Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Implementation of Low Power Efficient 8-bit Carry Look Ahead Adder using Adiabatic Technique

: For high speed and portable equipment, energy efficiency is the most important factor. By adopting different design styles, the power consumption of the electronic devices can be reduced. In this paper by applying power minimization techniques at circuit levels the power consumption of any combinational logic circuits can be reduced. Adiabatic logic style is said to be an attractive solution for such low power electronic applications. Adiabatic describe the thermo dynamic process in which there is no energy exchange with the environment, and therefore very less dissipated energy loss. With this technique, dissipation in pull up network can be minimized and some energy stored at load capacitance can be recycled instead of heat dissipation. In our proposed design the efficiency of a fully adiabatic logic circuit for 8-bit carry look ahead adder will be implemented and power dissipation of adiabatic and conventional circuits is compared. Using CADENCE simulation, the parameters like Transistor gate sizing, maximum frequency of operation, the minimum voltage of operation are used to measure the performance of each circuit. We are using ECRL (Efficient Charge Recovery Logic) logic families which is compared with conventional cmos circuit.
Die Beers-Liste : Ein Instrument zur Optimierung der Arzneimitteltherapie geriatrischer Patienten

Geriatrische Patienten sind bereits heute eine Patientengruppe von sehr hoher Bedeutung. Im Jahr 2005 lebten in Deutschland fast 16 Millionen über 65-Jährige; dies entspricht mehr als 19 % der Gesamtbevölkerung [1]. Für das Jahr 2040 wird prognostiziert, dass fast ein Drittel der deutschen Gesamtbevölkerung älter als 65 Jahre alt sein wird [1]. Geriatrische Patienten stellen in Bezug auf die Pharmakotherapie ein besonderes Patientenkollektiv dar. Sowohl Veränderungen in der Pharmakokinetik (z. B. durch sinkende Nierenfunktion und herabgesetzte Motilität des Gastrointestinaltrakts) als auch in der Pharmakodynamik sind zu beobachten: Beispielsweise funktionieren homöostatische Kontrollmechanismen (z. B. Barorezeptor) weniger gut. Dieser Verlust kann unerwünschte Arzneimittelwirkungen wie orthostatische Hypotonie mit erhöhter Sturzgefahr verstärken [2]. In vielen Fällen leiden geriatrische Patienten an verschiedenen Erkrankungen, häufig mit der Folge der Polymedikation. Im Durchschnitt nehmen diese Patienten drei oder mehr Arzneimittel regelmäßig ein [3]. Durch viele verordnete Arzneimittel von meist verschiedenen Ärzten und zusätzliche Selbstmedikation wird die Therapie oft sehr komplex. Dies führt häufig zu Problemen beispielsweise in der Compliance und klinisch relevanten Arzneimittelinteraktionen. Allgemein ist die Studienlage bei geriatrischen Patienten – ähnlich wie bei Kindern – eher unbefriedigend, da zu wenige Studien bei über 65-Jährigen durchgeführt werden (häufig ist dies sogar Ausschlusskriterium für die jeweilige Studie). Viele Arzneimittel führen bei diesen Patienten jedoch zu Problemen im Bereich Verträglichkeit und Wirksamkeit. Zum einen können verstärkt unerwünschte Arzneimittelwirkungen (UAW) auftreten, zum anderen kann die Arzneimitteltherapie bei alten Patienten ineffektiv werden.
Automating Visual Inspection with Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have become the recent tool of choice for many visual detection tasks, including object classification, localization, detection, and segmentation. CNNs are specialized neural networks composed of many layers and specifically designed to analyze grid-like data, e.g. images. One of the key features of a CNN is its ability to automatically detect important features within an image (e.g. edges, patterns, shapes); prior to CNNs, these features had to be manually engineered by subject matter experts. Inspired by the significant achievements and success that CNNs have experienced in the domain of computer vision, we examine a specific convolutional neural network (CNN) architecture, U-Net, suited for the task of visual defect detection. We identify and discuss situations for the use of this architecture in the specific context of external defect detection on aircraft and experimentally discuss its performance across a dataset of common visual defects. One requirement of training Convolution Networks on an image analysis task is the need for a large image (training) data set. We address this problem by using synthetically generated images from computer models of jets with varying angles and perspectives with and without induced faults in the generated images. This paper presents the initial results of using CNNs, specifically U-Net, to detect aerial vehicle surface defects of three categories. We further demonstrate that CNNs trained on synthetic images can then be used to detect faults in real images of jets with visual damages. The results obtained in this research, indicate that our approach has been quite effective in detecting surface anomalies in our tests.
Hardware-Conscious Hash-Joins on GPUs

Traditionally, analytical database engines have used task parallelism provided by modern multisocket multicore CPUs for scaling query execution. Over the past few years, GPUs have started gaining traction as accelerators for processing analytical queries due to their massively data-parallel nature and high memory bandwidth. Recent work on designing join algorithms for CPUs has shown that carefully tuned join implementations that exploit underlying hardware can outperform naive, hardware-oblivious counterparts and provide excellent performance on modern multicore servers. However, there has been no such systematic analysis of hardware-conscious join algorithms for GPUs that systematically explores the dimensions of partitioning (partitioned versus non-partitioned joins), data location (data fitting and not fitting in GPU device memory), and access pattern (skewed versus uniform). In this paper, we present the design and implementation of a family of novel, partitioning-based GPU-join algorithms that are tuned to exploit various GPU hardware characteristics for working around the two main limitations of GPUs–limited memory capacity and slow PCIe interface. Using a thorough evaluation, we show that: i) hardware-consciousness plays a key role in GPU joins similar to CPU joins and our join algorithms can process 1 Billion tuples/second even if no data is GPU resident, ii) radix partitioning-based GPU joins that are tuned to exploit GPU hardware can substantially outperform non-partitioned hash joins, iii) hardware-conscious GPU joins can effectively overcome GPU limitations and match, or even outperform, state-of-the-art CPU joins.
Sensor-Networked Underwater Target Tracking Based on Grubbs Criterion and Improved Particle Filter Algorithm

For target tracking in underwater wireless sensor networks (WSNs), the contributions of the measured values of each sensor node are different for data fusion, so a better weighted nodes fusion and participation planning mechanism can obtain better tracking performance. A distributed particle filter based target tracking algorithm with Grubbs criterion and mutual information entropy weighted fusion (GMIEW) is proposed in this paper. The Grubbs criterion is adopted to analyze and verify the information obtained by sensor nodes before the information fusion, and accordingly some interference information or error information can be excluded from the data set. In the process of calculating importance weight in particle filter, dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor nodes and the target state is used to reflect the amount of target information provided by sensor nodes, thus a dynamic weighting factor corresponding to each node can be obtained. The simulation results show that the proposed algorithm effectively improves the accuracy of prediction of target tracking system.
Enhanced Job Recommendation System

: We address the problem of recommending suitable jobs to people who are seeking a new job. We formulate this recommendation problem as a supervised machine learning problem. Our technique exploits all past job transitions as well as the data associated with employees and institutions to predict an employee’s next job transition. Dealing with the enormous amount of recruiting information on the Internet, a job seeker always spends hours to find useful ones. To reduce this laborious work, we design and implement a recommendation system for online job hunting. In this paper, we contrast user-based and item-based collaborative filtering algorithm to choose a better performed one. We also take background information including students’ resumes and details of recruiting information into consideration, bring weights of co-apply users (the users who had applied the candidate jobs) and weights of student used liked jobs into their recommendation algorithm. At last, the model we proposed is verified through experiments study which is using actual data. The recommended results can achieve higher score of precision and recall, and they are more relevant with users’ preferences.
Networking solutions for connecting bluetooth low energy devices - a comparison

The Bluetooth Low Energy (BLE) is an attractive solution for implementing low-cost, low power consumption, short-range wireless transmission technology and high flexibility wireless products, which working on standard coin-cell batteries for years. The original design of BLE is restricted to star topology networking, which limits network coverage and scalability. In contrast, other competing technologies like Wi-Fi and ZigBee overcome those constraints by supporting different topologies such as the tree and mesh network topologies. This paper presents a part of the researchers’ efforts in designing solutions to enable BLE mesh networks and implements a tree network topology which is not supported in the standard BLE specifications. In addition, it discusses the advantages and drawbacks of the existing BLE network solutions. During analyzing the existing solutions, we highlight currently open issues such as flooding-based and routing-based solutions to allow end-to-end data transmission in a BLE mesh network and connecting BLE devices to the internet to support the Internet of Things (IoT). The approach proposed in this paper combines the default BLE star topology with the flooding based mesh topology to create a new hybrid network topology. The proposed approach can extend the network coverage without using any routing protocol.
Research on Eigenvalue Analysis Method in Multi-Surface Metal Shell Vibratory Gyro

The metal shell vibratory gyro is a branch of Coriolis vibratory gyro, which has the advantage of overload resistance, simplify structure, low cost, wide range and long life. This paper focuses on multi-surface metal resonator. It is important that the eigenvalue analysis of multi-surface of hemisphere, cylinder and hyperbolic. Through analysis multi-surface characteristic, it presents a novel orthogonal curvilinear coordinate used machine error factor based on the traditional coordinate, which realizes boundary linear. We analyze its natural vibration characteristics, research on basic model distribution and built mode shape function of resonator; deeply research on dynamics characteristics of resonator under the condition of angular rate input, establish dynamics equation of multi-surface fusion metal shell vibratory gyroscope based on thin shell mechanics theory of elastic mechanics, solve out the eigenvalue of the resonator, and we also analyze this eigenvalue characteristics to lay the foundation of the follow-up study of the theory of the metal shell vibratory gyroscope. We test the gyro’s temperature performance on the last. Finally, we test on the metal shell vibratory gyroscope, the ranges to ±3600°/s, zero-bias stability is 7.862°/h (25 °C), angular random walk is 1.487°/h1/2.
Towards a Standardised Framework for Securing Connected Vehicles

Vehicular security was long limited to physical security - to prevent theft. However, the trend of adding more comfort functions and delegating advanced driving tasks back to the vehicle increased the magnitude of attacks, making cybersecurity inevitable. Attackers only need to find one vulnerability in the myriad of electronic control units (ECUs) and communication technologies used in a vehicle to compromise its functions. Vehicles might also be attacked by the owners, who want to modify or even disable certain vehicle functions. Many different parties are involved in the development of such a complex system as the functions are distributed over more than 100 ECUs, making it difficult to get an overall picture of the achieved security. Therefore, moving towards a standardised security framework tailored for the automotive domain is necessary. In this thesis we study various safety and security standards and proposed frameworks from different industrial domains with respect to their way of classifying demands in the form of levels and their methods to derive requirements. In our proposed framework, we suggest security levels appropriate for automotive systems and continue with a mapping between these security levels and identified security mechanisms and design rules to provide basic security. We further study in detail a mechanism which provides freshness to authenticated messages, namely AUTOSAR SecOC Profile 3, and present a novel extension that offers a faster synchronisation between ECUs and reduces the number of required messages for synchronisation.
ABC: A Simple Explicit Congestion Controller for Wireless Networks

We propose Accel-Brake Control (ABC), a simple and deployable explicit congestion control protocol for network paths with time-varying wireless links. ABC routers mark each packet with an "accelerate" or "brake", which causes senders to slightly increase or decrease their congestion windows. Routers use this feedback to quickly guide senders towards a desired target rate. ABC requires no changes to header formats or user devices, but achieves better performance than XCP. ABC is also incrementally deployable; it operates correctly when the bottleneck is a non-ABC router, and can coexist with non-ABC traffic sharing the same bottleneck link. We evaluate ABC using a Wi-Fi implementation and trace-driven emulation of cellular links. ABC achieves 30-40% higher throughput than Cubic+Codel for similar delays, and 2.2X lower delays than BBR on a Wi-Fi path. On cellular network paths, ABC achieves 50% higher throughput than Cubic+Codel.
An Explicit Construction of Optimal Streaming Codes for Channels With Burst and Arbitrary Erasures

This paper presents a new construction of error correcting codes which achieves optimal recovery of a streaming source over a packet erasure channel. The channel model considered is the sliding-window erasure model, with burst and arbitrary losses, introduced by Badr et al. We present a simple construction, when the rate of the code is at least 1/2, which achieves optimal error correction in this setup. Our proposed construction is explicit and systematic. It uses off-the-shelf maximum distance separable (MDS) codes and maximum rank distance (MRD) Gabidulin block codes as constituent codes and combines them in a simple manner. This is in contrast to other recent works, where the construction involves a careful design of the generator or parity check matrix from first principles. The field size requirement which depends on the constituent MDS and MRD codes is also analyzed.
Asymmetric Cryptographic Schemes Based on Noncommutative Structures

Asymmetric cryptography based on groups is mainly concerned with the role of noncommutative groups. The origin of group based cryptography goes back in the 1980s. Since then, numerous cryptographic proposals based on noncommutative groups have been evolved. In this thesis, we consider several noncommutative settings in different cryptographic context. This study adds to the cryptographic literature by demonstrating new asymmetric cryptographic schemes. On the one hand, two new public key exchange protocols and two asymmetric cryptosystems are constructed. On the other hand, different primitives like suggestion of platform, size of different parameters, security and efficiency aspects regarding these proposals, are also elaborated.
UTCPredictor: An uncertainty‐aware novel teaching cases predictor

Teaching cases are crucial for computer science (e.g., software testing) teaching. With the fast development of computer science, old and outdated teaching cases cannot meet the requirements of teaching. Therefore, teachers need to update teaching case repository continually and timely. However, teaching case development is an extremely time‐consuming work. Given today's complex and fast‐moving environment of computer science, teachers often feel blind about about what types of cases should be added for teaching. This paper presents UTCPredictor—an automated approach of predicting novel teaching cases from real production by identifying the most uncertain data, which are always with new features that reflect the latest developments and trends in the field. The implementation of UTCPredictor is based on the idea of interactive machine learning as well as several text mining techniques. To evaluate the effectiveness of UTCPredictor, we take bug report case building in software testing teaching as an example, using UTCPredictor to perform 10‐fold cross‐validation on an existing teaching case set. The performance in terms of indicators; Recall, Precision, and F1‐score, achieved three very competitive values—0.91, 0.94, and 0.85, respectively. We further evaluate the effectiveness of UTCPredictor through a user study and a questionnaire. The results are very positive; the user study indicates that educators can build a teaching case set from latest bug report repository by spending only 8.16%–18.11% time costs compared with traditional manual approach; the responses from 2,000 students for the questionnaire show that the teaching cases built with UTCPredictor are very popular among students.
Human feedback in continuous actor-critic reinforcement learning

Reinforcement learning is utilized in contexts where an agent tries to learn from the environment. Using continuous actions, the performance may be improved in comparison to using discrete actions, however, this leads to excessive time to ﬁnd a proper policy. In this work, we focus on including human feedback in reinforcement learning for a continuous action space. We unify the policy and the feedback to favor actions of low probability density. Furthermore, we compare the performance of the feedback for the continuous actor-critic algorithm and test our experiments in the cart-pole balancing task. The obtained results show that the proposed approach increases the accumulated reward in comparison to the autonomous learning method.
Automate Nuclei Detection Using Neural Networks

Nuclei identification is a pivotal first step in many areas of biomedical research. Pathologists often observe images containing microscopic nuclei as part of their day to day jobs. During research, pathologists must identify nuclei characteristics from microscopic images such as: volume of nuclei, size, density and individual position within image. The pathology field can benefit from image detection enhancements done through the use of computer image segmentation techniques. This research presents methods that can be used to identify all the cell nuclei contained in images. Multiple techniques were experimented with such as edge detection and Convolutional Neural Networks with U-Net architecture. The data for training these models was sourced from the 2018 Data Science Bowl sponsored by Kaggle and Booz, Allen, Hamilton. As a result, there were various methods identified to assist the pathology industry for automating nuclei detection by using computer image detection methods. These computer methods rapidly process images for research purposes, with a reasonably high accuracy which has the potential to greatly accelerate the pace of research.
Advancement in Depth Estimation for StereoImage Pair

The power energy distribution process is one of the great challenges, which both the consumers and power energy distributors normally face. Visualization has been recommended as useful approach to solve this problem. MATLAB is used to showcase the visualization via analysis of the data of energy production for the month of December 2012 and this was used to predict the energy estimate for each day in the month of January 2013.With the patterns generated, the pattern of energy distribution can easily be modeled. Such will assist in proper planning, sharing and management of energy to sustain the national need.
Remaining Useful Life Estimation Using CNN-XGB With Extended Time Window

The remaining useful life estimation has been widely studied for engineering systems. A system commonly works under varying operating conditions, which may affect the system degradation trajectory differently and consequently reduce the accuracy of remaining useful life estimation. In this paper, we propose CNN-XGB with extended time window to tackle this issue. Firstly, the extended time window is created by feature extension and time window processing in data preprocessing. In feature extension, multiple degradation features are extracted by an improved differential method, and these features are appended to the raw data as additional features. To make the time window cover more information for better prognostic accuracy, a time window padding method is used considering the problem of missing data in some samples. Secondly, a convolutional neural network architecture with multichannel 1 * 1 filter kernel is proposed considering the effect of varying operating conditions. Furthermore, to improve the prognostic robustness and avoid the sensitivity to the abnormal data, convolutional neural network and extreme gradient boosting are fused by model averaging (CNN-XGB). The validity of the proposed method is verified using aero-engine datasets from NASA.
Mobile-Controlled UAVs for Audio Delivery Service and Payload Tracking Solution

Unmanned Aerial Vehicles are self-driving vehicles in robotic technology, which are in incremental demand for transportation. In this paper, re-engineering the UAVs model for payload delivery service to enhance the feature of tracking, communication of recorded audio voices, and enlarging of new process to deliver a partial control of the UAVs especially for the customers is being discussed. In the present study, integration of UAVs and mobile technology has been adopted for re-engineering of the UAV based delivery process. The fundamental approach of the present study is to involve the customer in the entire process of payload delivery under effective coordination to provide customer-oriented functionality which will improve the customer encounter in logistics operations. People don’t get to visit his/her loved one’s grave due to lack of time, busy schedule or traffic congestion. Visiting of graveyard is unfriendly for elderly too because of their health issues. Today, by the coordination of developed sensor and IT innovation, visiting of graveyard has become simple. To be perfect and reasonable, artifacts of the proposed model can easily be implemented for the virtual visiting of graveyard. By utilization of the proposed model, individuals and particularly elderly or women can easily visit the loved one’s grave from the comfort of their home by watching ongoing recordings of the blooming activity on grave or tomb. Furthermore people can send recorded audio, voice message or Quran recitation from his/her registered mobile app at the graveyard. The proposed model is evaluated by conducting survey-based quantitative research approach based on the Delphi method. The performance of the model is analyzed by the goodness-of-fit (Chi-square) test. The inspection of the overall outcome against the re-engineering model and its provided service efficiency has a greater impact of acceptance.