Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultrasound Open Platforms for Next-Generation Imaging Technique Development

Open platform (OP) ultrasound systems are aimed primarily at the research community. They have been at the forefront of the development of synthetic aperture, plane wave, shear wave elastography, and vector flow imaging. Such platforms are driven by a need for broad flexibility of parameters that are normally preset or fixed within clinical scanners. OP ultrasound scanners are defined to have three key features including customization of the transmit waveform, access to the prebeamformed receive data, and the ability to implement real-time imaging. In this paper, a formative discussion is given on the development of OPs from both the research community and the commercial sector. Both software- and hardware-based architectures are considered, and their specifications are compared in terms of resources and programmability. Software-based platforms capable of real-time beamforming generally make use of scalable graphics processing unit architectures, whereas a common feature of hardware-based platforms is the use of field-programmable gate array and digital signal processor devices to provide additional on-board processing capacity. OPs with extended number of channels (>256) are also discussed in relation to their role in supporting 3-D imaging technique development. With the increasing maturity of OP ultrasound scanners, the pace of advancement in ultrasound imaging algorithms is poised to be accelerated.
Determination of the big mammals migration corridors in the particular areas using remotely-operating intelligent camera system

Large mammals free movement through the large areas is fundamental element of the healthy mammals ecosystem. The migrations keep the balance between animal populations, maintain sufficient genetic diversity or prevent the temporary lack of food. With the growing rate of transportation, urbanization and industrialization across the all countries, the barriers in the countrysides are created. Because of these barriers, the natural migration corridors of big mammals are endangered. Preserve the land connectivity is fundamental step for nature conservation and for healthy mammals ecosystem. To achieve this objective, we proposed a camera-based remotely operating intelligent system to identify the migration corridors. The system consists of one master device and more slave devices. The slave devices are deployed in the harsh environment of the countrysides and they use two camera devices to detect and track mammals across their surveillance zone. The information about mammals movement, movement trajectory and time in the scene are send to the master and stored in the database. The system uses the algorithm of computer vision to automatically classify appropriate animal species for moving mammals. The master collect data from all slave devices in the designated area and then the migration corridors for individual mammals species are created.
A Rollout Control Algorithm for Discrete-Time Stochastic Systems

The growing demand for making autonomous intelligent systems that can learn how to improve their performance while interacting with their environment has induced significant research on computational cognitive models. Computational intelligence, or rationality, can be achieved by modeling a system and the interaction with its environment through actions, perceptions, and associated costs. A widely adopted paradigm for modeling this interaction is the controlled Markov chain. In this context, the problem is formulated as a sequential decision-making process in which an intelligent system has to select those control actions in several time steps to achieve long-term goals. This paper presents a rollout control algorithm that aims to build an online decision-making mechanism for a controlled Markov chain. The algorithm yields a lookahead suboptimal control policy. Under certain conditions, a theoretical bound on its performance can be established. © 2010 ASME
Energy-Constrained Slot-Amplitude Modulation With Dimming Support

This letter proposes a modulation scheme that combines both pulse slots and amplitudes in a flexible manner to obtain a desired level of flicker-free dimming while efficiently conveying information for visible light communication. The scheme is referred to as energy-constrained slot-amplitude modulation (ECSAM). Specifically, the total energy for every macro-symbol, containing multiple pulse slots, is fixed at a level that depends on the requested dimming target by adjusting the number of active slots and their amplitude levels. By allocating a fixed amount of energy for every marco-symbol, of a period smaller than the human eye’s integrating time, ECSAM also ensures flicker-free data transmission for eye safety. The performance of ECSAM in terms of error rate and spectral efficiency is evaluated numerically. Measurement of the error rate, obtained from an experimental test-bed, is reported at distances ranging from 20 to 100 cm. Results show that ECSAM supports a continuous and wide range of dimming with high-spectral efficiency and low error rate compared with conventional techniques.
Full-duplex radio technology for simultaneously detecting and preventing improvised explosive device activation

Originating from civilian/commercial wireless networks, the progressive concept of same frequency simultaneous transmission and reception (SF-STAR), a.k. a. in-band full-duplex operation, has high potential also at the future battlefield. The prospects of a military full-duplex radio (MFDR) are not limited to enhancing the spectral efficiency of tactical communications, which would already be a significant advancement considering the universal congestion of electromagnetic spectrum. Perhaps even more importantly, armed forces could gain a major technical advantage by employing multifunction MFDRs that are capable of jointly conducting signals intelligence, electronic warfare, and tactical communications owing to their SF-STAR capability. This study focuses on one specific promising application, where a radio transceiver performs spectrum monitoring and signal surveillance for potential hostile transmissions when simultaneously performing an electronic attack against opposing forces' receivers at the same frequency band. In particular, we demonstrate by experiments in a laboratory environment that the MFDR technology can be successfully used for detecting an attempt to control remotely an improvised explosive device while also preventing its activation by transmitting a jamming signal.
WiAU: An Accurate Device-Free Authentication System with ResNet

The ubiquitous and fine-grained features of WiFi signals make it promising for achieving device-free authentication. However, traditional methods suffer from drawbacks such as sensitivity to environmental dynamics, low accuracy, long delay, etc. In this paper, we introduce how to validate human identity using the ubiquitous WiFi signals. We develop WiAU, a device-free authentication system which only utilizes a Commodity Off-The-Shelf (COTS) router and a laptop. We describe the constitutions of WiAU and how it works in detail. Through collecting channel state information (CSI) profiles, WiAU automatically segments coherent activities and walking gait using an automatic segment algorithm (ASA). Then, a ResNet algorithm with two dedicated loss functions is designed to validate legal users and recognize illegal ones. Finally, experiments are conducted from different scenes to highlight the superiorities of WiAU in terms of high accuracy, short delay and robustness, revealing that WiAU has an accuracy of over 98% in recognizing human identity and human activities respectively.
An accurate and extensible machine learning classifier for flow-level traffic classification

Machine Learning (ML) techniques have been widely applied in recent traffic classification. However, the problems of both discriminator bias and class imbalance decrease the accuracies of ML based traffic classifier. In this paper, we propose an accurate and extensible traffic classifier. Specifically, to address the discriminator bias issue, our classifier is built by making an optimal cascade of binary sub-classifiers, where each binary sub-classifier is trained independently with the discriminators used for identifying application specific traffic. Moreover, to balance a training dataset, we apply SMOTE algorithm in generating artificial training samples for minority classes. We evaluate our classifier on two datasets collected from different network border routers. Compared with the previous multi-class traffic classifiers built in one-time training process, our classifier achieves much higher F-Measure and AUC for each application.
An exact optimization strategy for the restoration problem in radial distribution systems with switches allocated in a reduced number of circuits

In this paper, an adapted mathematical model is presented to the optimization of the restoration problem in radial electric power distribution systems with the existence of a reduced number of switches, which is the typical characteristic of real distribution systems. The restoration problem consists in restoring the maximum possible of loads after the de-energization of parts of the system due to the occurrence of a permanent fault or due to the execution of expansion projects or preventive maintenance. The preparation and the implementation of the restoration plan should be performed in the shortest time possible. In the proposed mathematical model, the objective is to maximize the restoration of de-energized loads and to minimize the number of switching operations necessary for the reestablishment of these loads. The mathematical model formulated is of mixed-integer second order conic programming and can be efficiently solved by commercial solvers based on exact optimization techniques, such as the branch and bound method. Tests were performed simulating permanent faults in a 53-bus distribution system adapted to the proposed problem. The results obtained show the robustness and the efficiency of the mathematical model adapted to solve this problem.
Design and analysis of a compliant elbow-joint for arm rehabilitation robot

Rehabilitation robot aided sensorimotor training positively affects the reduction of impairment in patients. Due to the complexity of the physical structure of the upper limb of the human body, the joints of the robot are difficult to be aligned with the human joints in real time. This situation may cause pain or harm to the patient. Nowadays, more and more rehabilitation robots aim at clinical requirements such as being lightweight and the friendly interaction between robot and patient. So in this paper, we present the design and analysis of a compliant elbow-joint for arm rehabilitation robot which focuses on human-robot interaction. Firstly, a cable-driven arm rehabilitation robot is designed for the shoulder and elbow rehabilitation, which has the property of lightweight and flexibility. Secondly, an axis-adjustable elbow joint is designed to achieve real-time alignment with the skeletal elbow joint using a set of compliant mechanisms. Finally, the simulation results of the compliant elbow-joint can prove the design has a more friendly human-robot interaction.
A new clustering approach to identify the values to query the deep web access forms

The deep web is a huge part of the web only accessible by querying its access forms. To query these access forms, we need to know the possible values of each form field. But, some form fields have an undefined set of values and this makes their automatic query difficult or impossible. In this paper, we propose our new approach to identify the set of the possible values for these fields to query the deep web access forms. For this, we query first these fields with the values associated with the domain of the deep web source. After, we use the K-medoids clustering approach to classify these generated results in a K clusters. For this, our clustering approach uses the semantic similarity between these results. The elements of the generated clusters are used by our approach to define the set of the possible values of these analyzed fields. With this approach, we can apply efficient queries to all the fields of the deep web access forms and access the deep web information.
Development, growth, and reproduction of the cyclopoid copepod Limnoithona tetraspina in the upper San Francisco Estuary

Limnoithona tetraspina is a small cyclopoid copepod that was introduced to the San Francisco Estuary (SFE), USA, in 1993 and became the most abundant copepod species in the low- salinity zone (LSZ). Two previous studies have shown that it feeds only on motile prey, predominantly ciliates. Little is otherwise known of its biology or its role in the estuarine foodweb. We determined development times, growth rates, and fecundity of L. tetraspina from March to August of 2006 and 2007. The mean growth rate of copepodites in both years was 0.03 d -1 , which is low relative to values reported for related Oithona spp. Development times were longer in the field than in the laboratory at food saturation, indicating food limitation in the SFE. Mean weight-specific fecundity rate in 2007 was 0.10 d -1 , which is twice that of 2006, but within the range of reported values for Oithona spp. Low growth and fecundity rates indicate that the population success of L. tetraspina is due to low mortal- ity. L. tetraspina may avoid certain mortality agents (e.g. visual predation) to which larger copepods are susceptible.
CHIP: A children identification system based on mobile phone sensory data

Identifiable signatures about people can be left automatically and unobtrusively when they are using smartphones, which can help to identify different users and offer personalized service to them. Specifically, offering services to users according to their age is significant and necessary, as people with different ages have distinct requirements for phone use. However, the smartphone knows little about the user's age. In this paper, we introduce CHIP, a system that achieves CHildren user Identification based on smartphone sensory data only. The system first captures data in two scenes, i.e., unlocking screen and phone answering, by utilizing ubiquitous sensors available in commodity mobile phone. Based on these scenes, we then extract features in the stages of picking up the phone, swiping the screen, response time, and answering the phone. The Random Forest classifier is further used to identify children users. We evaluate the system through real-life experiments conducted by 8396 effective samples from 35 participants with different ages. Experimental results show that our approach achieves the precision of 94.80% in identifying children phone users.
HALA: Hive advanced lexical analyzer

Big Data Analytics has become an integral part of technology in the past decade. It has been the driving force behind some of the major advancements made, including machine learning, artificial intelligence, human behavior prediction and even genomic studies. Furthermore, the popularity of Hadoop and its ability to provide a highly scalable and available distributed computing on commodity machines has attracted various efforts into optimizing the framework itself. This research focuses on bridging the gap between the user experience seen in a relational database management system and the Hadoop framework. It is added as an external component to Apache Hive's architecture and introduces a novel tool, Hive Advanced Lexical Analyzer, or HALA, for 3 added functionalities: (1) Lexical Analysis, (2) Syntactic Analysis and (3) Multi-query processing. This paper also presents the results of a thorough analysis of the applicability for each of these components. 5 queries from the TPC-H series were selected and used as the base query model for multiple variations for information extraction. In addition to extracting query information, the authors have also performed detailed analysis on the syntax verifying component as well. Multiple runs of different query variations have returned a non-existent false positives and false negatives. Additionally, the modularity of this tool allows the usage of either or all of the components to realize the desired outcome.
Quantitative Evaluation of the Topologies and Electromagnetic Performances of Dual-Three-Phase Flux-Switching Machines

Hybrid-excited flux-switching machines based on permanent magnet (PM) flux-switching machines were proposed to achieve improved torque–speed characteristics. However, due to the reduction of PM volume, decreases in torque density and efficiency are usually inevitable. Thus, it is necessary to make a comprehensive comparison between the dual-three-phase hybrid-excited flux-switching machines and their PM flux-switching counterparts. To obtain a full understanding of flux-switching machines, PM flux-switching machines with U-shaped stator cores and alternate-pole magnets or C-shaped stator cores are considered. From the perspective of the frequency domain, this paper not only gives a comprehensive evaluation and comparison of the above-mentioned six flux-switching machines, but also reveals the theoretical mechanism that results in their different electromagnetic behavior in a unified way by the general field modulation theory, which is the main contribution of this paper.
RSUs placement based on vehicular social mobility in VANETs

Due to the distributed and highly dynamic nature, roadside units (RSUs) play an important role in vehicular ad hoc networks (VANETs). To improve the performance of Vehicles-RSUs-Vehicles (V-R-V) communications, designing a reasonable approach for RSUs placement becomes a problem. In this paper, we study the RSUs placement problem based on vehicular sociality Mobility. Given a limited budget, how to place the RSUs optimally so as to achieve the maximum V-R-V communications is NP-hard. By analyzing the traces of the taxis in Shanghai, we find that the vehicles have the nature of social mobility. Through this social mobility, we could improve the performance of V-R-V communications in VANETs. Then, we propose a greedy algorithm to solve the RSUs placement problem based on vehicular social mobility. Simulation results show that our method is more effective than others in improving the performance of V-R-V communications.
Moving target tracking based on improved Meanshift and Kalman filter algorithm

The target tracking technology on video is an indispensable technology in the field of computer vision research. MeanShift algorithm is one of the many excellent moving target tracking algorithms. This paper mainly research focuses on MeanShift theory and traditional MeanShift target tracking algorithm. MeanShift does not need parameters and has the advantage of small computation. However, under some conditions the MeanShift does not work well. For the problem of the failure to tracking target when the target move fast or the target occluded happend, improve the MeanShift algorithm and Kalman filter algorithm is introduced to deal with the problem. The improved algorithm is tested, and experimental result show that this algorithm can effectively track the target in occlusion enviorment.
A 60-GHz 8-Way Phased-Array Front-End With T/R Switching and Calibration-Free Beamsteering in 28-nm CMOS

An eight-way phased-array TRX front-end with RF phase shifting and on-chip transmit/receive switching is implemented in 28-nm CMOS. The TX <inline-formula> <tex-math notation="LaTeX">$P_{\mathbf {1dBout}}$ </tex-math></inline-formula> and RX noise figure (NF) are 10 dBm and 6.8 dB, respectively. The active phase shifter shows less than 5° phase resolution and amplitude errors within ± 0.35 dB. The 9.6-mm<sup>2</sup>chip consumes 231 mW in RX and 508 mW in the TX mode from a 0.9-V supply. When combined with PCB antennas, a ±46° scan angle is obtained with <0.4 dB peak-to-peak gain ripples without calibration. We also present an approach to express the noise performance of a phased-array receiver in terms of the effective isotropic NF, as an extension to the NF, which is only defined for a two-port system.
Design of cybernetic model for glucose clamp techniques

Glucose clamp techniques are used in clinical studies to determine glucose metabolism indices. In order to “clamp”the blood glucose levels, the negative feedback principle is used to maintain glycemia at desired levels, depending on the specific clamp technique. The purpose of this study is to develop dynamical multivariable system model of glucose metabolism for analysis of input/output interactions using cybernetic principles. The structure of model is chosen to study the direct impact of insulin and glucose infusion rate on glucose and insulin levels in blood. The model is derived from the linearization of widely used Bergman minimal model. Multiple input-output interaction can then be further analyzed in order to determine the quality and robustness of existing control strategies or to develop new ones Simulations of a more complex physiological Sorensen model are used to validate the resulting model.
ABC: Adaptive Beacon Control for Rear-End Collision Avoidance in VANETs

Vehicular ad hoc network (VANET) has been widely recognized as a promising solution to enhance driving safety, by keeping vehicles well aware of the nearby environment through frequent beacon message exchanging. Due to the dynamic of transportation traffic, especially for those scenarios where the density of vehicles is high, the naive beaconing scheme where vehicles send beacon messages at a fixed rate with a fixed transmission power can cause severe channel congestion. In this paper, we investigate the risk of rear-end collision model and define a danger coefficient \rho to characterize the danger threat of each vehicle being in a rear-end collision. We then propose a fully-distributed beacon congestion control scheme, referred to as ABC, which guarantees each vehicle to actively adapt a minimal but sufficient beacon rate to avoid a rear-end collision based on individual estimates of \rho. In essence, ABC adopts a TDMA- based MAC protocol and solves a NP-hard optimal distributed beacon rate adapting (DBRA) problem with a greedy heuristic algorithm, in which a vehicle with a higher \rho will be assigned with a higher beacon rate while keeping the total required beacon demand lower than the channel capacity. We conduct extensive simulations to demonstrate the efficiency of ABC design in different traffic density and a large variety of underlying road topologies.
FlowCog: Context-Aware Semantic Extraction and Analysis of Information Flow Leaks in Android Apps

Android apps having access to private information may be legitimate, depending on whether the app provides users enough semantics to justify the access. Existing works analyzing app semantics are coarse-grained, staying on the app-level. They can only identify whether an app, as a whole, should request special permission but cannot answer whether a specific app behavior under a particular runtime context, such as information flow, is correctly justified. We propose FlowCog, an automated system to extract semantics related to information flows and correlate such semantics with given information flows to address these issues. Particularly, FlowCog statically finds all the Android views related to the given flow via control or data dependencies and then extracts semantics, such as texts and images, from these views and associated layouts. Next, FlowCog adopts natural language processing and deep learning approaches to infer whether the extracted semantics correlate with the given flow. Our evaluation shows that FlowCog can achieve an accuracy rate of 95.4% and an F1 score of 0.953.