Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-Based Dynamic Virtual Fixtures for Tools Collision Avoidance in Robotic Surgery

In robot-aided surgery, during the execution of typical bimanual procedures such as dissection, surgical tools can collide and create serious damage to the robot or tissues. The da Vinci robot is one of the most advanced and certainly the most widespread robotic system dedicated to minimally invasive surgery. Although the procedures performed by da Vinci-like surgical robots are teleoperated, potential collisions between surgical tools are a very sensitive issue declared by surgeons. Shared control techniques based on Virtual Fixtures (VF) can be an effective way to help the surgeon prevent tools collision. This letter presents a surgical tools collision avoidance method that uses Forbidden Region Virtual Fixtures. Tool clashing is avoided by rendering a repulsive force to the surgeon. To ensure the correct definition of the VF, a marker-less tool tracking method, using deep neural network architecture for tool segmentation, is adopted. The use of direct kinematics for tools collision avoidance is affected by tools position error introduced by robot component elasticity during tools interaction with the environment. On the other hand, kinematics information can help in case of occlusions of the camera. Therefore, this work proposes an Extended Kalman Filter (EKF) for pose estimation which ensures a more robust application of VF on the tool, coupling vision and kinematics information. The entire pipeline is tested in different tasks using the da Vinci Research Kit system.
Sentiment Analysis in the Mobile Application Review Document Using the Improved K-Nearest Neighbor Method

The large number of smartphone users makes Indonesia a very potential market for business, especially mobile application developers. Each application store allows application users to provide a review of the applications used. From the existing application store, none has the sentiment analysis feature to filter or categorize positive reviews and negative reviews. Sentiment analysis is computational research of opinions, sentiments, and emotions that are expressed textually. The review document will go through several stages starting from preprocessing, calculation of term weighting to the calculation of cosine similarity (degree of similarity) to the training data used. The next process is sorting the level of similarity, determining the new k-values to produce a category for the document. The testing process of Sentiment Analysis in the Mobile Application Review Document Using the Improved K-Nearest Neighbor Method resulted in the best average accuracy of 88,76%. The best accuracy from all scenario test is 90,67 % and optimal k value is 15. Where the number of documents, comparison or balance of the proportion of training data and the determination of the value of k-values used influence the good or not classification process of the document in the form of an application review
Digital Reading Industry and Cost Analysis Take ZhangYue Technology co., LTD. as an Example

Digital reading is a new thing emerging with the rise of the Internet. With rapid development, it has begun to replace paper reading as the preferred way for people to read. Compared with the traditional paper reading, digital reading can not only reduce costs also can bring convenience to read, although compared with the traditional paper reading, digital reading has obvious advantages, but with the rapid development of Internet and the diversity of people pastime, digital reading industry also is full of fierce competition. Firstly, through the overall analysis of digital reading industry and other industries as well as the analysis within the digital industry, this paper has a preliminary understanding of the overall development level of the digital reading industry and the competitive trend within the digital industry. Then take ZhangYue Technology Co., Ltd as an example to further analyze the cost structure of digital reading enterprises. From the analysis in this paper, we can see that the digital reading industry has its own unique characteristics in the cost structure, which is different from the traditional manufacturing industry. Content cost and channel cost + selling cost are the main components of the cost of digital reading industry, which can account for more than 50% of the cost. However, selling cost is not actually related to the source of revenue. Therefore, through the analysis of this paper, enterprises should focus on the reduction of selling cost. More attention should be paid to improving the efficiency of content cost. As for the channel cost, it can be reduced under the circumstance that the enterprise's market share is basically full, and the efficiency of its use should be paid attention to at the same time.
Energy Management in Electrical Power System Employing Machine Learning

Fossil fuel depletion has led to the increase in usage of solar energy. Many plants have been setup in various parts of the India to harness this energy. One of the major contributors is Karnataka. The electricity demand here is met using both energy from non-renewable energy and solar energy. The technology used to meet the demand is by injecting all the power from all the sources to a single transmission line. This is then distributed to different feeders according to its needs. Solar energy is not used to its fullest since it is only injected to the transmission line and extra energy is only stored for reserve. A framework is been introduced in order to make this solar energy used to its full extent and usage of other power is only at times when solar energy is not able to meet the demand. This framework proposes a unique switching strategy by maximizing the usage of solar employing various machine learning algorithms. The data of all the powers and demand are taken from Karnataka power transmission corporation limited (KPTCL) official website for prediction using machine learning techniques. Out of many algorithms used such as linear regression, logistic regression, decision tree, random forest and support vector machines (SVM), it is found that random forest is most efficient and gives the best switching configuration for maximum usage of solar energy.
Efficient Wireless Power Transfer With Capacitively Segmented RF Coils

Wireless power transfer systems have been widely applied in the field of portable and implantable devices, featuring contact-free and reliable energy supply. Novel implant systems, such as brain-computer interfaces, impose the challenges of strong miniaturization and operation under loosely coupled conditions. Therefore, maximizing power transfer efficiency while decreasing the size of transmitter and receiver structures becomes a central research question. This paper presents a unified design strategy of modeling, analyzing and optimizing planar spiral coils with integrated capacitive elements, so-called capacitively segmented coils, for operation in wireless power transfer interfaces. It mathematically analyzes and experimentally verifies that the combination of capacitive coil segmentation, increased operational frequencies and geometrical coil optimization can be used to establish wireless power transfer links with comparatively high efficiency, small size and limited detuning effects in lossy dielectric environments. The paper embraces the formulation and verification of a broadband analytical link model based on partial element equivalent circuits, which is subsequently used to determine dominant coupling and loss mechanisms and to optimize the coils’ geometries for high efficiency. Moreover, an extended analysis shows how the capacitive coil segmentation can effectively suppress dielectric losses and non-uniform current distributions by canceling the inductive contribution of every coil segment at the frequency of operation. Utilizing these methods, an exemplary 40.68MHz wireless power link with a 30mm primary and a 10mm secondary coil is designed and evaluated: With a maximum efficiency of up to 31% in biological tissue at 20mm separation distance, it features efficiency levels which are up to ten times higher and a specific absorption rate which is up to five times lower compared to non-segmented systems. When operated at 150MHz in air, efficiency levels are up to 1.5 times higher than in state-of-the-art systems of the same size.
Improvement of COCOMO II Model to Increase the Accuracy of Effort Estimation

Effort estimation is one of the most important activities in the software development project, because the project managers need to bea able to estimate the amount of cost and time for developing software. There are several techniques and models that can be used to estimate effort such as COCOMO, COCOMO II, SLIM, SEER-SEM. One method that is widely used for effort estimation is COCOMO II. COCOMO II model is an improvement of COCOMO '81 model. However, COCOMO II estimation results are still not satisfying in terms of accuracy. To improve the accuracy of COCOMO II, many researchers are trying to combine COCOMO II with other methods. In this paper, we propose to combine COCOMO II with K-Means clustering method to improve the accuracy. K-Means clustering is used to determine the data that will be used in the COCOMO II calibration process. The COCOMO II calibration aims to determine the new A and B constant values based on software project dataset. Based on the results of the study, it can be concluded that the accuracy of the proposed method generally increased compared to the original COCOMO II model. The value of accuracy depends on the preprocessing technique performed and the number of clusters. The best accuracy is achieved when the preprocessing technique used is to multiply cost driver attributes by 100 and number of clusters is 5. This proposed method can reduce the value of MRE COCOMO II from 1.32 to 0.85 and increase the value of PRED (0.3) from 32% to 54% for COCOMO NASA 2 dataset and Turkish Software Industry.
Motion Planning for Elastic, Non-Controlled, Hyper-Redundant Serial Mechanism

This paper introduces the kinematics and two motion planning schemes for a planar, hyper-elastic leaf-spring that is fully supported at the proximal end. The path of the leaf-spring’s free-end depends on its zero-energy configuration and loaded initial configuration. These two configurations need to be determined such that the free-end’s actual path will go through a desired path, which we define as a set of planar points. Our solutions minimize a weight function which compares the actual and desired paths. We show how our mathematical model fits the behavior of a shape memory alloy Nitinol wire under uniform heating and simulate our motion planning schemes. Finally, we calculate the shape of a real shape memory alloy wire so its free-end’s path will follow a straight line.
A Survey on scheduling algorithm for downlink in LTE cellular network

Wireless internet access was introduced to the world, which led to more mobile phone subscriptions. Data services were first available in the second generation. Later after working past a lot of challenges to achieve the needs and higher performance, the Third Generation Partnership Project (3GPP) created High Speed Packet Access (HSPA). There is a lot of demand on the mobile applications, which then requires higher data rates and low latency. So the 3GPP further started working on Long Term Evolution (LTE), that improves all necessities. The introduction of Orthogonal Frequency Division Multiplexing (OFDM) further improved throughput rate but scheduling algorithm has a greater part in increasing overall performance of network. The scheduling algorithms are used to allocate time, space and resources to the users. These algorithms have also been employed in downlink (i.e. the communication from base station to the mobile) since it requires high data rates with lower packet loss compared to uplink therefore efficient algorithms are required for downlink. This paper focuses on the detailed study and comparisons of existing downlink scheduling algorithms which are namely Proportional Fair (PF), Max-Rate, Maximum Largest Weighted Delay First, Round Robin(RR), and Exponential Rule(EXP-Rule). Moreover, this paper provides some of improvements that can be done in the above mentioned algorithms.
A Comparative Study of Link Prediction Algorithms For Social Networks of Varying Sizes

Link prediction in social networks enables us to predict future links in an evolving social network as new nodes get added through the passage of time. It can also help to detect missing edges in the social graph. A successful link prediction method substantially reduces the experimental effort required to establish the topology of a network and can accelerate the mutually beneficial interactions that takes much longer to form by chance. In this work, the various state of art link prediction methods are analyzed while also looking at the kind of networks the algorithms work best with. Different social graph data sets are chosen based on their sizes and sparsity. Subsequently link prediction task on each of these data sets were done using various machine learning algorithms to understand the performance of several random walk based node embedding methods as compared with the performance of several classical approaches. It is found that the sparsity and size of the graph are important factors that determine the performance of random walk based node embedding methods.
MMW Receiver Front-End for Noninvasive Glucose Measurement

A millimeter wave (MMW) receiver front-end is designed and implemented using 90-nm CMOS technology for noninvasive glucose measurement. The LNA achieves the power gain of 19.43 dB at 29 GHz and the minimum noise figure of 6 dB at 29 GHz. The mixer performs frequency translation to convert the signals at 28–30 GHz to 10 MHz. The receiver front-end achieves the conversion gain of 14 dB and the PldB of −23 dBm. The VCO delivers the frequency band of 28–30 GHz while achieving the FOM of −182.3 dBc/Hz.
Fault-Tolerant Platoon Control of Autonomous Vehicles Based on Event-Triggered Control Strategy

This paper is concerned with the issue of platoon fault-tolerant control of the system with time-varying actuator faults. By obtaining the relative state information of neighboring vehicles, two event-triggered fault-tolerant controllers are designed for the two cases of the leader vehicle speed. Then the vehicle platoon system is transformed into error system, and the event-triggered control strategy is designed, in an effort to further save resources. Moreover, by the related theory of Lyapunov, it is shown that the error system is bounded. Finally, two simulation examples are given to show the effectiveness of the proposed approach.
Distributed compressed video sensing based on convolutional sparse coding

This paper discusses a Distributed Compressed Video Sensing (DCVS) framework using Convolutional Sparse Coding (CSC). CSC is a technique to represent a signal as convolutions of filters and corresponding coefficients. Conventional block based DCVS methods divide a given video sequence into key and non-key frames. The key frames are decoded independently like still images, and the non-key frames use Side Information (SI) generated with previously decoded key frames. The sparse dictionaries of the non-key frames are designed with the SIs. However, in CSC based methods, a non-key frame can use the dictionary of the nearest key frame in the temporal domain since the dictionary filters, namely features, are robuster against motions than those of block based methods.
Robust Control for a Quadrotor Transporting a Payload

This works presents a control strategy for a quadrotor carrying a cable-suspended payload. In order to stabilize the vehicle translational dynamics and to attenuate the payload swing, a robust control is developed. The controller is based on an energy approach and the passivity properties of the system. Stability proofs based on the Lyapunov theory for the controller design are presented. Numerical simulations were carried out to validate the nonlinear control behaviors.
DTC-Enabled Frequency-Tunable Inverted-F Antenna for IoT Applications

In this letter, an integrated frequency-tunable inverted-F antenna is presented. Thanks to a reconfigurable circuit using a digitally tunable capacitor (DTC), the antenna resonant frequency can be shifted over 32 different positions from 600 to 960 MHz. This tunability is used to compensate the frequency detuning caused by the change in the antenna close surrounding. The small dimensions as well as the digital control and the low power consumption of the reconfiguration circuit make the proposed antenna a good candidate for Internet of Things applications.
Design of Microstrip Patch Antenna with quarter wave transformer for ISM Band

This paper presents a design of microstrip patch antenna with quarter wave transformer for ISM Band. The antenna is designed over the operating frequency is 2.4 GHz using the substrate material as FR-4. The designed antenna can be used for ISM (industrial, scientific and medical) band applications in Wireless Body Area Networks (WBAN). Different performance parameters of the antenna such as operating frequency, Return Loss, Voltage Standing Wave Ratio (VSWR), directivity, SAR have been analyzed both in planar conditions on the human tissue model by using CST Microwave Studio. Finally, Specific Absorption Rate (SAR) is evaluated to satisfy the antenna safety concern on the human body.
Performance Evaluation of Supervised Machine Learning Algorithms Using Different Data Set Sizes for Diabetes Prediction

Data classification algorithm in machine learning is very helpful in analyzing several medical data with a large size and helps in making decisions to diagnose a disease. Not all supervised classification algorithms get accurate results in analyzing data sets. For this reason, testing the accuracy of each supervised classification algorithm is necessary, this can be used as a comparison in determining which types of algorithms are most accurate in measuring small amounts of data, and which algorithms are the most accurate in measuring large amounts of data. In this paper we will examine several classification algorithms including Naïve Bayes algorithms, functions (Support Vector Classifier algorithms), rules (decision table algorithms), trees (J48) by looking at the results of measurements made by each algorithm with measurement variables, which are Correctly Classified, incorrect classifieds, Precision, and Recall. The purpose of the study was to find the weaknesses and strengths of the supervised classification algorithm based on the measurement variables that have been determined against the testing of predictive databases of diabetes. Based on the results in this study, the best algorithm that can be used to help decide to diagnose a disease is the SVM algorithm with an accuracy value of 77.3%.
Financial Leasing, Optimal Financial Structure and Economic Growth: An Analysis Based on Financial Inclusion Perspective

From the view of financial inclusion, this paper studies the relationship among financing leasing, optimal financial structure and economic growth, based on the dynamic panel model, and applies system GMM estimation to explore the mechanism, the optimal financial structure and financial inclusion. The research conclusion shows that financial leasing helps to improve and promote the level of financial inclusion; because financial leasing has the advantages of product and agency cost compared with bank credit, it can promote social and technological progress, adjust the industrial and economic structure, and achieve economic growth.
Modeling and Control of Dish-Stirling Solar Thermal Integrated With PMDC Generator Optimized by Meta-Heuristic Approach

This paper proposes an autonomous microgrid system for the first time with solar thermal dish-Stirling engine coupled with permanent magnet direct current generator and battery energy storage system along with uncontrolled variable loads. Both proportional-integral (PI) and proportional-integral-derivative (PID) controllers are considered separately for the bidirectional charge controller and buck-boost converter controller for the dish-Stirling system. The modern meta-heuristic algorithms including recent ones like particle swarm optimization, mine blast algorithm and grey wolf optimizer (GWO), have been adopted for tuning the gain parameters of the controllers used for the direct current common bus voltage stability and power sharing. The work considers variable insolation level for somehow mimicking the natural solar variation all the day. The simulation of the proposed model is carried out in MATLAB/Simulink and the results are obtained with two types of controllers for comparison. The results demonstrate the superior performance of the PID controllers optimized with GWO algorithm in terms of faster convergence rate, DC voltage profile, and maintenance of SOC of battery and energy efficiency as compared to the other two algorithms. It is also to be noted that the use of a permanent magnet DC generator coupled to the solar dish-Stirling engine improves the electric conversion efficiency and reduces cost effectively as compared to AC generators.
FCS – MPC and Feedback Quantizer applied to a Multi-Cell AFE Rectifier

In this paper is presented a proposal of control strategy based on Finite Control Set – Model Predictive complemented with Feedback Quantizer. Finite Control Set - Model Predictive is a control scheme widely used in power converters. The advantages of this control are, among others, (i) the nonlinearities and constraints of the converter’s operation, (ii) the natural discrete operation of power converters, and (iii) the rapid response to changes in references or disturbances. Yet, one drawback to be mentioned in the Finite Control Set – Model Predictive Control would be the non-fixed switching frequency, which results in a dispersed harmonic spectrum, generating resonance or saturation in transformers. The proposed control strategy, improve the harmonic spectrum of input current and PWM voltage of the power cell in a Multi-Cell AFE Rectifier. Simulation results confirm the validity of the proposal.
Lossy Signal-Interference Filters and Applications

Lossy signal-interference transversal filtering sections (TFSs) composed of two in-parallel transmission-line segments with input–output matching lines—reflective-type part—and open-ended stubs with input resistors—lossy part—are presented. First, in their bandstop and dual-band bandpass versions, this class of lossy TFS is applied to the design of filtering networks with input-/two-port-quasi-reflectionless (QR) stopbands, such as wide-band bandstop filters (BSFs) and dual-band bandpass filters (BPFs). Furthermore, multistage circuit architectures made up of equal and dissimilar lossy TFSs for higher rejection and broader stopband realization, respectively, and higher selectivity dual-band BPF synthesis are described. Subsequently, in its bandpass counterpart, the conceived approach of lossy TFS is exploited to develop single-/multistage sharp-rejection bandpass filters (BPFs) with a flattened transmission band. The operational foundations of the engineered types of lossy TFSs for the suggested applications and theoretical design examples are provided. In addition, for experimental-validation purposes, four microstrip filter prototypes are manufactured and characterized, as follows: 2-GHz two-stage symmetrical QR BSFs with similar and frequency-contiguous bandstop TFSs for higher rejection and broader stopband shaping, respectively, a 1.4/2.6-GHz input-QR dual-band bandpass TFS, and a 2-GHz five-stage lossy BPF with enhanced passband amplitude flatness and spectrally enlarged attenuated bands.