Printed circuit boards (PCBs) are the first stage in manufacturing any electronic product. The reliability of the electronic product depends on the PCB. The presence of manufacturing defects in PCBs might affect the performance of the PCB and thereby the reliability of the electronic products. In this paper, the various challenges faced in identifying manufacturing defects along with a review of various learning methods employed for defect detection are presented. We compare the various techniques available in the literature for further understanding of the accuracy of these techniques in defect detection.

In this paper we consider first price auction (FPA) model for 5G wireless networks. FPA is credible auction and yields higher expected revenue compared to second price auction (SPA) with budget constraints users. We first describe the model of wireless communications and then present the equilibrium analysis for FPA. We next consider budget constraint FPA and also analyze FPA with the number of user increasing to infinity. With large number of users, FPA tend to be truthful and credible auction. Finally, we study budget constrained repeated first price auctions using mean field fluid approximations. We also analyze the equilibrium behavior of the user with mean field approximations.

Printed Circuit Boards (PCBs) are the building blocks for all electronic products. Fabrication of a PCB involves various mechanical and chemical processes. As obtaining accuracy in the mechanical and chemical processes is very difficult, various defects/faults are formed during PCBs fabrication. These fabrication defects lead to performance degradation of electronic products. In this review, we describe various defects present in PCBs under the Through hole and SMD categories. To understand the frequency of occurrence and reason for the occurrence of defects in both manual and machine, PCB fabrication data was collected and analysed from April 2017 to July 2020 as a part of industry collaboration.

Citation is a measure that quantifies the impact of the researcher, research article and journal’s quality. Investigating the citation of articles and/or researchers is one of the important tasks in the research community. So, understanding and predicting citation patterns of research articles has become popular in scientific research fields. In this work, we give a machine learning approach to predict the citations of research articles using the keywords. We study the citation impact based on keywords motioned in the articles using the data set of publications which are published in the various physical review journals from 1985-2012. In this dataset, for each publication is allocated some PACS codes (keywords) by their authors which represent a sub-field of Physics. In this work, we are investigating the impact of PACS codes of article on article’s citation. We are performing our analysis on the first (sub-field of physics), second (sub area of sub-field of physics) and third level of PACS codes. We observed that compared to the first level, every pair of citation patterns of the second level is highly correlated. We also obtained a universal approximation curve for the third level that matches with the average value of the first level. This curve looks like a shifted and scaled version of the Gaussian function and is right skewed. We can also predict the citations based on the keywords by using this universal curve.

Infrastructure plays an important role in the development of any nation. Roads are very important for the movement of humans and goods. Good road infrastructure reduces travel time and cost. Poor road infrastructure can cause an increase in travel time, more consumption of fuel and lead to accidents due to difficulty in driving. All these factors will have a cascading effect on the economy as the prices of essential commodities are directly related to the easy of transportation. Hence, it is necessary to detect the distresses on roads like potholes and do regular repairs. Initially, manual inspection was done by the concerned employers, who go check the roads and take note of the areas where there is a requirement of repair. This method involves a lot of time and labor. With the development in technology, the focus on implementing a smart method of inspection increased. To build an accurate and quick detection system, an Earth Mover's Distance (EMD) based model is proposed in this paper. This algorithm finds the presence of road distress by comparing the image of the current testing road with that of the reference image. The experiment also considers the chance of occurrence of Gaussian noise and Salt and Pepper noise while capturing the images, to fit the real-world situations.

Printed Circuit Boards (PCBs) have become an inevitable component in all electronic devices, including the gadgets that we use in our day-to-day life. To maintain the quality of a product it is necessary to make sure the PCB is void of defects. To identify whether PCB has defects or not, it has to undergo tests in various stages. In this paper, we propose a reference-based model using Wasserstein distance metric to detect short faults in PCBs. The study includes the effect of Gaussian and Salt-Pepper Noise have on fault detection, which usually occurs in the images procured by digital cameras.

We consider a system with multiple Femtocells (FCs) operating in a Macrocell. The transmissions in one Femtocell interfere with its neighboring Femtocells. Each Femtocell has multiple users, each requiring a minimum transmission rate. There is also peak transmit power constraint in each channel to control interference to the BS and the users in the Macrocell. We formulate the problem of channel allocation and power control in each Femtocell as a noncooperative Game. We develop efficient decentralized algorithms to obtain a Nash equilibrium (NE) that satisfies the Quality of Service (QoS) of each user, in an efficient way. We also obtain efficient decentralized algorithms to obtain fair NE when it may not be feasible to satisfy the QoS of all the users in FCs. Finally, we extend our algorithms to the case where there may be voice and data users in the system.

We consider a system with multiple Femtocells operating in a Macrocell. The transmissions in one Femtocell interfere with its neighboring Femtocells as well as with the Macrocell Base Station. We model Femtocells as selfish nodes and the Macrocell Base Station protects itself by pricing subchannels for each usage. We use Stackelberg game model to study this scenario and obtain equilibrium policies that satisfy certain quality of service.

We consider a system with multiple Femtocells operating in a Macrocell. The transmissions in one Femtocell interfere with its neighboring Femtocells. Each Femtocell has multiple users each requiring a minimum transmission rate. There is also peak transmit power constraint in each channel to control interference to the BS and the users in the Macrocell. We formulate the problem of channel allocation and power control at the Femtocells as a noncooperative Game. We develop efficient decentralized algorithms to obtain a Nash point and satisfy the QoS of each user.

Femtocells are a new concept which improves the coverage and capacity of a cellular system. We consider the problem of channel allocation and power control to different users within a Femtocell. Knowing the channels available, the channel states and the rate requirements of different users the Femtocell base station (FBS), allocates the channels to different users to satisfy their requirements. Also, the Femtocell should use minimal power so as to cause least interference to its neighboring Femtocells and outside users. We develop efficient, low complexity algorithms which can be used online by the Femtocell. The users may want to transmit data or voice. We compare our algorithms with the optimal solutions. © 2012 IEEE.