Timo Hämäläinen

TL;DR: This paper proposes a framework for EEG-based cross-subject emotion recognition using fused entropy features and a Bidirectional Long Short-term Memory (BiLSTM) network, and shows that MSE of EEG is more efficient than other single-entropy features in recognizing emotions.

TL;DR: It is given a hands-on example of how low-level optimization of the VHSIC Hardware Description Language (VHDL) code is extremely difficult within a contemporary Field Programmable Gate Array (FPGA) design flow, but by changing the VHDL coding style synthesis results can be improved.

TL;DR: This approach allows correcting detected problems by identifying the reasons for misbehavior, and has only a small overhead, less than 18 B/min per node in the implementation, allowing the use in bandwidth and energy constrained WSNs.

TL;DR: The optimization tool uses an iterative algorithm to optimize the interconnection parameters, such as data width, priorities, and the time an agent can reserve the inter connection, to fulfill the given constraints.

TL;DR: In this article, the authors considered optimal relay station deployment for IEEE 802.16j networks and proposed a relay deployment mechanism to maximize network capacity for every user or to maximize total network capacity, and therefore to reach greater network capacity values while employing smaller number of relay stations.