this paper uses the projective property of points as an index to introduce the projective transformation properties of geometric structure of three lines on two adjacent planes. Then using it as an index, the invariant of the geometric structure of seven lines on three adjacent planes is proposed, and the invariant of the geometric structure of five lines on three adjacent planes is obtained by using the algorithm in this paper. Finally, the accuracy and stability of the invariant geometry are verified by experiments.