Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulated Quantization, Real Power Savings

Reduced precision hardware-based matrix multiplication accelerators are commonly employed to reduce power consumption of neural network inference. Multiplier designs used in such accelerators possess an interesting property: When the same bit is 0 for two consecutive compute cycles, the multiplier consumes less power. In this paper we show that this effect can be used to reduce power consumption of neural networks by simulating low bit-width quantization on higher bit-width hardware. We show that simulating 4 bit quantization on 8 bit hardware can yield up to 17% relative reduction in power consumption on commonly used networks. Furthermore, we show that in this context, bit operations (BOPs) are a good proxy for power efficiency, and that learning mixed-precision configurations that target lower BOPs can achieve better trade-offs between accuracy and power efficiency.
On the Performance of DCF in Full Duplex WLANs with Hidden Terminals

Full Duplex (FD) technology is considered as one of the next big leap in the evolution of modern WLANs. Allowing a node to simultaneously transmit a data frame while in receive mode, can theoretically double the system throughput. However, several requirements must be fulfilled in order for FD operation to manifest. One obvious prerequisite is that the Medium Access Control (MAC) mechanism must allow two nodes to access the shared medium simultaneously. In modern WLANs the standard MAC layer mechanism is the Distributed Coordination Function (DCF), which is specifically designed to avoid such situations. FD communications may also take place when the physical placement of the communicating parts involves the existence of hidden terminals which, in standard Half Duplex (HD) communications, imposes a significant problem. This paper investigates the performance of the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, which constitutes the basis of the DCF mechanism, in FD WLANs with hidden terminals, and compares it with the standard HD case. Our analysis is based on performance modelling. Results indicate that, under the DCF regime, FD technology exhibits an exiguous performance improvement, in terms of saturation throughput, when compared with its half duplex counterpart.
Addressable WAT Test of Domestic Semitronix Tester

Traditional WAT test is executed on a series of testkeys which is located in the scribe line of wafers. A transparent disadvantage of this kind of test is large amount of test time and limited number of DUT(device under test). So a different kind of WAT test is urgently needed, which combines the advantages of testing efficiency and accuracy. This paper will introduce a novel WAT test, it adopts parallel test based on specially designed testkey structure. What's more, domestic Semitronix tester is able to conduct this test, huge amount of time is saved, and testing accuracy is guaranteed.
Identifying Prerequisite Relations Between Concepts In Wikipedia

Today, the Internet is flooded with a lot of learning resources, which are provided by different people. Because the relationship between these learning resources is unclear, it is difficult for instructors and students to use these learning resources for curriculum design and learning path planning. The order of learning resources is usually determined by the core knowledge concepts addressed in each resource. Therefore, identifying the prerequisite relations between concepts will be the key to solving the above problems. In this article, we take Wikipedia as an example and propose a new method for identifying concept prerequisite relations. We define five groups of features for concept pairs and predict whether there is a prerequisite relations between two concepts. Experimental results show that the performance of the proposed method exceeds the existing baselines.
QoE-aware Data Caching Optimization in Edge Computing Environment

Edge computing provides users with computing resources and storage capacity by deploying edge servers around users. Existing edge data caching researches mainly focused on minimizing users’ access latency and maximizing service providers’ revenue. In this paper, this QEDC problem is addressed from the service provider’s point of view. Firstly, this QoE-oriented Edge Data Caching(QEDC) problem is modeled as a combinatorial optimization problem. Users’ QoE achieves maximization while satisfying a constraint for service providers’ cache budget, and its NP-hardness is proved. Secondly, an edge data fusion method is introduced which is more suitable for real environment and the calculation method of data popularity. In addition, an approach named QEDC-IP is provided based on Integer Programming to tackle this QEDC problem. Finally, a large number of experiments are conducted by using a mixture of real-world datasets, and the results verify the performance of QEDC-IP approach by comparing it with five representative approaches in data caching.
Link State Estimation Under Cyber-Physical Attacks: Theory and Algorithms

Effective defense against cyber-physical attacks in power grids requires accurate damage assessment. While some solutions have been proposed to recover the line states within the attacked area, existing solutions are limited by the assumption of a connected post-attack grid and the lack of verifiable performance guarantees. To fill this gap, we study the recovery of line states under a cyber-physical attack that disconnects lines while blocking information from the attacked area. In contrast to existing solutions assuming a connected post-attack grid or known post-attack power injections, we consider a more challenging scenario where the attack may partition the grid into islands, which causes unknown changes in power injections. To address this problem, under the DC model we (i) propose a linear programming-based algorithm to recover the line states within the attacked area under unknown post-attack power injections, (ii) characterize the accuracy of the proposed recovery algorithm under certain conditions, and (iii) develop efficient algorithms to verify the recovery results using observable information. Then, we extend the DC-based algorithms to AC model. Our numerical evaluations demonstrate that the proposed recovery algorithm is highly accurate in localizing failed lines, most of which can be successfully verified by the proposed verification algorithms.
Decide the Next Pitch: A Pitch Prediction Model Using Attention-Based LSTM

Information collection and analysis have played a very important role in high-level baseball competitions. Knowing opponent’s possible strategies or weakness can help own team plan adequate countermeasures. The purpose of this study is to explore how artificial intelligence technology can be applied to this domain. This study focuses on the pitching events in baseball. The goal is to predict the pitch types that a pitcher may throw in the next pitch according to the situation on the field. To achieve this, we mine discriminative features from baseball statistics and propose a stacked long-term and short-term memory model (LSTM) with attention mechanism. Experimental data come from the pitching data of 201 pitchers in Major League Baseball from 2016 to 2021. By collecting information of pitchers’ pitching statistics and on-field situations, results show that the average accuracy rate reaches 76.7%, outperforming conventional machine learning prediction models.
Video-Analysis of “High-Risk” on-field Movements Biomechanics in Amateur Male Futsal Players: Preliminary Findings

BACKGROUND: Landings, decelerations, and changes of direction are key movements in Futsal matches. These playing situations can be associated with non-contact lower limb injuries, ranging from an ankle sprain to ACL tear, due to biomechanical risk factors, such as dynamic knee valgus. Evidence indicates that the identification of sport-specific Biomechanics is a necessary step to investigate the players' movement quality and implement injuries prevention programs. AIM: The main goal of the present study is to propose an affordable video-analysis methodology to identify biomechanical variables, useful to understand the biomechanical effects of a 4-week multifaceted training program intervention, which looked for modifying injury risk in sport-specific tasks. MATERIALS AND METHODS: In RCT study, 44 amateur Futsal players were equally divided into two groups: Intervention Group (IG, N=22), and Control Group (CG, N=22). The IG attended a 4-week intervention program, 2 times per week, with each session lasting up to 1 hour. An on-the field setting was used to deliver a three-task test to each group: Single-Leg Hop (SLH), Deceleration (DEC), and 90° Changing of Directions (COD). For each participant, bilaterally, pre-to-post intervention, we video-analyzed: seven variables for the SLH and DEC frames, including four frontal stability angles (trunk, hip, knee, and ankle), two sagittal flexion angles (hip and knee), the body Centre of Mass difference (ΔCM) in centimetres (cm) between landing and standing position, and the body mean acceleration on the x-axis during the deceleration. In addition, for the final-foot contact (FFC) frame during COD, we considered three frontal stability angles (trunk, knee, foot), two sagittal flexion angles (hip, knee) and the distance in cm between the FFC and the body CM on the y-axis. RESULTS: IG compared to the CG at T1 showed a significant improved frontal stability, in COD Knee angle, and foot angle; and a significant improved Hip and Knee co-flexion, in landing Hip Flexion, in landing Knee Flexion, and in COD Knee Flexion. IG, pre-to-post intervention, showed a significant improved frontal stability in landing Knee angle, in DEC Knee angle; and a significant improved Hip and Knee co-flexion, in SLH ΔCM, in DEC both Hip and Knee Flexion angles, in COD Knee Flexion angles. CONCLUSIONS: These results provide coaches with an affordable and feasible video-analysis methodology based on an on-the field setting to screen biomechanical variables in futsal-specific movements.
Demand based Proportional Fairness Scheduling for 5G eMBB Services

Millimeter wave (mmWave) based 5G networks have nowadays attracted researcher from both industry and academia. Because, mmWave can enable dominant 5G services such as ultra-high definition video streaming and virtual reality, by providing them with their requirements, e.g., high data rate and low latency time. But, mmWave channel dynamically changes, which effects on overall performance of 5G network. Hence, efficient radio resource management schemes are needed in medium access control (MAC) layer to wisely distribute mmWave band resources among user equipment (UEs). In this paper, we design a high performance MAC scheduling scheme by improving standard proportional fairness (SPF) scheduling algorithm. In which, UEs demands are considered besides their channel condition. The proposed demand based proportional fairness (D-PF) scheme firstly gives higher priority to UEs with lower data rates requirements and better channel condition, hence higher UEs satisfaction level can be obtained faster. Then, it prioritizes them only based on their channel qualities if their required rates are achieved. Through simulation, we prove that the proposed scheme outperforms the conventional round robin (RR) and SPF algorithms in terms of system throughput, where an increase of more than 300 Mbps can be achieved, with maintaining same fairness between UEs. Meanwhile, D-PF scheme provides better throughput and satisfaction for UEs. For example, it can satisfy 95% of network UEs by providing them with their required rates, while only 61% and 62% of UEs can be satisfied if RR or SPF is used, respectively.
State of the Art Metal Deposition System for Advanced UBM, RDL and Fan-Out Wafer Level Packaging

As we enter the fourth wave of computing, advanced packaging becomes a key enabler of Moore's law scaling. Two of the key building blocks: 1D Under Bump Metallization and 2D Re-Distribution Layer (RDL) form critical interconnects in Fan-Out Wafer Level Packaging (FOWLP) which deliver benefits in power, performance, area, cost and time to market (PPACt). The use of polymeric materials in FOWLP give rise to several challenges for PVD, such as low temperature processing, warpage control and contact resistance. This work presents a state-of-the-art multi-chamber PVD platform capable of achieving >45% contact resistance reduction, and >30% productivity improvement over legacy PVD solutions without exceeding thermal budgets required.
A Novel Asymmetric Multilevel Inverter with Reduced Components and Lower Source Variety

In this paper, a novel multilevel inverter with fewer components is described. This inverter can synthesize 25 output voltage levels with 6 DC sources with a variety of two. The magnitude ratio of DC sources is 1:1:4:4:1:1. It has a higher level by switch ratio as compared to similar inverters. A detailed operating principle, voltage stress analysis, and switching strategy have been discussed. The inverter provides the minimum total output voltage harmonic distortion without filters. An detailed simulation analysis in the MATLAB / Simulink platform has been used to confirm the effectiveness and advantages of the suggested inverter. Different load circumstances, including resistive (R), resistive-inductive (R-L), inductive (L), and dynamic load, have been applied to the inverter. It observes that the inverter performs well for various types of loads.
Quality of Measurement Information in the Context of Sports Applications

Under the assumption that information provided by measurements may be usefully exploited to improve sport performances, this paper addresses the problem of defining and assessing the quality of measurement information in the context of sports applications. By using some criteria organized, according to semiotics, into syntactic, semantic, and pragmatic layers, a previously published framework is adapted to interpret measurement information acquired for sports applications. The framework is illustrated, and a case study is presented to introduce its application.
Spatiotemporal distribution of cage and raft aquaculture in China's offshore waters using object-oriented random forest classifier

Cage and raft aquaculture (CRA) in China's offshore waters has been spreading in this decade. Exploring spatiotemporal distribution of CRA can provide basic data for predicting aquaculture yield, controlling the effect of CRA on sea environment, and optimizing CRA spatial arrangement. Supporting by Google Earth Engine (GEE) and ArcGIS platforms, and taking China's coastal areas as study area, this study adopted object-oriented random forest (OORF) classifier for detecting spatiotemporal distribution of CRA from remote sensing big data of Sentinel-2 and Sentinel-1 in 2016, 2018 and 2021. Firstly, the study area was divided into a number of computational regions, and principal component analysis was used to generate single-band images for segmentation using simple non-iterative clustering (SNIC) algorithm. Secondly, the selected important features of spectrum, index, shape and texture were computed and input into OORF classifier to detect CRA in GEE. Finally, spatiotemporal distribution of CRA was analyzed by spatial statistical analysis in ArcMap. The results show: 1) OORF is an effective classifier for acquiring CRA in GEE with overall accuracies in three years all around 0.9; 2) From 2016 to 2021, the total area of CRA has increased 14.86% and the gravity center of CF A moves toward the northeast.
Vocal cues of disordered voices: an overview

The purpose of the article is to give an overview of vocal cues of disordered voices and propose a classificatory framework. Generally speaking, vocal cues of disordered voices are numeric descriptors or measurements that are clinically relevant and that summarize properties of speech signals or other signals that are obtained non-invasively, and which report on a speaker's phonation in general and voice in particular. One known difficulty is the existence of a large variety of published experimental procedures and data. The diversity is indeed impressive with regard to the vocal pathologies or handicaps that have been studied, the vocal symptoms that have been described, the tasks speakers have been asked to carry out and the linguistic, paralinguistic and extralinguistic performances that have been examined, the sensors that have been used and the speech material that has been recorded, as well as the non-acoustic levels of description acoustic cues have been correlated with. As a consequence, one may experience difficulties distilling general rules or comparing results obtained in different frameworks.
Investigation of the attitude and competency toward the use of technology in a cohort of sport science students

The present research was based on a The present research was based on a nonexperimental descriptive design with cross-sectional approach and conducted through a online survey specifically designed after a structured review of the literature. The purpose of this study was to investigate the attitude and competency toward the use of technology in a cohort of sport science students. Three different language versions of the survey were developed on the Google online survey platform (Google Form) and the links were sent by email to the sport science students of three universities in Italy, Austria, and Lithuania, respectively. The questionnaire was composed by three matrix tables questions, two single select multiple choice questions, a multiple selects multiple choice question and a choice matrix question, which items investigated which digital tools are used most and their usefulness, the users' awareness level of the impact of technology on their professional work and their perceived level of proficiency. Data were collected by 327 respondents. A cluster analysis was provided by means of the k-modes algorithm, while to understand the optimal number of clusters the Elbow method has been used. The main findings of this study draw a profile of the surveyed sport science students as having a positive attitude toward technology, with a daily use mainly through mobile phone and laptop, perceiving a personal good level of competence to understand and operationally use applications and a diversified use related to their different professional tasks.
Heart Disease Diagnosis using Deep Learning

This paper deals with the design and implementation of an automated algorithm which will efficiently calculate Ejection Fraction of Left Ventricle (LV) of the heart from its MRI. For several years, cardiac Magnetic Resonance Imaging (MRI) has been used by medical professionals to treat patients with various cardiovascular ailments. This is because Magnetic Resonance Imaging is a great non-invasive medical imaging technique. Due to the variability of MRIs, it is very time consuming and strenuous for medical professionals to manually analyze them in order to diagnose and treat patients. This study deals with the automated segmentation of the LV in order to calculate ejection fraction which is in turn used to diagnose the patients.Images obtained from MRIs are generally in DICOM format, hence basic pre-processing techniques have been used to convert the DICOM images into PNG format. Then segmentation of Left Ventricle is performed, and required volumes are calculated which are utilized to compute ejection fraction. This algorithm will reduce the time taken to analyze MRIs by providing better accuracy and ease to specialists. To develop this automated algorithm, image processing along with deep learning (convolutional neural networks) has been used and an accuracy of over eighty-one percent has been achieved.
IoT Book Bot

In order to ease the process of library management many technologies have been adopted but most of them focus on inventory management. There has hardly been any progress of automation in the field of issuing and returning books to the library on time. In colleges and schools, hostellers often forget to timely return the issued books back to the library. To solve the above issue and to ensure timely submission of the issued books, this work develops a ‘Book-Bot’ which solves these complexities. The bot can commute from point A to point B, scan and verify QR Codes and Barcodes. The bot will have a certain payload capacity for carrying books. The QR code and Barcode scanning will be enabled by a Pi Camera, OpenCV and Raspberry Pi, thus making the exchange of books safe and secure. The odometry maneuvers of the bot will be controlled manually via a Blynk App. This paper focuses on how human intervention can be reduced and automates the issue part of library management system with the help of a bot.
Multi-Objective Robust Workflow Offloading in Edge-to-Cloud Continuum

Workflow offloading in the edge-to-cloud continuum copes with an extended calculation network among edge devices and cloud platforms. With the growing significance of edge and cloud technologies, workflow offloading among these environments has been investigated in recent years. However, the dynamics of offloading optimization objectives, i.e., latency, resource utilization rate, and energy consumption among the edge and cloud sides, have hardly been researched. Consequently, the Quality of Service(QoS) and offloading performance also experience uncertain deviation. In this work, we propose a multi-objective robust offloading algorithm to address this issue, dealing with dynamics and multi-objective optimization. The workflow request model in this work is modeled as Directed Acyclic Graph(DAG). An LSTM-based sequence-to-sequence neural network learns the offloading policy. We then conduct comprehensive implementations to validate the robustness of our algorithm. As a result, our algorithm achieves better offloading performance regarding each objective and faster adaptation to newly changed environments than fine-tuned typical single-objective RL-based offloading methods.
A Five-Level Inverter with Multiple DC Sources for Grid-Connected Systems

In this paper, a single phase five-level inverter configuration is proposed using two independent DC sources at the converter’s input side. The proposed inverter can provide real power to the grid with a reduced number of switches, which results in recused switching loss and simplifies the switching control technique. The proposed inverter is helpful where multiple dc-link voltages are available. The operation principle of the designed five-level inverter, design guidelines and modulation strategies are elaborated in detail. In the end, simulation waveforms and experimental results illustrate the desired performance and viability of the given multi-level inverter.
Calculating improved bounds and approximations for sequential testing procedures

Recently developed product type bounds are utilized for calculating improvements of the expected stopping time, the variance of the stopping time and the power of various sequential testing procedures. These are presented for testing the mean of a normal distribution, but the results and techniques apply more generally. These bounds have been easily calculated to 5th order by taking advantage of the dependence structure. It should be noted that these improvements are most useful when the distribution for the stopping time has its probability mass shifted towards later stopping times. Testing procedures that are developed with an early stopping time are easily handled with lower order bounds such as 3rd order, which are faster to calculate. This will be demonstrated