In a highly dynamic semiconductor manufacturing technology, the migration to the next process technology node tries to create a smaller-faster-cheaper chip. To achieve this, the integrated circuit (IC) designs are becoming more complex and new manufacturing materials/processes are introduced. Additionally, demands for sustainable yield and shorter time to volume production have to be met as they govern the profitability of the semiconductor industry. As a result, rapid yield improvement techniques are crucial to overcome huge yield losses. Any yield improvement technique typically involves detecting the yield loss (testing) and identifying its root cause (diagnosis). This paper presents a survey of the existing research work reported in the literature for test and diagnostics of nanometer chips and presents interesting open issues.

This is a review paper on the topic of multiple gate field effect transistors: MuGFETs, or FinFETs, as they are called. First, the motivation behind multiple gate FETs is presented. This is followed by looking at the evolution of FinFET technologies; the main flavors (variants) of multigate FETs; and their advantages/disadvantages. The physics and technology of these devices is briefly discussed. Results are then presented which show the performance figures of merit of FinFETs, and their strengths and weaknesses. Finally, a perspective on the future of the FinFET technology is presented. This paper is a judicious mix of the author's original work on FinFETs and other contemporary know-how available in the literature on this topic.

This paper presents a system to assist blind and low-vision people to walk around independently and safely in a university environment. Navigation is accomplished by providing speech guidance on their current location and navigation information on how to move to a particular location. The system utilizes RFID tags for localization to mark points of interest. A wireless sensor network (WSN) acts as the data transportation backbone. The system consists of a Windows and web-based graphical user interface (GUI) which allows remote monitoring by the caregivers and system administrators.

Identifying the file type (TXT, EXE, JPEG, etc.) is important for computer security applications such as computer forensics, steganalysis, and antivirus programs. The common approach for this is to use file extensions, magic numbers, or other header information. However, these are susceptible to tampering or corruption; for instance, the file extension can be easily spoofed and the magic numbers can be obfuscated. A more reliable approach may be to analyze the file content instead of using only the tip of the information (metadata). This paper proposes two methods based on the file content. First, we use the cosine distance as a similarity metric when comparing the file content rather than the Mahalanobis distance that is popular and has been used by the other related approaches. The cosine similarity (unlike the Mahalanobis distance) retains the classification accuracy on a small number of highly frequent byte patterns which leads to a smaller model size and faster detection rate. Second, we decompose the identification procedure into two steps by taking the divide and conquer: in the first step, the similar files in terms of byte pattern frequencies are grouped into several clusters. In the next step, the cluster which contains different file types is fed to the neural network in order for finer classification. The experiments showed that the classification followed by clustering leads to higher accuracies.

The explosive increase in the use of e-mails has produced a large amount of information, and caused a problem in that many spam or regular e-mails with the same or similar contents are duplicated over and over day-to-day. We often group e-mails into categories in order to maintain e-mails efficiently. However, reading the e-mail messages and classifying them is still a tedious task. Moreover, the number of e-mails and its manual classification is increasing every day. So, e-mail users are demanding methods that can classify e-mails more and more efficiently. In this paper, we propose an e-mail multicategory classification and e-mail message reorganization method using semantic features and a dynamic category hierarchy reconstruction method. The proposed method in this paper classifies multicategory e-mails automatically and supports keyword and directory search methods in the classified results so that a large volume of e-mails can be managed efficiently. In addition, we used an interactive dynamic category hierarchy reconstruction method in which the user reorganizes all e-mail messages into categories and thus enhances the efficiency of directory searches.

Third Generation Partnership Project Long Term Evolution (3GPP LTE) has adopted orthogonal frequency division multiple access (OFDMA) as the downlink and single-carrier frequency division multiple access (SC-FDMA) as the uplink for the multiple access scheme. SC-FDMA is adopted in the uplink due to its low peak-to-average-power ratio (PAPR). To reduce the PAPR in OFDMA, several techniques have been used in the literature. Many researches are still going on to find the impact of different parameters on PAPR in SC-FDMA. To further reduce the PAPR in SC-FDMA, techniques used to reduce the PAPR in OFDMA can be a potential choice. In this paper, we investigate some existing papers and find the opportunity to build a platform for further reduction in the PAPR in SC-FDMA environment.

With the fast development of the computer technology and information processing technology, the problem of information security is becoming more and more important. Information hiding is usually used to protect the important information from disclosing when it is transmitting over an insecure channel. Digital image encryption is one of the most important methods of image information hiding and camouflage. The image encryption techniques mainly include compression methodology, modern cryptography mechanism, chaos techniques, DNA techniques, and so on. In this paper, we summarize the main encryption algorithms and classify them based on the means. In particular, chaos-based and DNA cryptography-based image encryption algorithms are illustrated and analyzed in detail. Finally, the future direction in this field is discussed.