Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

Simultaneous Localization and Mapping (SLAM) is the process by which a mobile robot carrying specific sensors builds a map of the environment and at the same time uses this map to estimate its pose. Currently, SLAM has been proven its value and is a hot topic. However, challenges still exist: when the mobile robot stops in the process of motion and a large number of feature points in the environment move slightly, the feature matching process cannot eliminate the non-stationary feature point pairs. The introduction of a large number of outliers (non-stationary feature point pairs) seriously affects the observation process of SLAM. It directly leads to the estimation errors of mobile robot pose and the 3D features position, and further leads to keyframe trajectory drift. If there is a mechanism that allows the robot to accurately detect itself in a stop state, then the pose and map points could be locked, and the state variables could be optimized to make the system enter the positive succession. Therefore, detecting the stop status of the mobile robot is a significant work for SLAM. In this manuscript, an improved phase correlation method is proposed to solve the problem of stop detection for the autonomous driving vehicle in the dynamic street environment. After experiments, it is revealed that the stop detection is significant for the performance improvement to the state-of-the-art visual SLAM system, and the improved phase correlation method has higher stop detection accuracy than the conventional phase correlation in various scenarios.
Molecular authentication of Caulerpa (Chlorophyta) species along the eastern (Israeli) Mediterranean shores

Abstract Phylogenetic analyses based on ITS1, ITS2 and tufA gene sequences confirmed the earlier described presence of three Caulerpa species, namely C. prolifera, C. mexicana and C. scalpelliformis, along the Israeli Mediterranean shores. These analyses also demonstrated that C. mexicana is indeed widespread in this area, and ruled out the presence of the morphologically almost similar and invasive C. taxifolia. In the past, this species has apparently been misidentified by local phycologists. A consistent genotypic variability present in the Israeli C. mexicana sequences creates a well-supported separate clade when compared to their Mediterranean Sea counterparts.
Design and Synthesis of Multi-Segmented Robot to Mimic Serpentine Motion.

The type of locomotion achieved by reptilian organisms such as snakes is a truly fascinating phenomenon. Replicating the serpentine movement is a difficult task, but once such it is replicated to some degree, it could be used to make robots which have a large scope of application in many different fields. Replication of this type of motion makes it easier to access constricted areas in search and rescue operations carried out by response teams during, say, natural disasters. Also, if such movement can be replicated in an appropriately sized robot, it can be used in surveillance applications too. Serpentine motion, when properly replicated, can also open up new avenues for robots that are used to navigated clustered terrain. This paper represents a progress report on the research about different ways of achieving serpentine motion in robots using electromechanical and mechanical components. The robot shown in this paper was synthesized, and it was able to achieve serpentine and sidewinding motions.
A Coordinated Operation of Multiple Distributed Energy Resource Technologies for Arbitrage Benefit Enhancement

Electric utilities may own capital intensive battery energy storage systems and micro turbine (MT) to cope against the uncertain environment created by large-scale integration of renewable energy sources (RESs) such as solar photovoltaic (SPV) and wind turbine (WT), etc. The introduction of dynamic electricity price model in distribution system has shifted paradigm to optimally manage distributed storage in order to get maximum possible arbitrage benefits along with improvement in system performance. This paper proposes a heuristic method to enhance several techno-economic benefits of distribution systems by coordinating available energy storages in the presence of RESs. Proposed approach independently manages economic operation of BESS whereas MT unit improves load profile of the system. The methodology envisages optimal solution after trying just few combinations of charging and discharging states of BESS by utilizing knowledge base driven approach of optimizing arbitrage benefits under dynamic price signal. Moreover, the untapped potential of MT unit is amicably utilized to reduce system stress. Proposed method considers dynamically varying system states yet the approach is straightforward and non-iterative. This makes the proposed method class apart from existing methods. The application results on a standard 33-bus distribution system highlights importance of the proposed methodology.
Cloning and expression pattern of 14‐3‐3ε from Culex pipiens

14‐3‐3 proteins play various roles in eukaryotes, such as signal transduction, cell cycle regulation, apoptosis, stress response and cytoskeleton organization. We are interested in apoptosis induced by mosquito–virus interactions using Culex pipiens as an experimental model. To this end, we cloned a partial fragment (702 bp) of 14‐3‐3ε, one of the key proteins known to be involved in apoptosis, from Cx. pipiens. The deduced amino acid sequence (234 residues) had high homology to 14‐3‐3ε homologs from various organisms, such as Culex quinquefasciatus (99%), Anopheles gambiae (98%), Aedes aegypti (96%), Drosophila melanogaster (93%), Apis mellifera (91%) and Bombyx mori (89%). Developmental reverse transcription (RT)‐polymerase chain reaction (PCR) analysis showed that Cp14‐3‐3ε mRNA is expressed at very high levels in the egg, early pupa, and adult male and female, although it was expressed in other developmental stages, such as first and fourth instar larvae. Analysis of RT‐PCR for tissue specificity showed that Cp14‐3‐3ε is abundant in the abdomen and ovary, and it was also detectable in all other tissues, such as thorax, midgut and Malpighian tubule. Actinomycin‐D, an apoptosis‐inducing chemical, induces Cp14‐3‐3ε mRNA expression. Furthermore, poly I : C (RNA virus analog) induces Cp14‐3‐3ε mRNA 3 h after injection. Thus, although further investigation is necessary for elucidating the biological significance of the induced 14‐3‐3ε, our data suggest that Cp14‐3‐3ε is involved in cell survival and signal transduction in Cx. pipiens.
Optimal Allocation and Comparative Investigation of Unified Power Flow Controller using ASMO

With the widespread concern of the installation of FACTS devices in power systems for better controllability and voltage stability enhancement; appropriate placement of these devices is becoming imperative. This paper presents an Ageist Spider Monkey Optimization (ASMO) and a fuzzy logicbased approach to seek the optimal locations and sizing of FACTS devices to improve the voltage stability margin and decrease active and reactive power losses of the power systems. Attributes of FACTS devices are also compared based on the proposed algorithm. Membership values of variables defined in diverse objectives can be determined by a quarter cosine fuzzy membership function. The application of the proposed method has been investigated on standard IEEE 30 and 118-bus systems.
Modeling of Multiwave Pumped Fiber Raman Amplifier for C+L Telecommunication Windows

Modeling results for broadband fiber Raman amplifier (FRA) with flat gain parameters over very wide spectral range covering the C+L telecommunication windows are presented in our paper. Multiwave pumped FRA with active media True Wave singlemode fiber type is analyzed in comparison with erbium doped fiber amplifier. Numerical data for multimode Gaussian decomposition of Raman gain profile in True Wave fiber type for telecommunication are resulted. It is shown the almost exact approximation of the Raman gain profile can be obtained in analytical form for the True Wave fiber over the working bandwidth up to 20 THz. It is the analytical form of gain profile greatly simplifies the modeling procedure and main gain parameters for 2, 3, 4, 5, and 6 wavelengths pumped FRAs are presented.
Dynamic Supply Voltage Level Generation for Minimum Energy Real Time Tasks using Geometric Programming

Communication and computation are moving towards mobile platforms to address the demands of emerging applications. Despite advances in process and battery technologies that allow processors to provide much greater computation per unit of energy and longer life of battery, the fundamental tradeoff between performance and battery life continues to remain critical. To maximize energy efficiency of processors in mobile electronics, Dynamic Voltage Scaling (DVS) is conventionally deployed to dynamically vary supply voltage and hence speed, at run time. The nonlinear relationship between CPU speed and power consumption enables spread out of task execution in time domain by leveraging on the available slackness by reducing voltage, than to run the CPU at full speed for short bursts and then switch to idle state. The proposed work aims to minimize the energy consumption of each task of real time periodic task sets, in a uniprocessor environment, using task utilization factor as a control variable for generating the optimized supply voltage to every task of task sets. The energy minimization of a task is implemented using Geometric Programming (GP), by varying frequency over a range on fixed task sets and on randomly varying task set instances and hence generating supply voltage levels. Results demonstrate that energy savings vary between 18% to 34%, for standard task sets and an average of 77% for randomly generated task sets, depending on the power delay characteristics of task sets.
Utilization of Soft-Switched Boost Converter for MPPT Application in Photovoltaic Single-Phase Grid-Connected Inverter

A modified soft-switched boost converter with a lower number of auxiliary components is proposed in this paper that provides either zero-voltage-transition (ZVT) or zero-current-transition (ZCT) operation for main switch and diode. Proposed converter is utilized in a single-phase grid-tied photovoltaic power generation system. Due to importance of maximum power harvesting from PV, incremental conductance MPPT procedure is employed to control DC/DC converter. Simulation is performed in PLECS software to validate the advantageous introduced soft-switching technique.
Enhance the Lifespan of Underwater Sensor Network Through Energy Efficient Hybrid Data Communication Scheme

The work in this proposal suggests an energy balanced, depth aware data transmission approach to prolonging the network lifetime of Underwater Wireless Sensor Networks (UWSNs). As single-hop transmission is energy harvesting. Therefore, more often multi-hop communication paradigm is used to forward data towards the surface sink. However, multi-hop communication leads imbalanced consumption. Due to the fact that, nodes nearer to the surface may deplete more energy comparison to other nodes, this paper proposed a hybrid or mixed-hop transmission approach for balancing the energy consumption throughout the network. The selection of the relay node in mixed-hop transmission is based on optimal depth threshold and residual energy grades of the participating sensor nodes. This proposal uses energy of the nodes more efficiently thru balancing the energy consumption during data transmission as compared to other works proposed in the literature. The effectiveness of the proposal is evaluated and validated through simulation in terms of network lifetime, residual energy grades, and network throughput.
Combined Control of Machine & Grid Side Converters for PMSGs in Wind Energy Conversion Systems with Fast and Robust Performance

Fast dynamics and smooth performance are important requirements that ensure the secure connection of wind energy conversion systems to the network according to grid codes. In this paper, a system model in grid voltage reference frame is developed. The combined control is then applied to both machine side and grid side converters of a grid-connected PMSG to fulfill the code requirements where the task of dc-link voltage control is assigned to the machine side converter. The control system enjoys low computation, simplicity, robustness and less dependency on grid parameters. Simulation results on a 1.5MW PMSG approve the performance excellence of the proposed control system compared with conventional control systems under different grid operating conditions.
Optimal Design of Permanent Magnet Structure to Reduce Unbalanced Magnetic Pull in Surface-Mounted Permanent-Magnet Motors

For the surface-mount permanent magnet synchronous motors (SPMSM) with fractional slot concentrated windings, the winding distribution of per phase under different poles is uneven, which results in unbalanced magnetic pull (UMP) between the stator and rotor. UMP has an important impact on the performance and service life of the motor. In this paper, a new permanent magnet (PM) structure is proposed to reduce UMP and torque fluctuation of the fractional slot SPMSM. Furthermore, a 48p/54s SPMSM is taken as an example, the proposed PM structure is optimized by the Taguchi method which can comprehensively consider the control factors and noise factors. The effects of various variables and process error on UMP, torque fluctuation and robustness of results are analyzed respectively. Finally, the optimized PM structure is obtained, which improves the operation stationarity.
Bandwidth Research of Wireless IoT Switches

The paper presents the research and comparative analysis of the bandwidth of low-power wireless IoT devices as wireless switches. Investigated and analyzed sensors the dependence of FTP multimedia data transmission speed on wireless Wi-Fi network on the temperature of the switch processor. To get temperature indicators sufficiently built into Python libraries to read temperature files. The paper focuses on the synchronicity of measurement results records for more accurate analysis. As a result, the dependence of the measured factors was calculated using the Pearson correlation formula. These measurement factors affect the autonomy and energy consumption, which is important for IoT devices, and therefore, among the devices tested, recommendations were made regarding their choice when used depending on the conditions.
Genetic Diversity and Age Class Structure of Seedlings and Saplings after a Mast Flowering of Bamboo in the Brazilian Atlantic Forest

Premise of research. Aulonemia aristulata (Döll) McClure—an endemic Brazilian species—flowers, sets seed, and subsequently dies after many years of vegetative growth. After it has died, plants regenerate by seed germination on the forest floor. Because of the sporadic and unusual records of the flowering cycle and the difficulty of identifying individuals, few studies of the genetic structure of bamboo populations have been published. In this study, we investigated how the flowering event affects the genetic diversity among different populations and evaluated the genetic variability between two ontogenic stages. Methodology. We collected seedlings in July 2009 and saplings in February 2010 in two study areas. We investigated the genetic diversity using markers with different modes of inheritance: nuclear simple sequence repeats (SSRs) and chloroplast SSRs. Pivotal results. The nuclear genetic diversity of the study populations was higher than in other bamboo species. All populations had high and significant inbreeding values, indicating a deficiency of heterozygotes. In general, saplings exhibited less inbreeding than seedlings. Moreover, chloroplast genetic diversity was low, which may indicate that individuals share maternal ancestors. Conclusions. This study demonstrates that sexual reproduction, demographic history, and colonization patterns may contribute to population diversity. The decrease in plant density between ontogenetic stages (i.e., from seedling to sapling) was associated with a higher frequency of homozygotes in seedlings compared with saplings and could indicate intraspecific competition coupled with inbreeding depression as the main factor reducing plant density in bamboos.
Identifying episodes of sleep apnea in ECG by machine learning methods

The paper is devoted to the application of machine learning methods for computerized sleep apnea detection based on single lead electrocardiographic signal (ECG). In order to explore the possibilities of machine learning for ECG-based apnea detection, the Apnea-ECG database provided by the PhysioNet resource was used in this study. A distinctive peculiarity of this database is that it contains the annotations for each minute of each recording indicating the presence or absence of apnea at the current moment of time. To evaluate the effectiveness of ECG derived parameters for sleep apnea detection, 80 ECG segments of 10 minutes duration, annotated as apnea, and 73 ECG segments of the same duration, annotated as normal sleeping were extracted and investigated. The purpose of this work is to define and compare the informative features for identifying episodes of sleep apnea in ECG by heart rate variability analysis, as well as to choose the classification method that provides the highest accuracy for this task. The time-domain, frequency domain, spectral-temporal and wavelet features are considered. Using these feature sets, the performances of a number of classifiers based on decision trees, discriminant analysis, logistic regression, support vector machines, variations of k-nearest neighbors' method, and ensemble learning, were determined. Based on this, a combination of features and classifiers are proposed that provides the highest accuracy of sleep apnea episodes recognition in single lead ECG. The choice of model options for the best performing classifiers was investigated.
Distracted Driver Detection using Stacking Ensemble

Distracted driving is one of the primary causes of car crashes. While driving the vehicle, drivers frequently perform secondary activities that distract driving. A decrease in driver distraction is a critical aspect of the smart transportation system. To decrease accidents and improve safety, this paper proposes a distracted driver detection system that classifies various types of distracted activities using ensemble techniques. Different convolutional networks had been trained on images by eliminating the final layer to get there feature vectors. By using the stacking ensemble technique, we stack all the feature vectors to train it on a convolutional network. This stacking technique, which is used to detect the distracted driver posture, achieves 97% accuracy. The study shows how models predict the desired classes. The model proposed in this paper can be used in a real- time environment to detect activities done by the driver.
Securing Data Transmission against Multi Cell MIMO Eavesdropper in 5G Wireless Networks using MOSPR

The next generation network (5G) promises to offer a wide spectrum of advantages like massive user connections and high mobility. Massive user connection is achieved by incorporating Multiple Input Multiple Output (MIMO). Securing 5G wireless networks is of greater concern because they are developed with the intention of providing exponentially increasing wireless services where a trillion of legitimate users will be involved. Data transmission in 5G networks has been exposed to various security threats such as eavesdropping, jamming, Denial of Service (DoS) attack and Man-In-The-Middle (MITM) attack. The above mentioned threats have the ability of affecting the performance of 5G wireless networks since 5G is a major support system for new use cases. Some of the existing mechanisms employed to deal with the security threats are Physical Layer Security (PLS) and cryptography. PLS is preferred since its primary focus is on physical and Media Access Control (MAC) layer where variations in security between wireless and wired networks occur. The existing PLS approach for security against single cell MIMO eavesdropper is Original Symbol Phase Rotated (OSPR) secure transmission scheme where one base station serves the user terminals in a single cell. Our paper implements OSPR for a multi cell MIMO where multiple base stations serve multiple user terminals located in different cells. This architecture will be referred to as Multi cell MIMO Eavesdropping (MME). The efficiency of multi cell OSPR will be compared with Artificial Noise (AN) generation. The extent to which OSPR gives protection against MIMO eavesdropper is measured in terms of Symbol Error Rate (SER). As SER increases, security of data transmission increases. The efficiency of multi cell OSPR over AN is expected to be 8% and it will be verified in simulation using MATLAB.
The Minimum Cost of Information Erasure for Stationary Memoryless Sources under Restriction on the Output Distribution

In order to erase data including confidential in-formation stored in storage devices, an unrelated and random sequence is usually overwritten, which prevents the data from being restored. The problem of minimizing the cost for information erasure when the amount of information leakage of the confidential information should be less than or equal to a constant asymptotically has been introduced by T. Matsuta and T. Uyematsu. Whereas the minimum cost for overwriting has been given for general sources, a single-letter characterization for stationary memoryless sources is not easily derived. In this paper, we give single-letter characterizations for stationary memoryless sources under two types of restrictions: one requires the output distribution of the encoder to be independent and identically distributed (i.i.d.) and the other requires it to be memoryless but not necessarily i.i. d. asymptotically. The characterizations indicate the relation among the amount of information leakage, the minimum cost for information erasure and the rate of the size of uniformly distributed sequences. The obtained results show that the minimum costs are different between these restrictions.
Image Captioning: A Comprehensive Survey

The primary purpose of image captioning is to generate a caption for an image. Image captioning needs to identify objects in image, actions, their relationship and some silent feature that may be missing in the image. After identification the next step is to generate a most relevant and brief description for the image that must be syntactically and semantically correct. It uses both computer vision concepts for identification of objects and natural language processing methods for description. It’s difficult for a machine to imitate human brain ability however researches in this field have shown a great achievement. Deep learning techniques are enough capable to handle such problems using CNN and LSTM. It can be used in many intelligent control systems and IOT based devices. In this survey paper, we are presenting different approaches of image captioning such as retrieval based, template based and deep learning based as well as different evaluation techniques.