Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fast and Light Weight Deep Convolution Neural Network Model for Cancer Disease Identification in Human Lung(s)

In the proposed work, a convolution neural network (CNN) based model has been used to identify the cancer disease in human lung(s). Moreover, this approach identifies the single or multi-module in lungs by analyzing the Computer Tomography (CT) scan. For the purpose of the experiment, publicly available dataset named as Early Lung Cancer Action Program (ELCAP) has been used. Moreover, the performance of proposed CNN model has been compared with traditional machine learning approaches i.e. support vector machine, k-NN, Decision Tree, Random Forest, etc under various parameters i.e. accuracy, precision, recall, Cohen Kappa. The performance of proposed model is also compared with famous CNN models i.e. VGG16, Inception V3 in terms of accuracy, storage space and inference time. The experimental results show the efficacy of proposed algorithms over traditional machine learning and pre-trained models by achieving the accuracy of 99.5%
Optimal Scheduling for Emptying a Wireless Network: Solution Characterization, Applications, Including Deadline Constraints

Link scheduling, i.e., which links should transmit together and for how long, has been and remains a cornerstone optimization problem in wireless networking. In minimum-time scheduling, the task is to minimize the amount of time before emptying the data demand residing at the source nodes. We derive a complete structural characterization of the solution that unifies and significantly extends the known results. First, we approach link scheduling with a general system model without restrictions on the shape of the achievable rate region. Then, we give and prove a solution characterization of optimality that is conceptually simple yet powerful. We demonstrate several applications of this characterization for analysis of optimality and problem tractability. Next, we consider a significant extension by including deadline constraints, under which optimal scheduling becomes much more complex. Yet, we show how our formulation yields a solution description for that problem as well.
Millimeter-Wave Printed-RGW Hybrid Coupler With Symmetrical Square Feed

In this letter, a new hybrid coupler with symmetrical square feed using printed ridge gap waveguide (RGW) technology is introduced and investigated. This structure can find important applications in beamforming network designs. The proposed coupler has a compact size of <inline-formula> <tex-math notation="LaTeX">$1.12\lambda _{g} \times 1.12\lambda _{g} \times 0.14\lambda _{g}$ </tex-math></inline-formula> and shows promising results at millimeter-wave <italic>Ka</italic>-band around 30 GHz, especially in terms of amplitude balance bandwidth and loss. The simulated results are compared with the measured data and a good agreement is achieved.
Forecasting Residential Monthly Electricity Consumption using Smart Meter Data

If residential customers had an estimation of their monthly total electricity consumption, they could manage their electricity use more efficiently. The increasing deployment of residential advanced metering infrastructure (AMI), which measures and communicates electricity consumption information, has made electricity consumption data widely available. In this paper, we investigate a methodology for estimating a customer's monthly total electricity consumption at different time horizons of the end of the monthly billing cycle, using a shrinking time window- based forecasting approach and different statistical and machine learning models. The proposed technique is evaluated on sample smart meter data from one thousand households with five different regression model forms and a baseline method. Experiments show significant improvement of the proposed regression-based techniques over the baseline method, with gradient boosting having the best performance.
Improving Temperature Sensor Accuracy in the IoT Trainer Kit by Linear Regression Method

The rapid development of Internet of Things (IoT) makes people in higher education must train their students to be better prepared in advancing and implementing that topic. Therefore, to improve student comprehension, we need tools such as trainer kit as a learning media. The IoT Trainer Kit has been created in Bandung State Polytechnic called the I-Kit which has many features. Inputs include DHT 11 temperature, humidity sensors and RFID. The controller used is Arduino Nano. Output for features in the trainer kit will appear on the web page. This I-Kit also has several communication devices such as Bluetooth, LoRa, ESP 8266 and SIM 800. However, before using the I-Kit as a learning medium, we must make the features in this trainer kit precision first. But the training kit is also not necessarily reliable, it must be tested and improved for the performance of its features. In this paper, we have improved accuracy of the DHT 11 temperature sensor on the I-Kit. Improvement was carried out using the linear regression method, to find out the correlation between The temperature of the thermometer with the temperature read on the sensor in the trainer's kit. Then this regression equation is entered into the temperature program in Arduino. When comparison is made between error and deviation standard before and after doing regression, the error rate is decrease byd 80.9 % from 7.3 become 1.39. The deviation standard which represents tolerance from sensor decrease 20% from 0.88 becomes 0.704.
Current-Sensor-Based Contact Stiffness Detection for Prosthetic Hands

When a prosthetic hand grasps an object, a proper grasping force should be exerted according to the stiffness properties of the grasped object so that damage caused by excessive force or slide caused by insufficient force is prevented. To implement stiffness detection and simultaneously to prevent errors and defects caused by the use of force sensors and the difficulties in the direct measurement of the deformation of the grasped object, a force sensor-less method of contact stiffness detection is proposed for a single degree of freedom prosthetic hand with a self-locking mechanism. In this method, force sensor signals are replaced with motor current signals. An analytic solution of contact stiffness is obtained based on a linearized grasping model near the initial contact position between the prosthetic finger and the grasped object. Contact stiffness is thus calculated by using the current of the driving motor and the angular displacement of the prosthetic hand rather than directly through the ratio of the force to the deformation. Simulation and experiment results demonstrate the effectiveness and feasibility of the proposed method.
Observing and Mitigating Micro-Burst Traffic in Data Center Networks

Micro-burst traffic is not uncommon in data centers. It can cause packet dropping, which may result in serious performance degradation (e.g., Incast problem). However, current approaches to mitigate micro-burst is usually ad-hoc and not based on a principled understanding of the underlying behaviors. On the other hand, traditional studies focus on traffic burstiness in a single flow, while micro-burst traffic in the data centers could occur with highly fan-in communication pattern, and its dynamic behavior is still unclear. To this end, in this paper, we re-examine the micro-burst traffic in typical data center scenarios. We find that the evolution of micro-burst is determined by both TCP’s self-clocking mechanism and congestion control algorithm. Besides, dynamic behaviors of micro-burst under various scenarios can all be described by the time derivative of queue length evolution.Our observations also implicate that conventional solutions like absorbing and pacing are ineffective to mitigate micro-burst traffic.Instead, senders need to rapidly respond to some explicit signals of the queue buildup caused by the micro-burst traffic rather than independently and ineffectually pacing themselves in isolation. Inspired by the findings and insights from experimental observations, we propose Micro-burst-Aware Transport Control Protocol (MATCP), which leverages characteristic behaviors of micro-burst traffic derived from the time derivative of the queue occupancy. MATCP can suppress the sharp queue length increment by over $2\times $ and reduce the tail query completion time by up to 84.4%.
Energy-Based Modeling and Swing Up Control Synthesis of an Inverted Pendulum System

The inverted pendulum system (IPS) on a cart is an example benchmark that is often used to demonstrate the modeling and control design frameworks of modern control systems. This paper describes the derivation of dynamic model and control law of an IPS using energy-based methods. Specifically, this paper first develops the IPS' dynamic model using Euler-Lagrange method and then derives a control law to stabilize it on the desired position using Lyapunov method. Simulation results are presented to show the capability of the designed control law to stabilize the IVP on its equilibrium point.
Data Integrity of Industrial Controllers via Multi-resolution Hierarchical Time Series Clustering

Industrial controllers operate vital machinery and must always ensure reliability. This can be compromised during cyber-physical security breaches where the controller data fails to report the true machine status. In this paper, we develop a solution to estimate true machine status based on (uncompromised) external sensor data. This is a hard problem to solve as industrial machine behavior is dictated by multiple hidden underlying states with no direct physical interpretation. However, identification of these states will simplify monitoring of controller data, since the data could be expected to be more regular within a particular state. Accordingly, we solve two problems: We first propose a new multi-resolution, hierarchical clustering algorithm to identify the machine states. Our algorithm is an extension of the Toeplitz Inverse Covariance-based Clustering (TICC) introduced by Hallac et al., which we call Hierarchical TICC. It involves two new hyperparameters, but removes the need to specify the number of clusters in advance. Next we show that within a known state, data taken from sensors can be leveraged to produce an effective classifier of controller data. Tested on data from a controlled manufacturing testbed, we show that Hierarchical TICC, when applied to current and vibration sensor data, was able to recover almost the same clusters (97% in agreement) as TICC, but gives the flexibility of not having to provide the number of clusters in advance. Subsequent classification within a cluster is also capable of learning data reported by the controllers of those machines with accuracy generally above 90%.
A Cloud-Fog-Edge Closed-Loop Feedback Security Risk Prediction Method

In recent years, with the opening of the “smart age” curtain, smart devices dominated by technologies such as robots, drones, and intelligent perception have gradually moved to the center of the Intelligent CPSS stage. However, the new security risks of the Intelligent CPSS have also become increasingly prominent. Especially in recent years, in Ukraine and Venezuela’s power attack incidents, a series of related attacks always occur simultaneously. This is a multi-task compound attack. This paper designs a set of Cloud-Fog-Edge closed-loop feedback security risk prediction strategies for multi-task compound attacks based on the offensive and defensive ideas of intelligent games, combining classified deep Boltzmann machines and Markov time-varying models. This strategy can be used for various types of power intelligent system terminals, and realizes security risk prediction with modularity, interoperability, open interfaces and compliance with open standards. Interoperability with other safety equipment can also be achieved through standardized interfaces to form system security protection capabilities to meet the actual needs of the industry Internet system. Experimental results show that the method is superior to the typical traditional method.
Hybrid Dynamic Voltage Balancing Technique for Series-Connected SiC MOSFETs

It is an effective way to achieve higher-voltage switches by series connection of individual semiconductors. However, the unequal dynamic voltage sharing is the main problem needs to be solved, especially for the wide band gap devices with quite high switching speed. This problem can be solved by using active gate control techniques, snubber circuits and active clamping circuits. In this paper, a hybrid method is proposed for balancing the dynamic voltages among series-connected SiC MOSFETs with both an active voltage clamp circuit and an optimized passive snubber. Circuit and parameter design of the driver will be proposed and experimental results will be given to verify the performance of the proposed circuit.
Scalable Deep Learning for Stress and Affect Detection on Resource-Constrained Devices

Psychological stress in human beings has been on a meteoric rise over the last few years. Chronic stress can have fatal consequences such as heart disease, cancer, suicide and so on. It is thus imperative to detect stress early on to prevent health risks. In this work, we discuss efficient and accurate stress and affect detection using scalable Deep Learning methods, that can be used to monitor stress real-time on resource-constrained devices such as low-cost wearables. By making inferences on-device, we solve the issues of high latency and lack of privacy which are prevalent in cloud-based computation. Using the concept of Early Stopping - Multiple Instance Learning, we build specialized models for stress and affect detection for 3 popular datasets in the domain, that have very low inference times but high accuracy. We introduce a metric ηcomp to measure the computational savings from the use of these models. On average, our models show an absolute increase of 10% in overall accuracy over the benchmarks, computational savings of 95.39%, and an 18x reduction in inference times on a Raspberry Pi 3 Model B. This allows for efficient and accurate real-time monitoring of stress on low-cost resource-constrained devices.
Sparse Feature Extraction for Activity Detection Using Low-Resolution IR Streams

In this paper, we propose an ultra-low-resolution infrared (IR) images based activity recognition method which is suitable for monitoring in elderly care-house and modern smart home. The focus is on the analysis of sequences of IR frames, including single subject doing daily activities. The pixels are considered as independent variables because of the lacking of spatial dependencies between pixels in the ultra-low resolution image. Therefore, our analysis is based on the temporal variation of the pixels in vectorised sequences of several IR frames, which results in a high dimensional feature space and an "n<<p" problem. Two different sparse analysis strategies are used and compared: Sparse Discriminant Analysis (SDA) and Sparse Principal Component Analysis (SPCA). The extracted sparse features are tested with four widely used classifiers: Support Vector Machines (SVM), Random Forests (RF), K-Nearest Neighbours (KNN) and Logistic Regression (LR). To prove the availability of the sparse features, we also compare the classification results of the noisy data based sparse features and non-sparse based features respectively. The comparison shows the superiority of sparse methods in terms of noise tolerance and accuracy.
Comparison of Neural Biomarker Assessment Methods for Early Detection of Alzheimer's Disease

With growing age, the cognitive ability degrades gradually as an aging factor. For a portion of people, the cognitive capability diminishes to a great extent, which will eventually result in Alzheimer's disease (AD). Mild cognitive impairment (MCI) is considered as an intermediate stage of AD. Diagnosis of AD patients at an early stage can reduce the chance of developing into a severe condition for cognition. This study aims to investigate the MCI assessment methods (statistical analysis and individual classification) for distinguishing the healthy control (HC) and MCI patients via functional near-infrared spectroscopy (fNIRS). This study evaluated ten digital biomarkers from three brain regions and three mental tasks ($N$-back, Stroop, and verbal fluency task). Among these three tasks, the $N$-back task achieved the best accuracy (76.67 %) with biomarker 2 (HbO mean from 5 to 25 sec) and 7 (HbO slope from 0 to peak value) in the middle prefrontal cortex by linear discriminant analysis (LDA). Additionally, the statistical analysis results also indicated that a significant difference ($p$-value < 0.05) existed between MCI and HC. However, the biomarkers, which achieved an individual classification accuracy more than 70%, could not be consistent with the biomarkers with $p$-value < 0.05. It reveals that statistical analysis technique still should be improved for diagnosing MCI individuals. Machine learning (LDA) can contribute as a tool by early prediction of AD via analyzing digital biomarkers using a non-invasive technique.
Energy-Efficient Joint Power Allocation and Energy Cooperation for Hybrid-Powered Comp-Enabled HetNet

Base station (BS) coordination with respect to data and energy cooperation has recently emerged as a potential solution for enhancing the energy efficiency (EE) of multi-cell multi-tier cellular network architecture. This work studies the EE maximization problem in a hybrid-powered (grid and renewable energy source) heterogeneous network (HetNet) where the data and energy are jointly coordinated among the BSs. We propose a combinatorial optimization algorithm to maximize the system EE with the aim to reduce grid power consumption (GPC). Due to the complexity of the formulation, Lagrange dual decomposition and metaheuristic method are incorporated to solve the problem. Furthermore, the non-fractional programming EE problem is solved using the Dinkelbach’s method which converges faster with a lower complexity. Simulation results show that cooperation among the BSs to share the channel information and energy reduces the GPC by nearly 20% and increases EE around 10% during harvested energy scarcity among the BSs.
Optimal Joint Channel Estimation and Data Detection for Massive SIMO Wireless Systems: A Polynomial Complexity Solution

By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can greatly increase spectral and energy efficiency over traditional MIMO systems. However, increasing the number of antennas at the base station (BS) makes the uplink joint channel estimation and data detection (JED) challenging in massive MIMO systems. In this paper, we consider the JED problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact Generalized Likelihood Ratio Test (GLRT) optimal JED algorithms with low expected complexity, for both constant-modulus and nonconstant-modulus constellations. We show that, despite the large number of unknown channel coefficients, the expected computational complexity of these algorithms is polynomial in channel coherence time (<inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>) and the number of receive antennas (<inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>), even when the number of receive antennas grows polynomially in the channel coherence time (<inline-formula> <tex-math notation="LaTeX">$N=O(T^{11})$ </tex-math></inline-formula> suffices to guarantee an expected computational complexity cubic in <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> and linear in <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>). Simulation results show that the GLRT-optimal JED algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity.
Online Content Popularity Prediction and Learning in Wireless Edge Caching

Caching popular contents in advance is an important technique to achieve low latency and reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process, optimal content placement caching probabilities are obtained to maximize the average success probability (ASP) for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. In this paper, we first propose two online prediction (OP) methods for forecasting CP viz., popularity prediction model (PPM) and Grassmannian prediction model (GPM), where the unconstrained coefficients for linear prediction are obtained by solving constrained non-negative least squares. To reduce the higher computational complexity per online round, two online learning (OL) approaches viz., weighted-follow-the-leader and weighted-follow-the-regularized-leader are proposed, inspired by the OP models. In OP, ASP difference (i.e, the gap between the ASP achieved by prediction and that by known content popularity) is bounded, while in OL, sub-linear MSE regret and linear ASP regret bounds are obtained. With MovieLens dataset, simulations verify that OP methods are better for MSE and ASP difference minimization, while the OL approaches perform well for the minimization of the MSE and ASP regrets.
High Step-Up DC-DC Push-Pull Resonant Based on low Cost half-wave Class-D Rectifier

This paper presents high step-up DC-DC push-pull resonant based on low cost half-wave class-D rectifier, it provides high of voltage for a 12 VDc from battery via isolated transformer and half-wave class-D rectifier. The main switches of push-pull and diode half-wave rectifier operated under can be achieve to ZCS condition. The benefit propose of this technique is used self-leakage inductance for ZCS of both power switch and leakage inductance used designing resonant tank secondary side. A prototype high voltage conversion ratio push-pull resonant converter was built and operated at a 250 kHz fixed switching frequency, a rate 360 VDc output voltage and efficiency eques to 92 % at the 190 W maximum output full load. The objective analysis the effect of parasitic junction capacitance of half-wave rectifier which has significant include of this purpose is to operating point of resonant tank and voltage. In this paper introduced verify implement prototype. The circuit proposed and analysis was verified by experimental result. From a result is presented in agreement with the theoretical analysis and experimental work.
How can Automated Machine Learning Help Business Data Science Teams?

Artificial intelligence and machine learning have attracted the attention of many commercial and non-profit organizations aiming to leverage advanced analytics, in order to provide a better service to their customers, increase their revenues through creating new or improving their existing internal processes, and better exploit their data by discovering complex hidden patterns. Such advanced solutions require data scientists with rare (and generally expensive) skill sets. Moreover, such solutions are often perceived as complex black boxes to decision-makers. Automated machine learning tools aim to reduce the expertise gap between the technical teams and stakeholders involved in business data science projects, by reducing the amount of time and specialized skills required to generate predictive models. We systematically benchmarked five automated machine learning tools against seven supervised learning problems of a business nature. Our results suggest that such tools, in fully automated mode, must be used cautiously, only where predictive models support low-impact decisions and do not need to be explainable, and only by data scientists capable to ensure that all phases of the data mining process have been performed adequately.