A low-noise amplifier (LNA) topology with tunable input matching and noise cancellation is introduced and described in this paper, which was designed and optimized to interface with a magnetoelectric (ME) antenna in a 0.35 µm MEMS-compatible CMOS process. Compared to conventional antennas, acoustically actuated ME antennas have significantly smaller area for ease of integration. The LNA was simulated with an ME antenna model that was constructed based on antenna measurements. Input matching at the LNA-antenna interface is controlled with a circuit that varies the effective impedance of the gate inductor using a control voltage. Tunability of 455 MHz around 2.4 GHz is achieved for the optimum S11 frequency with a control voltage range of 0.3 V to 1.2 V. The proposed LNA has a noise cancelling feedback loop that improves the noise figure by 4.1 dB. The post-layout simulation results of the LNA show a 1-dB compression point of -7.4 dBm with an S of 17.8 dB.

This paper reports on a fully miniaturized brain-spinal interface (BSI) system for closed-loop cortically-controlled intraspinal microstimulation (ISMS). Fabricated in AMS 0.35μm two-poly four-metal complementary metal-oxide-semiconductor (CMOS) technology, this system-on-chip (SoC) measures ~ 3.46mm × 3.46mm and incorporates two identical 4-channel modules, each comprising a spike-recording front-end, embedded digital signal processing (DSP) unit, and programmable stimulating back-end. The DSP unit is capable of generating multichannel trigger signals for a wide array of ISMS triggering patterns based on real-time discrimination of a programmable number of intracortical neural spikes within a pre-specified time-bin duration via thresholding and user-adjustable time-amplitude windowing. The system is validated experimentally using an anesthetized rat model of a spinal cord contusion injury at the T8 level. Multichannel neural spikes are recorded from the cerebral cortex and converted in real time into electrical stimuli delivered to the lumbar spinal cord below the level of the injury, resulting in distinct patterns of hindlimb muscle activation.

Being able to rapidly detect a low air flow rate with high accuracy is essential for various applications in the automotive and biomedical industries. We have developed a thermal-based low air flow sensor with a low-power sensor readout for biomedical applications. The thermal-based air flow sensor comprises a heater and three pairs of temperature sensors that sense temperature differences due to laminar air flow. The thermal-based flow sensor was designed and simulated by using laminar flow, heat transfer in solids and fluids physics in COMSOL MultiPhysics software. The proposed sensor can detect air flow as low as 0.0064 m/sec. The readout circuit is based on a current- controlled ring oscillator in which the output frequency of the ring oscillator is proportional to the temperature differences of the sensors. The entire readout circuit was designed and simulated by using a 130-nm standard CMOS process. The sensor circuit features a small area and low-power consumption of about 22.6 µW with an 800 mV power supply. In the simulation, the output frequency of the ring oscillator and the change in thermistor resistance showed a high linearity with an R value of 0.9987. The low-power dissipation, high linearity and small dimensions of the proposed flow sensor and circuit make the system highly suitable for biomedical applications.

We present a new noise shaping method and a dual polarity calibration technique suited for successive approximation register type analog to digital converters (SAR-ADC). Noise is pushed to higher frequencies with the noise shaping by adding a switched capacitor. The SAR capacitor array mismatch has been compensated by the dual-polarity digital calibration with minimum circuit overhead. A proof-of-concept prototype SAR-ADC using the proposed techniques has been fabricated in a 0.5-μm standard CMOS technology. It achieves 67.7 dB SNDR at 62.5 kHz sampling frequency, while consuming 38.3μW power with 1.8 V supply.

3D-HEVC is the state-of-the-art standard to compress three-dimensional videos. One of the 3D-HEVC novel tools is the DIS tool, which is used to efficiently compress smooth and homogeneous areas of depth maps by using four different prediction modes. The decision of which DIS mode will be used is done through the SVDC similarity criterion in the DIS original definition. This article proposes the substitution of the complex SVDC criterion for simpler and more hardware friendly criteria as SATD, SSE, and SAD. These alternative criteria were evaluated in terms of encoding efficiency and hardware impacts in comparison with the SVDC. Dedicated DIS hardware were designed using each one of each criterion and these designs were described in VHDL and synthesized for TSMC 40 nm. The best results were found with SAD criteria, with losses of only 0.2% in coding efficiency and with expressive gains of more than 50 times in power and more than 35 times in area, when compared with SVDC. The reached results showed that the use of a simpler similarity criterion is an important alternative to be used in DIS tool, mainly if an efficient hardware design is required.

Chimp optimization algorithm (ChOA) is a robust nature-inspired technique, which was recently proposed for addressing real-world challenging engineering problems. Due to the novelty of the ChOA, there is room for its improvement. Recognition and classification of marine mammals using artificial neural networks (ANNs) are high-dimensional challenging problems. In order to address this problem, this paper proposed the using of ChOA as ANN's trainer. However, evolving ANNs using metaheuristic algorithms suffers from high complexity and processing time. In order to address this shortcoming, this paper proposes the fuzzy logic to adjust the ChOA's control parameters (Fuzzy-ChOA) for tuning the relationship between exploration and exploitation phases. In this regard, we collect underwater marine mammals sounds and then produce an experimental dataset. After pre-processing and feature extraction, the ANN is used as a classifier. Besides, for having a fair comparison, we used a benchmark audio database of marine mammals. The comparison algorithms include ChOA, coronavirus optimization algorithm, harris hawks optimization, black widow optimization algorithm, Kalman filter benchmark algorithms, and also comparative benchmarks include convergence speed, local optimal avoidance ability, classification rate, and receiver operating characteristics (ROC). The simulation results show that the proposed fuzzy model can tune the boundary between the exploration and extraction phases. The convergence curve and ROC confirm that the convergence rate and performance of the designed recognizer are better than benchmark algorithms.

Auricular vagus nerve stimulation (aVNS) is a novel neuromodulatory therapy used for treatment of various chronic systemic disorders. Currently, aVNS is non-individualized, disregarding the physiological state of the patient and therefore making it difficult to reach optimum therapeutic outcomes. A closed-loop aVNS system is required to avoid over-stimulation and under-stimulation of patients, leading to personalized and thus improved therapy. This can be achieved by continuous monitoring of individual physiological parameters that serve as a basis for the selection of optimal aVNS settings. In this work we developed a novel aVNS hardware for closed-loop application, which utilizes cardiorespiratory sensing using embedded sensors (and/or external sensors), processes and analyzes the acquired data in real-time, and directly governs settings of aVNS. We show in-lab that aVNS stimulation can be arbitrarily synchronized with respiratory and cardiac phases (as derived from respiration belt, electrocardiography and/or photo plethysmography) while mimicking baroreceptor-related afferent input along the vagus nerve projecting into the brain. Our designed system identified > 90% of all respiratory and cardiac cycles and activated stimulation at the target point with a precision of ± 100 ms despite the intrinsic respiratory and heart rate variability reducing the predictability. The developed system offers a solid basis for future clinical research into closed-loop aVNS in favour of personalized therapy.

This study presents a novel UWB flexible antenna with dual band-notched design for wearable biomedical devices. The proposed antenna is designed on Kapton Polyimide-based flexible substrate. This includes a CPW fed circular and triangle structure. The dual notched bands are realized by using two triangular-shaped spiral slots defected ground structures. The first notched band (2.4-3.7 GHz) is generated for rejecting WLAN and WiMAX, the second notch (5.15-5.725 GHz) is generated for rejecting HyperLAN/2. The designed UWB antenna has approximately a bandwith of 159% (2.05-14 GHz) in simulation. Thus, the designed UWB antenna meets FCC standards. The antenna has an omnidirectional radiation pattern with a maximum gain of 12.7 dB in 8.4 GHz. The proposed antenna is fabricated with the low-cost airbrush printed technique. In this technique, a higher gain value is obtained by controlling the thickness of the conductive layer. Effect of flexibility on the antenna performance is tested for different configurations in the simulation and anechoic chamber environments. According to the results obtained, the overall performance is not affected except for the shift in frequency. Since the antenna has a UWB structure, the frequency shift that occurs in bending is at a tolerable level. The proposed UWB antenna is suitable for wearable biomedical devices, with a high UWB performance.