Learning a perception and reasoning module for robotic assistants to plan steps to perform complex tasks based on natural language instructions often requires large free-form language annotations, especially for short high-level instructions. To reduce the cost of annotation, large language models (LLMs) are used as a planner with few data. However, when elaborating the steps, even the state-of-the-art planner that uses LLMs mostly relies on linguistic common sense, often neglecting the status of the environment at command reception, resulting in inappropriate plans. To generate plans grounded in the environment, we propose FLARE (Few-shot Language with environmental Adaptive Replanning Embodied agent), which improves task planning using both language command and environmental perception. As language instructions often contain ambiguities or incorrect expressions, we additionally propose to correct the mistakes using visual cues from the agent. The proposed scheme allows us to use a few language pairs thanks to the visual cues and outperforms state-of-the-art approaches. Our code and the dataset are publicly available to facilitate further research.

This paper presents a buffer-embedded noise-shaping SAR ADC, whose input buffer separates the capacitive DAC (CDAC) and the sampling capacitor (CS) at the input and output of the input buffer. This compensates for the non-linearity of the input buffer and reduces the CSvalue, resulting in a significant power saving in the input buffer. This buffer-embedded architecture enables the effective implementation of the following passive loop filter and enhances energy efficiency. A bootstrapping switch in the feedback CDAC is coupled to the output of the buffer, thereby avoiding a signal dependency due to the parasitic capacitance of the switch. The buffer-embedded noise-shaping SAR ADC occupies 0.08mm2in a 65 nm CMOS process and features a parasitic input capacitor of 0.2 pF. It achieves 73.8 dB SNDR, 77 dB DR and 87.3 dB SFDR in a 2 MHz bandwidth without any calibration. Including the power consumption of the input buffer, the ADC consumes only 2.1 mW.

We propose a novel approach to recognize positive or negative emotions from acoustic signals in movies by extracting musical components such as tempo, loudness and melody and then by applying ANFIS Model with fuzzy clustering. In order to extract emotional features in acoustic signals, we first transform the sound into a spectrogram. The spectrogram visually represents characteristic information of sound such as tempo, loudness and melody. Then, we apply the fuzzy model on spectrogram to get the effective emotion features of sound. The extracted tempo, loudness and melody information is used as inputs for an adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means clustering (FCM). Finally, the ANFIS classifies the sound as positive or negative emotion, which is compared with a mean opinion score of human in test movies.

The paper addresses the problem of scheduling periodic messages in demand priority network standardized by IEEE 802.12 Committee. As regards the real time property of the demand priority network, unnecessary blocking time due to its round robin based MAC protocol may cause periodic messages to miss their hard deadlines and result in low schedulability of periodic messages. We propose a new message scheduling algorithm to enforce a priority based preemptive message transmission on the frame basis. Before a node transmits a periodic message, it broadcasts the priority of message for all nodes to construct a network wide ready queue in order of priority. A node can transmit a periodic message only when its message is at the head of the ready queue. We have derived sufficient and necessary conditions for both static and dynamic priority assignment in order to determine the schedulability of periodic messages. The simulation study shows that the proposed algorithm significantly improves the guarantee ratio of periodic messages.