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This paper introduces the strategic efforts of universities in Indonesia in marketing their research results according to government recommendations through startups with digital marketing. Products marketed range from education, equipment, and training services. Preliminary studies aimed at accelerating digital marketing traffic have been carried out in the MATLAB framework. We use the Queen honey bee colony migration (QHBM) method to accurately launch promo coupons so that they can change the tendency of ordinary visitors to become buyers. As a fair comparison, we use a moving average (MA) with the same threshold as QHBM. The simulation results show the fact that QHBM is more accurate than MA. After using the simulation results in real situations, this startup was able to achieve BEP in 6 months with an average monthly turnover of US 2,045. Based on the results of this evaluation, it was found that consumer satisfaction reached 85%, the effectiveness of digital marketing reached the level and consumer trends for renewable energy in the future reached 55%. [ABSTRACT FROM AUTHOR]

A new S2 and S3 sound detection by using the heart rate signal taken from dorsalis pedis artery as a reference is presented in this paper. This approach aims to evaluate the accuracy of the S1 and S2 sounds detection by using dorsalis pedis pulse measurement as a reference. The dorsalis pedis pulse was measured by using MAX 30100 sensor controlled by a microcontroller. Meanwhile, the heart sounds were captured by using the ECG device. The measurement was taken for 5 minutes for a total of ten male and ten female subjects, with the age of 20 years old. This experiment has been conducted several times, and the average was calculated and compared to the existing solution. This proposed approach gives a small error compared to the standard S1 and S2 detection, leading it to be an acceptable and promising solution of a detection method. [ABSTRACT FROM AUTHOR]

The wearable devices are gaining its popularity in recent years. This paper presents the performances of our smart shoes as a wearable biomedical device in capturing the heartbeat signal from dorsalis pedis. Generally, the heartbeat measured from an artery was located on the chest, hand, fingertip, and ear or neck. Even though the pulse from dorsalis pedis has a smaller amplitude compared to the other artery location, the integrated sensor on our smart shoes can capture the signal with high precision. The experiment was conducted several times to three groups of healthy participants; they were kid, mature, and elder. The results show that the smart shoe sensor could measure the dorsalis pedis pulse and has a maximum error rate of 11.76% compared to the digital tensimeter. Unlike the standard measurement and other commercial devices, our device gives a small gap in RMS error, which is acceptable for clinical needs. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive method for power management in smart shoes data transmission. The device communication module consumes a lot of energy. The transmission power is set to maximum by default. The maximum transmission power is not needed when the node is near the sink. But, the low transmission power is not guaranteed data successfully sent when the device is far from the sink. The transmission power control method is important to avoiding retransmission and improve energy efficiency. The proposed method uses a fuzzy logic controller (FLC) to develop an adaptive system. The FLC uses RSSI and the amount of available energy as an input parameter. The system will choose the shortest path based on RSSI. Data exchange between nodes scattered in smart and moving shoes with users can be set by Fuzzy placed on the sink. Sink collects data and sends it to the cloud. The test is carried out on the general device using conventional method compared to the proposed method. The lifetime of both devices compared. Based on experiments, sports fields with several users with relaxed, brisk and running modes show significant power savings compared to conventional methods. The proposed method provides energy efficiency up to 9.2% better than conventional power management. [ABSTRACT FROM AUTHOR]

This paper presents a battery management system on a self-powered smart shoes application. As a mobile device, smart shoes have limited battery reserves and are separated from installed energy sources. The use of batteries must be as save as possible so that the device can still function while in use. Smart shoes can sense heart rate and oxygen levels in blood. Both collected data is encapsulated in a single frame prior to sending it to the database server through wireless sensor network. We developed an energy-saving method to extend battery life through duty cycle. The proposed method used sleep feature, data reduction, wireless transceiver optimization, and fixed network authentication configuration. The sleep feature allows the device active during data acquisition and delivery, the rest it will be going to sleep. The data is reduced to minimize power transmission. Testing energy consumption on devices with proposed battery management is carried out on wireless network ecosystem that uses static addressing. The results are compared with energy consumption from devices with conventional battery management. The results show that the proposed battery management system can reduce energy consumption up to 4.85%. Overall the proposed method has better performance than the conventional method. [ABSTRACT FROM AUTHOR]