Miracle-dz/UniFAR-FTUs huggingface dataset:
Probability-Based Address Translationfor Flash SSDs

Thanks to the advance of NAND scaling technologies, an ultra-scale SSD (e.g., <inline-formula><tex-math notation="LaTeX">$>$</tex-math><alternatives><mml:math><mml:mo>></mml:mo></mml:math><inline-graphic xlink:href="im-ieq1-3006529.gif"/></alternatives></inline-formula> 100 TB) is introduced to markets. This rapid increase of SSD capacity, however, comes at the cost of more DRAM which resides in an SSD controller for logical-to-physical (L2P) address translation. Many have proposed various address translation algorithms to reduce DRAM, but they fail to provide short read latency, in particular when a workload has weak locality. This letter proposes a novel probability-based address translation algorithm, called <italic>ProbFTL</italic>. In contrast to existing translation techniques that maintain exact L2P mapping, ProbFTL employs a probability-based data structure, a bloom filter, for address translation. By leveraging a space-efficient nature of a bloom filter, ProbFTL reduces the amount of DRAM for address translation to 20 percent of the existing techniques. The read latency of ProbFTL is not affected from locality of a workload; ProbFTL guarantees a read amplification factor of 1.1 even under a random read workload. ProbFTL exhibits slightly worse garbage collection efficiency, but its write amplification factor is maintained sufficiently low.
Recursive Engine In-Cylinder Pressure Estimation Merely Using Structural Vibration Signal

The cylinder pressure signal is a very useful indicator for advanced high-performance internal-combustion engines equipped in modern automobiles. In this paper, our previous framework for cylinder pressure estimation is revisited, and based on which a novel strategy for cylinder pressure estimation merely using vibration signal is proposed. The main idea behind the previous framework is to use the information of both engine structural vibration signal and crank angular speed signal to recursively estimate the cylinder pressure based on the Kalman filter. Different with the previous work, here, the engine speed is estimated using the vibration signal instead of being measured by a crank angular speed sensor, which leads to the so-called solely vibration-based cylinder pressure estimation method. The estimated crank angular speed is compared with the calculated one. Two combustion metrics, pressure peak Pmax and peak location Ploc, are used for evaluating the proposed method. The cylinder pressure estimation results are compared with both experimental data collected from a four-cylinder diesel engine and previous estimation results, and illustrate the effectiveness of the proposed method.
Hi-End: Hierarchical, Endurance-Aware STT-MRAM-Based Register File for Energy-Efficient GPUs

Modern Graphics Processing Units (GPUs) require large hardware resources for massive parallel thread executions. In particular, modern GPUs have a large register file composed of Static Random Access Memory (SRAM). Due to the high leakage current of SRAM, the register file consumes approximately 20% of the total GPU energy. The energy efficiency of the register file becomes more critical as the throughput of GPUs increases. For more energy-efficient GPUs, the usage of non-volatile memory such as Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM) as the GPU register file has been studied extensively. STT-MRAM requires a lower leakage current compared to SRAM and provides an appropriate read performance. However, using STT-MRAM directly in the GPU register file causes problems in performance and endurance because of complicated write procedures and material characteristics. To overcome these challenges, we propose a novel register file architecture and its management system for GPUs, named Hi-End, which exploits the data locality and compressibility of the register file. For STT-MRAM-based GPU register files, Hi-End increases the data write performance and endurance by caching and data compression, respectively. In our evaluation, Hi-End enhances the energy efficiency of a GPU register file by 70.02% and reduces the write operations by up to 95.98% with negligible performance degradation compared to SRAM-based register files.
Energy Efficiency Optimization in Heterogeneous Networks Based on Deep Reinforcement Learning

To meet the rapid increasing requirement of service and application of communication, heterogeneous wireless networks with the macrocell and the femtocells are considered in this paper. How to deal with resource allocation efficiently and dynamically in heterogeneous wireless networks puts forward to an urgent problem. In this paper, we aim to maximize the overall network's energy efficiency, where multiple femto base stations are randomly and densely distributed in the coverage of macro base station. We first construct an EE model and formulate the problem for optimization. Then we propose a Nature DQN algorithm in deep reinforcement learning to solve it with power discretization. Finally, in simulations, it demonstrates that the proposed Nature DQN can not only achieve better energy efficiency than Q-learning and water-filling but also accelerate the convergence.
Programming Language Support for Multisensor Data Fusion: The Splash Approach*

We present the Splash programming framework to support the effective implementation of multisensor data fusion. Multisensor data fusion has been widely exploited in autonomous machines since it outperforms algorithms using only a single sensor, in terms of accuracy, reliability and robustness. Knowing that developers have long been lacking programming language support for multisensor data fusion, we offer a dedicated Splash language construct along with formal semantics for multisensor data fusion. Specifically, we analyze the structural characteristics of multisensor data fusion algorithms and derive technical issues that the language construct must tackle. We then give a detailed account of the language construct along with its formal semantics. Finally, we validate its utility and effectiveness via its application to a lane keeping assist system.
5G Radio Access Network Slicing in Massive MIMO Systems for Industrial Applications

A key enabler for Industry 4.0 is Fifth Generation Wireless Specifications (5G), within which network slicing is a promising technique to ensure customized quality of service for specific end-user groups in industrial scenarios. Massive Multiple Input Multiple Output (MIMO) plays a significant role in 5G but network slicing for massive MIMO has not yet been addressed. In this paper, we propose a network slicing scheme for a 5G Radio Access Network (RAN) with massive MIMO technology. Our simulations show that it is feasible to provide guaranteed performance in terms of high reliability, low latency, and a large number of connections by deploying our proposed scheme at the mMedium Access Control (MAC) layer of a massive MIMO base station.
A Bioinspired Airfoil Optimization Technique Using Nash Genetic Algorithm

Natural fliers glide and minimize wing articulation to conserve energy for endured and long range flights. Elucidating the underlying physiology of such capability could potentially address numerous challenging problems in flight engineering. However, primitive nature of the bioinspired research impedes such achievements, hence to bypass these limitations, this study introduces a bioinspired non-cooperative multiple objective optimization methodology based on a novel fusion of PARSEC, Nash strategy, and genetic algorithms to achieve insect-level aerodynamic efficiencies. The proposed technique is validated on a conventional airfoil as well as the wing crosssection of a desert locust (Schistocerca gregaria) at low Reynolds number, and we have recorded a 77% improvement in its gliding ratio.
Optimal time-domain moment matching with partial placement of poles and zeros

In this paper we consider a minimal, linear, time-invariant (LTI) system of order n, large. Our goal is to compute an approximation of order ν < n that simultaneously matches ν moments, has ℓ poles and k zeros fixed, with ℓ + k < ν, and achieves minimal H2 norm of the approximation error. For this, in the family of ν order parametrized models that match ν moments we impose ℓ+k linear constraints yielding a subfamily of models with ℓ poles and k zeros imposed. Then, in the subfamily of ν order models matching ν moments, with ℓ poles and k zeros imposed we propose an optimization problem that provides the model yielding the minimal H2-norm of the approximation error. We analyze the first-order optimality conditions of this optimization problem and compute explicitly the gradient of the objective function in terms of the controllability and the observability Gramians of the error system. We then propose a gradient method that finds the (optimal) stable model, with fixed ℓ poles and k zeros.
Object Mobility classification based Visual SLAM in Dynamic Environments

Most of the existed visual odometry methods cannot work in dynamic environments since the dynamic objects lead to wrong uncertain feature associations. In this paper, we involved a learning-based object classification front end to recognize and remove the dynamic object, and thereby ensure our ego-motion estimator’s robustness in high dynamic environments. Moreover, we newly classify the environmental objects into static, movable and dynamic three classes. This processing not only enables the ego-motion estimation in the dynamic environment but also leads to clean and complete map-ping results. The experimental results indicate that the proposed method outperformed the other state-of-the-art SLAM solutions in both dynamic and static indoor environments.
Optimal parameter estimation in a landslide motion model using the adjoint method

This work proposes an optimal approach for parameter estimation in a landslide motion, based on the so-called adjoint method. The system is described by an extended sliding-consolidation model composed of an ordinary differential equation and 1D parabolic partial differential equation that represents landslide motion and pore pressure evolution respectively. The key feature of this model is pore pressure feedback, which regulates landslide motion and leads to coupling between both differential equations. Parameters to be estimated include the friction and dilatancy angle of the material. The objective functional for the optimal estimation is composed of: i) a cost function defined as the least square error between measurements and related simulated values, and ii) a product of Lagrange variables and system dynamics. A variational approach is applied to get the gradients of the cost functional with respect to parameters to be estimated and adjoint model. The cost functional is optimized, employing the steepest descent method to estimate parameters. Finally, the presented optimal estimation method is validated on a simulated test case.
Using Digital Twin Data for the Attribute-Based Usage Control of Value-Added Networks

The great progress in the field of digitization and networking of the industry brings many advantages as well as new challenges. On the one hand, it enables new business models that hold great economic growth potential. On the other hand, it leads to an increased heterogeneity of the resulting value- added networks. This poses a striking problem when it comes to the interconnection of industrial devices and the establishment of new business models. Since the disadvantages can quickly outweigh the advantages, activities in this area proceed still very cautiously. A solution is offered by highly dynamic attribute-based usage control architectures at network-level, which monitor the entire communication in the value-added networks. Previous approaches, however, either have the disadvantage that they can only use communication metadata as attributes, or are highly specialized and tailored to the respective systems of the individual network nodes. In addition, they are usually pure access control systems instead of usage control systems. In this paper, the authors propose a novel attribute-based usage control model (ABUC) that allows simulation models in form of digital twins of the respective network components to be used in order to predict their behavior and thus control network traffic according to predicted usage. The ABUC model is then implemented in a use case and validated with several performance tests.
Target Positioning Based on Particle Centroid Drift in Large-Scale WSNs

The localization problem of target nodes remains unresolved, especially in large-scale and complex environments. In this paper, we propose a particle centroid drift (PCD) algorithm to reduce the distance errors between nodes and obtain the particle aggregation region by using the drift vector. First, we use the particle quality prediction function to obtain the particles in a high-likelihood region. The high-quality particles have high probability in the calculation, which can increase the number of effective particles and enable avoiding particle degradation. Then, the centroid drift vector is used to make the particle distribution similar to the actual reference distribution. Experiments are conducted on state-space models: the local movement where 55% nodes are moving and the globe movement where 100% nodes are moving. The results show that the proposed algorithm has low estimation errors, a good tracking effect and an acceptable time complexity.
A Non Linear Registration Without the Use of the Brightness Constancy Hypothesis

Image Registration is first step in analysis of the same point of the patient over some time. This work proposes a new approach to define the same anatomical point in a set of images without the intensity of the content or the constant intensity hypothesis. It considers Möbius transformation of matched points having affine and projective transformations as particular cases. The experiments consider breast infrared dynamic examination of 23 patients, each one with 20 images. They are registered in two diverse ways in a total of 874 registrations. Results are evaluated qualitatively and quantitatively. The used examinations are public available and the achieved results are even compared with other well know approach resulting 1748 registration on real images. The proposed approach improves the image matching in 92%, 92%, 81%, 85%, and 81% according to Dice, Jaccard, Superposition of Reference, False Negative and False Positive respectively.
EARLY MOLECULAR RESPONSE WITH IMATINIB THERAPY IN CHRONIC MYELOID LEUKEMIA AND ITS ASSOCIATION WITH BASELINE WHITE BLOOD CELL COUNT AND SPLEEN SIZE

Objectives: To determine the frequency of early molecular response in patients of chronic myeloid leukemiatreated with Imatinib and its association with baseline white blood cell (WBC) count and spleen size.Study Design: Cross sectional study.Place and Duration of Study: Combined Military Hospital, Rawalpindi Pakistan, from May to Nov 2017.Material and Methods: Seventy eight patients of Chronic Myeloid Leukemia (CML) in chronic phase (CP) wereincluded in the study. Inclusion criteria were: 18 years or older, diagnosed with CML in CP with positive BCRABL1. Patients who were in accelerated/blast phase, or already taking any Tyrosine Kinase Inhibitors orchemotherapy were excluded from the study. Base line WBC count, spleen size and BCR-ABL1 IS values wererecorded. All the enrolled patients were placed on Imatinib therapy (400 mg/day) and RT-PCR for BCR ABL1transcript was repeated after three months.Results: In our study, 60.15% of patients achieved EMR at 3 months after Imatanib therapy (p-value <0.001). Inunivariate analysis, there was significant association of spleen size, baseline WBC count and percentages ofblasts in bone marrow with BCR ABL1 (IS) at 3 months (p-value <0.001), while on multivariate regression model,significant association was found only in spleen size (p-value <0.001) with EMR.Conclusion: A significant number of patients achieved EMR with Imatinib therapy. Spleen size at diagnosis wasthe only significant factor associated with achieving EMR. It is imperative to identify patients at an early stagewho are unlikely to achieve EMR and therefore have poor over-all survival.
A Novel Homopolar Four Degrees of Freedom Hybrid Magnetic Bearing

In this article, a novel four degrees of freedom (4-DOF) homopolar hybrid magnetic bearing (HMB) is put forward to reduce the magnetic field coupling of radial 4-DOF. It consists of two pairs of symmetrical radial stators and one rotor, and the radial stators on each side have two pieces, namely, X and Y stator core. Due to the special design of X and Y magnetic poles in the different radial stator cores, the magnetic circuits of X and Y channels are independent of each other. The configuration and operating principle of the homopolar HMB are first discussed, and then, the mathematical model of radial suspension force is deduced by the equivalent magnetic circuit method (EMCM). Besides, some main parameters are given in accordance with the given maximum radial suspension force. Finally, 3-D finite-element (FE) model of the homopolar 4-DOF HMB is established with MagNet software. Meanwhile, the magnetic flux distributions, the magnetic flux density, the force–current relationship of X channel with displacement and current of Y channel are simulated and analyzed. The simulation results indicate that the presented 4-DOF HMB has good performance, and the magnetic fields of X and Y channels decouple well from each other.
Mask Overlaying: a Deep Learning Approach for Individual Optic Cup Segmentation from Fundus Image

Glaucoma is a group of eye diseases characterized by progressive degeneration of the optic nerve, which can lead the patient to blindness, however it can be prevented through diagnosis and treatment of the disease. It is the leading cause of irreversible blindness worldwide and is expected to affect 76 million by 2020. The cup-to-disc ratio technique is a structural examination of the optic nerve used to glaucoma diagnose, and requires an accurate segmentation of the optic disc and cup. Thus, this work proposes an automated method for cup segmentation in fundus images of DRISHTI-GS dataset using a combination of the green channel of RGB image and the provided optic disc mask as input to a modified U-Net [1] convolutional neural network. The results achieved were promising, yielding a mean dice coefficient of 94%, much better than state-of-the-art algorithms. The method could be used for glaucoma diagnosis.
RRAM Crossbar-Based In-Memory Computation of Anisotropic Filters for Image Preprocessingloa

Anisotropic-diffusion is a commonly used signal preprocessing technique that allows extracting meaningful local characteristics from a signal, such as edges in an image and can be used to support higher-level processing tasks, such as shape detection. This paper presents a fully scalable CMOS-RRAM architecture of an edge-aware-anisotropic filtering algorithm aimed at computer vision applications. The CMOS circuitry controls the scale-space image data to perform pseudo-parallel in-memory computing and nonlinear processing through RRAM crossbar. The arithmetic operations for in-memory computation of brightness gradients are efficiently accumulated to produce the enhanced image in several iterations. The proposed architecture uses single RRAM as a computing and storage element to perform both arithmetic operations and accumulations. Thanks to the in-memory computation, memory accesses and arithmetic operations are reduced by 64% and 92%, respectively, compared to traditional digital implementations. This, in turn, results in a potential reduction of power and area costs of about 75% and 85%, respectively. The processing time is also reduced by 67%.
Integrated Control of Yaw Moment and Wheel Slip for Heavy Commercial Road Vehicles*

A Direct Yaw-moment Controller (DYC) is proposed to ensure the directional stability of Heavy Commercial Road Vehicles (HCRVs) during combined cornering and braking maneuvers. A wheel slip controller is integrated with DYC to prevent locking of wheels during the full application of brakes. DYC utilizes a Sliding Mode Controller (SMC) to generate the required yaw moment to be produced by the longitudinal forces. A reaching law based SMC for wheel slip controller is implemented to coordinate with the proposed DYC. To compensate for the time-delay in the pneumatic brake system of HCRVs, a brake controller with a predictor-based delay handling technique has also been incorporated in the design. The importance of inclusion of delay compensation and the performance of an integrated control strategy is demonstrated through Hardware-in-Loop (HiL) experiments.
Reinforcement Learning for Task Offloading in Mobile Edge Computing for SDN based Wireless Networks

The explosive growth of the distributed computing resources in mobile edge computing (MEC) create a necessity to have a reasonable controller to ensure efficient utilization of distributed computing resources. The software defined networks (SDNs) capabilities can be used in MEC environment to reduce the energy consumption of user device without service disruptions. SDN, which is not proposed specifically for edge computing, can in fact serve as an enabler to let the real potential of edge computing be achieved and to lower the complexity barriers involved. This paper investigates resource allocation problem in wireless MEC by aiming to save the battery power of user device. Learning from experience plays an important role in a SDN based MEC infrastructure where reinforcement learning (RL) takes a long term goal into consideration besides immediate reward, which is very important for a dynamic environment. A novel software defined edge cloudlet (SDEC) based RL optimization framework is proposed in this paper to tackle the energy minimization problem in wireless MEC. Specifically, Q-learning and cooperative Q-learning based RL schemes are proposed for the intractable problem. Simulation results reveal that the proposed scheme achieves superior performance in saving the battery power of a user device compared to other benchmark methods such as Q-learning with a random algorithm and Q-learning with epsilon greedy.
Neighborhood estimation in sensitivity-based update rules for real-time optimal control

In the domain of optimal control theory, an optimal control problem can be formulated with respect to a predefined task parameter set, leading to a parametric optimal control problem. In application areas such as spacecraft guidance and robotics, optimal control and trajectory planning inevitably involve dealing with motion constraints and imposing a requirement of feasibility, as well as optimality. By virtue of the Sensitivity Theorem, real-time solutions of such constrained optimal control problems can be approximated through a sensitivity-based update of a given nominal solution for nominal task parameter values. There are, however, strict requirements placed on the task parameters and on the optimization solutions to make this approximation possible, so as to place guarantees on the existence and optimality of solutions in the vicinity of the nominal solution. One of these requirements is a neighborhood of validity about the nominal parameter, the size of which is, however, not specified by the Sensitivity Theorem. In this paper, we therefore present a method by which to estimate the size of this neighborhood. We then judiciously apply the result to the whole task parameter space of interest, so as to be able to place weak guarantees on the existence and optimality of solutions throughout it. We demonstrate the efficacy of our approach on a two-link planar robot arm.