Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unified QoS and Security Provisioning Framework for Wiretap Cognitive Radio Networks: A Statistical Queueing Analysis Approach

Due to the spectrum-sharing feature of cognitive radio networks (CRNs) and the broadcasting nature of wireless channels, providing quality-of-service (QoS) provisioning for primary users (PUs) and protecting information security for secondary users (SUs) are two crucial and fundamental issues for CRNs. Consequently, in this paper, we establish a unified QoS and security provisioning framework for wiretap CRNs. Specifically, different from the widely used deterministic QoS provisioning method and information-theoretical security protection approach, our established framework, which is built on the theory of statistical queueing analysis, can quantitatively characterize the PU’s QoS and the SU’s security requirements. By adopting the theories of effective capacity and effective bandwidth, we further convert the QoS and security requirements to the equivalent PU’s effective capacity and SU’s effective bandwidth constraints. Following our developed framework, we formulate the nonconvex optimization problem, which aims at maximizing the average throughput of SU subject to PU’s QoS requirement, SU’s security constraint, as well as SU’s average and peak transmit power limitations. Then, we adopt the techniques of convex hull and probabilistic transmission to convert the original nonconvex problem to the equivalent convex problem and obtain the optimal power allocation scheme through the Lagrangian method. Moreover, we also develop a fixed power allocation scheme which is suboptimal but has low complexity. The simulation results are also provided, which demonstrate the impact of the PU’s QoS and the SU’s security requirements on SU’s throughput as well as the advantage of our proposed optimal power allocation scheme over the fixed power allocation scheme and the conventional security-based water-filling policy.
The Playback Trajectory Optimization Algorithm for Collaborative Robot

Teaching is a basic function of many collaborative robots. The recorded trajectories with teaching may contain noises and jumps due to hand trembling and sensor errors. When the robot replays these trajectories, these noises and jumps may cause vibrations and shocks, even damage the body of the robot. An algorithm based on CHOMP (Covariant Hamiltonian Optimization for Motion Planning) algorithm is proposed to optimize the playback trajectories of collaborative robot. The algorithm is composed of two norms in the Hilbert space. One Euclidian norm is used for the evaluation of the replay of trajectories. The other norm that combines the first and second order derivatives of trajectories is used for the evaluation of trajectory dynamic characters. Experiments of UR3 robot in both obstacle-free and obstacle environments prove that the proposed algorithm can optimize the recorded trajectories of a collaborative robot as well as enabling the trajectory to maintain useful.
Voltage and Frequency Control of PV, Micro-hydro and Biomass Based Islanded Microgrid

The environment friendly technology is making the renewable energy sources like micro-hydro, photovoltaic (PV) and biomass to grow rapidly. Another advantage of have these renewables is the convenient and cost effective installation at remote sites with no or limited access to the grid electricity. This paper presents a modeling based methodology for interconnected power system scheme having PV, micro-hydro and biomass generatiuon systems with control unit to maintain a constant frequency and voltage. The distributed control scheme is applied to control the frequency and voltage. Furthermore, this paper also investigated the dynamic performance of micro-hydro and PV after the occurrence of transient disturbances due to a quick change in electric load.
Acquisition of sociolinguistic variation in a dialect contact situation : the case of Palestinian children and adolescents in Syria

The present study investigates patterns of variation in the speech of 40 girls and boys (3;717;9) in a Bedouin speech community of Palestinian refugees outside the Syrian capital Damascus. It contributes to the knowledge on the acquisition of variation in Arabic speaking communities, especially in situations of contact and diffusion (Britain 2002). The project focuses on the emergence of variation and its development as a function of age and gender by examining speakers’ use of the phonological variables (dˁ), (ðˁ), (θ), (ð), (q), and the morphophonological feminine suffix (a), which are realized differently in urban and Bedouin dialects. Patterns of accommodation and register variation in the speech of these participants are also tested to further understand their linguistic behaviour and tap into their sociolinguistic awareness. Sociolinguistic interviews and a picture-naming task were carried out by two female fieldworkers, a local and an urban speaker, in order to elicit spontaneous data and examine variation patterns across different interlocutors and in diverse contexts. The general linear model was used to test the effect of age, gender, and their interaction on variation, and a paired-samples t test was employed to investigate the occurrence of accommodation with the urban interviewer and register variation in the picture task. Accommodation to the urban interviewer occurred in the realization of all variables. Style variation appeared in the realization of (dˁ), (q) and the plain interdentals. The most interesting patterns of variation were in relation to age and gender. Older speakers used the local variants more than younger speakers and girls generally favoured the urban variants. However, a further breakdown by age and gender revealed an intriguing pattern whereby gender differences were limited to speakers between the ages of 6 and 14. Use of the local variants showed a linear increase in the speech of boys older than 5. Girls, on the other hand, showed an increase in using the urban variants up to age 14 followed by a sharp decline, as older girls strongly favoured the local variants. This pattern persisted with all variables, but the degree of variation was dependent on specific variables as one might expect (Eckert 1997; Smith et al. 2007). For example, interdental fricative and (dˁ) showed the greatest amount of variation, with frequency and lexical diffusion (Bybee 2002) emerging as possible forces of change in the case of (θ) and (ð). In contrast, the morphophonological feminine suffix (a) was highly resistant to variation. Realizations of (q)
Fast Defect Inspection Based on Data-Driven Photometric Stereo

Fast inspection of a defect is a challenging task in mass production of curved surfaces, and photometric stereo (PS) utilizing multiple images from a single camera under a number of different illumination directions is a promising technique for this task due to its high sensitivity to surface normal perturbations. This paper adapts conventional PS and extends the technique to the inspection of non-Lambertian surfaces with high accuracy and efficiency. A data-driven PS is presented by establishing the Gaussian process (GP) model to represent the nonlinear reflectance behavior of various materials based on measured reflectance data sets. With the trained GP model, the surface normal can be estimated in two steps: prediction of bidirectional reflectance distribution function values under different light directions and the subsequent least-squares estimation of a surface normal. Comparison tests with other algorithms on the Mitsubishi Electric Research Laboratories data set and real workpieces with non-Lambertian materials show the superior accuracy and efficiency of the proposed method in surface normal estimation. After the surface normal of the workpiece is recovered, the defects can be detected by filtering out the perturbation of the surface normal. Experiments on steel and glossy polyester workpieces validate the efficacy of the proposed approach in detecting defects on curved non-Lambertian surfaces.
Driver Drowsiness Monitoring System Using Fusion of Facial Features & EEG

Driver tiredness or unconsciousness is one of the main reasons of accidents in the entire world. Monitoring the drowsiness of the driver is one of the best ways of measuring driver fatigue state. This system works by monitoring the system application using fusion of facial feature and EEG of the driver and giving an alarm when the person is sleepy. This system is designed in a non-intrusive manner for real-time monitoring. The main purpose is to improve the road safety of the driver. In this application the eyes of the driver are observed and brain waves are measured. If the driver's eyes remain closed for more than a threshold period and if the frequency of brain waves is less than the normal active state, the driver is said to be drowsy and an alarm is provided by application software.
Improved Asymptotic Sphere-Packing Bounds for Subblock-Constrained Codes

Subblock-constrained codes are an important class of constrained codes, having applications in many diverse fields. In this paper, we provide closed-form expressions for the best known upper bounds on the asymptotic rates of subblock-constrained codes for a range of relative distance values via a generalized sphere-packing approach. In particular, we study binary subblock energy-constrained codes (SECCs), characterized by the property that the number of ones in each subblock exceeds a certain thresh-old, and binary constant subblock-composition codes (CSCCs), characterized by the property that the number of ones in each subblock is constant. Improved bounds on the optimal asymptotic rate for SECCs and CSCCs are obtained by applying a generalized sphere-packing approach and judiciously choosing appropriate constrained spaces for estimating asymptotic ball sizes. We also use numerical examples to highlight the improvement.
FIRE DETECTION USING QUADCOPTER

Fire breakout in buildings is a major threat now days. These accidents cause loss of life and property. To find the reason behind, frequency and giving protection to all type of buildings became challenges to the fire rescue team. There are a lot of problems faced by fire fighters in the event of a fire breakout. Some of these include lack of knowledge about the building construction, fire behavior, communication between firefighters and knowing about the building tenability and victim viability. This work deals with the application of a Quadcopter with an IR camera to overcome the major problems faced during firefighting. The components of our project include fire detection using IR camera and sending the information received by the quad copter to the concerned fire rescue team.
A Comparative Study of Unsupervised Learning Algorithms for Software Fault Prediction

Software fault prediction is an important task in software development process which enables software practitioners to easily detect and rectify the errors in modules or classes. Various fault prediction techniques have been studied in the past and unsupervised learning methods such as clustering techniques are drawing much attention in the recent years. K-means is a well known clustering algorithm which is applied on various exploratory analysis including software fault prediction. This paper provides a comparative study on software fault prediction using K-means clustering algorithm and its variants. We use five software fault prediction datasets taken from PROMISE repository to evaluate the prediction accuracy of the clustering algorithms. Experimental results indicate that proper initial seed selection enables K-means algorithm to effectively group the faulty modules.
Towards Deep Reinforcement Learning Based Chinese Calligraphy Robot

Learning how to write Chinese character strokes from the stroke images directly, has a great significance to the inheritance of calligraphy art and to imitate the writing style of Chinese calligraphers. However, most of the existing methods directly applied existing samples with action labels. The performance of these methods is often limited by the quality and number of samples. Thus, these methods cannot be used to learn calligraphy from unlabeled samples. To address this problem, a calligraphy robotic model based on deep reinforcement learning is proposed in this paper, which enables a robotic arm to write fundamental Chinese character strokes from stroke images. In the model, writing task is seen as the process of interaction between the robot and the environment. The robot makes appropriate writing action based on the state information provided by the environment. In order to evaluate the writing action of the robot, a reward function is designed on the model. In addition, the stochastic policy gradient method is used in training on the model. Finally, the model was extensively experimented on a stroke data set. Environmental results demonstrate that the proposed model allows a calligraphy robot to successfully write fundamental Chinese character strokes from stroke images. This model provides a promising solution for reconstructing writing actions from images.
A Review on Power Supply Induced Jitter

The primary focus of this paper is to discuss the modeling of jitter caused by power supply noise (PSN), named power supply induced jitter (PSIJ). A holistic discussion is presented from the basics of power delivery networks to PSN and eventually to the modeling of PSIJ. The in-depth details and a review of several methodologies available in the literature for the estimation of PSIJ are presented.
Web-Based Automobile Service Management System for MAS Motors LLC

This project endeavored to design and develop a Web-based Automobile Service Management System for MAS Motors LLC, an accredited Toyota dealership in Libya. The system will help reduce the manual process from the everyday activities of the service division in all branches. The system is dedicated to be used by the following individuals: (1) Service Advisers, (2) Workshop Managers, (3) Storekeepers, (4) Technicians, (5) Customers, and (6) Upper Management.The company’s growth made the manual process ineffective and incompetent in handling the evolving business activities. The project aimed to provide a centralized system that will operate in all of the branches and accommodate the daily business needs.The software produced from the project was tested using Alpha and Beta software testing. The researcher also conducted an interview concerning the business process, current business difficulties, and features of the proposed system. Hewlett-Packard FURPS (Functionality, Usability, Reliability, Performance, and Supportability) model was used to assess the software quality, and questions were answered by a group of respondents. The system was found to be functional, meeting the requirements of the client.
Development of a Portable Specie Identifier for Dried Sea Cucumber

Portable systems are the trend nowadays, especially during this time that handheld devices have almost the same capability as with most of our computers. Developing a portable system for identifying the specie of a dried sea cucumber would be the focus of this study. Sea cucumber is one of the highest valued marine products of the Philippines. When in dried form, a sea cucumber would be difficult to identify its specie. To correctly identify, the researcher proposed a system that could be use by the traders or collectors. Since most of the collectors and buyers of the dried sea cucumber were from different islands and barangays, having a portable device would be convenient. The prototype of the specie identifier was made up of two layers, the lower box was the base holding the retractable tray while upper box or top cover was the enclosure of most of the electronic components. Raspberry pi was the main controller, processing the data captured by the pi camera and executing different image processing techniques like feature extraction and classification to produce a predicted output. The whole system was powered by 18650 batteries and a power bank. The operation of the system can be controlled through a 7-inch touchscreen Pi display. The portable specie identifier system works properly according to its designed features and functionality. The system was tested using test data employing k-fold cross validation technique, it yields 75.26% overall accuracy in identifying dried sea cucumber specie.
Predict Robot Grasp Outcomes based on Multi-Modal Information

In the service robot application scenario, the stable grasp requires careful balancing the contact forces and the property of the manipulation objects, such as shape, weight. Deducing whether a particular grasp would be stable from indirect measurements, such as vision, is therefore quite challenging, and direct sensing of contacts through tactile sensor provides an appealing avenue toward more successful and consistent robotic grasping. Other than this, an object's shape and weight would also decide whether to grasping stabilize or not. In this work, we investigate the question of whether tactile information and object intrinsic property aid in predicting grasp outcomes within a multi-modal sensing framework that combines vision, tactile and object intrinsic property. To that end, we collected more than 2550 grasping trials using a 3-finger robot hand which mounted with multiple tactile sensors. We evaluated our multi-modal deep neural network models to directly predict grasp stability from either modality individually or multimodal modalities. Our experimental results indicate the visual combination of tactile readings and intrinsic properties of the object significantly improve grasping prediction performance.
The Epidemiology of Delays in a Teaching Hospital

This study aims to describe the causes of medically unnecessary hospital stay at a teaching tertiary hospital, using modified version of Delay Tool in which the causes of delay are divided into slx major categories : delay related to test scheduling, test results, surgery, medical staff, patient/family, and administration. For the analysis of hospital stay, 6,479 inpatient-days were reviewed in two medical and four surgical departments for one month. Initially inappropriate hospital stays were identified using Appropriateness Evaluation Protocol (AEP), and causes of delay listed in Delay Tool were assigned to each of them. In both medical and surgical services, the most important cause of delay was related to medical staffs, ranging from 3.6% to 51.6% of total inpatient days. Next important category was delay related to test scheduling in medical services (), and delay related to surgery in surgical services (). Among subcategories of delay related to medical staffs, delay due to conservative care was the most important cause of inappropriate hospital stay (). Each clinical departments had different distribution among delay categories, which could not be fully justified by their clinical charateristics. The Delay Tool would be helpful in exploring factors related to the inefficient use of hospital beds. As a measurement tool of inappropriate hospital stay, however, the Delay Tool should be refined in the definitions of categories and its contents.
A Triple-Parameter Based Multi-Relay Selection Strategy for Wireless-Powered Cooperative Network

This paper studies a wireless-powered cooperative network consisting of a single source, multiple wireless-powered relays, and a single destination. The relays harvest radio frequency energy from the source, decode the information, and use beamforming to forward the information to the destination. We develop a triple-parameter-based multi-relay selection (TPMRS) strategy to determine how to assign the energy harvesting and information decoding tasks to different relays. By modeling the TPMRS strategy as a finite state Markov chain, we derive the stationary distribution of each state and obtain an analytical expression of the system throughput. The optimal parameters that maximize the system throughput are obtained via exhaustive search. The numerical and simulation results validate the correctness of the model and derivations. In addition, our proposed TPMRS strategy outperforms the existing multi-relay selection scheme.
Impact of angular separation between adjacent users on beamforming performance

The standards of wireless communication systems have been developed from single-user to multi-user communications so that users can gain a higher data transmission speed, a reduction of power consumption and wider coverage. For the multi-user transmission, a base station transmits data to a number of users employing the same frequency at the same time. So far, a beamforming technology has been taken into account to upgrade the multi-user communication systems. The beamforming technology helps the systems avoid interference in the direction of main beam. In general, the interference cannot be eliminated when it comes in the region of main beam. This means that the beam width directly affects the distance between adjacent users. Furthermore, the angular separation between adjacent users affects the beamforming performance. Therefore, this paper analyzes and simulates the effect of angular separation between adjacent users. In case that desired and interference signals are both located in the main beam region, the Signal to Interference plus Noise Ratio (SINR) must be very low. However, producing a narrow beam width lets users come close to one another. This can be achieved by employing a large number of antenna elements.
Performance Improvement of Dorsal Hand Recognition via Multi-band Selection

A great advantage of multispectral technique is to pursue better recognition performance through band fusion. Adding more bands can build larger feature dimension space while bringing in more redundant information. This paper tries to optimize band set for multispectral dorsal hand recognition mainly in near infrared (NIR) light. Images at 35 bands are sampled uniformly from 700nm to 1040nm, and then band distribution is analyzed for multispectral biometric specialty. Multi-band selection is processed in two steps. First, the whole NIR region is divided into several band clusters according to maximum irrelevance principle. Second, representative bands are chosen from these clusters for recognition rate ranking. Our scheme focuses on accuracy and rapidity simultaneously. Experiment shows the robustness of improved clustering method and the high fusion performance. The proposed band selection method can be applied to other multispectral database with consecutive band feature change.
An OFDM Multi-User Spectrum Resource Allocation Algorithm Based on Joint Access Mechanism

Aiming at the problem of spectrum resource allocation of multi-cognitive users in CR-OFDM systems, this paper proposes a joint subcarrier and power allocation method based on access mechanism. In addition, the cognitive user's interference to the primary user communication is defined as the opportunity interference constraint. The algorithm first allocates subcarriers, takes into account the different communication needs of each cognitive user in the allocation, and introduces an on-demand allocation scale factor to ensure the fairness of spectrum resource allocation. Then, when the subcarrier allocation is completed, add a power lower bound constraint to each subcarrier allocated by the cognitive user, through a ladder algorithm to complete the power allocation optimization of the subcarrier finally. Simulation results show that the proposed algorithm not only ensures the fairness of capacity allocation among cognitive users but also guarantees the communication quality of each user. In addition, the proposed algorithm is more suitable for practical application background due to the introduction of opportunities to interfere with constraints.