Miracle-dz/UniFAR-FTUs huggingface dataset:
FastJoin: A Skewness-Aware Distributed Stream Join System

In the bigdata era, many applications are required to perform quick and accurate join operations on large-scale real-time data streams, such as stock trading and online advertisement analysis. To achieve high throughput and low latency, distributed stream join systems explore efficient stream partitioning strategies to execute the complex stream join procedure in parallel. Existing systems mainly deploy two kinds of partitioning strategies, i.e., random partitioning and hash partitioning. Random partitioning strategy partitions one data stream uniformly while broadcasting all the tuples of the other data stream. This simple strategy may incur lots of unnecessary computations for low-selectivity stream join. Hash partitioning strategy maps all the tuples of the two data streams according to their attributes for joining. However, hash partitioning strategy suffers from a serious load imbalance problem caused by the skew distribution of the attributes, which is common in real-world data. The skewed load may seriously affect the system performance. In this paper, we carefully model the load skewness problem in distributed join systems. We explore the key tuples which lead to the heavy load skewness, and propose an efficient key selection algorithm, GreedyFit to find out these key tuples. We design a lightweight tuple migration strategy to solve the load imbalance problem in real-time and implement a new distributed stream join system, FastJoin. Experimental results using real-world data show that FastJoin can significantly improve the system performance in terms of throughput and latency compared to the state-of-the-art stream join systems.
Hotspots Infrared detection of photovoltaic modules based on Hough line transformation and Faster-RCNN approach

In the last two decades, the installation and production of photovoltaic (PV) plants increased widely. As the PV system operation time grew, more and more defect occurred on the PV modules. One of the most significant open issues in the PV sector is to find appropriate inspection methods to detect PV modules' failures. In this paper, two approaches are proposed to detect the hotspots in the infrared image of PV modules. The classical digital image processing technology mainly uses Hough line transformation and canny operator to detect hotspots. The deep learning model is based on Faster-RCNN and transfer learning, which perform better with more compute resources.
Efficient Triple Output Network for Vertebral Segmentation and Identification

Precise vertebral segmentation provides the basis for spinal image analyses and interventions, such as vertebral compression fracture detection and other abnormalities. Deep learning is a popular and useful paradigm for medical image process. In this paper, we proposed an iterative vertebrae instance segmentation model, which has good generalization ability for segmenting all types of vertebrae, including cervical, thoracic, and lumbar vertebrae. In experimental results, our model not only used 17% less memory but also achieves better performance on vertebrae segmentation compared to existing methods. The existing method provides only two output for segmentation and classification respectively. However, with more memory available, our model is capable of providing third output for accurate anatomical prediction under the same amount of memory.
6G Visions: Mobile ultra-broadband, super internet-of-things, and artificial intelligence

With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation (6G) mobile communications be on the eve of the fifth-generation (5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things (IoT), and artificial intelligence (AI). Then, we review key technologies to realize each aspect. In particular, teraherz (THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions.
A Reference-Optimizing Antiwindup Control for Input-Constrained Systems

Control limits due to saturation constraints may result in the directionality problem in addition to the controller windup effect. In solving the directionality problem, this paper explores the concept of modifying the reference signal to be an element of the maximal output admissible set of the closed-loop dynamics. This is achieved by an online constrained optimization of a tracking-related cost function associated with the closed-loop system. The design procedure for this reference-optimizing directional compensator is applicable to most existing antiwindup schemes. The effectiveness of the proposed control structure is demonstrated via simulation of benchmark case study examples.
Lossless Image Compression using Proposed Equations and JPEG-LS Prediction

This paper proposes a new lossless image compression technique which makes use of simple mathematical equations along with Joint Photographic Expert Group Lossless (JPEG-LS) prediction technique to compress the image. Context of four pixels is used and the four pixels are varied in their signs which constitute a system of sixteen equations. Among the four context pixels, three pixels are actual pixels whereas the fourth pixel is the predicted value of the current pixel. Equations are solved to obtain the equation which result in minimum positive equation. Having obtained the minimum positive equation, it is solved by replacing the predicted pixel by the actual pixel value. Resulting absolute value is written in the image which results in transformed image and the sign of the result is written in a file. Both the transformed image and the sign file are Huffman encoded to reduce their size further. Compression ratio achieved for Lena image using the proposed compression technique is 1.6.
Effective Parallel Computing via a Free Stale Synchronous Parallel Strategy

As the data becomes bigger and more complex, people tend to process it in a distributed system implemented on clusters. Due to the power consumption, cost, and differentiated price-performance, the clusters are evolving into the system with heterogeneous hardware leading to the performance difference among the nodes. Even in a homogeneous cluster, the performance of the nodes is different due to the resource competition and the communication cost. Some nodes with poor performance will drag down the efficiency of the whole system. Existing parallel computing strategies such as bulk synchronous parallel strategy and stale synchronous parallel strategy are not well suited to this problem. To address it, we proposed a free stale synchronous parallel (FSSP) strategy to free the system from the negative impact of those nodes. FSSP is improved from stale synchronous parallel (SSP) strategy, which can effectively and accurately figure out the slow nodes and eliminate the negative effects of those nodes. We validated the performance of the FSSP strategy by using some classical machine learning algorithms and datasets. Our experimental results demonstrated that FSSP was 1.5- $12\times $ faster than the bulk synchronous parallel strategy and stale synchronous parallel strategy, and it used $4\times $ fewer iterations than the asynchronous parallel strategy to converge.
Performance of Frequency Translation in Separating a Photoplethysmographic Signal from an Additive Motion Artifact

Obtaining red and infrared (IR) photoplethysmo-graphic (PPG) signals by a general method in a commercial pulse oximeter is overlapped with an additive motion artifact (MA) signal routinely. When the red and IR PPG signals are combined with the MA signal, a percentage of oxygen saturation (SpO2) is unreliable. To prevent the overlapping problem, a technique of frequency translation is introduced to shift the PPG frequency components away from the MA frequency components. The introduced approach remodels an LED-driving system by substituting an alternating current (AC) source for a traditional direct current (DC) source in the commercial pulse oximeter. To assess the performance, the SpO2 values computed from the red and IR PPG signals acquired by the presented solution are evaluated when four natural poses of motion occur. Besides, the well-known methods are used to calculate the SpO2 values from the red and IR PPG signals sensed by the conventional LED-emitting system during motion for the efficient comparison. The well-known methods are discrete saturation transform (DST), fast independent component analysis (fICA) and compression of Fourier coefficients (CFC). The resulting SpO2 values show that the technique of frequency translation provides overall mean error lower than the selected schemes for all postures. The overall mean error of the introduced technique is 1.1% while the approaches of DST, fICA and CFC yield the overall mean errors by 2.9%, 15.4% and 8.4%, respectively.
Joint Trajectory Design, Task Data, and Computing Resource Allocations for NOMA-Based and UAV-Assisted Mobile Edge Computing

Mobile edge computing (MEC) has been considered as a promising technique to address the explosively growing computation-intensive applications. Thanks to the flexibility of the unmanned aerial vehicles (UAVs), the UAV-assisted MEC can serve mobile terminals (MTs) effectively, i.e., the computing server installed on the UAV can flexibly change its location to serve MTs. Moreover, since non-orthogonal multiple access (NOMA) is able to accommodate massive connectivity, the NOMA-based and UAV-assisted MEC can provide flexible computing services for MTs in large-scale access networks (e.g., sensor networks and Internet of Things). However, due to the diversity of the UAV’s trajectory and the interference among MTs introduced by NOMA, the performance (e.g., energy consumption and delay) of the NOMA-based and UAV-assisted MEC system is adversely affected. Therefore, in this paper, we formulate an optimization problem to minimize the largest energy consumption among MTs by jointly optimizing the trajectory, task data and computing resource allocations, and then propose an iterative algorithm to solve the optimization problem. Furthermore, to minimize the largest energy consumption among MTs with lower complexity, we propose a fixed point service scheme and optimize the location of the fixed point. The simulation results show that the proposed optimization algorithms can effectively reduce the largest energy consumption among MTs and ensure the fairness among MTs.
An Improved Squirrel Search Algorithm for Maximum Likelihood DOA Estimation and Application for MEMS Vector Hydrophone Array

Maximum likelihood (ML) method for direction of arrival (DOA) estimation achieves an excellent performance in array signal processing, but the complexity and computational load of searching the multidimensional nonlinear function prevented it from practical application. Based on squirrel search algorithm (SSA), an improved SSA (ISSA) for ML DOA estimation is proposed in this paper, which can reduces the computational complexity. The idea of spatial variation and diffuse inspired by the invasive weed optimization(IWO) algorithm is applied to ISSA. The simulation experiments compared ISSA with SSA, IWO, seeker optimization algorithm(SOA), sine cosine algorithm (SCA), genetic algorithm (GA), particle swarm optimization (PSO) and differential evolution (DE) method for ML DOA estimator show that the proposed algorithm has faster convergence speed, fewer iterations and lower root mean square error(RMSE) under different number of signal sources, different signal to noise ratio(SNR) and different population size. Therefor the proposed algorithm does not only ensure the estimation accuracy, but also greatly reduce the computation complexity of multidimensional nonlinear optimization for the ML method. Finally, the test experiment using Micro Electronic Mechanical Systems(MEMS) vector hydrophone array in Fenhe lake show the engineering practicability of proposed ML DOA estimator with ISSA.The results obtained will be valuable in the application of engineering.
Classification of Scalp EEG States Prior to Clinical Seizure Onset

Objective: To investigate the feasibility of improving the performance of an EEG-based multistate classifier (MSC) previously proposed by our group. Results: Using the random forest (RF) classifiers on the previously reported dataset of patients, but with three improvements to classification logic, the specificity of our alarm algorithm improves from 82.4% to 92.0%, and sensitivity from 87.9% to 95.2%. Discussion: The MSC could be a useful approach for seizure-monitoring both in the clinic and at home. Methods: Three improvements to the MSC are described. Firstly, an additional check using RF outputs is made prior to alarm to confirm increasing probability of a seizure onset state. Secondly, a post-alarm detection horizon that accounts for the seizure state duration is implemented. Thirdly, the alarm decision window is kept constant.
Assessing the Relation between Family Background and Juvenile Delinquency using Data Mining

The soft minds of children can easily be shaped and subjected to twist and bends towards criminal acts. In India the criminal offence by children or Juvenile delinquency by youth is spreading. The main risk factors tendering for the juvenile criminal offences are family & parents, financial problems, education, peer groups etc. In this paper, we use association rule mining technique of data mining to make analyses on family background risk factor in juvenile delinquency with help of Indian juvenile crime dataset. The experiment results are investigated for assessing the relation between family background and juvenile delinquency in India
Community Detection in Social Graph Using Nature-Inspired Based Artificial Bee Colony Algorithm with Crossover and Mutation

Many types of social network are modelled as graphs. Community detection has been an important research area in social graph analysis. Community detection can be viewed as an optimization problem. Nowadays, researchers use nature-inspired algorithms to solve optimization problem. Their goal is to find the optimal solution for a given problem. In this paper, nature-inspired based artificial bee colony algorithm with crossover and mutation is used to detect community in social graphs. GraphX is built as a library on the top of Spark by encoding graph as a collection of vertices and edges. Comparative studies describe that the proposed algorithm and other nature-inspired algorithms can effectively detect the community structure on real world social graphs as other traditional community detection algorithms.
Image classification with Local Directional Decoded Ternary Pattern

This paper presents an efficient handcrafted texture operator for texture modeling and classification. The proposed descriptor, referred to as local directional decoded ternary pattern (LDDTP), consists in encoding both directional pattern features and contrast information in a compact way based on local derivative variations. The proposed operator first computes for each pixel within its $3\times 3$ overlapping square neighborhood, on the one hand, central edge response through the 2nd derivative of Gaussian filter, and on the other hand, eight directional edge responses using the eight Frei-Chen masks to capture more detailed information. Then, spatial relationships among the neighboring pixels through the generated edge responses are exploited independently with the help of the concepts of LTP and LDP operators to enhance the discriminative power. Finally, the produced LDDTP pattern is splitted into two distinct parts: local directional decoded ternary pattern lower ($LDDTP_{L}$) and local directional decoded ternary pattern upper ($LDDTP_{U}$), which are combined into hybrid distributions to form the final LDDTP feature descriptor. Experimental results on eight publicly available texture datasets showed that the proposed LDDTP descriptor achieves classification performance, which is competitive or better than several old and recent state-of-the-art LBP variants.
NMOS Only Schmitt Trigger Based SRAM Cell

The scaling of transistors to nanoscale and even lower requires them to be operated in sub threshold region of operation. Negative Bias Temperature Instability (NBTI) is a quantum effect observed in p-channel Metal Oxide Semiconductor Field Effect Transistor (MOSFET) that takes place due to operation at sub threshold voltages. NBTI reduces the lifetime of the circuit by degrading the performance. The effect of NBTI in standard 6T Static Random Access Memory (SRAM) cell can be reduced by replacing the inverter with NMOS only Schmitt trigger inverter. The simulation of proposed NMOS only Schmitt trigger based SRAM cell is carried out at 0.6 V in 45 nm gpdk technology in Cadence Virtuoso. The Static Noise Margin (SNM) of the proposed design with respect to standard 6T SRAM cell has doubled in write mode and has increased by 66.66 percent in read mode as seen in the simulation results obtained.
Identification of Autism Based on SVM-RFE and Stacked Sparse Auto-Encoder

In order to improve the classification accuracy of patients with autism based on the full Autism Brain Imaging Data Exchange dataset, a total of 501 subjects with autism and 553 subjects with typical control across 17 sites were involved in the study. Firstly, we applied the resting-state functional magnetic resonance imaging data to calculate the functional connectivity (FC) based on the automated anatomical labeling atlas with 116 brain regions. Secondly, we adopted the support vector machine-recursive feature elimination algorithm to select top 1000 features from the primitive FC features. Thirdly, we trained a stacked sparse auto-encoder with two hidden layers to extract the high-level latent and complicated features from the 1000 features. Finally, the optimal features obtained were fed into the softmax classifier. Experimental results demonstrate that the proposed classification algorithm is able to identify the autism with a state-of-the-art accuracy of 93.59% (sensitivity 92.52%, specificity 94.56%).
A Driver-Centric Carpooling: Optimal Route-Finding Model Using Heuristic Multi-Objective Search

Traffic congestion is the most crucial problem faced in the 21st century. To ease the severity of this problem, ‘Road-Space Rationing’ has been adopted by many countries. Carpooling service, also known as ride-sharing, is a by-product of ‘Road-Space Rationing’ feature. With intelligent carpooling service, people can share rides more elegantly by maximizing the seat occupancy rate. In this paper, we introduce an efficient carpooling system that can provide the optimal route for a driver. To find the optimal route we use the Non-Dominated Genetic Sorting Algorithm (NSGA). In case of multiple near-optimal results, we Figure out a single result by applying the proposed Crowding Distance Sorting Algorithm (CDSA). Finally, we show the comparison between each of the selected objectives and the optimal result. Our comparison shows that the optimal outcome has a minimum trade-off in respect of all individual objective best results.
A Hybrid Framework to Evaluate Breast Abnormality Using Infrared Thermal Images

Medical image assessment is an essential practice in most of the disease identification events. A recent imaging procedure, infrared thermal imaging, has attracted wide consumers due to its noninvasive nature, cost, and accuracy. This paper considers the inspection of breast malignancy. This paper presents a hybrid framework with a heuristic algorithm-driven preprocessing practice and a semi/fully automated postprocessing. The result of the proposed technique is also validated against other existing segmentation methods.
Predicting Students' Involvement in Blended Learning Environment

It is very important to know students' involvement in the classroom. Nonetheless, it becomes challenging for a teacher to predict the same in real-time, particularly when the classroom is large. Assuming blended learning platform in BYOD paradigm of teaching-learning process, we propose a method to systematically predict students' involvement in the classroom without using the privacy sensitive data. Initial observations indicate that it is possible to predict students' involvement from their device handling pattern and resource utilization while interacting with the devices.
Drowsy-DC: Data Center Power Management System

In a modern data center (DC), the large majority of costs arise from the energy consumption. The most popular technique used to mitigate this issue in a virtualized DC is the virtual machine (VM) consolidation. Although the latter may increase server utilization by about 5-10%, it is difficult to actually notice server loads greater than 50%. By analyzing the traces from our cloud provider partner, confirmed by previous research work, we have identified that some VMs have sporadic periods of data computation followed by large intervals of idleness. These VMs often hinder the consolidation system to further increase the energy efficiency of the DC. In this paper we propose a novel DC power management system called Drowsy-DC, which is able to identify the aforementioned VMs that have similar periods of idleness. Further, these VMs are colocated on the same server so that their idle periods are exploited to put the server to a low power mode (suspend to RAM) until some data computation is required. By introducing a negligible overhead, our system is able to improve any VM consolidation system (up to 81% for OpenStack Neat).
Joint Power Allocation and Beamforming in Heterogeneous Cloud Radio Access Networks

In this paper, resource allocation for heterogeneous cloud radio access network (HC-RAN) network is investigated. By maximizing the sum rate of all the users in HC-RAN under the power constraints of macro base station (MBS) and remote radio heads (RRHs), the MBS beamforming vectors and RRHs transmit power are jointly optimized. To solve the above nonlinear and non convex programming problem, we propose an alternate iterative algorithm. In the algorithm, the original problem is first divided into two subproblems, one is the power allocation problem for RRHs and the other is the beamforming problem for MBS. For the former problem, the power allocation problem is transformed into equivalent difference-of-convex (D.C.) problem and an iterative power allocation algorithm is proposed. For the latter problem, we derive the structure of the optimal beamforming vectors according to the Lagrangian function, and propose a novel iterative beamforming algorithm. Finally, an alternate iterative power allocation and beamforming algorithm is proposed. The experimental results show that benefit from the joint power and beamforming optimization, the proposed algorithm is effective in improving the sum rate of HC-RAN.
A Power and Static Noise Margin Analysis of different SRAM cells at 180nm Technology

SRAM cell stability and leakage power consumption is one of the primary concern in nanotechnology era. Threshold voltage (Vth) of MOS changes with temperature and thereby leakage power of MOS. We analyze static noise margin (SNM) and leakage power at different Temperature and Power supply for different SRAM cells 6T, 8T, 9T and 10T. Simulations are done on cadence virtuoso tool. The result shows that 9T SRAM cell have better performance, power consumption and static noise margin compared to other three structures. Results shows that 9T SRAM cell has at least 20% less leakage Power with respect to 10T SRAM cell and have 150% increased static noise margin over conventional 6T SRAM cell.