Miracle-dz/UniFAR-FTUs huggingface dataset:
Waveform weighting matrix design for robust transmit beampattem formation of MIMO radar

The waveform weighting matrix design for robust transmit beampattern formation of multiple-input multiple-output radar in presence of transmit steering vector error is studied. Unlike waveform covariance matrix design for transmit beampattern formation, each transmit array element emits a weighted sum of orthogonal waveforms which persues good auto- and cross-correlation properties of waveforms. The weighting matrix is designed by minimizing integrated side-lobe level, under the worst-case 3 dB main-beam width constraint. By employing semidefinite relax and first-order Taylor series expansion, non-convex optimization problem can be converted to a convex one. And a cyclic minimization algorithm based on second-order cone programming is given to synthesize transmit waveform weighting matrix. Simulations show that the proposed method can form robust beampattern against transmit steering vector error and the cyclic optimization algorithm can synthesize transmit waveform weighting matrix effectively.
Modeling of Channel Current in Sub-threshold Region for Poly-Si based Macaroni Structure in 3D NAND Flash Memories

In this paper, sub-threshold region I-V model for poly-Si based macaroni structure in 3D NAND Flash memories is presented. The model is based on analytical I-V solution for macaroni structure which was previously developed for single crystal-Si channel. Starting from the existing model, the characteristics of polysilicon, such as sub-threshold slope (S.S.) and mobility degradation and drain induced barrier lowering (DIBL), were added. Moreover, we considered shift in I-V curve due to different distribution of traps for each cell.
Deficiency in the membrane protein Tmbim3a/Grinaa initiates cold-induced ER stress and cell death by activating an intrinsic apoptotic pathway in zebrafish

Most members of the family of proteins containing a transmembrane BAX inhibitor motif (TMBIM) have anti-apoptotic activity, but their in vivo functions and intracellular mechanisms remain obscure. Here, we report that zebrafish Tmbim3a/Grinaa functions in the prevention of cold-induced endoplasmic reticulum (ER) stress and apoptosis. Using a gene-trapping approach, we obtained a mutant zebrafish line in which the expression of the tmbim3a/grinaa gene is disrupted by a Tol2 transposon insertion. Homozygous tmbim3a/grinaa mutant larvae exhibited time-dependently increased mortality and apoptosis under cold exposure (at 16 °C). Mechanistically, using immunofluorescence, fluorescence-based assessments of intracellular/mitochondrial Ca2+ levels, mitochondrial membrane potential measurements, and Ca2+-ATPase assays, we found that cold exposure suppresses sarcoplasmic/ER Ca2+-ATPase (SERCA) activity and induces the unfolded protein response (UPR) and ER stress. We also found that the cold-induced ER stress is increased in homozygous tmbim3a/grinaa mutant embryos. The cold-stress hypersensitivity of the tmbim3a/grinaa mutants was tightly associated with disrupted intracellular Ca2+ homeostasis, followed by mitochondrial Ca2+ overload and cytochrome c release, leading to the activation of caspase 9– and caspase-3–mediated intrinsic apoptotic pathways. Treatment of zebrafish larvae with the intracellular Ca2+ chelator 1,2-bis(2-aminophenoxy)ethane-N,N,N′,N′-tetraacetate-acetoxymethyl ester (BAPTA-AM) or with 2-aminoethoxydiphenyl borate (2-APB), an inhibitor of the calcium-releasing protein inositol 1,4,5-trisphosphate receptor (IP3R), alleviated cold-induced cell death. Together, these findings unveil a key role of Tmbim3a/Grinaa in relieving cold-induced ER stress and in protecting cells against caspase 9– and caspase 3–mediated apoptosis during zebrafish development.
Bone Reconstruction of the Lower Extremity: Complications and Outcomes

A study was performed to analyze the results and final outcomes of bone reconstruction of the lower extremity. Twenty-six patients presented with type IIIB open fractures, nine with type IIIC open fractures, and 15 with chronic osteomyelitis. Seven patients underwent primary amputation, and reconstruction was attempted for 43 patients. The mean bone defect size was 7.7 cm (range, 3 to 20 cm). Bone reconstruction was achieved with conventional bone grafts in 16 cases, in association with either local (13 cases) or free (three cases) flaps. Vascularized bone transfer was performed in 24 cases, with either osteocutaneous groin flaps (10 cases), soleus-fibula flaps (12 cases), or osteocutaneous lateral arm flaps (two cases). For three patients, bone reconstruction was performed with a technique that combines the induction of a membrane around a cement spacer with the use of an autologous cancellous bone graft. Infections were observed to be responsible for prolonged hospital stays and treatment failures. The cumulative rates of sepsis were 4.6 percent at 1 week after injury and 62.8 percent at 2 months. Vascular complications were also related to infections and were responsible for four secondary amputations. One patient asked for secondary amputation because of a painful nonfunctional lower limb. Bone healing occurred in 37 of 43 cases, and the average time to union was 9.5 months, with an average of 8.7 procedures. The mean lengths of stay were 49 days for conventional bone grafts and 62 days for vascularized bone grafts. All of the 50 patients were able to walk, with an average time of 14 months. All of the patients with amputations underwent prosthetic rehabilitation. Patients mostly complained about the reconstructed limb (62.8 percent). Joint stiffness was present in 40 percent of the cases. Other long-term complications were pain (nine cases), lack of sensation (five cases), infection (five cases), and pseudarthrosis (one case). However, all of the patients with successful reconstructions preferred their salvaged leg to an amputation. Of 41 patients who were working before the injury, 26 returned to work.
PREVIRNEC: A cognitive telerehabilitation system based on Virtual Environments

In this paper, we describe PREVIRNEC, a distributed system for cognitive telerehabilitation based on virtual environments. Our system allows personalized treatments by means of 2D and 3D exercises that can be built according to single patient's characteristics. Patients realize their exercises remotely. Depending on their obtained results, the system readjusts automatically the levels of difficulty of the tasks, and switches from one task to the other. The paper focuses on technological issues of the system design. We analyze its structure, components, and we discuss the decisions adopted in the interaction mode design and well as in the tasks layout.
Tenofovir disoproxil fumarate for the treatment of hepatitis B virus infection: pharmacokinetics and clinical efficacy

Tenofovir is an acyclic nucleotide phosphonate diester analogue of adenosine monophosphate with antiviral activity against HIV-1 and hepatitis B virus. After two phosphorylations, tenofovir inhibits HIV-1 reverse transcriptase by competing with the natural substrate, deoxyadenosine 5´-triphosphate. Tenofovir disoproxil fumarate has been approved by the US FDA for the treatment of HIV-1 infection in combination with other antiretroviral agents. Although not approved by the FDA for the treatment of chronic hepatitis B virus infection, tenofovir has anti-hepatitis B virus activity in patients co-infected with HIV and hepatitis B. This paper will review the pharmacology and clinical experience with tenofovir, with a focus on hepatitis B infection.
Thin-Film Magnetic Inductors for High-Frequency Switching Regulator

This paper presents the design aspects of thin-film magnetic inductors for their use in high-frequency integrated voltage regulators (IVR). Performance metrics for solenoid inductors such as inductance density, quality factor and operating frequency are maximized using optimum design parameters. These inductors are developed using thin-film magnetic core within the redistribution (RDL) layers and the measurement results are presented. The results show achieving $Q\gt 10$ at 100MHz. Finally, these inductors are used in a two-phase PWM controlled interleaved IVR switching at 100MHz.
BENU: Distributed Subgraph Enumeration with Backtracking-Based Framework

Given a small pattern graph and a large data graph, the task of subgraph enumeration is to find all the subgraphs of the data graph that are isomorphic to the pattern graph. The state-of-the-art distributed algorithms like SEED and CBF turn subgraph enumeration into a distributed multi-way join problem. They are inefficient in communication as they have to shuffle partial matching results that are much larger than the data graph itself during the join. They also spend non-trivial costs on constructing indexes for data graphs. Different from those join-based algorithms, we develop a new backtracking-based framework BENU for distributed subgraph enumeration. BENU divides a subgraph enumeration task into a group of local search tasks that can be executed in parallel. Each local search task follows a backtracking-based execution plan to enumerate subgraphs. The data graph is stored in a distributed database and is queried as needed. BENU only queries the necessary edges of the data graph and avoids shuffling partial matching results. We also develop an efficient implementation for BENU. We set up an in-memory database cache on each machine. Taking advantage of the inter-task and intra-task locality, the cache significantly reduces the communication cost with controllable memory usage. We conduct extensive experiments to evaluate the performance of BENU. The results show that BENU is scalable and outperforms the state-of-the-art methods by up to an order of magnitude.
Environmental tobacco smoke exposure and children's health

Almost half of the child population is involuntarily exposed to environmental tobacco smoke (ETS). The ETS exposure gives rise to an excessive risk of several diseases in infancy and childhood, including sudden infant death syndrome, upper and lower respiratory infections, asthma and middle ear diseases. It is also linked to cancer, and behavioural problems and neurocognitive deficits in children.
Robust Query Processing: Mission Possible

Robust query processing with strong performance guarantees is an extremely desirable objective in the design of industrial-strength database engines. However, it has proved to be a largely intractable and elusive challenge in spite of sustained efforts spanning several decades. The good news is that in recent times, there have been a host of exciting technical advances, at different levels in the database architecture, that collectively promise to materially address this problem. In this tutorial, we will present these novel research approaches, characterize their strengths and limitations, and enumerate open technical problems that remain to be solved to make robust query processing a contemporary reality.
Moving Object Linking Based on Historical Trace

The prevalent adoption of GPS-enabled devices has witnessed an explosion of various location-based services which produce a huge amount of trajectories monitoring an individual's movement. This triggers an interesting question: is movement history sufficiently representative and distinctive to identify an individual? In this work, we study the problem of moving object linking based on their historical traces. However, it is non-trivial to extract effective patterns from moving history and meanwhile conduct object linking efficiently. To this end, we propose four representation strategies (sequential, temporal, spatial, and spatiotemporal) and two quantitative criteria (commonality and unicity) to construct the personalised signature from the historical trace. Moreover, we formalise the problem of moving object linking as a k-nearest neighbour (k-NN) search on the collection of signatures, and aim to improve efficiency considering the high dimensionality of signatures and the large cardinality of the candidate object set. A simple but effective dimension reduction strategy is introduced in this work, which empirically outperforms existing algorithms including PCA and LSH. We propose a novel indexing structure, Weighted R-tree (WR-tree), and two pruning methods to further speed up k-NN search by combining weight and spatial information contained in the signature. Our extensive experimental results on a real world dataset verify the superiority of our proposals, in terms of both accuracy and efficiency, over state-of-the-art approaches.
Application of Gaussian Mixture Clustering Algorithm in Template Attack

Traditional template attack is more severe to attack conditions. It requires a large amount of energy traces and plaintext and ciphertext pairs. In some cases, attackers cannot obtain plaintext or ciphertext so that traditional template attack fails. In this paper, Gaussian Mixture Clustering Algorithm is introduced to solve the problem, which enables attackers to still perform template attacks and relaxes the attack conditions. In addition, this paper uses a strategy to select appropriate plaintext to improve the accuracy of attacks.
Intrusion Detection System Based on Data Mining Technics for Industrial Networks

Security of the critical infrastructure of automated process control system under the conditions of the automation level of modern production in the Russian Federation and around the world is becoming an increasing priority and is one of the most important components of the digital economy. The attacks of malicious persons are constantly improving, becoming combined and spread almost instantly. Intrusion detection systems implement monitoring functions and detect attacks that have bypassed the firewall. The means to ensure the information security of process control systems at this stage of their development are not able to withstand such threats. To solve this problem, network attacks detection systems based on the use of artificial intelligence methods is proposed. The research describes the analysis of modern approaches to the network attacks detection, structure of detection system based on data mining methods and comparison between different data mining algorithms.
Path Planning for Bathymetry-aided Underwater Navigation

It has been shown that significant variation in the bottom topography of underwater terrain could be used to dramatically improve underwater localization accuracy. However, path planning with bathymetric aids has not been extensively explored in literature. Localization accuracy strongly depends on the path taken by an underwater vehicle. Given a starting point and a destination, we develop an algorithm to plan a path that will ensure good localization. We adopt an information entropy measure to assess the localization uncertainty of a particle filter, such that a generic posterior can be described. We use this to drive a path planning algorithm that minimizes uncertainty using reinforcement learning and Gaussian process regression. We test our algorithm using bathymetric data and show that it generates near-optimal paths with good localization accuracy at the destination.
Bartonella and Rickettsia From Fleas (Siphonaptera: Ceratophyllidae) of Prairie Dogs (Cynomys spp.) From the Western United States

Fleas of prairie dogs have been implicated in the transmission of Bartonella spp. We used PCR to test DNA extracts from 47 fleas of prairie dogs from 6 states. We amplified DNA from 5 unique genotypes of Bartonella spp. and 1 Rickettsia sp. from 12 fleas collected in North Dakota, Oklahoma, Texas, and Wyoming. Sequences from the Bartonella spp. were similar, but not identical, to those from prairie dogs and their fleas in Colorado.
A Model-Based Approach to Generate Dynamic Synthetic Test Data

Having access to high-quality test data is an important requirement to ensure effective cross-organizational integration testing. The common practice for addressing this need is to generate synthetic data. However, existing approaches cannot generate representative datasets that can evolve to allow the simulation of the dynamics of the systems under test. In this PhD project, and in collaboration with an industrial partner, we investigate the use of machine learning techniques for developing novel solutions that can generate synthetic, dynamic and representative test data.
Novel on-chip high-Q power inductor for high-efficient PowerSoC Applications

In this paper, novel on-chip inductors with thick copper and thick magnetic core are designed and fabricated. With improved magnetic core lamination and thicker Cu layers, these inductors have achieved a high Q factor of 21.1 at 20MHz, as well as a high L/Rdc of 420nH/ohm, which is the best-known reported Q factor and L/Rdc for on-chip inductors of large inductances (>100nH).
Iot Based Smart Window using Sensor Dht11

In India, Smart City is a mission started by our P.M, Which aims to develop the infrastructure digitally of India’s cities and rural areas. Focusing towards the mission, we have provided an efficient solution for operation of the window in a smart way by monitoring the temperature level inside the closed environment on the real time basis. Whole system is IOT based. The sensors in the windows detect the temperature level continuously and accordingly the system will be performed by iot. This will helps to maintain the temperature level in our surrounding and also provides an smart window operation without the help of manpower.
A Wide Tuning Range and Low Phase Noise Quotient Frequency Synthesizer for Multiband Millimeter-Wave Applications

This paper presents an integer-N quotient frequency synthesizer (QFS) for V, E, and W multiband millimeter-wave transceiver applications. Design considerations of passive and active components to improve tuning range and phase noise of LC-VCO are presented. A complementary dual-injection injection-locked divider (ILFD) structure with independent gate biasing is proposed to improve injection efficiency and frequency locking range. A pseudo-differential cascode buffer structure is developed to improve the stability of the VCO buffer. The QFS is fabricated in a 0.13 μm CMOS process, achieving a wide frequency range of 27.38 to 33.5 GHz while drawing 70mA of current. At 27.4 GHz, the measured closed-loop phase noises are −80.2, −111.3 and −125.5 dBc at 1, 10 and 100 MHz offset, respectively.
Deep Learning Framework for Domain Generation Algorithms Prediction Using Long Short-term Memory

Real-time prediction of domain names that are generated using the Domain Generation Algorithms (DGAs) is a challenging cyber security task. Scope to collect the vast amount of data for training favored data-driven techniques and deep learning architectures have the potential to address this challenge. This paper proposes a deep learning framework using long short-term memory (LSTM) architecture for prediction of the domain names that are generated using the DGAs. Binary classification had benign and DGA domain names and multiclass classification was performed using 20 different DGAs. For the binary classification, LSTM model gave accuracy of 98.7% and 71.3% on two different test data sets and for the multi-class classification, it gave accuracy of 68.3% and 67.0% respectively. Two diversified data sets were used to analyze the robustness of the LSTM architecture.
An indoor pedestrian navigation algorithm based on smartphone mode recognition

In recent years, the rapid development of smartphone-based navigation has been proven to have great application prospects. However most smartphone-based navigation technologies only applicable to a fixed mode, which has a significant decline in user experience. Therefore, we design a multi-mode smartphone recognition method based on neural network. On the basis of it, a heading correction method based on mode-change detection is proposed to reduce the heading angle error of smartphone navigation in multi-mode. In addition, we study an intelligent step length estimation method to improve the accuracy of pedestrian navigation. And the experimental results demonstrate the effectiveness of the proposed methods, i.e. the average heading error and the maximum positioning error obtained by the proposed algorithm is 94.5% and 91.6% less than these of traditional PDR algorithm respectively.
HyMJ: A Hybrid Structure-Aware Approach to Distributed Multi-way Join Query

The multi-way join query plays a fundamental role in many big data analytic scenarios. Recently, the hybrid join query is becoming increasingly important. However, the existing one-round and multi-round algorithms have limitations in the process of the hybrid query. In this paper, we present a novel hybrid structure-aware multi-way join algorithm called HyMJ, which combines the one-round and multi-round algorithms to compute the hybrid query efficiently. First, we propose the query structure graph (QSG) to represent the internal query structure of a given join query and the query structure decomposition tree (QSDT) to represent the structure-aware query plan. Each internal node of the QSDT denotes a subquery with a cyclic or acyclic query structure. Then, we design a graph contraction based algorithm to construct QSDT from QSG. Furthermore, to select the optimal join strategy for each subquery in the QSDT, we introduce a heuristic strategy selection model. Experimental results on Apache Spark reveal that HyMJ outperforms both the one-round and multi-round algorithms for hybrid multi-way join queries on real-world datasets.
Low-Loss SISL Patch-Based Phase Shifters and the Mixer Application

This paper proposes a new class of phase shifters using right-angled triangular patches based on the substrate integrated suspended line (SISL) platform. The reference line and the main line of the phase shifter are both designed in the patch form, which is different from previous works. With the common reference patch fixed, multiway and polyphase SISL phase shifters can be achieved by adjusting the main patch. As demonstration, three SISL patch-based phase shifters with phase shifts of 45°, 60°, and 90°, respectively, have been designed at 24-GHz band and fabricated by the printed circuit board (PCB) process. The radiation loss, conductor loss, and dielectric loss of the proposed patch-based phase shifters have been reduced based on the self-packaged SISL platform, and the measured insertion loss of the SISL phase shifter has a typical value of 0.3 dB at 24-GHz band, which is much smaller as compared with the state of the art. Moreover, the 90° case has been applied to an SISL single-balanced mixer design. Besides the low-loss phase shifter, other components of the mixer are all implemented on the common SISL platform with low loss, and the overall loss of the SISL mixer is very small as compared with the other works. The SISL mixer has a compact size and low conversion loss with a minimum of 5.8 dB at the 24-GHz band.