A Topology-Independent Mapping Technique for Application-Specific Networks-on-Chip

The design of Networks-on-Chip (NoCs) involves several key issues, including the topological mapping, that is, the mapping of the processing elements or Intellectual Properties (IPs) to the network nodes. Although several proposals have been focused on topological mapping last years, this topic is still an open issue. In this paper, we propose, in an extended manner, a topology-independent mapping technique for application-specific NoCs that can be used with regular or irregular topologies, and with any routing algorithm. This technique globally matches the communication pattern generated by the IPs with the available network bandwidth in the different parts of the network. The evaluation results show that the proposed technique can provide better performance than other mapping techniques not only in terms of average latency and network throughput, but also in terms of power consumption

Integrating knowledge tracing and item response theory: A tale of two frameworks

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

Annual Report 2018-19

Not AvailableA large number of germplasm accessions of horticultural crops are being conserved and maintained in the field gene banks. Among fruit crops, a total of 1110,190,759 and 54 viable germplasm are being conserved at main station ICAR-IIHR, CHES, Bhubaneswar, Chettalli and Hirehalli, respectively. Whereas in vegetable crops, a total of 5694 and 842 viable germplasm are being conserved at main station and at CHES Bhubaneswar, respectively, including leafy and other underutilized vegetables. A total of 459 germplasm in flower crops and 225 in medicinal crops and 33 accessions of mushroom are also conserved at ICAR-IIHR, Bengaluru. ICARIIHR has been identified as the National Repository for Rose by PPV&FRA, under which the digital rose repository of 75 rose varieties have been built for easy identification, grouping and selection of varieties. The germplasm collected and conserved has also been characterized using Bioversity International or NBPGR descriptors. In fruit crops, characters of 17 Appemidi and 26 mango accessions were morphologically characterized and fruit parameters of 25 accessions from FGB were characterized based on Bioversity International descriptors. Nine USDA germplasm of pomegranate were characterized for vegetative and fruit traits. Two varieties of guava, two exotic varieties of papaya, and three custard apple varieties were characterized for fruit traits as per the DUS descriptor. Three g e r m p l a s m o f p i n e a p p l e h a v e b e e n morphologically characterized and evaluated for yield and quality. In vegetable crops, 5 in chilli, 54 in brinjal, 45 in radish, 5 in onion were characterized using NBPGR descriptors. A total of 1000 accessions were evaluated for 24 characters based on NBPGR descriptors for growth, yield and quality. A total of 42 cylindrical and 18 round types of bottle gourd were evaluated for resistance to powdery mildew. Forty-six accessions comprising 15 summer squash and 26 butternut types were characterized for 14 quantitative and 11 qualitative traits based on NBPGR descriptors. Eighty six drumstick germplasm along with released varieties were evaluated for leaf nutritional parametersNot Availabl