ERROR IDENTIFICATION IN RAM USING INPUT VECTOR MONITORING CONCURRENT BIST ARCHITECTURE

Abstract— Input vector monitoring concurrent built-in self test (BIST) schemes perform testing during the normal operation of the Random Access Memory without imposing a need to set the RAM offline to perform the test. These schemes are evaluated based on the hardware overhead and the concurrent test latency (CTL), i.e., the time required for the test to complete, whereas the circuit operates normally. In this brief, we present a novel input vector monitoring concurrent BIST scheme, which is based on the idea of monitoring a set (called window) of vectors reaching the circuit inputs during normal operation, and the use of a static-RAM-like structure to store the relative locations of the vectors that reach the circuit inputs in the examined window; the proposed scheme is shown to perform significantly better than previously proposed schemes with respect to the hardware overhead and CTL tradeoff.

Impact of Human Capital Management on Organizational Performance With the Mediation Effect of Human Resource Analytics

Purpose: The main objective of the study is to examine the relationship between Human Capital Management, Human resource analytics and Organizational performance through systematic literature survey   Theoretical Framework: The authors developed a conceptual framework to examine the relationship between the components of the Human Capital Management (HCM ) and its effect on  organizational performance with the mediation of HR analytics   Design/methodology/approaches: The study is based on extensive systematic literature review collected from previous studies   Findings: The systematic review validated the proposed conceptual model and found that HR analytics help organizations track their human capital management and improves organizational performance   Research practical social implication: This study makes significant contributions to the existing body of knowledge of Human capital management and application of innovative tool of HR analytics .There are several implications for practitioners based on the findings   Originality/value: The study is more relevant and practically applied to the organization who are in the nurturing stage of implementing HR analytics towards Human Capital Management for increasing organizational performanc

Uma nova espécie de lagarto do gênero Eublepharis (Squamata: Eublepharidae) da Índia

Descrevemos aqui uma nova espécie de lagarto do gênero Eublepharis das Montanhas Satpura, Índia central. A nova espécie assemelha-se intimamente a E. fuscus, mas pode ser distinguida desta pelo seguinte conjunto de caracteres: SVL 125–130 mm; tubérculos em forma de domo, sem quilhas, arranjados em ~20 fileiras no dorso, espaço intertubercular maior que a largura de um tubérculo; 46–48 escamas na franja ocular, três faixas claras entre a curva nucal e a constrição caudal; lamelas subdigitais mediais lisas; 13–14 poros pré-anais, que podem estar interrompidos medialmente por uma única escama sem poro. A descrição da nova espécie aumenta o conhecimento limitado de Eublepharis na Índia. Fornecemos uma chave de identificação para as espécies do gênero Eublepharis.We describe here a new species of the genus Eublepharis from the Satpura Hills in central India. The new species closely resembles E. fuscus, but can be differentiated from it by the following suite of characters: SVL 125–130 mm; dome shaped tubercles lacking keels arranged in ~20 rows on dorsum, inter-tubercular space greater than width of a tubercle; 46–48 ocular fringe scales, three pale bands between the nuchal loop and caudal constriction; medial subdigital lamellae smooth; 13–14 preanal pores, which may be interrupted medially by a single poreless scale. Description of the new species sheds light on the limited knowledge of Eublepharis in India. We provide an identification key to the species of the genus Eublepharis

Rice GIs of Kerala: Gap in Desired and Achieved Outcomes

83-91Case studies on Geographical Indications (GIs) prove that that it is essential to include a quality assurance clause within the legal framework of GIs, if the benefits of registration are to be accrued to the farming community. The potential positive impact of the GI for stake holders can be initiated through a strong institutional context and well organised supply chain. Support from governmental agencies is essential in this regard to build up effective promotional strategies to promote the product and its intrinsic qualities across markets. The paper analyses the performance of rice GIs of Kerala, initiatives put after the registration, the gaps between desired and achieved outcomes of the policy initiatives and the bottlenecks of the implementation of the innovation. The studies analysed recommend that revival of the producer society is essential in order to take collective decisions on defining the production limits, agreeing up on code of conduct, identifying indicators of quality, and building up strategies for marketing and consumer orientation

ଜେଲି ତଥ୍ୟ (Jelly facts-Odiya Version)

Jelly fact

Cassava storage : post-harvest deterioration and storage of fresh cassava roots

<p>Sum of squares of the errors (SSE) between data from patients <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014531#pone.0014531-Shankarappa1" target="_blank">[36]</a> and our predictions of viral diversity, <i>d_G</i>, and divergence, <i>d_S</i>, for different values of the population size, <i>C</i>, (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014531#pone-0014531-g005" target="_blank">Fig. 5</a>) and the viral generation time, τ, shown for each of the nine patients. <i>C</i> and τ that yield the lowest SSE provide the best fit to the data. The best-fit value of <i>C</i> yields <i>N_e</i> (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0014531#pone-0014531-t001" target="_blank">Table 1</a>).</p

Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks

Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images. In literature, the problem of image orientation detection is mostly confronted by using low-level vision features, while some approaches incorporate few easily detectable semantic cues to gain minor improvements. The vast amount of semantic content in images makes orientation detection challenging, and therefore there is a large semantic gap between existing methods and human behavior. Also, existing methods in literature report highly discrepant detection rates, which is mainly due to large differences in datasets and limited variety of test images used for evaluation. In this work, for the first time, we leverage the power of deep learning and adapt pre-trained convolutional neural networks using largest training dataset to-date for the image orientation detection task. An extensive evaluation of our model on different public datasets shows that it remarkably generalizes to correctly orient a large set of unconstrained images; it also significantly outperforms the state-of-the-art and achieves accuracy very close to that of humans