This article presents a study of the relationship between electromyographic (EMG) signals from vastus lateralis, rectus femoris, biceps femoris and semitendinosus muscles, collected during fatiguing cycling exercises, and other physiological measurements, such as blood lactate concentration and oxygen consumption. In contrast to the usual practice of picking one particular characteristic of the signal, e.g., the median or mean frequency, multiple variables were used to obtain a thorough characterization of EMG signals in the spectral domain. Based on these variables, linear and non-linear (random forest) models were built to predict blood lactate concentration and oxygen consumption. The results showed that mean and median frequencies are sub-optimal choices for predicting these physiological quantities in dynamic exercises, as they did not exhibit significant changes over the course of our protocol and only weakly correlated with blood lactate concentration or oxygen uptake. Instead, the root mean square of the original signal and backward difference, as well as parameters describing the tails of the EMG power distribution were the most important variables for these models. Coefficients of determination ranging from R² = 0:77 to R² = 0:98 (for blood lactate) and from R² = 0:81 to R² = 0:97 (for oxygen uptake) were obtained when using random forest regressors.

A set of novel time-domain features characterizing multi-channel surface EMG (sEMG) signals of six muscles (rectus femoris, vastus lateralis, and semitendinosus of each leg) is proposed for prediction of physiological parameters considered important in cycling: blood lactate concentration and oxygen uptake. Fifty one different features, including phase shifts between muscles, active time percentages, sEMG amplitudes, as well as symmetry measures between both legs, were defined from sEMG data and used to train linear and random forest models. The random forests models achieved the coefficient of determination R2 = 0:962 (lactate) and R2 = 0:980 (oxygen). The linear models were less accurate. Feature pruning applied enabled creating accurate random forest models (R2 >0:9) using as few as 7 (lactate) or 4 (oxygen) time-domain features. sEMG amplitude was important for both types of models. Models to predict lactate also relied on measurements describing interaction between front and back muscles, while models to predict oxygen uptake relied on front muscles only, but also included interactions between the two legs. © 2015 The authors and IOS Press. All rights reserved.

In this paper we present a novel active radio frequency identification system consisting of transponders with low complexity, low power consumption, and long system reading range. The transponder’s low complexity and small circuit integration area indicate that the production cost is comparable to the one of a passive tag. The hardware keystone is the transponder’s radio wake-up transceiver, which is a single oscillator with very low power consumption. The communication protocol, based on frequency signalling binary tree, contributes to the low complexity of the tag architecture. More than 1500 tags can be read per second. The average transponder ID read-out delay is 319 ms when there are 1000 transponders within reach of the interrogator. The calculated expected life time for a transponder is estimated to be almost three years.

A novel radio receiver circuit, functioning as a tuned active, detecting antenna, is described. The receiver is suggested to be part of a new radio system with the potential of competing with the range capability of active RFID-tags and, through its low power and long lifetime, with passive RFID-tags. The circuit is outlined and the functionality is verified by simulations and measurements.

A 24 MHz discrete prototype showed better than -70 dBm sensitivity and 5 kHz bandwidth, with a power consumption of 102 μW. Simulations of a monolithic implementation were performed at 2.5 GHz. The detector is modeled by using 180 nm CMOS transistors. In simulations the power consumption for the detector is below 125 μW at a sensitivity of -83 dBm and a bandwidth of 9 MHz.

Our conclusion is that this novel simple circuit architecture is well suited for monolithic implementation of a low power transceiver.

In this paper we present a Radio Frequency Identification (RFID) protocol used to wake up and extract the ID of every tag (or a subset thereof) within reach of a reader in an active backscatter RFID system. We also study the effect on tag energy cost and read-out delay incurred when using the protocol, which is based on a frequency binary tree. Simulations show that, when using the 2.45 GHz ISM band, more than 1500 tags can be read per second.With a population of 1000 tags, the average read-out delay is 319 ms, and the expected lifetime of the RFID tags is estimated to be more than 2.5 years, even in a scenario when they are read out very often.

The use of Radio Frequency Identification systems (RFID) is growing rapidly. Today, mostly “passive” RFID systems are used because no onboard energy source is needed on the transponders. However, “active” RFID technology, with onboard power sources in the transponders, gives a range of opportunities not possible with passive systems. To obtain energy efficiency in an Active RFID system the protocol to be used should be carefully designed with energy optimization in mind. This paper describes how energy consumption can be calculated, to be used in protocol definition, and how evaluation of protocol in this respect can be made. The performance of such a new protocol, in terms of energy efficiency, aggregated throughput, delay, and number of air collisions is evaluated and compared to an existing, commercially available protocol for Active RFID, as well as to the IEEE standard 802.15.4 (used e.g. in the Zigbee mediumaccess layer).

Active Radio Frequency Identification (A-RFID) extends the functionality from the predecessor passive RFID trough adding a power source to the transponder device (device used on a product to identify it). This power source enables more advanced functions in the radio interface such as listening (doing a carrier sense) to the radio channel (carrier of data information) finding out if it is unengaged, and free to use. In this paper we study the carrier sense functionality and its effects in lowering the tag energy consumption. Simulation results show that the life time of a tag, in an A-RFID system, using carrier sense is more than doubled compared to one not using carrier sense. The increased lifetime of the tag is due to the lowered energy consumption caused by the improved throughput and the decreased payload delay, which in turn is thanks to using carrier sense and naturally then give a better utilization of the radio channel.

This paper suggests a cluster collision avoidance mechanism and a dual transceiver architecture to be used in a clustered wireless multihop network. These two contributions make the clustered wireless multihop network the preferred architecture for future industrial wireless networks. The wireless multihop cluster consists of one master and several slaves, where some of the slaves will act as gateways between different clusters. Frequency hopping spread spectrum is used on a cluster level and to avoid frequency collisions between clusters a "neighbor cluster collision avoidance mechanism" is proposed and evaluated through simulations. To break up the dependence between the clusters, introduced by the gateway nodes, each node is equipped with two transceivers. The paper is concluded with a suggestion to use a clustered wireless multihop network with orthogonal hopping sequences for an industrial setting.

In this paper an implementation of a wireless sensor network is described. The aim with the implementation is to investigate if present design patterns are applicable on wireless sensor networks. A 3-tier model is adopted as a possible candidate for the software as well as for the network architecture. The implemented wireless sensor network consists of a heterogeneous set of hardware devices such as sensors, sensor hubs, beepers, PDAs and connectors. Most of the hardware components are COTS and most of the software is GNU licensed. All these form a 3-tier hierarchical network architecture.