As artificial intelligence (AI) technology advances, Internet of Things (IoT) devices, such as mobile phones and augmented reality devices, are increasingly becoming crucial enablers of user-device interactions. Among the various methods of interaction, hand pose recognition and analysis is a crucial method to understand the intentions of users and perform precise functions. However, to perform such functions, a substantial amount of computation and resources are required, making it challenging to implement them on small form-factor devices with low-power consumption. For this reason, improving energy efficiency is a crucial objective in real-time hand pose estimation (HPE) applied to low-power platforms with limited resources. In this article, we introduce an FPGA-based energy-efficient real-time HPE system with an integrated image signal processor (ISP). The proposed system uses several low-power design techniques, including a systolic array with dynamic on/off control per processing element (PE), to minimize power consumption and save energy when not in use. In addition, we improve area efficiency by reducing the buffer size in the systolic array using a half-size shift buffer stack. Furthermore, the use of parallel and pipelined structures improved operational efficiency, resulting in a reduction in both operational time and power consumption. The evaluation results on a KU115 FPGA board show that the system achieves an error of 7.78 mm and can process 52 fps, demonstrating its capability for real-time HPE. Moreover, this system achieves high-energy efficiency, up to 61.74 GOPs/W, making it suitable for energy-efficient and accurate HPE in low-power environments.

Cells execute their functions through dynamic operations of biological networks. Dynamic networks delineate the operation of biological networks in terms of temporal changes of abundances or activities of nodes (proteins and RNAs), as well as formation of new edges and disappearance of existing edges over time. Global genomic and proteomic technologies can be used to decode dynamic networks. However, using these experimental methods, it is still challenging to identify temporal transition of nodes and edges. Thus, several computational methods for estimating dynamic topological and functional characteristics of networks have been introduced. In this review, we summarize concepts and applications of these computational methods for inferring dynamic networks and further summarize methods for estimating spatial transition of biological networks.

MOTIVATION: Time-evolving differential protein-protein interaction (PPI) networks are essential to understand serial activation of differentially regulated (up- or downregulated) cellular processes (DRPs) and their interplays over time. Despite developments in the network inference, current methods are still limited in identifying temporal transition of structures of PPI networks, DRPs associated with the structural transition and the interplays among the DRPs over time. RESULTS: Here, we present a probabilistic model for estimating Time-Evolving differential PPI networks with MultiPle Information (TEMPI). This model describes probabilistic relationships among network structures, time-course gene expression data and Gene Ontology biological processes (GOBPs). By maximizing the likelihood of the probabilistic model, TEMPI estimates jointly the time-evolving differential PPI networks (TDNs) describing temporal transition of PPI network structures together with serial activation of DRPs associated with transiting networks. This joint estimation enables us to interpret the TDNs in terms of temporal transition of the DRPs. To demonstrate the utility of TEMPI, we applied it to two time-course datasets. TEMPI identified the TDNs that correctly delineated temporal transition of DRPs and time-dependent associations between the DRPs. These TDNs provide hypotheses for mechanisms underlying serial activation of key DRPs and their temporal associations. AVAILABILITY AND IMPLEMENTATION: Source code and sample data files are available at http://sbm.postech.ac.kr/tempi/sources.zip. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Systems biology attempts to describe complex systems behaviors in terms of dynamic operations of biological networks. However, there is lack of tools that can effectively decode complex network dynamics over multiple conditions. RESULTS: We present principal network analysis (PNA) that can automatically capture major dynamic activation patterns over multiple conditions and then generate protein and metabolic subnetworks for the captured patterns. We first demonstrated the utility of this method by applying it to a synthetic dataset. The results showed that PNA correctly captured the subnetworks representing dynamics in the data. We further applied PNA to two time-course gene expression profiles collected from (i) MCF7 cells after treatments of HRG at multiple doses and (ii) brain samples of four strains of mice infected with two prion strains. The resulting subnetworks and their interactions revealed network dynamics associated with HRG dose-dependent regulation of cell proliferation and differentiation and early PrPSc accumulation during prion infection. AVAILABILITY: The web-based software is available at: http://sbm.postech.ac.kr/pna.

A new air interface scheme based on adaptive resource allocation method is proposed for downlink in cellular OFDM systems. The proposed method is featured as a novel technique for high spectral efficiency (i.e. frequency reuse factor approaching one) and high power efficiency. In order to reduce the co-channel interference (CCI) and improve the capacity in a cell, a is partitioned into three 120/spl deg/ sectors and three adjacent sectors from different cells are composed as a virtual cell which is centrally controlled. Assuming knowledge of instantaneous channel information for all sub-channels, bit rates and quality of service (QoS) requirements of all active users in base-stations (BSs), an adaptive resource allocations based wireless multi-user OFDM access is employed independently in each virtual cell by using the techniques such as multi-user diversity, interference measurement and bad channel avoidance over the whole system bandwidth. It is demonstrated that the system performance is greatly improved compared to the traditional systems with fixed channel allocation in cellular communications.