Miracle-dz/UniFAR-FTUs huggingface dataset:
High Reliability Solution of 2.5D Package Technologies

In recent years, high performance computing (HPC) and networking products have been integrating more functions into the larger die size with high density transistor. For High-end Router/ Switch product requirement, its wafer Si node development trend is from 28nm to 5nm but with low wafer yield and high wafer cost at large advanced Si node SoC die. Semiconductor industry also is facing the limit of Moore’s Law. The Heterogeneous/ homogeneous integration and chiplet idea come out and be popular in recently years due to separated small die can increase gross die and higher wafer yield for cost saving. Heterogeneous/ homogeneous integration package is a solution to support multiple die package requirements and get higher system performance in parallel with Moore’s Law. This Package is like 3D IC platform for heterogeneous/ homogeneous integration between two chips, it provides high computing speed and high bandwidth memory for massive data transmission. It is important for HPC product that networking server and AI training need lower latency. HPC package evaluation is a key item for foundry house and OSAT. 2.5D (Chip on Wafer on Substrate, CoWoS) package with a through silicon via (TSV) interposer already production in market. 2.5D advantages are finest L/S, more bump density, bandwidth density, and interposer provide better chip module warpage control, no CTE mismatch cause Underfill crack like FOMCM (Fan Out Muti-Chip Module) and more mature process for multiple dies integration solution. SPIL have develop 2.5D structure with molding chip module for years to provide high end product protection and reliability for advanced die function and whole package. Chip module warpage is a key and define by chips module layout design. If two small chips place different side of ASIC die, 2.5D package will have unbalance chip module stress condition due to larger mold area than 2 small chips place in same side of ASIC. There might be risk like C4 bump non-wetting or bridge bump, TSI crack and bad package coplanarity control. These issue need extra time for material and recipe fine-tune or need to change ASIC design. In this paper, we use filler die as solution for unbalance chip module 2.5D by increasing Si area in multichip module to keep original design for electric path. The simulation result shows more filler dies has better chip module warpage, low risk for package warpage and TSI stress because Si filler die help whole wafer warpage control in Mechanical properties result. In real sample result, 2.5D with filler dies structure shows all data in spec, better chip module warpage control and package coplanarity Cpk than no filler die device. Crosssection image of real structure show no not-wetting bump, bridge bump and TSI crack issue during process. This paper demonstrate 2.5D with filler die solution to be high reliability solution for heterogeneous/ homogeneous integration solution.
Image Classification and Recognition of Medicinal Plants Based on Convolutional Neural Network

With the development of computer vision, plant identification systems have helped botanists recognize and identify plant species more quickly. We usually differentiate plants of different species based on morphology, texture, color, and other characteristics. These features can exhibit high degrees of similarity, and the differences between species can be minor. This paper proposes a classification and recognition method for medicinal plants based on the ResNet34 convolutional neural network, focusing on the image recognition of medicinal grassland plants. First, we use targeted and effective classifications for species tasks based on the Plant-Net. Then, weakly supervised attention functions are used to learn to locate and recognize more discriminative features and improve the efficiency and accuracy of recognition. Moreover, data augmentation methods combined with label-smooth loss functions are used to improve the generalization of model by introducing more data variances. A comprehensive experiment on a grassland plant dataset demonstrates the effectiveness and superiority of our method over other state-of-the-art methods. The total number of images from the dataset is 5000, and the accuracy rate reaches 96.8%.
Steady and Mutation Flow Field Analysis of Butterfly Valve Based on FLUENT

Using software FLUENT,steady analysis of flow field were carried out for large-diameter center butterfly valve.On the basis of steady results,cons idering the case of sudden increase in speed,it underwent the unsteady analyses of the corresponding flow field.The results showed that the sudden increase in speed cause instant promotion of the field pressure and velocity.And the peaks of pressure and velocity were bigger than that in the case of high speed steady state.And then the pressure and velocity gradually decreased to reach basic stability. On the contrary,when the inlet velocity suddenly returned to low speed,the flow field pressure and velocity instantaneously reduced.The valleys of pressure and velocity were lower than that case of low velocity steady state.Then the flow field pressure and velocity gradually increased to reach the basic stability.Otherwise,the opening size had an effect on mutation flow field parameters as well as the speed to restore balance of butterfly valve.
QoS-Aware Energy Efficient Power Allocation for OFDM Based DF Relay Networks

This paper investigates power allocation strategies in an orthogonal frequency division multiplexing (OFDM) based decode-and-forward (DF) relay network consisting of one source, one relay and one destination. Different from conventional power allocation schemes which mainly focus on total throughput optimization, we aim at maximizing the energy efficiency with quality of service (QoS) guarantees. By simplifying a three-node relay network to a two-node relay network with equivalent channel gain, an effective energy efficient power allocation scheme with closed form solution based on water filling method is proposed to address the problem. Numerical results are shown to demonstrate the effectiveness of our proposed power allocation scheme.
ARPD: Anchor-free Rotation-aware People Detection using Topview Fisheye Camera

People detection in top-view, fish-eye images is challenging as people in fish-eye images often appear in arbitrary directions and are distorted differently. Due to this unique radial geometry, axis-aligned people detectors often work poorly on fish-eye frames. Recent works account for this variability by modifying existing anchor-based detectors or relying on complex pre/post-processing. Anchor-based methods spread a set of pre-defined bounding boxes on the input image, most of which are invalid. In addition to being inefficient, this approach could lead to a significant imbalance between the positive and negative anchor boxes. In this work, we propose ARPD, a single-stage anchor-free fully convolutional network to detect arbitrarily rotated people in fish-eye images. Our network uses keypoint estimation to find the center point of each object and regress the object’s other properties directly. To capture the various orientation of people in fish-eye cameras, in addition to the center and size, ARPD also predicts the angle of each bounding box. We also propose a periodic loss function that accounts for angle periodicity and relieves the difficulty of learning small-angle oscillations. Experimental results show that our method competes favorably with state-of-the-art algorithms while running significantly faster.
Clock Drift Analysis In a Multihost System

The demand for new software-based features in automotive systems is increasing day by day. This leads to the increasing complexity of embedded systems. A distributed real-time system consists of a set of nodes that are interconnected by a local area network and communicate via message passing only. The order of the events can be interpreted by time. Clocks are typically local counters that are increased with a predefined rate according to real-time. Clock synchronization algorithms ensure that any two clocks in the system read the same value at about the same point in realtime. This is achieved by clock synchronization that changes the current values of the clocks, the clocks' rate, or both. Synchronization ensures that operations occur in the logically correct order. Clock drift happens when the local clock gets calculated incorrectly, causing it to slowly drift in real-time. Larger deviations and increased drift are observed while the system is running without any synchronization. This paper is aimed to present a process to find the root cause of a clock drift problem.
Semi-Autonomous Cooperative Tasks in a Multi-Arm Robotic Surgical Domain

This paper addresses collaborative multi-arm task execution in a robot-aided surgical domain. The proposed approach aims to assist the surgeon by recognizing their intent and autonomously controlling some of the robotic arms. We lean on Learning from Demonstration paradigm, where knowledge is gained by observing humans performing a task. Example cooperative movements are gained in a leader/follower scenario with an operator controlling both arms. Example leader arm movements are encoded in a hierarchical database, which consists of a binary tree enhanced with weighted directed graphs. It is used for online leader movement recognition and prediction. Example follower arm movements are encoded as a set of Dynamic Movement Primitives, which are used for online synthesis of appropriate follower movements, based on recognition and prediction of leader movements. The proposed approach is evaluated on a dual arm robotic surgical system, showing that the operator can perform the cooperative task by only controlling the leader arm. The proposed database structure enables recognition and prediction fast enough for real-time task execution, and the novel approach of speed adaptation ensures both arms are in sync regardless of the operators speed of execution.
Influence of BGA Design Parameters on Solder Joint Reliability

Autonomous vehicles are predicted to constitute 90% of all U.S. vehicle sales by the year 2050. This will greatly expand the use of semiconductor devices in an already growing market. Electronic components in automotive environments are subjected to harsh conditions: including large temperature variations, mechanical shock, vibration and other wear-out failures. Of the different types of failures, second level interconnects are most vulnerable to fatigue failures. These solder interconnects are particularly sensitive to thermal and mechanical loading due to Coefficient of Thermal Expansion (CTE) mismatch with printed circuit boards (PCBs). To counter this effect, a robust package design approach is necessary across all material layers that can survive the required field life. Accelerated tests are run to assess the package performances and if the package survives a certain target cycles in -40C to 125C environment, it is deemed qualified to meet the customer requirements. This is at least a 5x harsher than the performance requirements for mobile or compute type applications. The cycle of design iterations and qualification tests take several months from start to finish which is very inefficient and doesn’t nearly keep up with the customer needs. A robust and accurate life prediction model [1] is an effective engineering tool to improve designs and accurately predict fatigue life of the package within a short time without need to perform expensive and time-consuming reliability testing. However, the life prediction accuracy of finite element simulation models is dependent on modeling assumptions, geometrical complexity, creep constitutive model of solder and fatigue life model calculation [2–9]. In the current study we first build a simulation framework that can sufficiently predict the semiconductor package life with varying design and material parameters (see Figure 1 and 2). This model is fine-tuned for material behavior, predicted life and test vehicle data. Following that, we used the model to understand the influence of various design parameters to the predicted life. This provided the first level screening of identifying the right knobs that will be critical in enhancing package life. The information was very critical to guide the design of next generation packages as well as modify some of the existing packages that are not meeting the customer requirements. This paper details the parametric study and key parameters for improved solder joint reliability in harsh automotive environments.
mmWave MIMO: An LAMP-Based Network with Deep Residual Learning Combining the Prior Channel Information for Beamspace Sparse Channel Estimation

In mmWave MIMO systems relying on lens antenna arrays, beamspace sparse channel estimation is one of the key technologies to improve system performance. One new beamspace sparse channel estimation scheme with deep residual learning is proposed, which introduces the channel prior information following the Gaussian mixture distribution and the residual processing unit. Firstly, the prior-aided GM-LAMP network is employed to obtain a preliminary estimation; then the deep residual learning network is designed to reduce the impact of channel noise. Compared with existing methods, the proposed algorithm can improve estimation accuracy with the lower computational complexity.
Symbol Error Rate of M-ary PSK with I/Q Imbalances Over an Impulsive Noise Channel

In this paper, we analyze the effect of the in-phase/quadrature imbalances on the error performance of M-ary phase shift keying (PSK) systems in the presence of impulsive noise. We adopt Middleton class A model as an impulsive noise model and derive symbol error rate (SER) and SER floor expressions of M-ary PSK over an impulsive noise channel. Through computer simulations, we validate the theoretical results.
CoLoss-GAN: Collision-Free Human Trajectory Generation with a Collision Loss and GAN

Humans navigate through crowds in a socially compliant manner and usually try to avoid close contact to strangers. An important skill that enables humans to achieve this is the accurate prediction of human movement. This skill is also desirable for autonomous mobile platforms for safe and socially conforming actions. Current research explores the challenging stochastic nature of a human's future trajectory using deep learning. However, it neglects the property that people move predominantly collision-free. We address this problem and introduce in this paper the CoLoss-GAN, a generative adversarial network (GAN) that encodes historical trajectories and generatively decodes future socially conforming trajectories. We propose an optimized state refinement and an effective pooling module, which learn feature representations that reliably encode human-to-human interactions and current human intentions. We encourage collision-freeness by formulating a crucial collision loss (CoLoss) that penalizes colliding predicted trajectories. Our experiments demonstrate performance on challenging real-world benchmarks, outperforming several state-of-the-art deterministic and generative baselines, in terms of accuracy and collision-freeness, with more plausible and nonlinear trajectory predictions.
ROBCO Drone - Service Robot for Transport and Delivery of Grocery Products

in this paper a service robot-drone, which is intended for transport and delivery of the finished production from animal goods, is presented. The robot will operate in a dynamic environment in grocery stores and their warehouses. The purpose of the robot is to relieve the people by performing transport tasks in the grocery stores themselves and their warehouses. The mechanical, hardware and software systems of the robot is described as well as an innovative and modern design is proposed. Special attention is paid on development of specialized mechanical system that allows the robot to have a lifting mechanism. Experiment on the accuracy of movement of the robot have been performed and obtained result are presented.
Understanding and exploiting object interaction landscapes

Interactions play a key role in understanding objects and scenes for both virtual and real-world agents. We introduce a new general representation for proximal interactions among physical objects that is agnostic to the type of objects or interaction involved. The representation is based on tracking particles on one of the participating objects and then observing them with sensors appropriately placed in the interaction volume or on the interaction surfaces. We show how to factorize these interaction descriptors and project them into a particular participating object so as to obtain a new functional descriptor for that object, its interaction landscape, capturing its observed use in a spatiotemporal framework. Interaction landscapes are independent of the particular interaction and capture subtle dynamic effects in how objects move and behave when in functional use. Our method relates objects based on their function, establishes correspondences between shapes based on functional key points and regions, and retrieves peer and partner objects with respect to an interaction.
Computer vision algorithms for retinal vessel detection and width change detection

Detection of width changes in blood vessels of the retina may be indicative of eye or systemic disease. However, fundus images taken at different times have different scales and are difficult to compare side by side. This research presents automated techniques for detection of vessel width change from two images acquired at different points in time. In order to detect vessel width change; vessels must first be identified in available images. This research starts by making numerous improvements to an existing vessel tracing algorithm. However, even the improved results exhibit too much variance, primarily attributable to the discrete nature of the tracing algorithm. Thus new methods for estimating vessels are explored to address the limitations of the vessel tracing algorithm. These methods are designed to provide smooth, continuous boundaries. Five ribbon-like objects are put into an active contour framework, all initialized using the results from tracing. These ribbons are shown to provide more repeatable vessel boundaries than tracing with an innovative technique named in this research as cross section snakes selected as best. In addition to estimating vessels from a single image, a technique is explored that uses information from multiple images acquired in a single sitting to estimate vessel boundaries in a single image with greater accuracy. This technique was subjectively evaluated by five people and was preferred at a rate that is double the sum of two other single image estimation techniques. In detecting change, two approaches are tried. One that alternately tests vessel boundaries in the other image before determining change, and one that uses a statistical hypothesis test as the basis for the determination of change. The first method is the better of the two based on three criteria. First, method 1 achieves a positive predictive power of 78% and method 2, 57%. Second, method 1 correctly identifies 15 out of 20 vessel segments as changed compared with only 7 in method 2. Finally, method 1 is shown to be superior at correctly identifying no change (i.e. less false positives) over method 2.
Depth Estimation Inside 3 D Maps based on eye-tracker

The measurement tool in the 3D map is the bridge between the map reader and the 3D map information. They can help map readers get depth, distance and angle information from 3D maps. Although many 3D map measurement tools have been designed, but little is known about their impact on map readers and verify the role of 3D measurement tools in eye tracker experiments. The authors first designed some new 3D map measurement tools to obtain the information in the 3D map, and then conducted an eye tracker experiment to verify the effectiveness of these 3D map measurement tools and the recognition mode of the experimental participants. This experiment includes an eye tracker experiment and a post-test interview. A total of 26 people participated in this experiment. The results of the experiment show the effectiveness and efficiency of the measurement tools in the 3D map. At the same time, this experiment also revealed some key points on how to improve the 3D map measurement tools.
Jurnal Statistika dan Aplikasinya Vol.5 No.2 December 2021

Poverty is a multidimensional problem, one of the dimensions related to poverty is the employment dimension. A person who works is not necessarily free from the problem of poverty. The phenomenon of people who have worked but are still classified as poor is referred to as working poor. The rising poverty conditions and the declining open unemployment rate in South Sumatra Province indicate a phenomenon of working poor. The causes of poverty can be approached from various aspects which can be in the form of regional level characteristics, community level characteristics, as well as household and individual level characteristics. Therefore, this study aims to determine the general description of the characteristics of the working poor, analyze individual level and district/city level variables that affect the working poor and their trends in South Sumatra Province in 2020 using binary logistic multilevel regression analysis. Individual level variables that affect the poverty status of workers in South Sumatra Province in 2020 are marital status variables, age, education level, area of residence, employment, and number of hours worked. Then, the district/city variable that has a significant effect is HDI.
State set estimation based MPC for LPV systems with input constraint

: This paper considers a state set estimation (SSE) based model predictive control (MPC) for linear parameter-varying (LPV) systems with input constraint. We estimate, at each time instant, a feasible set of all states which are consistent with system model, measurements and a priori information, rather than the state itself. By combining a state-feedback MPC and an SSE, we design an SSE-based MPC algorithm that stabilizes the closed-loop system. The proposed algorithm is solved by semi-deﬁnite program involving linear matrix inequalities. A numerical example is included to illustrate the performance of the proposed algorithm.
Response of Cropping System in China to Global Climatic Changes II. Effect of Cropping System in China to Global Climatic Changes

The effect of global climatic change and the characteristics of China’s agriculture was studied. The main problems of China’s cropping system in response to climatic change were discussed from the cropping plan, input in science and technology, protective tillage breed good varieties, and using water - saving culture of the point of view. Some available countermeasures and technological steps were recommended. Keyword: Cropping system; Climatic change; Responsible countermeasures
A Case Study on Biomechanical Analysis of Kneeling and Squatting Methods while Manual Lifting Using Motion Capture Analysis

Weight lifting techniques had been long discussed and debated between squatting and semisquatting (kneeling) techniques. Using motion analysis and force plates, the weight lifting tasks were measured for comparison. Participant was required to lift different weights of 5kg, 10kg and 15kg using squat and semisquat techniques. The tasks were randomized and the performance was measured using Cortex 7.0 and Mokka 6.0. Then kneeling technique was indeed a more efficient and safe posture as compare to the squatting technique that would expose an acute onset of spinal injury. The case study illustrated that kneeling technique was the better technique in protecting the spinal health from any chronic musculoskeletal disorders. Therefore, the study recommended that weight lifting training exercises to be promoted for kneeling postures and provide core muscle in strengthening exercises as an important intervention and training program at workplace.