Phasor Measurement Units (PMUs) are state-of-the-art measuring devices that capture high-resolution time-synchronized voltage and current phasor measurements in wide area monitoring systems (WAMS). Their usage for various real-time applications demands a huge amount of data collected from multiple PMUs to be transmitted from the local phasor data concentrator (PDC) to the control centre. To optimize the requirements of bandwidth to transmit the data as well as to store the data, an efficient synchrophasor data compression technique is desired. To this end, this paper presents a 3-stage data compression scheme in which Stage-1 performs the accumulation of the data matrix from the optimally placed PMUs in WAMS into the local PDC. The data is then passed through a novel Ramanujan's sum-based fault window detection algorithm to identify the fault within the PMU data matrix in Stage-2. Finally, Stage-3 proposes an enhanced graph filtering-enabled principal component analysis scheme which expands the notion of conventional PCA techniques into the graph domain to compress the data. The performance of the proposed scheme is verified on the IEEE 14-bus system and New England 39-bus system. Further, practical applicability of the proposed method is validated on field PMU data collected from EPFL campus in Switzerland.

Prediction of protein-protein interactions (PPIs) is an important problem in biology, since interactions play key role in most biological processes and functions in living cells. PPIs have been studied from many perspectives. Of these, an important problem is prediction of different complex types such as obligate vs. non obligate and transient vs. permanent, among others. We focus on prediction of obligate protein complexes, which are more stable and perform a specific function, as opposed to transient and non-obligate complexes which last for a short period of time. We have modeled the prediction problem using minimotifs, aka short-linear motifs, to extract information contained in the protein sequences to distinguish between obligate and non-obligate PPIs. Incorporating different classifiers such as the k-nearest neighbor (k-NN), the support vector machine (SVM) and linear dimensionality reduction (LDR) yields a very powerful scheme for prediction. On two well-known datasets, the model delivers classification accuracies as high as 99%. Analysis and cross-dataset validation show that the information contained in the training sequences is crucial for prediction and determination of stability in PPIs.